iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

On 27 June, 2022

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM – GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r=0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.

Availability: iCOMIC source code is available here https://github.com/RamanLab/iCOMIC. iCOMIC user manual can be accessed using the link,   https://icomic-doc.readthedocs.io/en/latest/user-guide.html.

Original Paper: 

  • [DOI] S. Venkatraghavan, S. Anantakrishnan, and K. Raman, “Probing patterning in microbial consortia with a cellular automaton for spatial organisation,” Scientific Reports, vol. 12, iss. 1, p. 17159, 2022.
    [bibtex]
    @article{Venkatraghavan2022Probing,
      title = {Probing patterning in microbial consortia with a cellular automaton for spatial organisation},
      volume = {12},
      copyright = {2022 The Author(s)},
      issn = {2045-2322},
      url = {https://www.nature.com/articles/s41598-022-20705-7},
      doi = {10.1038/s41598-022-20705-7},
      abstract = {Microbial consortia exhibit spatial patterning across diverse environments. Since probing the self-organization of natural microbial communities is limited by their inherent complexity, synthetic models have emerged as attractive alternatives. In this study, we develop novel frameworks of bacterial communication and explore the emergent spatiotemporal organization of microbes. Specifically, we built quorum sensing-mediated models of microbial growth that are utilized to characterize the dynamics of communities from arbitrary initial configurations and establish the effectiveness of our communication strategies in coupling the growth rates of microbes. Our simulations indicate that the behavior of quorum sensing-coupled consortia can be most effectively modulated by the rates of secretion of acyl homoserine lactones. Such a mechanism of control enables the construction of desired relative populations of constituent species in spatially organized populations. Our models accurately recapitulate previous experiments that have investigated pattern formation in synthetic multi-cellular systems. Additionally, our software tool enables the easy implementation and analysis of our frameworks for a variety of initial configurations and simplifies the development of sophisticated gene circuits facilitating distributed computing. Overall, we demonstrate the potential of spatial organization as a tunable parameter in synthetic biology by introducing a communication paradigm based on the location and strength of coupling of microbial strains.},
      language = {en},
      number = {1},
      urldate = {2022-11-15},
      journal = {Scientific Reports},
      author = {Venkatraghavan, Sankalpa and Anantakrishnan, Sathvik and Raman, Karthik},
      month = oct,
      year = {2022},
      keywords = {Computational biology and bioinformatics, Microbiology, Systems biology},
      pages = {17159},
    }
  • [DOI] A. Anilkumar Sithara, D. Maripuri, K. Moorthy, S. Amirtha Ganesh, P. Philip, S. Banerjee, M. Sudhakar, and K. Raman, “iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data,” NAR Genomics and Bioinformatics, vol. 4, iss. 3, p. lqac053, 2022.
    [bibtex]
    @article{AnilkumarSithara2022ICOMIC,
      title = {{iCOMIC}: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data},
      volume = {4},
      issn = {2631-9268},
      shorttitle = {{iCOMIC}},
      url = {https://doi.org/10.1093/nargab/lqac053},
      doi = {10.1093/nargab/lqac053},
      abstract = {Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM—GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.},
      number = {3},
      urldate = {2022-10-06},
      journal = {NAR Genomics and Bioinformatics},
      author = {Anilkumar Sithara, Anjana and Maripuri, Devi Priyanka and Moorthy, Keerthika and Amirtha Ganesh, Sai Sruthi and Philip, Philge and Banerjee, Shayantan and Sudhakar, Malvika and Raman, Karthik},
      month = sep,
      year = {2022},
      pages = {lqac053},
      file = {Full Text PDF:C\:\\Users\\Karthik\\Zotero\\storage\\TZS9AURX\\Anilkumar Sithara et al. - 2022 - iCOMIC a graphical interface-driven bioinformatic.pdf:application/pdf;Snapshot:C\:\\Users\\Karthik\\Zotero\\storage\\ZH78XTHR\\6649537.html:text/html},
    }
  • [DOI] R. K. Kumar, N. K. Singh, S. Balakrishnan, C. W. Parker, K. Raman, and K. Venkateswaran, “Metabolic Modeling of the International Space Station Microbiome Reveals Key Microbial Interactions,” Microbiome, vol. 10, iss. 1, p. 102, 2022.
    [bibtex]
    @article{Kumar2022Metabolic,
      title = {Metabolic Modeling of the {{International Space Station}} Microbiome Reveals Key Microbial Interactions},
      author = {Kumar, Rachita K. and Singh, Nitin Kumar and Balakrishnan, Sanjaay and Parker, Ceth W. and Raman, Karthik and Venkateswaran, Kasthuri},
      year = {2022},
      month = jul,
      journal = {Microbiome},
      volume = {10},
      number = {1},
      pages = {102},
      issn = {2049-2618},
      doi = {10.1186/s40168-022-01279-y},
      abstract = {Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.},
      keywords = {Community modeling,Metabolic networks,Microbial communities,Network biology,Space microbiome},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\TGT93TYG\\Kumar et al. - 2022 - Metabolic modeling of the International Space Stat.pdf}
    }
  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “Discovering design principles for biological functionalities: Perspectives from systems biology,” Journal of Biosciences, vol. 47, iss. 4, p. 56, 2022.
    [bibtex]
    @article{Bhattacharya2022bDiscovering,
      title = {Discovering design principles for biological functionalities: {Perspectives} from systems biology},
      volume = {47},
      issn = {0973-7138},
      shorttitle = {Discovering design principles for biological functionalities},
      url = {https://doi.org/10.1007/s12038-022-00293-4},
      doi = {10.1007/s12038-022-00293-4},
      abstract = {Network architecture plays a crucial role in governing the dynamics of any biological network. Further, network structures have been shown to remain conserved across organisms for a given phenotype. Therefore, the mapping between network structures and the output functionality not only aids in understanding of biological systems but also finds application in synthetic biology and therapeutics. Based on the approaches involved, most of the efforts hitherto invested in this field can be classified into three broad categories, namely, computational efforts, rule-based methods and systems-theoretic approaches. The present review provides a qualitative and quantitative study of all three approaches in the light of three well-researched biological phenotypes, namely, oscillation, toggle switching, and adaptation. We also discuss the advantages, limitations, and future research scope for all three approaches along with their possible applications to other emergent properties of biological relevance.},
      language = {en},
      number = {4},
      urldate = {2022-10-06},
      journal = {Journal of Biosciences},
      author = {Bhattacharya, Priyan and Raman, Karthik and Tangirala, Arun K.},
      month = sep,
      year = {2022},
      keywords = {Adaptation, biological networks, bioswitches, computational approach, oscillator, rule-based, systems biology, systems theory},
      pages = {56},
      file = {Full Text PDF:C\:\\Users\\Karthik\\Zotero\\storage\\FAU3CNJ4\\Bhattacharya et al. - 2022 - Discovering design principles for biological funct.pdf:application/pdf},
    }
  • [DOI] S. S. M. Das and K. Raman, “Effect of Dormant Spare Capacity on the Attack Tolerance of Complex Networks,” Physica A: Statistical Mechanics and its Applications, vol. 598, p. 127419, 2022.
    [bibtex]
    @article{Das2022Effect,
      title = {Effect of Dormant Spare Capacity on the Attack Tolerance of Complex Networks},
      author = {M. Sai Saranga Das and Raman, Karthik},
      year = {2022},
      journal = {Physica A: Statistical Mechanics and its Applications},
      pages = {127419},
      issn = {0378-4371},
      volume = {598},
      doi = {10.1016/j.physa.2022.127419},
      abstract = {The vulnerability of networks to targeted attacks is an issue of widespread interest for policymakers, military strategists, network engineers and systems biologists alike. Current approaches to circumvent targeted attacks seek to increase the robustness of a network by adding or swapping edges (Edge Addition (EA) or Edge Swapping (ES) method respectively) that ultimately leads to a higher size of the largest connected component for a given fraction of nodes removed when compared to that of the original network. In this work, we propose a strategy in which there is a pre-existing, dormant spare capacity already built into the network for an identified vulnerable node, such that the traffic of the disrupted node can be diverted to another pre-existing node/set of nodes in the network. Using our algorithm, the increase in robustness of canonical scale-free networks was nearly 14-fold. We also analysed real-world networks using our algorithm, where the mean increase in robustness was nearly 5-fold. We have compared our work with the results obtained from other EA and ES algorithms and have found the increase in robustness using our algorithm to be significant in light of what has been reported to our knowledge in the literature. The cost of this spare capacity and its effect on the operational parameters of the network have also been discussed.},
      langid = {english},
      keywords = {Canonical networks,Edge addition algorithm,Networks,Robustness optimisation},
    }
  • [DOI] M. Sudhakar, R. Rengaswamy, and K. Raman, “Multi-Omic Data Helps Improve Prediction of Personalised Tumor Suppressors and Oncogenes,” Frontiers in Genetics, vol. 13, p. 854190, 2022.
    [bibtex]
    @article{Sudhakar2022Multiomic,
      title = {Multi-Omic Data Helps Improve Prediction of Personalised Tumor Suppressors and Oncogenes},
      author = {Sudhakar, Malvika and Rengaswamy, Raghunathan and Raman, Karthik},
      year = {2022},
      doi = {10.3389/fgene.2022.854190},
      journal = {Frontiers in Genetics},
      volume = {13},
      pages = {854190},
      issn = {1664-8021},
      abstract = {The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalised driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy {$\geq$} 0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\FN55QAEI\\Sudhakar et al. - 2022 - Multi-omic data helps improve prediction of person.pdf}
    }
  • [DOI] D. Chakraborty, R. Rengaswamy, and K. Raman, “Designing Biological Circuits: From Principles to Applications,” ACS Synthetic Biology, vol. 11, iss. 4, pp. 1377-1388, 2022.
    [bibtex]
    @article{Chakraborty2022Designing,
      title = {Designing {{Biological Circuits}}: {{From Principles}} to {{Applications}}},
      shorttitle = {Designing {{Biological Circuits}}},
      author = {Chakraborty, Debomita and Rengaswamy, Raghunathan and Raman, Karthik},
      year = {2022},
      month = apr,
      journal = {ACS Synthetic Biology},
      volume = {11},
      number = {4},
      pages = {1377--1388},
      doi = {10.1021/acssynbio.1c00557},
      abstract = {Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits-the repressilator and the toggle switch-were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.}
    }
  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “Discovering adaptation-capable biological network structures using control-theoretic approaches,” PLoS Computational Biology, vol. 18, iss. 1, p. e1009769, 2022.
    [bibtex]
    @article{Bhattacharya2022Discovering,
      title = {Discovering adaptation-capable biological network structures using control-theoretic approaches},
      volume = {18},
      issn = {1553-7358},
      url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009769},
      doi = {10.1371/journal.pcbi.1009769},
      abstract = {Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.},
      language = {en},
      number = {1},
      urldate = {2022-01-27},
      journal = {PLoS Computational Biology},
      author = {Bhattacharya, Priyan and Raman, Karthik and Tangirala, Arun K.},
      month = jan,
      year = {2022},
      note = {Publisher: Public Library of Science},
      keywords = {Network analysis, Network motifs, Eigenvalues, Control theory, Directed graphs, Dynamical systems, Peak values, Transfer functions},
      pages = {e1009769},
      file = {Full Text PDF:C\:\\Users\\Karthik\\Zotero\\storage\\5THKKWIH\\Bhattacharya et al. - 2022 - Discovering adaptation-capable biological network .pdf:application/pdf;Snapshot:C\:\\Users\\Karthik\\Zotero\\storage\\JXV5MYW8\\article.html:text/html},
    }
  • [DOI] M. Sudhakar, R. Rengaswamy, and K. Raman, “Novel Ratio-Metric Features Enable the Identification of New Driver Genes across Cancer Types,” Scientific Reports, vol. 12, iss. 1, p. 5, 2022.
    [bibtex]
    @article{Sudhakar2022Novel,
      title = {Novel Ratio-Metric Features Enable the Identification of New Driver Genes across Cancer Types},
      author = {Sudhakar, Malvika and Rengaswamy, Raghunathan and Raman, Karthik},
      year = {2022},
      month = jan,
      journal = {Scientific Reports},
      volume = {12},
      number = {1},
      pages = {5},
      issn = {2045-2322},
      doi = {10.1038/s41598-021-04015-y},
      abstract = {An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG.},
      copyright = {2022 The Author(s)},
      langid = {english},
      keywords = {Cancer genomics,Genomics,Machine learning,Oncogenes,Tumour-suppressor proteins},
      annotation = {Bandiera\_abtest: a Cc\_license\_type: cc\_by Cg\_type: Nature Research Journals Primary\_atype: Research Subject\_term: Cancer genomics;Genomics;Machine learning;Oncogenes;Tumour-suppressor proteins Subject\_term\_id: cancer-genomics;genomics;machine-learning;oncogenes;tumour-suppressor-proteins},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\4JL5BBQN\\Sudhakar et al. - 2022 - Novel ratio-metric features enable the identificat.pdf;C\:\\Users\\Karthik\\Zotero\\storage\\WG3T7Z3B\\s41598-021-04015-y.html}
    }
  • [DOI] L. Raajaraam and K. Raman, “A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 1330, 2022.
    [bibtex]
    @article{Raajaraam2022Computational,
      title = {A {{Computational Framework}} to {{Identify Metabolic Engineering Strategies}} for the {{Co-Production}} of {{Metabolites}}},
      author = {Raajaraam, Lavanya and Raman, Karthik},
      year = {2022},
      journal = {Frontiers in Bioengineering and Biotechnology},
      volume = {9},
      pages = {1330},
      issn = {2296-4185},
      doi = {10.3389/fbioe.2021.779405},
      abstract = {Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\73PVPMHZ\\Raajaraam and Raman - 2022 - A Computational Framework to Identify Metabolic En.pdf}
    }
  • [DOI] M. Ibrahim and K. Raman, “Two-Species Community Design of Lactic Acid Bacteria for Optimal Production of Lactate,” Computational and Structural Biotechnology Journal, vol. 19, pp. 6039-6049, 2021.
    [bibtex]
    @article{Ibrahim2021Twospecies,
      title = {Two-Species Community Design of Lactic Acid Bacteria for Optimal Production of Lactate},
      author = {Ibrahim, Maziya and Raman, Karthik},
      year = {2021},
      month = jan,
      journal = {Computational and Structural Biotechnology Journal},
      volume = {19},
      pages = {6039--6049},
      issn = {2001-0370},
      doi = {10.1016/j.csbj.2021.11.009},
      abstract = {Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, resulting in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational analysis approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on the optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging to Lactobacillus, Leuconostoc, and Pediococcus species from the Virtual Metabolic Human (VMH; https://vmh.life/) resource and well-curated GSMMs of L. plantarum WCSF1 and L. reuteri JCM 1112. We analysed 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in the communities. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, acetate, among other metabolites, were found to be cross-fed between community members. L. plantarum WCSF1 was found to be a member of communities with high lactate yields. In silico community optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.},
      langid = {english},
      keywords = {Constraint-based modelling,Cross-feeding,Genome-scale metabolic models,Metabolic engineering,Microbial consortia}
    }
  • [DOI] V. Senthamizhan, B. Ravindran, and K. Raman, “NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks,” Frontiers in Genetics, vol. 12, p. 1666, 2021.
    [bibtex]
    @article{Senthamizhan2021NetGenes,
      title = {{{NetGenes}}: {{A Database}} of {{Essential Genes Predicted Using Features From Interaction Networks}}},
      shorttitle = {{{NetGenes}}},
      author = {Senthamizhan, Vimaladhasan and Ravindran, Balaraman and Raman, Karthik},
      year = {2021},
      journal = {Frontiers in Genetics},
      volume = {12},
      pages = {1666},
      issn = {1664-8021},
      doi = {10.3389/fgene.2021.722198},
      abstract = {Essential gene prediction models built so far are heavily reliant on sequence-based features, and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we apply our approach for the prediction of essential genes to organisms from the STRING database and host the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2,700+ bacteria predicted using features derived from STRING protein\textendash protein functional association networks. Housing a total of over 2.1 million genes, NetGenes offers various features like essentiality scores, annotations, and feature vectors for each gene. NetGenes database is available from https://rbc-dsai-iitm.github.io/NetGenes/.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\9SUY67AY\\Senthamizhan et al. - 2021 - NetGenes A Database of Essential Genes Predicted .pdf}
    }
  • K. Raman, An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks, 1st edition ed., Boca Raton, FL: Chapman and Hall/CRC, 2021.
    [bibtex]
    @book{Raman2021Introduction,
      title = {An {{Introduction}} to {{Computational Systems Biology}}: {{Systems-Level Modelling}} of {{Cellular Networks}}},
      shorttitle = {An {{Introduction}} to {{Computational Systems Biology}}},
      author = {Raman, Karthik},
      year = {2021},
      month = may,
      edition = {1st edition},
      publisher = {{Chapman and Hall/CRC}},
      address = {{Boca Raton, FL}},
      abstract = {"This is a very comprehensive read that provides a solid base in computational biology. The book is structured in 4 parts and 14 chapters which cover all the way from the more basic concepts to advanced material, including the state-of-the-art methodologies in synthetic and systems biology. This is a bedside book for those researchers embarking to do investigation in computational biology and a great office companion for anyone working on systems and synthetic biology." -- Rodrigo Ledesma Amaro, Lecturer, Imperial College London  "This is a fantastic book. It offers an elegant introduction to both classical and modern concepts in computational biology. To the uninitiated, it is a terrific first read, bringing alive the glory of the past and the promise of the future. To the interested, it handholds and offers a springboard to dive deep. To the practitioner, it serves as a valuable resource bringing together in a panoramic view many diverse streams that adorn the landscape." -- Narendra M. Dixit, Professor, Indian Institute of Science An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks delivers a comprehensive and insightful account of applying mathematical modelling approaches to very large biological systems and networks\rule{1em}{1pt}a fundamental aspect of computational systems biology. The book covers key modelling paradigms in detail, while at the same time retaining a simplicity that will appeal to those from less quantitative fields.  Features   A hands-on approach to modelling   Covers a broad spectrum of modelling, from static networks to dynamic models and constraint-based models   Thoughtful exercises to test and enable understanding of concepts   State-of-the-art chapters on exciting new developments like community modelling and biological circuit design   Emphasis on coding and software tools for systems biology  This book is highly multi-disciplinary and will appeal to biologists, engineers, computer scientists, mathematicians and others.},
      isbn = {978-1-138-59732-7},
      langid = {english}
    }
  • [DOI] M. Ibrahim, L. Raajaraam, and K. Raman, “Modelling Microbial Communities: Harnessing Consortia for Biotechnological Applications,” Computational and Structural Biotechnology Journal, vol. 19, pp. 3892-3907, 2021.
    [bibtex]
    @article{Ibrahim2021Modelling,
      title = {Modelling Microbial Communities: {{Harnessing}} Consortia for Biotechnological Applications},
      shorttitle = {Modelling Microbial Communities},
      author = {Ibrahim, Maziya and Raajaraam, Lavanya and Raman, Karthik},
      year = {2021},
      month = jan,
      journal = {Computational and Structural Biotechnology Journal},
      volume = {19},
      pages = {3892--3907},
      issn = {2001-0370},
      doi = {10.1016/j.csbj.2021.06.048},
      abstract = {Microbes propagate and thrive in complex communities, and there are many benefits to studying and engineering microbial communities instead of single strains. Microbial communities are being increasingly leveraged in biotechnological applications, as they present significant advantages such as the division of labour and improved substrate utilisation. Nevertheless, they also present some interesting challenges to surmount for the design of efficient biotechnological processes. In this review, we discuss key principles of microbial interactions, followed by a deep dive into genome-scale metabolic models, focussing on a vast repertoire of constraint-based modelling methods that enable us to characterise and understand the metabolic capabilities of microbial communities. Complementary approaches to model microbial communities, such as those based on graph theory, are also briefly discussed. Taken together, these methods provide rich insights into the interactions between microbes and how they influence microbial community productivity. We finally overview approaches that allow us to generate and test numerous synthetic community compositions, followed by tools and methodologies that can predict effective genetic interventions to further improve the productivity of communities. With impending advancements in high-throughput omics of microbial communities, the stage is set for the rapid expansion of microbial community engineering, with a significant impact on biotechnological processes.},
      language = {en},
      keywords = {Constraint-based modelling,Genome-scale models,Metabolic engineering,Metabolic modelling,Microbial consortia,Microbiome},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\DGLGYXTK\\Ibrahim et al. - 2021 - Modelling microbial communities Harnessing consor.pdf;C\:\\Users\\Karthik\\Zotero\\storage\\U5JXRK6P\\S2001037021002865.html}
    }
  • [DOI] S. Gangadharan and K. Raman, “The Art of Molecular Computing: Whence and Whither,” BioEssays, vol. 43, iss. 8, p. 2100051, 2021.
    [bibtex]
    @article{Gangadharan2021Art,
      title = {The Art of Molecular Computing: {{Whence}} and Whither},
      shorttitle = {The Art of Molecular Computing},
      author = {Gangadharan, Sahana and Raman, Karthik},
      year = {2021},
      journal = {BioEssays},
      volume = {43},
      number = {8},
      pages = {2100051},
      issn = {1521-1878},
      doi = {10.1002/bies.202100051},
      abstract = {An astonishingly diverse biomolecular circuitry orchestrates the functioning machinery underlying every living cell. These biomolecules and their circuits have been engineered not only for various industrial applications but also to perform other atypical functions that they were not evolved for\textemdash including computation. Various kinds of computational challenges, such as solving NP-complete problems with many variables, logical computation, neural network operations, and cryptography, have all been attempted through this unconventional computing paradigm. In this review, we highlight key experiments across three different ``eras'' of molecular computation, beginning with molecular solutions, transitioning to logic circuits and ultimately, more complex molecular networks. We also discuss a variety of applications of molecular computation, from solving NP-hard problems to self-assembled nanostructures for delivering molecules, and provide a glimpse into the exciting potential that molecular computing holds for the future. Also see the video abstract here: https://youtu.be/9Mw0K0vCSQw},
      language = {en},
      keywords = {DNA computing,DNA data storage,logic gates,molecular machines,unconventional computing},
      annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bies.202100051},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\4ZMB2FZ5\\Gangadharan and Raman - 2021 - The art of molecular computing Whence and whither.pdf;C\:\\Users\\Karthik\\Zotero\\storage\\D8SJ9RAV\\bies.html}
    }
  • [DOI] S. Banerjee, K. Raman, and B. Ravindran, “Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes,” Cancers, vol. 13, iss. 10, p. 2366, 2021.
    [bibtex]
    @article{Banerjee2021Sequence,
      title = {Sequence {{Neighborhoods Enable Reliable Prediction}} of {{Pathogenic Mutations}} in {{Cancer Genomes}}},
      author = {Banerjee, Shayantan and Raman, Karthik and Ravindran, Balaraman},
      year = {2021},
      month = jan,
      volume = {13},
      pages = {2366},
      doi = {10.3390/cancers13102366},
      abstract = {Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. ``Driver'' mutations are primarily responsible for cancer progression, while ``passengers'' are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5{${'}$} and 3{${'}$} from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.},
      copyright = {http://creativecommons.org/licenses/by/3.0/},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\MQ74LXQA\\htm.html},
      journal = {Cancers},
      keywords = {cancer driver mutations,context of mutations,machine learning,missense mutations,neighborhood sequences},
      language = {en},
      number = {10}
    }
  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities,” in Methods in Molecular Biology, , 2021, vol. 2189, pp. 133-155.
    [bibtex]
    @inbook{Bhattacharya2021SystemsTheoretic,
      title = {Systems-{{Theoretic Approaches}} to {{Design Biological Networks}} with {{Desired Functionalities}}},
      author = {Bhattacharya, Priyan and Raman, Karthik and Tangirala, Arun K.},
      year = {2021},
      volume = {2189},
      pages = {133--155},
      issn = {1940-6029},
      doi = {10.1007/978-1-0716-0822-7_11},
      abstract = {The deduction of design principles for complex biological functionalities has been a source of constant interest in the fields of systems and synthetic biology. A number of approaches have been adopted, to identify the space of network structures or topologies that can demonstrate a specific desired functionality, ranging from brute force to systems theory-based methodologies. The former approach involves performing a search among all possible combinations of network structures, as well as the parameters underlying the rate kinetics for a given form of network. In contrast to the search-oriented approach in brute force studies, the present chapter introduces a generic approach inspired by systems theory to deduce the network structures for a particular biological functionality. As a first step, depending on the functionality and the type of network in consideration, a measure of goodness of attainment is deduced by defining performance parameters. These parameters are computed for the most ideal case to obtain the necessary condition for the given functionality. The necessary conditions are then mapped as specific requirements on the parameters of the dynamical system underlying the network. Following this, admissible minimal structures are deduced. The proposed methodology does not assume any particular rate kinetics in this case for deducing the admissible network structures notwithstanding a minimum set of assumptions on the rate kinetics. The problem of computing the ideal set of parameter/s or rate constants, unlike the problem of topology identification, depends on the particular rate kinetics assumed for the given network. In this case, instead of a computationally exhaustive brute force search of the parameter space, a topology-functionality specific optimization problem can be solved. The objective function along with the feasible region bounded by the motif specific constraints amounts to solving a non-convex optimization program leading to non-unique parameter sets. To exemplify our approach, we adopt the functionality of adaptation, and demonstrate how network topologies that can achieve adaptation can be identified using such a systems-theoretic approach. The outcomes, in this case, i.e., minimum network structures for adaptation, are in agreement with the brute force results and other studies in literature.},
      booktitle = {Methods in Molecular Biology},
      keywords = {Adaptation,Design principles,Stability,Systems biology,Systems theory},
      language = {eng},
      pmid = {33180299}
    }
  • [DOI] S. M. Keating, D. Waltemath, M. König, F. Zhang, A. Dräger, C. Chaouiya, F. T. Bergmann, A. Finney, C. S. Gillespie, T. Helikar, S. Hoops, R. S. Malik-Sheriff, S. L. Moodie, I. I. Moraru, C. J. Myers, A. Naldi, B. G. Olivier, S. Sahle, J. C. Schaff, L. P. Smith, M. J. Swat, D. Thieffry, L. Watanabe, D. J. Wilkinson, M. L. Blinov, K. Begley, J. R. Faeder, H. F. Gómez, T. M. Hamm, Y. Inagaki, W. Liebermeister, A. L. Lister, D. Lucio, E. Mjolsness, C. J. Proctor, K. Raman, N. Rodriguez, C. A. Shaffer, B. E. Shapiro, J. Stelling, N. Swainston, N. Tanimura, J. Wagner, M. Meier-Schellersheim, H. M. Sauro, B. Palsson, H. Bolouri, H. Kitano, A. Funahashi, H. Hermjakob, J. C. Doyle, M. Hucka, and S. L. C. 3. members, “SBML Level 3: An Extensible Format for the Exchange and Reuse of Biological Models,” Molecular Systems Biology, vol. 16, iss. 8, p. e9110, 2020.
    [bibtex]
    @article{Keating2020SBML,
      title = {{SBML Level} 3: An Extensible Format for the Exchange and Reuse of Biological Models},
      shorttitle = {{SBML Level} 3},
      author = {Keating, Sarah M and Waltemath, Dagmar and König, Matthias and Zhang, Fengkai and Dräger, Andreas and Chaouiya, Claudine and Bergmann, Frank T and Finney, Andrew and Gillespie, Colin S and Helikar, Tomáš and Hoops, Stefan and {Malik-Sheriff}, Rahuman S and Moodie, Stuart L and Moraru, Ion I and Myers, Chris J and Naldi, Aurélien and Olivier, Brett G and Sahle, Sven and Schaff, James C and Smith, Lucian P and Swat, Maciej J and Thieffry, Denis and Watanabe, Leandro and Wilkinson, Darren J and Blinov, Michael L and Begley, Kimberly and Faeder, James R and Gómez, Harold F and Hamm, Thomas M and Inagaki, Yuichiro and Liebermeister, Wolfram and Lister, Allyson L and Lucio, Daniel and Mjolsness, Eric and Proctor, Carole J and Raman, Karthik and Rodriguez, Nicolas and Shaffer, Clifford A and Shapiro, Bruce E and Stelling, Joerg and Swainston, Neil and Tanimura, Naoki and Wagner, John and {Meier-Schellersheim}, Martin and Sauro, Herbert M and Palsson, Bernhard and Bolouri, Hamid and Kitano, Hiroaki and Funahashi, Akira and Hermjakob, Henning and Doyle, John C and Hucka, Michael and {SBML Level 3 Community members}},
      year = {2020},
      month = aug,
      volume = {16},
      pages = {e9110},
      issn = {1744-4292},
      doi = {10.15252/msb.20199110},
      abstract = {Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\KCPCXI42\\msb.html},
      journal = {Molecular Systems Biology},
      keywords = {computational modeling,file format,interoperability,reproducibility,systems biology},
      number = {8}
    }
  • [DOI] P. Jagadeesan, K. Raman, and A. K. Tangirala, “A New Index for Information Gain in the Bayesian Framework,” IFAC-PapersOnLine, vol. 53, iss. 1, pp. 634-639, 2020.
    [bibtex]
    @article{Jagadeesan2020New,
      title = {A New Index for Information Gain in the {{Bayesian}} Framework},
      author = {Jagadeesan, Prem and Raman, Karthik and Tangirala, Arun K.},
      year = {2020},
      month = jan,
      volume = {53},
      pages = {634--639},
      issn = {2405-8963},
      doi = {10.1016/j.ifacol.2020.06.106},
      abstract = {In data-driven dynamical modeling, precise estimation of the parameters of large models from limited data has been considered a challenging task. The precision of the parameter estimates is highly dependent upon the information contained in the data; Loss of practical identifiability and sloppiness in the model structure are major challenges in estimating parameters precisely and closely related to the information contained in the data. Therefore, quantifying information is an important step in data-driven modeling. Quantifying information is a well-studied problem in the frequentist approach, where Fisher Information is one of the widely used metrics. However, Fisher Information computed via maximum likelihood estimation cannot accommodate any known prior knowledge about the parameters. Prior knowledge of the parameters along with informative experiments will improve the precision of the estimates. Bayesian estimation accommodates prior information in the form of a p.d.f. There has been very little work in the literature for quantifying information in the Bayesian framework. In this work, we introduce a new method for estimating information gain in the Bayesian framework using what is known as the Bhattacharyya coefficient. It is seen that the bounds of the coefficient have an insightful interpretation naturally in terms of information gain on the parameter of interest. We also demonstrate using case studies that the information gain of each parameter is an indication of loss of practical identifiability and sloppy parameters. It is also shown that the proposed information gain can be used as a model selection tool in black-box identification.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\78YIN2SV\\Jagadeesan et al. - 2020 - A new index for information gain in the Bayesian f.pdf;C\:\\Users\\Karthik\\Zotero\\storage\\SQFTHQHN\\S2405896320301257.html},
      journal = {IFAC-PapersOnLine},
      keywords = {Approximate Bayesian Computation (ABC),Bhattacharyya Coefficient,Information Gain,Model selection,Model Sloppiness,Practical Identifiability},
      language = {en},
      number = {1},
      series = {6th {{Conference}} on {{Advances}} in {{Control}} and {{Optimization}} of {{Dynamical Systems ACODS}} 2020}
    }
  • [DOI] K. R. Chng, T. S. Ghosh, Y. H. Tan, T. Nandi, I. R. Lee, A. H. Q. Ng, C. Li, A. Ravikrishnan, K. M. Lim, D. Lye, T. Barkham, K. Raman, S. L. Chen, L. Chai, B. Young, Y. Gan, and N. Nagarajan, “Metagenome-Wide Association Analysis Identifies Microbial Determinants of Post-Antibiotic Ecological Recovery in the Gut,” Nature Ecology & Evolution, vol. 4, iss. 1256-1267, pp. 1-12, 2020.
    [bibtex]
    @article{Chng2020Metagenomewide,
      title = {Metagenome-Wide Association Analysis Identifies Microbial Determinants of Post-Antibiotic Ecological Recovery in the Gut},
      author = {Chng, Kern Rei and Ghosh, Tarini Shankar and Tan, Yi Han and Nandi, Tannistha and Lee, Ivor Russel and Ng, Amanda Hui Qi and Li, Chenhao and Ravikrishnan, Aarthi and Lim, Kar Mun and Lye, David and Barkham, Timothy and Raman, Karthik and Chen, Swaine L. and Chai, Louis and Young, Barnaby and Gan, Yunn-Hwen and Nagarajan, Niranjan},
      year = {2020},
      month = jul,
      volume = {4},
      number = {1256-1267},
      pages = {1--12},
      issn = {2397-334X},
      doi = {10.1038/s41559-020-1236-0},
      abstract = {Loss of diversity in the gut microbiome can persist for extended periods after antibiotic treatment, impacting microbiome function, antimicrobial resistance and probably host health. Despite widespread antibiotic use, our understanding of the species and metabolic functions contributing to gut microbiome recovery is limited. Using data from 4 discovery cohorts in 3 continents comprising {$>$}500 microbiome profiles from 117 individuals, we identified 21 bacterial species exhibiting robust association with ecological recovery post antibiotic therapy. Functional and growth-rate analysis showed that recovery is supported by enrichment in specific carbohydrate-degradation and energy-production pathways. Association rule mining on 782 microbiome profiles from the MEDUSA database enabled reconstruction of the gut microbial ‘food web’, identifying many recovery-associated bacteria as keystone species, with the ability to use host- and diet-derived energy sources, and support repopulation of other gut species. Experiments in a mouse model recapitulated the ability of recovery-associated bacteria (Bacteroides thetaiotaomicron and Bifidobacterium adolescentis) to promote recovery with synergistic effects, providing a boost of two orders of magnitude to microbial abundance in early time points and faster maturation of microbial diversity. The identification of specific species and metabolic functions promoting recovery opens up opportunities for rationally determining pre- and probiotic formulations offering protection from long-term consequences of frequent antibiotic usage.},
      copyright = {2020 The Author(s), under exclusive licence to Springer Nature Limited},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\YM5BDE79\\s41559-020-1236-0.html},
      journal = {Nature Ecology \& Evolution},
      language = {en}
    }
  • [DOI] U. W. Liebal, A. N. T. Phan, M. Sudhakar, K. Raman, and L. M. Blank, “Machine Learning Applications for Mass Spectrometry-Based Metabolomics,” Metabolites, vol. 10, iss. 6, p. 243, 2020.
    [bibtex]
    @article{Liebal2020Machine,
      title = {Machine {{Learning Applications}} for {{Mass Spectrometry}}-{{Based Metabolomics}}},
      author = {Liebal, Ulf W. and Phan, An N. T. and Sudhakar, Malvika and Raman, Karthik and Blank, Lars M.},
      year = {2020},
      month = jun,
      volume = {10},
      pages = {243},
      doi = {10.3390/metabo10060243},
      abstract = {The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.},
      copyright = {http://creativecommons.org/licenses/by/3.0/},
      journal = {Metabolites},
      keywords = {machine learning,metabolic engineering,metabolic flux analysis,MS-based metabolomics,multi-omics,synthetic biology},
      language = {en},
      number = {6}
    }
  • [DOI] K. Sachdeva, M. Goel, M. Sudhakar, M. Mehta, R. Raju, K. Raman, A. Singh, and V. Sundaramurthy, “Mycobacterium Tuberculosis (Mtb) Lipid–Mediated Lysosomal Rewiring in Infected Macrophages Modulates Intracellular Mtb Trafficking and Survival,” Journal of Biological Chemistry, vol. 295, pp. 9192-9210, 2020.
    [bibtex]
    @article{Sachdeva2020Mycobacterium,
      title = {Mycobacterium Tuberculosis ({{Mtb}}) Lipid–Mediated Lysosomal Rewiring in Infected Macrophages Modulates Intracellular {{Mtb}} Trafficking and Survival},
      author = {Sachdeva, Kuldeep and Goel, Manisha and Sudhakar, Malvika and Mehta, Mansi and Raju, Rajmani and Raman, Karthik and Singh, Amit and Sundaramurthy, Varadharajan},
      year = {2020},
      month = may,
      issn = {0021-9258, 1083-351X},
      doi = {10.1074/jbc.RA120.012809},
      abstract = {Intracellular pathogens commonly manipulate the host lysosomal system for their survival. However, whether this pathogen-induced alteration affects the organization and functioning of the lysosomal system itself is not known. Here, using in vitro and in vivo infections and quantitative image analysis, we show that the lysosomal content and activity are globally elevated in Mycobacterium tuberculosis (Mtb) infected macrophages. We observed that this enhanced lysosomal state is sustained over time and defines an adaptive homeostasis in the infected macrophage. Lysosomal alterations are caused by mycobacterial surface components, notably the cell wall associated lipid sulfolipid-1 (SL-1), which functions through the mTOR complex 1 (mTORC1) – transcription factor EB (TFEB) axis in the host cells. An Mtb mutant lacking SL-1, MtbDpks2, shows attenuated lysosomal rewiring compared with the wild type Mtb in both in vitro and in vivo infections. Exposing macrophages to purified SL-1 enhanced the trafficking of phagocytic cargo to lysosomes. Correspondingly, MtbDpks2 exhibited a further reduction in lysosomal delivery compared with the wild type. Reduced trafficking of this mutant Mtb strain to lysosomes correlated with enhanced intracellular bacterial survival. Our results reveal that global alteration of the host lysosomal system is a defining feature of Mtb infected macrophages and suggest that this altered lysosomal state protects host cell integrity and contributes to the containment of the pathogen.},
      journal = {Journal of Biological Chemistry},
      volume = {295},
      pages = {9192-9210},
      keywords = {cell wall lipid,host-pathogen interaction,innate immunity,lysosome,macrophage,mTOR complex (mTORC),mycobacteria,Mycobacterium tuberculosis,pathogenesis,phagocytosis,sulfolipid-1 (SL-1)},
      language = {en},
      pmid = {32424041}
    }
  • [DOI] G. Sambamoorthy and K. Raman, “MinReact: A Systematic Approach for Identifying Minimal Metabolic Networks,” Bioinformatics (Oxford, England), vol. 36, iss. 15, pp. 4309-4315, 2020.
    [bibtex]
    @article{Sambamoorthy2020MinReact,
      title = {{{MinReact}}: A Systematic Approach for Identifying Minimal Metabolic Networks},
      shorttitle = {{{MinReact}}},
      author = {Sambamoorthy, Gayathri and Raman, Karthik},
      year = {2020},
      month = may,
      issn = {1367-4811},
      doi = {10.1093/bioinformatics/btaa497},
      abstract = {MOTIVATION: Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimise the metabolic networks into smaller networks that retain the functionality of the original network.
    RESULTS: Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrate that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner.
    AVAILABILITY: Algorithm is available from https://github.com/RamanLab/MinReact.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
      journal = {Bioinformatics (Oxford, England)},
      language = {eng},
      volume = {36},
      number = {15},
      pages = {4309-4315},
      pmid = {32407533}
    }
  • [DOI] A. Ravikrishnan, L. M. Blank, S. Srivastava, and K. Raman, “Investigating Metabolic Interactions in a Microbial Co-Culture through Integrated Modelling and Experiments,” Computational and Structural Biotechnology Journal, vol. 18, pp. 1249-1258, 2020.
    [bibtex]
    @article{Ravikrishnan2020Investigating,
      title = {Investigating Metabolic Interactions in a Microbial Co-Culture through Integrated Modelling and Experiments},
      author = {Ravikrishnan, Aarthi and Blank, Lars M. and Srivastava, Smita and Raman, Karthik},
      year = {2020},
      month = mar,
      issn = {2001-0370},
      doi = {10.1016/j.csbj.2020.03.019},
      volume = {18},
      pages = {1249--1258},
      abstract = {Microbial co-cultures have been used in several biotechnological applications. Within these co-cultures, the microorganisms tend to interact with each other and perform complex actions. Investigating metabolic interactions in microbial co-cultures is crucial in designing microbial consortia. Here, we present a pipeline integrating modelling and experimental approaches to understand metabolic interactions between organisms in a community. We define a new index named “Metabolic Support Index (MSI)”, which quantifies the benefits derived by each organism in the presence of the other when grown as a co-culture. We computed MSI for several experimentally demonstrated co-cultures and showed that MSI, as a metric, accurately identifies the organism that derives the maximum benefit. We also computed MSI for a commonly used yeast co-culture consisting of Saccharomyces cerevisiae and Pichia stipitis and observed that the latter derives higher benefit from the interaction. Further, we designed two-stage experiments to study mutual interactions and showed that P. stipitis indeed derives the maximum benefit from the interaction, as shown from our computational predictions. Also, using our previously developed computational tool MetQuest, we identified all the metabolic exchanges happening between these organisms by analysing the pathways spanning the two organisms. By analysing the HPLC profiles and studying the isotope labelling, we show that P. stipitis consumes the ethanol produced by S. cerevisiae when grown on glucose-rich medium under aerobic conditions, as also indicated by our in silico pathway analyses. Our approach represents an important step in understanding metabolic interactions in microbial communities through an integrated computational and experimental workflow.},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\ELL68Q8M\\Ravikrishnan et al. - 2020 - Investigating metabolic interactions in a microbia.pdf;C\:\\Users\\Karthik\\Zotero\\storage\\XLV67CIY\\S2001037019303484.html},
      journal = {Computational and Structural Biotechnology Journal},
      keywords = {Metabolic exchanges,Metabolic Support Index,Microbial co-cultures,Microbial interactions,Pathway analyses},
      language = {en}
    }
  • [DOI] N. T. Devika and K. Raman, “Deciphering the Metabolic Capabilities of Bifidobacteria Using Genome-Scale Metabolic Models,” Scientific Reports, vol. 9, iss. 1, p. 18222, 2019.
    [bibtex]
    @article{Devika2019Deciphering,
      title = {Deciphering the Metabolic Capabilities of {{Bifidobacteria}} Using Genome-Scale Metabolic Models},
      author = {Devika, N. T. and Raman, Karthik},
      year = {2019},
      month = dec,
      volume = {9},
      pages = {18222},
      issn = {2045-2322},
      doi = {10.1038/s41598-019-54696-9},
      abstract = {Bifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.},
      copyright = {2019 The Author(s)},
      file = {C\:\\Users\\Karthik\\Zotero\\storage\\U6ZJK899\\Devika and Raman - 2019 - Deciphering the metabolic capabilities of Bifidoba.pdf},
      journal = {Scientific Reports},
      language = {en},
      number = {1}
    }
  • [DOI] S. Choobdar, M. E. Ahsen, J. Crawford, M. Tomasoni, T. Fang, D. Lamparter, J. Lin, B. Hescott, X. Hu, J. Mercer, T. Natoli, R. Narayan, F. Aicheler, N. Amoroso, A. Arenas, K. Azhagesan, A. Baker, M. Banf, S. Batzoglou, A. Baudot, R. Bellotti, S. Bergmann, K. A. Boroevich, C. Brun, S. Cai, M. Caldera, A. Calderone, G. Cesareni, W. Chen, C. Chichester, S. Choobdar, L. Cowen, J. Crawford, H. Cui, P. Dao, M. De Domenico, A. Dhroso, G. Didier, M. Divine, A. del Sol, T. Fang, X. Feng, J. C. Flores-Canales, S. Fortunato, A. Gitter, A. Gorska, Y. Guan, A. Guénoche, S. Gómez, H. Hamza, A. Hartmann, S. He, A. Heijs, J. Heinrich, B. Hescott, X. Hu, Y. Hu, X. Huang, K. V. Hughitt, M. Jeon, L. Jeub, N. T. Johnson, K. Joo, I. Joung, S. Jung, S. G. Kalko, P. J. Kamola, J. Kang, B. Kaveelerdpotjana, M. Kim, Y. Kim, O. Kohlbacher, D. Korkin, K. Krzysztof, K. Kunji, Z. Kutalik, K. Lage, D. Lamparter, S. Lang-Brown, T. D. Le, J. Lee, S. Lee, J. Lee, D. Li, J. Li, J. Lin, L. Liu, A. Loizou, Z. Luo, A. Lysenko, T. Ma, R. Mall, D. Marbach, T. Mattia, M. Medvedovic, J. Menche, J. Mercer, E. Micarelli, A. Monaco, F. Müller, R. Narayan, O. Narykov, T. Natoli, T. Norman, S. Park, L. Perfetto, D. Perrin, S. Pirrò, T. M. Przytycka, X. Qian, K. Raman, D. Ramazzotti, E. Ramsahai, B. Ravindran, P. Rennert, J. Saez-Rodriguez, C. Schärfe, R. Sharan, N. Shi, W. Shin, H. Shu, H. Sinha, D. K. Slonim, L. Spinelli, S. Srinivasan, A. Subramanian, C. Suver, D. Szklarczyk, S. Tangaro, S. Thiagarajan, L. Tichit, T. Tiede, B. Tripathi, A. Tsherniak, T. Tsunoda, D. Türei, E. Ullah, G. Vahedi, A. Valdeolivas, J. Vivek, C. von Mering, A. Waagmeester, B. Wang, Y. Wang, B. A. Weir, S. White, S. Winkler, K. Xu, T. Xu, C. Yan, L. Yang, K. Yu, X. Yu, G. Zaffaroni, M. Zaslavskiy, T. Zeng, J. D. Zhang, L. Zhang, W. Zhang, L. Zhang, X. Zhang, J. Zhang, X. Zhou, J. Zhou, H. Zhu, J. Zhu, G. Zuccon, A. Subramanian, J. D. Zhang, G. Stolovitzky, Z. Kutalik, K. Lage, D. K. Slonim, J. Saez-Rodriguez, L. J. Cowen, S. Bergmann, D. Marbach, and T. D. M. I. C. Consortium, “Assessment of network module identification across complex diseases,” Nature Methods, vol. 16, iss. 9, pp. 843-852, 2019.
    [bibtex]
    @article{Choobdar2019Assessment,
      abstract = {Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.},
      added-at = {2019-08-31T09:26:34.000+0200},
      author = {Choobdar, Sarvenaz and Ahsen, Mehmet E. and Crawford, Jake and Tomasoni, Mattia and Fang, Tao and Lamparter, David and Lin, Junyuan and Hescott, Benjamin and Hu, Xiaozhe and Mercer, Johnathan and Natoli, Ted and Narayan, Rajiv and Aicheler, Fabian and Amoroso, Nicola and Arenas, Alex and Azhagesan, Karthik and Baker, Aaron and Banf, Michael and Batzoglou, Serafim and Baudot, Anaïs and Bellotti, Roberto and Bergmann, Sven and Boroevich, Keith A. and Brun, Christine and Cai, Stanley and Caldera, Michael and Calderone, Alberto and Cesareni, Gianni and Chen, Weiqi and Chichester, Christine and Choobdar, Sarvenaz and Cowen, Lenore and Crawford, Jake and Cui, Hongzhu and Dao, Phuong and De Domenico, Manlio and Dhroso, Andi and Didier, Gilles and Divine, Mathew and del Sol, Antonio and Fang, Tao and Feng, Xuyang and Flores-Canales, Jose C. and Fortunato, Santo and Gitter, Anthony and Gorska, Anna and Guan, Yuanfang and Guénoche, Alain and Gómez, Sergio and Hamza, Hatem and Hartmann, András and He, Shan and Heijs, Anton and Heinrich, Julian and Hescott, Benjamin and Hu, Xiaozhe and Hu, Ying and Huang, Xiaoqing and Hughitt, V. Keith and Jeon, Minji and Jeub, Lucas and Johnson, Nathan T. and Joo, Keehyoung and Joung, InSuk and Jung, Sascha and Kalko, Susana G. and Kamola, Piotr J. and Kang, Jaewoo and Kaveelerdpotjana, Benjapun and Kim, Minjun and Kim, Yoo-Ah and Kohlbacher, Oliver and Korkin, Dmitry and Krzysztof, Kiryluk and Kunji, Khalid and Kutalik, Zoltàn and Lage, Kasper and Lamparter, David and Lang-Brown, Sean and Le, Thuc Duy and Lee, Jooyoung and Lee, Sunwon and Lee, Juyong and Li, Dong and Li, Jiuyong and Lin, Junyuan and Liu, Lin and Loizou, Antonis and Luo, Zhenhua and Lysenko, Artem and Ma, Tianle and Mall, Raghvendra and Marbach, Daniel and Mattia, Tomasoni and Medvedovic, Mario and Menche, Jörg and Mercer, Johnathan and Micarelli, Elisa and Monaco, Alfonso and Müller, Felix and Narayan, Rajiv and Narykov, Oleksandr and Natoli, Ted and Norman, Thea and Park, Sungjoon and Perfetto, Livia and Perrin, Dimitri and Pirrò, Stefano and Przytycka, Teresa M. and Qian, Xiaoning and Raman, Karthik and Ramazzotti, Daniele and Ramsahai, Emilie and Ravindran, Balaraman and Rennert, Philip and Saez-Rodriguez, Julio and Schärfe, Charlotta and Sharan, Roded and Shi, Ning and Shin, Wonho and Shu, Hai and Sinha, Himanshu and Slonim, Donna K. and Spinelli, Lionel and Srinivasan, Suhas and Subramanian, Aravind and Suver, Christine and Szklarczyk, Damian and Tangaro, Sabina and Thiagarajan, Suresh and Tichit, Laurent and Tiede, Thorsten and Tripathi, Beethika and Tsherniak, Aviad and Tsunoda, Tatsuhiko and Türei, Dénes and Ullah, Ehsan and Vahedi, Golnaz and Valdeolivas, Alberto and Vivek, Jayaswal and von Mering, Christian and Waagmeester, Andra and Wang, Bo and Wang, Yijie and Weir, Barbara A. and White, Shana and Winkler, Sebastian and Xu, Ke and Xu, Taosheng and Yan, Chunhua and Yang, Liuqing and Yu, Kaixian and Yu, Xiangtian and Zaffaroni, Gaia and Zaslavskiy, Mikhail and Zeng, Tao and Zhang, Jitao D. and Zhang, Lu and Zhang, Weijia and Zhang, Lixia and Zhang, Xinyu and Zhang, Junpeng and Zhou, Xin and Zhou, Jiarui and Zhu, Hongtu and Zhu, Junjie and Zuccon, Guido and Subramanian, Aravind and Zhang, Jitao D. and Stolovitzky, Gustavo and Kutalik, Zoltán and Lage, Kasper and Slonim, Donna K. and Saez-Rodriguez, Julio and Cowen, Lenore J. and Bergmann, Sven and Marbach, Daniel and Consortium, The DREAM Module Identification Challenge},
      biburl = {https://www.bibsonomy.org/bibtex/2b16329a3c248adb971d62f5565b27a0e/karthikraman},
      description = {Assessment of network module identification across complex diseases | Nature Methods},
      doi = {10.1038/s41592-019-0509-5},
      interhash = {9fe2b286802d6572f14a8a2040d5dcc2},
      intrahash = {b16329a3c248adb971d62f5565b27a0e},
      issn = {15487105},
      journal = {Nature Methods},
      keywords = {community-detection myown networks},
      number = 9,
      pages = {843--852},
      refid = {Choobdar2019},
      timestamp = {2019-08-31T09:26:34.000+0200},
      title = {Assessment of network module identification across complex diseases},
      url = {https://doi.org/10.1038/s41592-019-0509-5},
      volume = 16,
      year = 2019
    }
  • [DOI] A. Srinivasan, V. S, K. Raman, and S. Srivastava, “Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture,” Biochemical Engineering Journal, vol. 151, p. 107256, 2019.
    [bibtex]
    @article{Srinivasan2019Rational,
      abstract = {Alpha-tocopherol, an essential dietary supplement, synthesized by photosynthetic organisms is the most biologically active antioxidant component of vitamin E in humans. Attempts to improve the yield of alpha-tocopherol using plant cell cultures has gained significance in recent years. Here, we developed a high alpha-tocopherol yielding cell line of Helianthus annuus using a model based metabolic engineering approach. To this end, we adapted an available genome-scale model of Arabidopsis for simulating H. annuus metabolism using constraint-based analysis to identify and rank suitable enzyme targets for overexpression. Of the various model-predicted enzyme targets, majority belonged to the vitamin E pathway and the MEP pathway while the others included reactions from the nucleotide biosynthesis and amino acid metabolism. Experimental validation of the top strategy (overexpression of p-hydroxyphenylpyruvate dioxygenase,) resulted in a high alpha-tocopherol yielding transformed cell line (up to ˜240 µg g-1), which was ˜10-fold more than in the untransformed cell line. A cell suspension was developed from the selected transformed cell line for in vitro production of alpha-tocopherol, which resulted in a maximum alpha-tocopherol yield of 412.2 µg g-1 and titre of 6.4 mg L-1.We thus demonstrate the utility of model-based metabolic engineering for multi-fold yield enhancement of commercially important plant secondary metabolites.},
      added-at = {2019-06-04T18:25:58.000+0200},
      author = {Srinivasan, Aparajitha and S, Vijayakumar and Raman, Karthik and Srivastava, Smita},
      biburl = {https://www.bibsonomy.org/bibtex/2952f0d54abbfa79c5603816ece8ed97e/karthikraman},
      description = {Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture - ScienceDirect},
      doi = {https://doi.org/10.1016/j.bej.2019.107256},
      interhash = {4844a665eb5d7dfaccdf0b0dbdbf7c60},
      intrahash = {952f0d54abbfa79c5603816ece8ed97e},
      issn = {1369-703X},
      journal = {Biochemical Engineering Journal},
      keywords = {flux-analysis fseof in-silico metabolic-engineering myown},
      volume = 151,
      pages = 107256,
      timestamp = {2019-09-06T09:18:53.000+0200},
      title = {Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture},
      url = {http://www.sciencedirect.com/science/article/pii/S1369703X19301834},
      year = 2019
    }
  • [DOI] A. Badri, K. Raman, and G. Jayaraman, “Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation,” Processes, vol. 7, iss. 6, p. 343, 2019.
    [bibtex]
    @article{Badri2019Uncovering,
      abstract = {Hyaluronan (HA), a glycosaminoglycan with important medical applications, is commercially produced from pathogenic microbial sources. The metabolism of HA-producing recombinant generally regarded as safe (GRAS) systems needs to be more strategically engineered to achieve yields higher than native producers. Here, we use a genome-scale model (GEM) to account for the entire metabolic network of the cell while predicting strategies to improve HA production. We analyze the metabolic network of Lactococcus lactis adapted to produce HA and identify non-conventional strategies to enhance HA flux. We also show experimental verification of one of the predicted strategies. We thus identified an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, that can function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report to use a nucleoside to improve HA production, we demonstrate the role of experimental validation in model refinement and strategy improvisation. Overall, we point out that well-constructed GEMs could be used to derive efficient strategies to improve the biosynthesis of high-value products.},
      added-at = {2019-07-31T18:39:08.000+0200},
      article-number = {343},
      author = {Badri, Abinaya and Raman, Karthik and Jayaraman, Guhan},
      biburl = {https://www.bibsonomy.org/bibtex/29a5a609c325d08b2d3c6a14bf22e2216/karthikraman},
      doi = {10.3390/pr7060343},
      interhash = {4c176d125a427b479f6ffa2cb1993edf},
      intrahash = {9a5a609c325d08b2d3c6a14bf22e2216},
      issn = {2227-9717},
      journal = {Processes},
      keywords = {in-silico metabolic-engineering metabolic-networks myown systems-biology},
      number = 6,
      timestamp = {2019-07-31T18:39:08.000+0200},
      title = {Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation},
      url = {https://www.mdpi.com/2227-9717/7/6/343},
      volume = 7,
      pages = 343,
      year = 2019
    }
  • [DOI] B. Tripathi, S. Parthasarathy, H. Sinha, K. Raman, and B. Ravindran, “Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks,” Frontiers in Genetics, vol. 10, p. 164, 2019.
    [bibtex]
    @article{Tripathi2019adapting,
      added-at = {2019-03-13T06:50:21.000+0100},
      author = {Tripathi, Beethika and Parthasarathy, Srinivasan and Sinha, Himanshu and Raman, Karthik and Ravindran, Balaraman},
      biburl = {https://www.bibsonomy.org/bibtex/2feed1e151a466869cb37f3968233fdad/karthikraman},
      description = {Frontiers | Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks | Genetics},
      doi = {10.3389/fgene.2019.00164},
      interhash = {44d8a15cfa518d0b07490773e9aef0ef},
      intrahash = {feed1e151a466869cb37f3968233fdad},
      journal = {Frontiers in Genetics},
      keywords = {community-detection myown networks},
      month = mar,
      timestamp = {2019-03-13T06:50:21.000+0100},
      title = {Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks},
      url = {https://doi.org/10.3389%2Ffgene.2019.00164},
      volume = 10,
      pages = 164,
      year = 2019
    }
  • [DOI] G. Sambamoorthy, H. Sinha, and K. Raman, “Evolutionary design principles in metabolism,” Proc Biol Sci, vol. 286, iss. 1898, p. 20190098, 2019.
    [bibtex]
    @article{Sambamoorthy2019Evolutionary,
      abstract = {Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.},
      added-at = {2019-03-12T15:30:51.000+0100},
      author = {Sambamoorthy, G and Sinha, H and Raman, K},
      biburl = {https://www.bibsonomy.org/bibtex/218e773209ccb6dbc7c85a479f28dffc7/karthikraman},
      description = {Evolutionary design principles in metabolism. - PubMed - NCBI},
      doi = {10.1098/rspb.2019.0098},
      interhash = {ffc1e60a56be2fff817f640c85863eeb},
      intrahash = {18e773209ccb6dbc7c85a479f28dffc7},
      journal = {Proc Biol Sci},
      keywords = {design-principles metabolic-networks myown},
      month = mar,
      number = 1898,
      pages = {20190098},
      pmid = {30836874},
      timestamp = {2019-03-13T06:51:56.000+0100},
      title = {Evolutionary design principles in metabolism},
      url = {https://www.ncbi.nlm.nih.gov/pubmed/30836874?dopt=Abstract},
      volume = 286,
      year = 2019
    }
  • [DOI] K. Azhagesan, B. Ravindran, and K. Raman, “Network-based features enable prediction of essential genes across diverse organisms,” PLOS ONE, vol. 13, iss. 12, pp. 1-13, 2018.
    [bibtex]
    @article{Azhagesan2018networkbased,
      abstract = {Machine learning approaches to predict essential genes have gained a lot of traction in recent years. These approaches predominantly make use of sequence and network-based features to predict essential genes. However, the scope of network-based features used by the existing approaches is very narrow. Further, many of these studies focus on predicting essential genes within the same organism, which cannot be readily used to predict essential genes across organisms. Therefore, there is clearly a need for a method that is able to predict essential genes across organisms, by leveraging network-based features. In this study, we extract several sets of network-based features from protein–protein association networks available from the STRING database. Our network features include some common measures of centrality, and also some novel recursive measures recently proposed in social network literature. We extract hundreds of network-based features from networks of 27 diverse organisms to predict the essentiality of 87000+ genes. Our results show that network-based features are statistically significantly better at classifying essential genes across diverse bacterial species, compared to the current state-of-the-art methods, which use mostly sequence and a few ‘conventional’ network-based features. Our diverse set of network properties gave an AUROC of 0.847 and a precision of 0.320 across 27 organisms. When we augmented the complete set of network features with sequence-derived features, we achieved an improved AUROC of 0.857 and a precision of 0.335. We also constructed a reduced set of 100 sequence and network features, which gave a comparable performance. Further, we show that our features are useful for predicting essential genes in new organisms by using leave-one-species-out validation. Our network features capture the local, global and neighbourhood properties of the network and are hence effective for prediction of essential genes across diverse organisms, even in the absence of other complex biological knowledge. Our approach can be readily exploited to predict essentiality for organisms in interactome databases such as the STRING, where both network and sequence are readily available. All codes are available at https://github.com/RamanLab/nbfpeg.},
      added-at = {2019-02-10T15:54:54.000+0100},
      author = {Azhagesan, Karthik and Ravindran, Balaraman and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2a545ac728afbb08f70604a4d626d8485/karthikraman},
      description = {Network-based features enable prediction of essential genes across diverse organisms},
      doi = {10.1371/journal.pone.0208722},
      interhash = {2cb983d56364987d368433ecb61efac3},
      intrahash = {a545ac728afbb08f70604a4d626d8485},
      journal = {PLOS ONE},
      keywords = {myown},
      month = {12},
      number = 12,
      pages = {1-13},
      timestamp = {2019-02-10T15:54:54.000+0100},
      title = {Network-based features enable prediction of essential genes across diverse organisms},
      url = {https://doi.org/10.1371/journal.pone.0208722},
      volume = 13,
      year = 2018
    }
  • [DOI] S. Sahoo, R. K. Ravi Kumar, B. Nicolay, O. Mohite, K. Sivaraman, V. Khetan, P. Rishi, S. Ganesan, K. Subramanyan, K. Raman, W. Miles, and S. V. Elchuri, “Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma,” FEBS Lett, vol. 593, iss. 1, pp. 23-41, 2019.
    [bibtex]
    @article{sahoo2019metabolite,
      abstract = {Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint-based reconstruction and analysis approaches to identify and explain RB-specific survival strategies, which are RB tumor specific. Importantly, our model-specific secretion profile is also found in RB1-depleted human retinal cells in�vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB-specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.},
      added-at = {2019-02-10T15:54:29.000+0100},
      author = {Sahoo, S and Ravi Kumar, R K and Nicolay, B and Mohite, O and Sivaraman, K and Khetan, V and Rishi, P and Ganesan, S and Subramanyan, K and Raman, K and Miles, W and Elchuri, S V},
      biburl = {https://www.bibsonomy.org/bibtex/2bfb9bd46463772c47c2ecd07257ff644/karthikraman},
      description = {Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma. - PubMed - NCBI},
      doi = {10.1002/1873-3468.13294},
      interhash = {910f4596de609292b08b2c8135a6756d},
      intrahash = {bfb9bd46463772c47c2ecd07257ff644},
      journal = {FEBS Lett},
      keywords = {myown},
      month = jan,
      number = 1,
      pages = {23-41},
      pmid = {30417337},
      timestamp = {2019-02-10T15:54:29.000+0100},
      title = {Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma},
      url = {https://www.ncbi.nlm.nih.gov/pubmed/30417337},
      volume = 593,
      year = 2019
    }
  • [DOI] G. Sambamoorthy and K. Raman, “Understanding the evolution of functional redundancy in metabolic networks,” Bioinformatics, vol. 34, iss. 17, p. i981–i987, 2018.
    [bibtex]
    @article{Sambamoorthy2018Understanding,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Sambamoorthy, Gayathri and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2b0a7eb7388e821b81bb497e570031740/karthikraman},
      day = 01,
      doi = {10.1093/bioinformatics/bty604},
      interhash = {9fc9b6007dfaf58e87216ab61954fb35},
      intrahash = {b0a7eb7388e821b81bb497e570031740},
      issn = {1367-4803},
      journal = {Bioinformatics},
      keywords = {evolution flux-analysis metabolic-networks myown synthetic-lethality},
      month = sep,
      number = 17,
      pages = {i981--i987},
      posted-at = {2018-09-12 08:32:13},
      priority = {2},
      timestamp = {2019-04-17T13:00:23.000+0200},
      title = {Understanding the evolution of functional redundancy in metabolic networks},
      url = {http://dx.doi.org/10.1093/bioinformatics/bty604},
      volume = 34,
      year = 2018
    }
  • A. Ravikrishnan, M. Nasre, and K. Raman, “Enumerating all possible biosynthetic pathways in metabolic networks.,” Scientific reports, vol. 8, p. 9932+, 2018.
    [bibtex]
    @article{Ravikrishnan2018Enumerating,
      abstract = {Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed {MetQuest}, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways of a specified size producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from identifying amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying large graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein. {MetQuest} is available as a Python package, and the source codes can be found at {https://github.com/RamanLab}/metquest .},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Ravikrishnan, Aarthi and Nasre, Meghana and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2c419ad1c7be0bacd7a0e7a4e4e564804/karthikraman},
      day = 02,
      interhash = {ba035a5173fb8ca04837270dc8d7aed6},
      intrahash = {c419ad1c7be0bacd7a0e7a4e4e564804},
      issn = {2045-2322},
      journal = {Scientific reports},
      keywords = {communities metabolic-networks myown pathfinding},
      month = jul,
      pages = {9932+},
      pmid = {29967471},
      posted-at = {2018-09-12 08:30:30},
      priority = {2},
      timestamp = {2019-04-17T13:00:35.000+0200},
      title = {Enumerating all possible biosynthetic pathways in metabolic networks.},
      url = {http://view.ncbi.nlm.nih.gov/pubmed/29967471},
      volume = 8,
      year = 2018
    }
  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “A systems-theoretic approach towards designing biological networks for perfect adaptation,” IFAC-PapersOnLine, vol. 51, iss. 1, pp. 307-312, 2018.
    [bibtex]
    @article{bhattacharya2018systemstheoretic,
      abstract = {Designing biological networks that are capable of achieving specific functionality has been of sustained interest in the field of synthetic biology for nearly a decade. Adaptation is one such important functionality that is observed in bacterial chemotaxis, cell signalling and homoeostasis. It refers to the ability of a cell to cope with environmental perturbations. All of these adaptation networks, involve negative feedback loops or open loop control strategies. A typical enzymatic network is a circuit of enzymes whose connections are characterized by enzymatic reactions that exhibit non-linear dynamics. Previous approaches to design of enzymatic networks capable of perfect adaptation have used brute force searches encompassing the complete set of possibilities to identify suitable circuit designs. In contrast, this work presents a systematic algorithm for circuit design, using a linear systems-theoretic approach. The key idea is to set up a design-oriented problem formulation as against employing a brute force search in the space of possible circuits. To this effect, we first linearize the non-linear dynamical circuit, subsequently, we translate the requirements for adaptation to design specifications for a linear time-invariant system and imposing these design specifications on the linearized system, we obtain the minimal topologies or motifs that can perform perfect adaptation, with an optimal set of rate constants. The optimal set of rate constants is obtained by solving a structure-specific constrained optimisation problem. In effect, we demonstrate that the proposed approach identifies the key motifs of the biological network that were identified by the existing brute force approach, albeit in a systematic manner and with very little computational effort.},
      added-at = {2019-02-10T15:52:53.000+0100},
      author = {Bhattacharya, Priyan and Raman, Karthik and Tangirala, Arun K.},
      biburl = {https://www.bibsonomy.org/bibtex/2be67777cc574df5ebbdd0a530ea35766/karthikraman},
      description = {A systems-theoretic approach towards designing biological networks for perfect adaptation - ScienceDirect},
      doi = {https://doi.org/10.1016/j.ifacol.2018.05.033},
      interhash = {a4db070aeafa2704eeefc27c651507b6},
      intrahash = {be67777cc574df5ebbdd0a530ea35766},
      issn = {2405-8963},
      journal = {IFAC-PapersOnLine},
      keywords = {myown},
      note = {5th IFAC Conference on Advances in Control and Optimization of Dynamical Systems ACODS 2018},
      number = 1,
      pages = {307 - 312},
      timestamp = {2019-02-10T15:52:53.000+0100},
      title = {A systems-theoretic approach towards designing biological networks for perfect adaptation},
      url = {http://www.sciencedirect.com/science/article/pii/S2405896318301939},
      volume = 51,
      year = 2018
    }
  • K. Raman, A. Pratapa, O. Mohite, and S. Balachandran, “Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL.,” in Methods in molecular biology, , 2018, vol. 1716, pp. 315-336.
    [bibtex]
    @inbook{Raman2018Computational,
      abstract = {In this chapter, we describe {Fast-SL}, an in silico approach to predict synthetic lethals in genome-scale metabolic models. Synthetic lethals are sets of genes or reactions where only the simultaneous removal of all genes or reactions in the set abolishes growth of an organism. In silico approaches to predict synthetic lethals are based on Flux Balance Analysis ({FBA}), a popular constraint-based analysis method based on linear programming. {FBA} has been shown to accurately predict the viability of various genome-scale metabolic models. {Fast-SL} builds on the framework of {FBA} and enables the prediction of synthetic lethal reactions or genes in different organisms, under various environmental conditions. Predicting synthetic lethals in metabolic network models allows us to generate hypotheses on possible novel genetic interactions and potential candidates for combinatorial therapy, in case of pathogenic organisms. We here summarize the {Fast-SL} approach for analyzing metabolic networks and detail the procedure to predict synthetic lethals in any given metabolic model. We illustrate the approach by predicting synthetic lethals in Escherichia coli. The {Fast-SL} implementation for {MATLAB} is available from {https://github.com/RamanLab}/{FastSL}/ .},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Pratapa, Aditya and Mohite, Omkar and Balachandran, Shankar},
      biburl = {https://www.bibsonomy.org/bibtex/250d15a815f673cc94b94361201051258/karthikraman},
      interhash = {6c803ebaa9bd716bd0c5671aa199c5a9},
      intrahash = {50d15a815f673cc94b94361201051258},
      issn = {1940-6029},
      booktitle = {Methods in molecular biology},
      keywords = {flux-analysis myown synthetic-lethality},
      pages = {315--336},
      pmid = {29222760},
      posted-at = {2018-03-29 08:38:26},
      priority = {2},
      timestamp = {2019-04-17T12:56:37.000+0200},
      title = {Computational Prediction of Synthetic Lethals in {Genome-Scale} Metabolic Models Using {Fast-SL}.},
      url = {http://view.ncbi.nlm.nih.gov/pubmed/29222760},
      volume = 1716,
      year = 2018
    }
  • [DOI] A. Sankar, S. Ranu, and K. Raman, “Predicting novel metabolic pathways through subgraph mining,” Bioinformatics, vol. 33, iss. 24, pp. 3955-3963, 2017.
    [bibtex]
    @article{Sankar2017Predicting,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Sankar, Aravind and Ranu, Sayan and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2a7ac388a8414223f3a952639ccc904ee/karthikraman},
      day = 15,
      doi = {10.1093/bioinformatics/btx481},
      interhash = {3421686ce8c1133bb8fe492e19f0185e},
      intrahash = {a7ac388a8414223f3a952639ccc904ee},
      issn = {1460-2059},
      journal = {Bioinformatics},
      keywords = {myown retrosynthesis},
      month = dec,
      number = 24,
      pages = {3955--3963},
      posted-at = {2017-07-28 06:26:31},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Predicting novel metabolic pathways through subgraph mining},
      url = {http://dx.doi.org/10.1093/bioinformatics/btx481},
      volume = 33,
      year = 2017
    }
  • [DOI] P. Bhatter, K. Raman, and V. Janakiraman, “Elucidating the biosynthetic pathways of volatile organic compounds in Mycobacterium tuberculosis through a computational approach,” Mol. BioSyst., vol. 13, iss. 4, pp. 750-755, 2017.
    [bibtex]
    @article{Bhatter2017Elucidating,
      abstract = {Microbial volatile organic compounds ({VOCs}) have gained prominence in the recent past for their potential use as disease markers. The discovery of microbial {VOCs} has benefited 'difficult to detect' diseases such as tuberculosis ({TB}). Few of the identified {VOCs} of Mycobacterium tuberculosis (Mtb) are currently being explored for their diagnostic potential. However, very little is known about the biosynthesis of these small lipophilic molecules. Here, we propose putative biosynthetic pathways in Mycobacterium tuberculosis for three {VOCs}, namely methyl nicotinate, methyl phenylacetate and methyl p-anisate, using computational approaches. In particular, we identify S-adenosyl methionine ({SAM}) transferases that play a crucial role in esterification of the acids to the final product. Our results provide important insights into the specificity of these pathways to Mtb species.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Bhatter, Purva and Raman, Karthik and Janakiraman, Vani},
      biburl = {https://www.bibsonomy.org/bibtex/25290d6defdd39c5fb68632c458706553/karthikraman},
      day = 22,
      doi = {10.1039/c6mb00796a},
      interhash = {ac4e545bf994682667b8bd59807586ea},
      intrahash = {5290d6defdd39c5fb68632c458706553},
      issn = {1742-2051},
      journal = {Mol. BioSyst.},
      keywords = {metabolism mtb myown},
      month = feb,
      number = 4,
      pages = {750--755},
      pmid = {28225105},
      posted-at = {2017-02-24 10:20:13},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Elucidating the biosynthetic pathways of volatile organic compounds in Mycobacterium tuberculosis through a computational approach},
      url = {http://dx.doi.org/10.1039/c6mb00796a},
      volume = 13,
      year = 2017
    }
  • [DOI] N. Rajasekaran, S. Suresh, S. Gopi, K. Raman, and A. N. Naganathan, “A General Mechanism for the Propagation of Mutational Effects in Proteins.,” Biochemistry, vol. 56, iss. 1, pp. 294-305, 2017.
    [bibtex]
    @article{Rajasekaran2017General,
      abstract = {Mutations in the hydrophobic interior of proteins are generally thought to weaken the interactions only in their immediate neighborhood. This forms the basis of protein engineering-based studies of folding mechanism and function. However, mutational work on diverse proteins has shown that distant residues are thermodynamically coupled, with the network of interactions within the protein acting as signal conduits, thus raising an intriguing paradox. Are mutational effects localized, and if not, is there a general rule for the extent of percolation and the functional form of this propagation? We explore these questions from multiple perspectives in this work. Perturbation analysis of interaction networks within proteins and microsecond long molecular dynamics simulations of several aliphatic mutants of ubiquitin reveal strong evidence of the distinct alteration of distal residue-residue communication networks. We find that mutational effects consistently propagate into the second shell of the altered site (even up to 15-20 \AA{}) in proportion to the perturbation magnitude and dissipate exponentially with a decay distance constant of ∼4-5 \AA{}. We also report evidence for this phenomenon from published experimental nuclear magnetic resonance data that strikingly resemble predictions from network theory and molecular dynamics simulations. Reformulating these observations onto a statistical mechanical model, we reproduce the stability changes of 375 mutations from 19 single-domain proteins. Our work thus reveals a robust energy dissipation-cum-signaling mechanism in the interaction network within proteins, quantifies the partitioning of destabilization energetics around the mutation neighborhood, and presents a simple theoretical framework for modeling the allosteric effects of point mutations.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Rajasekaran, Nandakumar and Suresh, Swaathiratna and Gopi, Soundhararajan and Raman, Karthik and Naganathan, Athi N.},
      biburl = {https://www.bibsonomy.org/bibtex/2d210b9287ce8c580c937de79d8919803/karthikraman},
      day = 10,
      doi = {10.1021/acs.biochem.6b00798},
      interhash = {fff52ca0df58eea214c4401c91b9ae3a},
      intrahash = {d210b9287ce8c580c937de79d8919803},
      issn = {1520-4995},
      journal = {Biochemistry},
      keywords = {myown networks protein-structures},
      month = jan,
      number = 1,
      pages = {294--305},
      pmid = {27958720},
      posted-at = {2016-12-16 12:30:11},
      priority = {2},
      timestamp = {2019-04-17T12:58:46.000+0200},
      title = {A General Mechanism for the Propagation of Mutational Effects in Proteins.},
      url = {http://dx.doi.org/10.1021/acs.biochem.6b00798},
      volume = 56,
      year = 2017
    }
  • [DOI] A. Badri, A. Srinivasan, and K. Raman, “In Silico Approaches to Metabolic Engineering,” in Current Developments in Biotechnology and Bioengineering, First ed., P. Gunasekaran, S. Noronha, and A. Pandey, Eds., , 2016.
    [bibtex]
    @inbook{Badri2016In,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Badri, Abinaya and Srinivasan, Aparajitha and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2bbdb07d2f3ddddc17d0041ec70ffbb35/karthikraman},
      booktitle = {Current Developments in Biotechnology and Bioengineering},
      doi = {10.1016/B978-0-444-63667-6.00008-0},
      edition = {First},
      editor = {Gunasekaran, P. and Noronha, Santosh and Pandey, Ashok},
      interhash = {d21db8b0d707f610aba6f93dc9a8171d},
      intrahash = {bbdb07d2f3ddddc17d0041ec70ffbb35},
      isbn = {9780444636676},
      keywords = {in\_silico metabolic\_engineering myown review},
      month = sep,
      posted-at = {2016-09-17 11:16:31},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {In Silico Approaches to Metabolic Engineering},
      url = {http://store.elsevier.com/Current-Developments-in-Biotechnology-and-Bioengineering/isbn-9780444636676/},
      year = 2016
    }
  • [DOI] A. Pratapa, S. Balachandran, and K. Raman, “Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks,” Bioinformatics, vol. 31, iss. 20, pp. 3299-3305, 2015.
    [bibtex]
    @article{Pratapa2015FastSL,
      abstract = {Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis ({FBA}), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming ({MILP}) problem. We here propose an algorithm, {Fast-SL}, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Pratapa, A. and Balachandran, S. and Raman, K.},
      biburl = {https://www.bibsonomy.org/bibtex/2c6d35c9c94986676e22b1fd132d7475b/karthikraman},
      day = 15,
      doi = {10.1093/bioinformatics/btv352},
      interhash = {417235ec10ac24def5c5e2cc4a920517},
      intrahash = {c6d35c9c94986676e22b1fd132d7475b},
      issn = {1460-2059},
      journal = {Bioinformatics},
      keywords = {flux-analysis myown synthetic-lethality},
      month = oct,
      number = 20,
      pages = {3299--3305},
      pmid = {26085504},
      posted-at = {2015-06-18 08:56:24},
      priority = {2},
      timestamp = {2019-04-17T12:59:47.000+0200},
      title = {{Fast-SL}: an efficient algorithm to identify synthetic lethal sets in metabolic networks},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/26085504},
      volume = 31,
      year = 2015
    }
  • [DOI] A. Ravikrishnan and K. Raman, “Critical assessment of genome-scale metabolic networks: the need for a unified standard,” Briefings in Bioinformatics, vol. 16, iss. 6, pp. 1057-1068, 2015.
    [bibtex]
    @article{Ravikrishnan2015Critical,
      abstract = {Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Ravikrishnan, Aarthi and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2fb4a81257d4d951ca3087c1a3a061630/karthikraman},
      day = 28,
      doi = {10.1093/bib/bbv003},
      interhash = {528b1969ad37bb112da7e20e995bace1},
      intrahash = {fb4a81257d4d951ca3087c1a3a061630},
      issn = {1477-4054},
      journal = {Briefings in Bioinformatics},
      keywords = {cobra constraint-based\_modelling flux-analysis myown sbml},
      month = feb,
      number = 6,
      pages = {1057--1068},
      pmid = {25725218},
      posted-at = {2015-03-01 12:12:55},
      priority = {2},
      timestamp = {2019-04-17T12:56:37.000+0200},
      title = {Critical assessment of genome-scale metabolic networks: the need for a unified standard},
      url = {http://dx.doi.org/10.1093/bib/bbv003},
      volume = 16,
      year = 2015
    }
  • [DOI] R. Partha and K. Raman, “Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces,” PLoS ONE, vol. 9, iss. 11, p. e112792+, 2014.
    [bibtex]
    @article{Partha2014Revisiting,
      abstract = {Robustness and evolvability are highly intertwined properties of biological systems. The relationship between these properties determines how biological systems are able to withstand mutations and show variation in response to them. Computational studies have explored the relationship between these two properties using neutral networks of {RNA} sequences (genotype) and their secondary structures (phenotype) as a model system. However, these studies have assumed every mutation to a sequence to be equally likely; the differences in the likelihood of the occurrence of various mutations, and the consequence of probabilistic nature of the mutations in such a system have previously been ignored. Associating probabilities to mutations essentially results in the weighting of genotype space. We here perform a comparative analysis of weighted and unweighted neutral networks of {RNA} sequences, and subsequently explore the relationship between robustness and evolvability. We show that assuming an equal likelihood for all mutations (as in an unweighted network), underestimates robustness and overestimates evolvability of a system. In spite of discarding this assumption, we observe that a negative correlation between sequence (genotype) robustness and sequence evolvability persists, and also that structure (phenotype) robustness promotes structure evolvability, as observed in earlier studies using unweighted networks. We also study the effects of base composition bias on robustness and evolvability. Particularly, we explore the association between robustness and evolvability in a sequence space that is {AU}-rich – sequences with an {AU} content of 80\% or higher, compared to a normal (unbiased) sequence space. We find that evolvability of both sequences and structures in an {AU}-rich space is lesser compared to the normal space, and robustness higher. We also observe that {AU}-rich populations evolving on neutral networks of phenotypes, can access less phenotypic variation compared to normal populations evolving on neutral networks.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Partha, Raghavendran and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2124b29e36d14280222afd19457b34aad/karthikraman},
      day = 12,
      doi = {10.1371/journal.pone.0112792},
      interhash = {5a3aa793e4b4ab6e5e91429698317c65},
      intrahash = {124b29e36d14280222afd19457b34aad},
      journal = {PLoS ONE},
      keywords = {evolvability myown neutral\_networks robustness},
      month = nov,
      number = 11,
      pages = {e112792+},
      posted-at = {2014-11-13 20:16:45},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces},
      url = {http://dx.doi.org/10.1371/journal.pone.0112792},
      volume = 9,
      year = 2014
    }
  • A. Ravikrishnan and K. Raman, Systems-level modelling of microbial communities : theory and practice, CRC Press, 2018.
    [bibtex]
    @book{Ravikrishnan2018Systemslevel,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Ravikrishnan, Aarthi and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2e38f01569051048cde184b7e68bd10bd/karthikraman},
      interhash = {25e27fbb8696fd0a8803a5abe3306094},
      intrahash = {e38f01569051048cde184b7e68bd10bd},
      isbn = {113859671},
      keywords = {books communities myown},
      posted-at = {2018-10-04 12:47:00},
      priority = {2},
      publisher = {CRC Press},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Systems-level modelling of microbial communities : theory and practice},
      url = {http://www.worldcat.org/isbn/113859671},
      year = 2018
    }
  • [DOI] V. V. Kulkarni, G. Stan, and K. Raman, A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems, Springer, 2014.
    [bibtex]
    @book{Kulkarni2014bSystems,
      abstract = {The complexity of biological systems has intrigued scientists from many disciplines and has given birth to the highly influential field of systems biology wherein a wide array of mathematical techniques, such as flux balance analysis, and technology platforms, such as next generation sequencing, is used to understand, elucidate, and predict the functions of complex biological systems. More recently, the field of synthetic biology, i.e., de novo engineering of biological systems, has emerged. Scientists from various fields are focusing on how to render this engineering process more predictable, reliable, scalable, affordable, and easy. Systems and control theory is a branch of engineering and applied sciences that rigorously deals with the complexities and uncertainties of interconnected systems with the objective of characterising fundamental systemic properties such as stability, robustness, communication capacity, and other performance metrics. Systems and control theory also strives to offer concepts and methods that facilitate the design of systems with rigorous guarantees on these properties. Over the last 100 years, it has made stellar theoretical and technological contributions in diverse fields such as aerospace, telecommunication, storage, automotive, power systems, and others. Can it have, or evolve to have, a similar impact in biology? The chapters in this book demonstrate that, indeed, systems and control theoretic concepts and techniques can have a significant impact in systems and synthetic biology. Volume {II} contains chapters contributed by leading researchers in the field of systems and synthetic biology that concern modeling physiological processes and bottom-up constructions of scalable biological systems. The modeling problems include characterisation and synthesis of memory, understanding how homoeostasis is maintained in the face of shocks and relatively gradual perturbations, understanding the functioning and robustness of biological clocks such as those at the core of circadian rhythms, and understanding how the cell cycles can be regulated, among others. Some of the bottom-up construction problems investigated in Volume {II} are as follows: How should biomacromolecules, platforms, and scalable architectures be chosen and synthesised in order to build programmable de novo biological systems? What are the types of constrained optimisation problems encountered in this process and how can these be solved efficiently? As the eminent computer scientist Donald Knuth put it, "biology easily has 500 years of exciting problems to work on". This edited book presents but a small fraction of those for the benefit of (1) systems and control theorists interested in molecular and cellular biology and (2) biologists interested in rigorous modelling, analysis and control of biological systems.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Kulkarni, Vishwesh V. and Stan, Guy-Bart and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/29e498c006dfed648787dfcf506484e27/karthikraman},
      doi = {10.1007/978-94-017-9047-5},
      interhash = {432ca91f767ab6392fa7531245b568ee},
      intrahash = {9e498c006dfed648787dfcf506484e27},
      isbn = {9789401790468},
      keywords = {books library myown systems\_biology},
      posted-at = {2015-01-05 07:21:03},
      priority = {2},
      publisher = {Springer},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {A Systems Theoretic Approach to Systems and Synthetic Biology {II}: Analysis and Design of Cellular Systems},
      url = {http://dx.doi.org/10.1007/978-94-017-9047-5},
      year = 2014
    }
  • [DOI] V. V. Kulkarni, K. Raman, and G. Stan, A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations, Springer, 2014.
    [bibtex]
    @book{Kulkarni2014aSystems,
      abstract = {The complexity of biological systems has intrigued scientists from many disciplines and has given birth to the highly influential field of systems biology wherein a wide array of mathematical techniques, such as flux balance analysis, and technology platforms, such as next generation sequencing, is used to understand, elucidate, and predict the functions of complex biological systems. More recently, the field of synthetic biology, i.e., de novo engineering of biological systems, has emerged. Scientists from various fields are focusing on how to render this engineering process more predictable, reliable, scalable, affordable, and easy. Systems and control theory is a branch of engineering and applied sciences that rigorously deals with the complexities and uncertainties of interconnected systems with the objective of characterising fundamental systemic properties such as stability, robustness, communication capacity, and other performance metrics. Systems and control theory also strives to offer concepts and methods that facilitate the design of systems with rigorous guarantees on these properties. Over the last 100 years, it has made stellar theoretical and technological contributions in diverse fields such as aerospace, telecommunication, storage, automotive, power systems, and others. Can it have, or evolve to have, a similar impact in biology? The chapters in this book demonstrate that, indeed, systems and control theoretic concepts and techniques can have a significant impact in systems and synthetic biology. Volume {II} contains chapters contributed by leading researchers in the field of systems and synthetic biology that concern modeling physiological processes and bottom-up constructions of scalable biological systems. The modeling problems include characterisation and synthesis of memory, understanding how homoeostasis is maintained in the face of shocks and relatively gradual perturbations, understanding the functioning and robustness of biological clocks such as those at the core of circadian rhythms, and understanding how the cell cycles can be regulated, among others. Some of the bottom-up construction problems investigated in Volume {II} are as follows: How should biomacromolecules, platforms, and scalable architectures be chosen and synthesised in order to build programmable de novo biological systems? What are the types of constrained optimisation problems encountered in this process and how can these be solved efficiently? As the eminent computer scientist Donald Knuth put it, "biology easily has 500 years of exciting problems to work on". This edited book presents but a small fraction of those for the benefit of (1) systems and control theorists interested in molecular and cellular biology and (2) biologists interested in rigorous modelling, analysis and control of biological systems.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Kulkarni, Vishwesh V. and Raman, Karthik and Stan, Guy-Bart},
      biburl = {https://www.bibsonomy.org/bibtex/2df0f62b97fc2c2379f8d84d53232cbc5/karthikraman},
      doi = {10.1007/978-94-017-9041-3},
      interhash = {77f78b90807b24afc7bd3d1a9f26327b},
      intrahash = {df0f62b97fc2c2379f8d84d53232cbc5},
      isbn = {9789401790406},
      keywords = {library myown systems\_biology},
      posted-at = {2015-01-05 07:19:58},
      priority = {2},
      publisher = {Springer},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations},
      url = {http://dx.doi.org/10.1007/978-94-017-9041-3},
      year = 2014
    }
  • K. Raman, Y. Kalidas, and N. Chandra, “Model Driven Drug Discovery: Principles and Practices,” in Biological Database Modeling, 1 ed., J. Chen and A. S. Sidhu, Eds., Artech House Publishers, 2007, pp. 163-188.
    [bibtex]
    @inbook{Raman2007Model,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Kalidas, Yeturu and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/27d18fc2c7fd904e25f25c17e37b8917b/karthikraman},
      booktitle = {Biological Database Modeling},
      chapter = 9,
      edition = 1,
      editor = {Chen, Jake and Sidhu, Amandeep S.},
      howpublished = {Hardcover},
      interhash = {cffbd51fb8eeac591669f292b4c24ca9},
      intrahash = {7d18fc2c7fd904e25f25c17e37b8917b},
      isbn = {1596932589},
      keywords = {drug\_discovery myown},
      pages = {163--188},
      posted-at = {2012-02-07 09:31:29},
      priority = {2},
      publisher = {Artech House Publishers},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Model Driven Drug Discovery: Principles and Practices},
      year = 2007
    }
  • [DOI] K. Raman, N. Damaraju, and G. K. Joshi, “The organisational structure of protein networks: revisiting the centrality–lethality hypothesis,” Systems and Synthetic Biology, vol. 8, iss. 1, pp. 73-81, 2014.
    [bibtex]
    @article{Raman2014Organisational,
      abstract = {Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the {STRING} provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality–lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the {STRING} database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality–lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality–lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Damaraju, Nandita and Joshi, Govind K.},
      biburl = {https://www.bibsonomy.org/bibtex/20691cb0a4fc6369cd4a1a26431f4ec25/karthikraman},
      booktitle = {Systems and Synthetic Biology},
      doi = {10.1007/s11693-013-9123-5},
      interhash = {f6d38f674f700948f52f300f89824891},
      intrahash = {0691cb0a4fc6369cd4a1a26431f4ec25},
      issn = {1872-5333},
      journal = {Systems and Synthetic Biology},
      keywords = {centrality gene-essentiality lethality myown networks protein-protein-interactions},
      month = mar,
      number = 1,
      pages = {73--81},
      posted-at = {2013-08-27 04:40:08},
      priority = {2},
      timestamp = {2019-04-17T12:59:21.000+0200},
      title = {The organisational structure of protein networks: revisiting the centrality--lethality hypothesis},
      url = {http://dx.doi.org/10.1007/s11693-013-9123-5},
      volume = 8,
      year = 2014
    }
  • [DOI] A. Kulkarni, L. Ananthanarayan, and K. Raman, “Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach,” Computational Biology and Chemistry, vol. 47, pp. 149-155, 2013.
    [bibtex]
    @article{Kulkarni2013Identification,
      abstract = { First study reported on identification of putative allergens in chickpea using in silico approaches Seven novel putative allergens from chickpea (Cicer arietinum) have been identified, based on sequence, structural and physicochemical similarities. Four out of seven putative allergens may also show cross reactivity with reported allergens, since the potential allergens had common sequence and structural featureswith the reported allergens. Allergy has become a key cause of morbidity worldwide. Although many legumes (plants in the Fabaceae family) are healthy foods, they may have a number of allergenic proteins. A number of allergens have been identified and characterized in Fabaceae family, such as soybean and peanut, on the basis of biochemical and molecular biological approaches. However, our understanding of the allergens from chickpea (Cicer arietinum L), belonging to this family, is very limited. In this study, we aimed to identify putative and cross-reactive allergens from Chickpea (Cicer areitinum) by means of in silico analysis of the chickpea protein sequences and allergens sequences from Fabaceae family. We retrieved known allergen sequences in Fabaceae family from the {IUIS} Allergen Nomenclature Database. We performed a protein {BLAST} ({BLASTp}) on these sequences to retrieve the similar sequences from chickpea. We further analysed the retrieved chickpea sequences using a combination of in silico tools, to assess them for their allergenicity potential. Following this, we built structure models using {FUGUE}: Sequence-structure homology; these models generated by the recognition tool were viewed in {Swiss-PDB} viewer. Through this in silico approach, we identified seven novel putative allergens from chickpea proteome sequences on the basis of similarity of sequence, structure and physicochemical properties with the known reported legume allergens. Four out of seven putative allergens may also show cross reactivity with reported allergens since potential allergens had common sequence and structural features with the reported allergens. The in silico proteomic identification of the allergen proteins in chickpea provides a basis for future research on developing hypoallergenic foods containing chickpea. Such bioinformatics approaches, combined with experimental methodology, will help delineate an efficient and comprehensive approach to assess allergenicity and pave the way for a better understanding of the biological and medical basis of the same. },
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Kulkarni, Anuja and Ananthanarayan, Laxmi and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2a988f6d798b2356bd568c881bb0c32bf/karthikraman},
      doi = {10.1016/j.compbiolchem.2013.08.003},
      interhash = {f68d3a595249259057e08ac9a5e4045f},
      intrahash = {a988f6d798b2356bd568c881bb0c32bf},
      issn = {14769271},
      journal = {Computational Biology and Chemistry},
      keywords = {allergy myown sequence\_analysis},
      month = dec,
      pages = {149--155},
      posted-at = {2013-09-20 08:17:36},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach},
      url = {http://dx.doi.org/10.1016/j.compbiolchem.2013.08.003},
      volume = 47,
      year = 2013
    }
  • [DOI] K. Raman and A. Wagner, “Evolvability and robustness in a complex signalling circuit,” Molecular BioSystems, vol. 7, iss. 4, pp. 1081-1092, 2011.
    [bibtex]
    @article{Raman2011bEvolvability,
      abstract = {Biological systems at various levels of organisation exhibit robustness, as well as phenotypic variability or evolvability, the ability to evolve novel phenotypes. We still know very little about the relationship between robustness and phenotypic variability at levels of organisation beyond individual macromolecules, and especially for signalling circuits. Here, we examine multiple alternate topologies of the Saccharomyces cerevisiae target-of-rapamycin ({TOR}) signalling circuit, in order to understand the circuit's robustness and phenotypic variability. We consider each of the topological variants a genotype, a set of alternative interactions between {TOR} circuit components. Two genotypes are neighbours in genotype space if they can be reached from each other by a single small genetic change. Each genotype (topology) has a signalling phenotype, which we define via the concentration trajectories of key signalling molecules. We find that the circuits we study can produce almost 300 different phenotypes. The number of genotypes with a given phenotype varies very widely among these phenotypes. Some phenotypes have few associated genotypes. Others have many genotypes that form genotype networks extending far through genotype space. A minority of phenotypes accounts for the vast majority of genotypes. Importantly, we find that these phenotypes tend to have large genotype networks, greater robustness and a greater ability to produce novel phenotypes. Thus, over a broad range of phenotypic robustness, robustness facilitates phenotypic variability in our study system. Our observations show parallels to studies on macromolecules, suggesting that similar principles might govern robustness and phenotypic variability in biological systems. Our approach points a way towards mapping genotype spaces in complex circuitry, and it exposes some challenges such mapping faces.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Wagner, Andreas},
      biburl = {https://www.bibsonomy.org/bibtex/22f0f196c64eb6217cea3c9817797c402/karthikraman},
      doi = {10.1039/c0mb00165a},
      interhash = {efa04c66f7c329b02845fe8c5139c406},
      intrahash = {2f0f196c64eb6217cea3c9817797c402},
      issn = {1742-2051},
      journal = {Molecular BioSystems},
      keywords = {evolvability myown neutral\_networks robustness signalling tor},
      number = 4,
      pages = {1081--1092},
      posted-at = {2011-01-11 16:30:15},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Evolvability and robustness in a complex signalling circuit},
      url = {http://dx.doi.org/10.1039/c0mb00165a},
      volume = 7,
      year = 2011
    }
  • [DOI] K. Raman and A. Wagner, “The evolvability of programmable hardware,” Journal of The Royal Society Interface, vol. 8, iss. 55, pp. 269-281, 2010.
    [bibtex]
    @article{Raman2011aEvolvability,
      abstract = {In biological systems, individual phenotypes are typically adopted by multiple genotypes. Examples include protein structure phenotypes, where each structure can be adopted by a myriad individual amino acid sequence genotypes. These genotypes form vast connected 'neutral networks' in genotype space. The size of such neutral networks endows biological systems not only with robustness to genetic change, but also with the ability to evolve a vast number of novel phenotypes that occur near any one neutral network. Whether technological systems can be designed to have similar properties is poorly understood. Here we ask this question for a class of programmable electronic circuits that compute digital logic functions. The functional flexibility of such circuits is important in many applications, including applications of evolutionary principles to circuit design. The functions they compute are at the heart of all digital computation. We explore a vast space of 1045 logic circuits ('genotypes') and 1019 logic functions ('phenotypes'). We demonstrate that circuits that compute the same logic function are connected in large neutral networks that span circuit space. Their robustness or fault-tolerance varies very widely. The vicinity of each neutral network contains circuits with a broad range of novel functions. Two circuits computing different functions can usually be converted into one another via few changes in their architecture. These observations show that properties important for the evolvability of biological systems exist in a commercially important class of electronic circuitry. They also point to generic ways to generate fault-tolerant, adaptable and evolvable electronic circuitry.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Wagner, Andreas},
      biburl = {https://www.bibsonomy.org/bibtex/2521a26440c38a29056b78d17ed271df0/karthikraman},
      day = 9,
      doi = {10.1098/rsif.2010.0212},
      interhash = {bd545f94ea83c7569ddc2b0471812e45},
      intrahash = {521a26440c38a29056b78d17ed271df0},
      issn = {1742-5662},
      journal = {Journal of The Royal Society Interface},
      keywords = {evolvable\_hardware myown neutral\_networks},
      month = jun,
      number = 55,
      pages = {269--281},
      pmid = {20534598},
      posted-at = {2010-06-10 10:48:24},
      priority = {0},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {The evolvability of programmable hardware},
      url = {http://dx.doi.org/10.1098/rsif.2010.0212},
      volume = 8,
      year = 2010
    }
  • [DOI] K. Raman and N. Chandra, “Systems Biology of Tuberculosis: Insights for Drug Discovery,” in Understanding the Dynamics of Biological Systems, W. Dubitzky, J. Southgate, and H. Fuß, Eds., New York, NY: , 2011, pp. 83-110.
    [bibtex]
    @inbook{Raman2011Systems,
      abstract = {Tuberculosis has been a global health concern for decades and the emergence of resistant strains and co-infection with {HIV} warrant newer approaches to identify anti-tubercular drugs and targets. The availability of many `omics'-scale datasets, together with the advances in computation and modelling have enabled the application of several systems-level modelling techniques in drug discovery. In this chapter, we focus on how systems-level modelling of Mycobacterium tuberculosis can provide us insights on various aspects of the pathogen, from metabolic pathways to protein--protein interaction networks, and how such models lend themselves to the identification of new and potentially improved drug targets. We present a brief overview of the modelling of mycobacterial metabolism, transcriptome and host-pathogen interactions, as well as how various models can be exploited for a rational identification of potential drug targets. Systems-level modelling and simulation of pathogenic organisms has an immense potential to impact most drug discovery programmes.},
      added-at = {2018-12-02T16:09:07.000+0100},
      address = {New York, NY},
      author = {Raman, Karthik and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/221371d1c5f9b49639d6f1f73c86c5a24/karthikraman},
      booktitle = {Understanding the Dynamics of Biological Systems},
      chapter = 5,
      doi = {10.1007/978-1-4419-7964-3\_5},
      editor = {Dubitzky, Werner and Southgate, Jennifer and Fu{\ss}, Hendrik},
      interhash = {4cb079c5956a461fca055a476ee79cbb},
      intrahash = {21371d1c5f9b49639d6f1f73c86c5a24},
      isbn = {978-1-4419-7963-6},
      keywords = {myown review systems\_biology tuberculosis},
      pages = {83--110},
      posted-at = {2011-01-20 07:08:12},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Systems Biology of Tuberculosis: Insights for Drug Discovery},
      url = {http://dx.doi.org/10.1007/978-1-4419-7964-3\_5},
      year = 2011
    }
  • [DOI] K. Raman, A. G. Bhat, and N. Chandra, “A systems perspective of host–pathogen interactions: predicting disease outcome in tuberculosis,” Molecular BioSystems, vol. 6, iss. 3, pp. 516-530, 2010.
    [bibtex]
    @article{Raman2010systems,
      abstract = {The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as {TNF}-alpha and {IFN}-gamma, greatly impaired bacterial clearance, while removing cytokines such as {IL}-10 alongside bacterial defence proteins such as {SapM} greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Bhat, Ashwini G. and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/2502045242bdfaf98e5faf3cc4b33049b/karthikraman},
      doi = {10.1039/b912129c},
      interhash = {f77dc1753daf598f373f35ea34d7d86c},
      intrahash = {502045242bdfaf98e5faf3cc4b33049b},
      issn = {1742-2051},
      journal = {Molecular BioSystems},
      keywords = {boolean-networks host-pathogen myown tuberculosis},
      month = mar,
      number = 3,
      pages = {516--530},
      pmid = {20174680},
      posted-at = {2009-12-14 17:04:35},
      priority = {5},
      timestamp = {2019-08-22T14:04:11.000+0200},
      title = {A systems perspective of host–pathogen interactions: predicting disease outcome in tuberculosis},
      url = {http://dx.doi.org/10.1039/b912129c},
      volume = 6,
      year = 2010
    }
  • [DOI] K. Raman, “Construction and analysis of protein-protein interaction networks,” Automated Experimentation, vol. 2, iss. 1, p. 2+, 2010.
    [bibtex]
    @article{Raman2010Construction,
      abstract = {Protein-protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein-protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/284c34055aec510d9bfdf2c61e496827e/karthikraman},
      doi = {10.1186/1759-4499-2-2},
      interhash = {0289d6969ef228a3c6e727ee1a4f4e39},
      intrahash = {84c34055aec510d9bfdf2c61e496827e},
      issn = {1759-4499},
      journal = {Automated Experimentation},
      keywords = {myown protein-protein-interactions review},
      number = 1,
      pages = {2+},
      pmid = {20334628},
      posted-at = {2010-02-15 15:30:09},
      priority = {0},
      timestamp = {2019-04-17T12:59:02.000+0200},
      title = {Construction and analysis of protein-protein interaction networks},
      url = {http://dx.doi.org/10.1186/1759-4499-2-2},
      volume = 2,
      year = 2010
    }
  • [DOI] K. Raman and N. Chandra, “Systems biology,” Resonance, vol. 15, iss. 2, pp. 131-153, 2010.
    [bibtex]
    @article{Raman2010Systemsbiology,
      abstract = {Abstract  Systems biology seeks to study biological systems as a whole, contrary to the reductionist approach that has dominated biology. Such a view of biological systems emanating from strong foundations of molecular level understanding of the individual components in terms of their form, function and interactions is promising to transform the level at which we understand biology. Systems are defined and abstracted at different levels, which are simulated and analysed using different types of mathematical and computational techniques. Insights obtained from systems level studies readily lend to their use in several applications in biotechnology and drug discovery, making it even more important to study systems as a whole.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/27c1c7ac6b91a8ecec703d693a1ddc9fc/karthikraman},
      day = 1,
      doi = {10.1007/s12045-010-0015-7},
      interhash = {0bbbd515c06d9094b0454d654e6d96e9},
      intrahash = {7c1c7ac6b91a8ecec703d693a1ddc9fc},
      issn = {0971-8044},
      journal = {Resonance},
      keywords = {myown systems\_biology},
      month = feb,
      number = 2,
      pages = {131--153},
      posted-at = {2010-03-05 05:37:36},
      priority = {0},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Systems biology},
      url = {http://dx.doi.org/10.1007/s12045-010-0015-7},
      volume = 15,
      year = 2010
    }
  • [DOI] K. Raman, R. Vashisht, and N. Chandra, “Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis,” Mol. BioSyst., vol. 5, iss. 12, pp. 1740-1751, 2009.
    [bibtex]
    @article{Raman2009Strategies,
      abstract = {Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Vashisht, Rohit and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/21a1dea73336dfbad656de276ef17a96b/karthikraman},
      doi = {10.1039/b905817f},
      interhash = {adfb2c8b22933b7e78fc12a27e944e78},
      intrahash = {1a1dea73336dfbad656de276ef17a96b},
      journal = {Mol. BioSyst.},
      keywords = {drug\_discovery drug\_targets fba myown networks tuberculosis},
      number = 12,
      pages = {1740--1751},
      posted-at = {2009-11-14 17:19:00},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis},
      url = {http://dx.doi.org/10.1039/b905817f},
      volume = 5,
      year = 2009
    }
  • [DOI] K. Raman and N. Chandra, “Flux balance analysis of biological systems: applications and challenges.,” Briefings in bioinformatics, vol. 10, iss. 4, pp. 435-449, 2009.
    [bibtex]
    @article{Raman2009Flux,
      abstract = {Systems level modelling and simulations of biological processes are proving to be invaluable in obtaining a quantitative and dynamic perspective of various aspects of cellular function. In particular, constraint-based analyses of metabolic networks have gained considerable popularity for simulating cellular metabolism, of which flux balance analysis ({FBA}), is most widely used. Unlike mechanistic simulations that depend on accurate kinetic data, which are scarcely available, {FBA} is based on the principle of conservation of mass in a network, which utilizes the stoichiometric matrix and a biologically relevant objective function to identify optimal reaction flux distributions. {FBA} has been used to analyse genome-scale reconstructions of several organisms; it has also been used to analyse the effect of perturbations, such as gene deletions or drug inhibitions in silico. This article reviews the usefulness of {FBA} as a tool for gaining biological insights, advances in methodology enabling integration of regulatory information and thermodynamic constraints, and finally addresses the challenges that lie ahead. Various use scenarios and biological insights obtained from {FBA}, and applications in fields such metabolic engineering and drug target identification, are also discussed. Genome-scale constraint-based models have an immense potential for building and testing hypotheses, as well as to guide experimentation.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/237ace952c0bfd6e28ef63a92ad09b39b/karthikraman},
      booktitle = {Brief Bioinform},
      day = 1,
      doi = {10.1093/bib/bbp011},
      interhash = {b85005d083305486bb15609aa20c4835},
      intrahash = {37ace952c0bfd6e28ef63a92ad09b39b},
      issn = {1477-4054},
      journal = {Briefings in bioinformatics},
      keywords = {fba myown reconstruction review},
      month = jul,
      number = 4,
      pages = {435--449},
      pmid = {19287049},
      posted-at = {2009-11-10 14:20:16},
      priority = {0},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Flux balance analysis of biological systems: applications and challenges.},
      url = {http://dx.doi.org/10.1093/bib/bbp011},
      volume = 10,
      year = 2009
    }
  • [DOI] K. Raman, K. Yeturu, and N. Chandra, “targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.,” BMC systems biology, vol. 2, iss. 1, p. 109+, 2008.
    [bibtex]
    @article{Raman2008TargetTB,
      abstract = {Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. We report a comprehensive in silico target identification pipeline, {targetTB}, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Yeturu, Kalidas and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/2f97981864a35573f5d32126f1daa8875/karthikraman},
      doi = {10.1186/1752-0509-2-109},
      interhash = {130adbb0e4bf774d82b07ee8e71e50e2},
      intrahash = {f97981864a35573f5d32126f1daa8875},
      issn = {1752-0509},
      journal = {BMC systems biology},
      keywords = {mycobacteria myown target\_identification},
      number = 1,
      pages = {109+},
      pmid = {19099550},
      posted-at = {2013-03-26 06:49:05},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {{targetTB}: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.},
      url = {http://dx.doi.org/10.1186/1752-0509-2-109},
      volume = 2,
      year = 2008
    }
  • [DOI] K. Raman and N. Chandra, “Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance,” BMC Microbiology, vol. 8, p. 234, 2008.
    [bibtex]
    @article{Raman2008b,
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/2dcbdf553bf832a4bc468f33f38125cab/karthikraman},
      comment = {10.1186/1471-2180-8-234},
      doi = {10.1186/1471-2180-8-234},
      interhash = {01ab5a3332335c3bbbb83113fb04bfec},
      intrahash = {dcbdf553bf832a4bc468f33f38125cab},
      journal = {BMC Microbiology},
      keywords = {mycobacteria myown},
      month = dec,
      pages = 234,
      posted-at = {2009-11-10 13:53:25},
      priority = {2},
      privnote = {10.1186/1471-2180-8-234},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance},
      url = {http://dx.doi.org/10.1186/1471-2180-8-234},
      volume = 8,
      year = 2008
    }
  • [DOI] K. D. Verkhedkar, K. Raman, N. R. Chandra, and S. Vishveshwara, “Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis,” PLoS ONE, vol. 2, iss. 9, p. e881+, 2007.
    [bibtex]
    @article{Verkhedkar2007Metabolome,
      abstract = {Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the {KEGG} {LIGAND} database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Verkhedkar, Ketki D. and Raman, Karthik and Chandra, Nagasuma R. and Vishveshwara, Saraswathi},
      biburl = {https://www.bibsonomy.org/bibtex/27d6f235a2d59da66f2e9c1e644593fa0/karthikraman},
      day = 12,
      doi = {10.1371/journal.pone.0000881},
      interhash = {6d8a391fc8acdc8599957cc4b2032e99},
      intrahash = {7d6f235a2d59da66f2e9c1e644593fa0},
      issn = {1932-6203},
      journal = {PLoS ONE},
      keywords = {comparative\_analysis mtb myown networks},
      month = sep,
      number = 9,
      pages = {e881+},
      pmid = {17849010},
      posted-at = {2012-02-07 09:31:05},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis},
      url = {http://dx.doi.org/10.1371/journal.pone.0000881},
      volume = 2,
      year = 2007
    }
  • [DOI] K. Raman, P. Rajagopalan, and N. Chandra, “Hallmarks of mycolic acid biosynthesis: A comparative genomics study,” Proteins: Structure, Function, and Bioinformatics, vol. 69, iss. 2, pp. 358-368, 2007.
    [bibtex]
    @article{Raman2007Hallmarks,
      abstract = {Mycolic acids, which render unique qualities to mycobacteria, are known to be important for mycobacterial growth, survival, and pathogenicity. It is of interest to understand the evolutionary origins of the mycolic acid pathway ({MAP}), as well as the common minimum principles critical for generating the capability of mycolic acid biosynthesis. The recent curation of a comprehensive model of the {MAP} in Mycobacterium tuberculosis and the availability of a large number of genome sequences make it feasible to carry out detailed sequence and phylogenetic analyses, to address these questions. A comprehensive phylogenetic pathway profile analysis was carried out for 318 fully sequenced bacterial genomes, for each of the proteins present in the {MAP}. The organisms were clustered on the basis of co-occurrence of the {MAP} proteins in their proteome, while the proteins were clustered on the basis of their phylogenetic profiles. The {MAP} proteins were also searched against the nonredundant sequence database, to identify similar proteins from other phyla. The pathway profiles indicate that four proteins and certain protein domains stand out as more characteristic to mycolate producing organisms. Further analysis leads to the identification of the desaturases {DesA1} and {DesA2} and certain domains of Fas and Pks13 as hallmarks of the pathway. The roles of these proteins in some other organisms, as well as the distribution of these proteins across all known genome sequences are also briefly discussed. The clustering of organisms, carried out to group organisms with similar profiles, provides a means of obtaining finer classification as compared to the standard taxonomic method. The results indicate that the {MAP} and hence the capacity of mycolic acid production in mycobacteria is an example of an emergent property that has come about by recruiting enzymes from unrelated pathways in plants, presumably through lateral gene transfer. The understanding of the hallmarks of mycolic acid biosynthesis will also find application in evaluating drug targets. Proteins 2007. {\copyright} 2007 {Wiley-Liss}, Inc.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Rajagopalan, Preethi and Chandra, Nagasuma},
      doi = {10.1002/prot.21591},
      interhash = {082b9fa12e5161e2255c2454ff21f5d7},
      intrahash = {7bc667b3440d2fd8fd4027d9362f5cc6},
      issn = {1097-0134},
      journal = {Proteins: Structure, Function, and Bioinformatics},
      keywords = {comparative\_analysis mtb myown sequence\_analysis},
      number = 2,
      pages = {358--368},
      posted-at = {2012-02-07 09:25:27},
      priority = {2},
      timestamp = {2019-02-10T16:15:28.000+0100},
      title = {Hallmarks of mycolic acid biosynthesis: A comparative genomics study},
      url = {http://dx.doi.org/10.1002/prot.21591},
      volume = 69,
      year = 2007
    }
  • [DOI] K. Raman, P. Rajagopalan, and N. Chandra, “Principles and Practices of Pathway Modelling,” Current Bioinformatics, vol. 1, iss. 2, pp. 147-160, 2006.
    [bibtex]
    @article{Raman2006Principles,
      abstract = {The potential of systems-based approaches are increasingly being realised in drug discovery, metabolic engineering and related areas. Developments in high-throughput experimental techniques and explosion of genomic data have fuelled progress in this area. Modelling and simulation of metabolic and regulatory pathways is an important step in systems analysis. In this review, we discuss the principles of pathway modelling, simulation techniques and current practices. A pre-requisite for modelling and simulating metabolic pathways is an accurate description of the pathway landscape. Despite availability of hundreds of annotated genome sequences, accurate information about pathways is still largely incomplete. We highlight some of the methods for deriving pathway landscapes from biochemical literature and high-throughput experimental data. The conceptual framework for modelling in terms of abstraction levels and schema for representation is also presented.  Next, several classes of techniques available for modelling and simulating such systems formulated from pathway landscapes, viz. kinetic pathway modelling, interaction-based modelling and constraint-based modelling are discussed. The Systems Biology Markup Language as well as various pathway design and simulation tools are reviewed. The usefulness of various concepts and methodologies in areas such as drug discovery and metabolic engineering are illustrated with examples from literature, with a note on future perspectives.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Rajagopalan, Preethi and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/27a8e52b18dc81e992837410e80a7339b/karthikraman},
      doi = {10.2174/157489306777011914},
      interhash = {ed098c430508609085979d3efe80a45d},
      intrahash = {7a8e52b18dc81e992837410e80a7339b},
      issn = {1574-8936},
      journal = {Current Bioinformatics},
      keywords = {metabolic-networks myown pathway-modelling},
      month = may,
      number = 2,
      pages = {147--160},
      posted-at = {2009-11-10 13:53:27},
      priority = {2},
      timestamp = {2019-04-17T13:00:55.000+0200},
      title = {Principles and Practices of Pathway Modelling},
      url = {http://www.ingentaconnect.com/content/ben/cbio/2006/00000001/00000002/art00003},
      volume = 1,
      year = 2006
    }
  • [DOI] K. Raman, P. Rajagopalan, and N. Chandra, “Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-Tubercular Drugs,” PLoS Computational Biology, vol. 1, iss. 5, p. e46+, 2005.
    [bibtex]
    @article{Raman2005Flux,
      abstract = {Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway ({MAP}) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis ({FBA}) has been performed on the {MAP} model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of {InhA} by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis {H37Rv} and human proteomes indicate that, apart from the known {InhA}, potential targets for anti-tubercular drug design are {AccD3}, Fas, {FabH}, Pks13, {DesA1}/2, and {DesA3}. Proteins identified as essential by {FBA} correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of {FBA} for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Rajagopalan, Preethi and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/288ac3c18a35aa2bb2afa68e558b07f55/karthikraman},
      day = 14,
      doi = {10.1371/journal.pcbi.0010046},
      interhash = {2929de77c714ca6187d716da66df0e4a},
      intrahash = {88ac3c18a35aa2bb2afa68e558b07f55},
      issn = {1553-7358},
      journal = {PLoS Computational Biology},
      keywords = {flux-analysis mtb myown},
      month = oct,
      number = 5,
      pages = {e46+},
      pmcid = {PMC1246807},
      pmid = {16261191},
      posted-at = {2012-02-07 09:26:29},
      priority = {2},
      timestamp = {2019-04-17T12:56:37.000+0200},
      title = {Flux Balance Analysis of Mycolic Acid Pathway: Targets for {Anti-Tubercular} Drugs},
      url = {http://dx.doi.org/10.1371/journal.pcbi.0010046},
      volume = 1,
      year = 2005
    }
  • [DOI] K. Raman, N. Chandra, K. Raman, and N. Chandra, PathwayAnalyser: A Systems Biology Tool for Flux Analysis of Metabolic Pathways, 2008.
    [bibtex]
    @misc{Raman2008PathwayAnalyser,
      abstract = {Stoichiometric and constraint-based analyses of metabolic pathways have been gaining ground in the recent past with the increase in the quality and number of pathway databases available and the curation of genome-scale metabolic models. Genome-scale metabolic models of several organisms such as Escherichia coli, Saccharomyces cerevisiae and Staphylococcus aureus have already been constructed. Flux Balance Analysis ({FBA}) and Minimisation of Metabolic Adjustment ({MoMA}) are two of the popular techniques for the constraint-based analysis of metabolic {pathways.We} have developed a computational tool, {PathwayAnalyser}, for the analysis of metabolic pathways, particularly by {FBA} and {MoMA}. {PathwayAnalyser} interfaces with the open-source {GNU} Linear Programming Toolkit ({GLPK}) for linear {programming/FBA} and Object Oriented Quadratic Programming ({OOQP}) for quadratic {programming/MoMA}. It gives a comprehensive report on gene deletions from the Systems Biology Markup Language ({SBML}) Model and objective function input for {FBA}. {PathwayAnalyser} is open-source and is available at http://sourceforge.net/projects/pathwayanalyser},
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Raman, Karthik and Chandra, Nagasuma and Raman, Karthik and Chandra, Nagasuma},
      biburl = {https://www.bibsonomy.org/bibtex/2fd19a7161f946327fd0aa87c407a6ecd/karthikraman},
      day = 8,
      doi = {10.1038/npre.2008.1868.1},
      interhash = {b8c151169b1a33540be7a37483117317},
      intrahash = {fd19a7161f946327fd0aa87c407a6ecd},
      issn = {1756-0357},
      journal = {Nature Precedings},
      keywords = {flux-analysis myown pathway\_analysis software systems\_biology},
      month = may,
      number = 713,
      posted-at = {2010-03-09 15:03:49},
      priority = {0},
      timestamp = {2019-04-17T12:56:37.000+0200},
      title = {{PathwayAnalyser}: A Systems Biology Tool for Flux Analysis of Metabolic Pathways},
      url = {http://dx.doi.org/10.1038/npre.2008.1868.1},
      year = 2008
    }


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