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 three related themes:

  1. In silico metabolic engineering
  2. Theoretical investigations of biological networks
  3. Biological data analysis

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

(1) 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
  • Over production 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
  • 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,
      interhash = {ba035a5173fb8ca04837270dc8d7aed6},
      intrahash = {c419ad1c7be0bacd7a0e7a4e4e564804},
      issn = {2045-2322},
      journal = {Scientific reports},
      keywords = {communities metabolic-networks myown pathfinding},
      month = jul,
      pages = {9932+},
      pmid = {29967471},
      posted-at = {2018-09-12 08:30:30},
      priority = {2},
      timestamp = {2019-04-17T13:00:35.000+0200},
      title = {Enumerating all possible biosynthetic pathways in metabolic networks.},
      url = {http://view.ncbi.nlm.nih.gov/pubmed/29967471},
      volume = 8,
      year = 2018
  • [DOI] 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},
      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},
      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

(2) 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, 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

(3) 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
  • Identifying disease modules in biological networks [DREAM Challenge]
  • Identifying the context of mutations in cancer (DBT Project)

Relevant Publications

  • [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




  • 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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