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

Curriculum Vitaé PDF


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.


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

Relevant Publications

  • [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, pp. 1-12, 2020.
      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,
      pages = {1--12},
      publisher = {{Nature Publishing Group}},
      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] 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.
      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.
      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},
      citeulike-article-id = {14635351},
      citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/29967471},
      citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=29967471},
      day = 02,
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      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] 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.
      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},
      publisher = {{Nature Publishing Group}},
      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.


  • 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

  • [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.
      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, 2019.
      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,
      year = 2019
  • [DOI] A. Sankar, S. Ranu, and K. Raman, “Predicting novel metabolic pathways through subgraph mining,” Bioinformatics, vol. 33, iss. 24, pp. 3955-3963, 2017.
      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},
      citeulike-article-id = {14401087},
      citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btx481},
      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] 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.
      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},
      citeulike-article-id = {14139063},
      citeulike-linkout-0 = {http://dx.doi.org/10.1016/B978-0-444-63667-6.00008-0},
      citeulike-linkout-1 = {http://store.elsevier.com/Current-Developments-in-Biotechnology-and-Bioengineering/isbn-9780444636676/},
      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. 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.
      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},
      citeulike-article-id = {13530311},
      citeulike-linkout-0 = {http://dx.doi.org/10.1093/bib/bbv003},
      citeulike-linkout-1 = {http://bib.oxfordjournals.org/content/early/2015/02/27/bib.bbv003.abstract},
      citeulike-linkout-2 = {http://bib.oxfordjournals.org/content/early/2015/02/27/bib.bbv003.full.pdf},
      citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/25725218},
      citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=25725218},
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      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},
      publisher = {Oxford University Press},
      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.


  • 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

  • [DOI] G. Sambamoorthy and K. Raman, “MinReact: A Systematic Approach for Identifying Minimal Metabolic Networks,” Bioinformatics (Oxford, England), 2020.
      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},
      pmid = {32407533}
  • [DOI] P. Bhattacharya, K. Raman, and A. K. Tangirala, “Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities,” Methods in Molecular Biology (Clifton, N.J.), vol. 2189, pp. 133-155, 2021.
      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.},
      journal = {Methods in Molecular Biology (Clifton, N.J.)},
      keywords = {Adaptation,Design principles,Stability,Systems biology,Systems theory},
      language = {eng},
      pmid = {33180299}
  • [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.
      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
  • [DOI] G. Sambamoorthy, H. Sinha, and K. Raman, “Evolutionary design principles in metabolism,” Proc Biol Sci, vol. 286, iss. 1898, p. 20190098, 2019.
      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] G. Sambamoorthy and K. Raman, “Understanding the evolution of functional redundancy in metabolic networks,” Bioinformatics, vol. 34, iss. 17, p. i981–i987, 2018.
      added-at = {2018-12-02T16:09:07.000+0100},
      author = {Sambamoorthy, Gayathri and Raman, Karthik},
      biburl = {https://www.bibsonomy.org/bibtex/2b0a7eb7388e821b81bb497e570031740/karthikraman},
      citeulike-article-id = {14635354},
      citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/bty604},
      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
  • [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.
      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},
      citeulike-article-id = {13648461},
      citeulike-linkout-0 = {http://www.ncbi.nlm.nih.gov/pubmed/26085504},
      citeulike-linkout-1 = {http://dx.doi.org/10.1093/bioinformatics/btv352},
      citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/early/2015/06/16/bioinformatics.btv352.abstract},
      citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/content/early/2015/06/16/bioinformatics.btv352.full.pdf},
      citeulike-linkout-4 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/31/20/3299},
      citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/26085504},
      citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=26085504},
      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},
      publisher = {Oxford University Press},
      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] R. Partha and K. Raman, “Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces,” PLoS ONE, vol. 9, iss. 11, p. e112792+, 2014.
      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},
      citeulike-article-id = {13427861},
      citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pone.0112792},
      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},
      publisher = {Public Library of Science},
      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
  • [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.
      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},
      citeulike-article-id = {12595680},
      citeulike-linkout-0 = {http://dx.doi.org/10.1007/s11693-013-9123-5},
      citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s11693-013-9123-5},
      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},
      publisher = {Springer Netherlands},
      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.


  • 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

  • [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.
      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}
  • [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.
      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},
      publisher = {{Multidisciplinary Digital Publishing Institute}},
      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] 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.
      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] 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, 2019.
      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,
      publisher = {Frontiers Media {SA}},
      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,
      volume = 164,
      year = 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)



  • 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”


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


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


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


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


  • BT3190 Metabolic Regulation (Jul-Nov)


  • BT3190 Metabolic Regulation (Jul-Nov)


Other Stuff

Read about my other interests and some personal stuff
A collection of interesting quotes, related to biology/systems biology/modelling
Links I use regularly
Friends, colleagues, random interesting people
What I Use
Some of the tools I use to get my work done


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