Computational Systems Biology Lab

Karthik Raman

Associate Professor
Co-ordinator, Initiative for Biological Systems Engineering
BT 221, Block II, Department of Biotechnology
Bhupat & Jyoti Mehta School of Biosciences
Indian Institute of Technology Madras
Chennai – 600 036
kraman@iitm·ac·in

Curriculum Vitaé PDF

Research

We are an interdisciplinary group focussing on the development of algorithms and computational tools to understand, predict and manipulate complex biological networks.

Work in our lab broadly falls under four related themes:

1. Understanding microbial interactions in microbiomes
2. In silico metabolic engineering
3. Theoretical investigations of biological networks
4. Biological data analysis

We are also an active member of the Centre for Integrative Biology and Systems mEdicine (IBSE), and the Robert Bosch Centre for Data Sciences and Artificial Intelligence (RBC-DSAI) at IIT Madras.

(1) Understanding microbial interactions in microbiomes

Building on our expertise in metabolic modelling, we have now initiated studies of several microbiomes and microbial interactions in these microbiomes. Building on our MetQuest algorithm and Metabolic Support Index to understand microbial metabolic

interactions, we are now studying microbiomes as diverse as the Guaymas Basic hydrothermal vents to the International Space Station microbiome. We are also very much interested in studying the gut microbiome and the role of keystone species in any microbiome.

Projects

• Studying microbial interactions in the ISS microbiome
• Variations in the ocular microbiome between healthy and keratitis conditions
• Gut microbiome: interactions and organisation

Relevant Publications

• 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},
    pmid = {35791019},
    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}
  }

• 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}
  }

• 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}
  }

• 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
  }

• 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}
  }

(2) In silico Metabolic Engineering

We develop computational approaches to predict ways to manipulate metabolic networks, for the over-production of commercially important molecules, e.g. hyaluronic acid, α-tocopherol. We primarily focus on constraint-based approaches to study metabolic networks, using tools such as flux balance analysis. We also have collaborations with experimental labs, to validate the predicted metabolic engineering strategies.

Projects

• Rational design of a consortium for metabolic engineering
• Overproduction of α-tocopherol in H. annuus cell lines
• Overproduction of hyaluronic acid in L. lactis
• Predicting novel metabolic pathways for retrosynthesis

Relevant Publications

• 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},
    pmid = {35071202},
    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}
  }

• 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},
    pmid = {34849207},
    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}
  }

• 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}
  }

• 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
  }

• 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
  }

• 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
  }

• 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
  }

• 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
  }

(3) Theoretical Investigations of Biological Networks

A lot of our work involves a fundamental understanding of biological networks, such as metabolic networks. We try to answer fundamental questions about the organisation and evolution of these networks, in a bid to better understand the constraints that permeate their design. This, in turn, will be very helpful towards designing and manipulating such networks, for various applications, including metabolic engineering.

Projects

• Understanding synthetic lethality in metabolic networks
• Robustness and plasticity of metabolic networks
• Design principles of oscillators, network design for synthetic biology
• Systems-theoretic approaches to design adaptive networks
• Sloppiness in biological systems

Relevant Publications

• 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},
    pmid = {35061660},
    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},
  }

• 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}
  }

• 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}
  }

• 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
  }

• 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
  }

• 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. 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
  }

• 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
  }

• 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
  }

(4) Biological Data Analysis

We are also working on a data-centric investigation of biological networks. For example, how can we understand biological networks, by using multi-dimensional data (e.g. genomic, transcriptomic, proteomic, phosphoproteomic etc.)? We also apply techniques from machine learning to study biological networks and datasets  alike, to make testable predictions and generate hypotheses for wet lab experimentation.

Projects

• Predicting essential proteins in protein interaction networks (See NetGenes database)
• Identifying disease modules in biological networks [DREAM Challenge]
• Identifying the context of mutations in cancer (DBT Project)

Relevant Publications

• 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},
    pmid = {35620468},
    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}
  }

• 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},
    pmid = {34997044},
    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}
  }

• 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},
    pmid = {34630517},
    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}
  }

• 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}
  }

• 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
  }

• 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
  }

Teaching

NPTEL

• VideoKen indexed “searchable” videos

2021

• BT5240 Computational Systems Biology (Jan-May)
• Computational Systems Biology NPTEL Course (Jul-Oct)
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4110 Computational Biology Laboratory (Jul-Nov)

2020

• BT5240 Computational Systems Biology (Jan-May)
• Computational Systems Biology NPTEL Course (Jul-Oct)
• NPTEL Lab Workshop (Aug 2020)

2019

• BT5240 Computational Systems Biology (Jan-May)
• BT2020 Numerical Methods for Biology (Jan-May), along with Dr. Athi N. Naganathan
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4310 Current Topics in Synthetic Biology (Jul-Nov)

2018

• BT5240 Computational Systems Biology (Jan-May)
• BT2020 Numerical Methods for Biology (Jan-May), along with Dr. Athi N. Naganathan
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• Computational Systems Biology NPTEL Course (Jul-Oct)

2017

• BT5240 Computational Systems Biology (Jan-May)
• AICTE STTP on Computational Systems Biology (Feb 6-11)
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4110 Computational Biology Laboratory (Jul-Nov)
• BT1010 Life Sciences – Module on “Big Data in Biology”

2016

• BT5240 Computational Systems Biology (Jan-May)
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4110 Computational Biology Laboratory (Jul-Nov)

2015

• BT5240 Computational Systems Biology (Jan-May)
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4110 Computational Biology Laboratory (Jul-Nov)

2014

• BT5240 Computational Systems Biology (Jan-May)
• BT3051 Data Structures and Algorithms for Biology (Jul-Nov)
• BT4310 Current Topics in Synthetic Biology (Jul-Nov)

2013

• BT5240 Computational Systems Biology (Jan-May)
• BT3240 Metabolic Regulation (Jul-Nov)

2012

• BT3190 Metabolic Regulation (Jul-Nov)

 2011

• BT3190 Metabolic Regulation (Jul-Nov)

 

Other Stuff

Personal

Read about my other interests and some personal stuff

Quotes

A collection of interesting quotes, related to biology/systems biology/modelling

Links

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People

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What I Use

Some of the tools I use to get my work done