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Faculty

Karthik Raman

Karthik Raman

Ph. D., Indian Institute of Science, Bangalore

Professor

kraman@iitm.ac.in

Office : Block:2 BT 221

+91-44-2257-4139

Research Interests

  • Computational Approaches to Understand and Manipulate Biological Networks

  • Biological Big Data Analysis

  • High-performance Computing for Systems Biology

  • In silico metabolic engineering

  • Theoretical investigations into biological networks

Publications

Complete list of publications can be found on Scopus

Articles (52)

  • R. K. Kumar, N. K. Singh, S. Balakrishnan, C. W. Parker, K. Raman, and K. Venkateswaran (2022) Metabolic Modeling of the International Space Station Microbiome Reveals Key Microbial Interactions Microbiome 10(1):102 DOI

  • S. S. M. Das and K. Raman (2022) Effect of Dormant Spare Capacity on the Attack Tolerance of Complex Networks Physica A: Statistical Mechanics and its Applications 598:127419 DOI

  • M. Sudhakar, R. Rengaswamy, and K. Raman (2022) Multi-Omic Data Helps Improve Prediction of Personalised Tumor Suppressors and Oncogenes Frontiers in Genetics 13:854190 DOI

  • D. Chakraborty, R. Rengaswamy, and K. Raman (2022) Designing Biological Circuits: From Principles to Applications ACS Synthetic Biology 11(4):1377-1388 DOI

  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2022) Discovering adaptation-capable biological network structures using control-theoretic approaches PLoS Computational Biology 18(1):e1009769 DOI

  • M. Sudhakar, R. Rengaswamy, and K. Raman (2022) Novel Ratio-Metric Features Enable the Identification of New Driver Genes across Cancer Types Scientific Reports 12(1):5 DOI

  • L. Raajaraam and K. Raman (2022) A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites Frontiers in Bioengineering and Biotechnology 9:1330 DOI

  • M. Ibrahim and K. Raman (2021) Two-Species Community Design of Lactic Acid Bacteria for Optimal Production of Lactate Computational and Structural Biotechnology Journal 19:6039-6049 DOI

  • V. Senthamizhan, B. Ravindran, and K. Raman (2021) NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks Frontiers in Genetics 12:1666 DOI

  • M. Ibrahim, L. Raajaraam, and K. Raman (2021) Modelling Microbial Communities: Harnessing Consortia for Biotechnological Applications Computational and Structural Biotechnology Journal 19:3892-3907 DOI

  • S. Gangadharan and K. Raman (2021) The Art of Molecular Computing: Whence and Whither BioEssays 43(8):2100051 DOI

  • S. Banerjee, K. Raman, and B. Ravindran (2021) Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes Cancers 13(10):2366 DOI

  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2021) Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities Methods in Molecular Biology (Clifton, N.J.) 2189:133-155 PMID DOI

  • S. M. Keating, D. Waltemath, M. K nig, F. Zhang, A. Dr ger, C. Chaouiya, F. T. Bergmann, A. Finney, C. S. Gillespie, T. Helikar, S. Hoops, R. S. Malik-Sheriff, S. L. Moodie, I. I. Moraru, C. J. Myers, A. Naldi, B. G. Olivier, S. Sahle, J. C. Schaff, L. P. Smith, M. J. Swat, D. Thieffry, L. Watanabe, D. J. Wilkinson, M. L. Blinov, K. Begley, J. R. Faeder, H. F. G mez, T. M. Hamm, Y. Inagaki, W. Liebermeister, A. L. Lister, D. Lucio, E. Mjolsness, C. J. Proctor, K. Raman, N. Rodriguez, C. A. Shaffer, B. E. Shapiro, J. Stelling, N. Swainston, N. Tanimura, J. Wagner, M. Meier-Schellersheim, H. M. Sauro, B. Palsson, H. Bolouri, H. Kitano, A. Funahashi, H. Hermjakob, J. C. Doyle, M. Hucka, and S. L. C. 3. members (2020) SBML Level 3: An Extensible Format for the Exchange and Reuse of Biological Models Molecular Systems Biology 16(8):e9110 DOI

  • P. Jagadeesan, K. Raman, and A. K. Tangirala (2020) A New Index for Information Gain in the Bayesian Framework IFAC-PapersOnLine 53(1):634-639 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 (2020) Metagenome-Wide Association Analysis Identifies Microbial Determinants of Post-Antibiotic Ecological Recovery in the Gut Nature Ecology & Evolution 4(1256-1267):1-12**DOI**

  • U. W. Liebal, A. N. T. Phan, M. Sudhakar, K. Raman, and L. M. Blank (2020) Machine Learning Applications for Mass Spectrometry-Based Metabolomics Metabolites 10(6):243 DOI

  • K. Sachdeva, M. Goel, M. Sudhakar, M. Mehta, R. Raju, K. Raman, A. Singh, and V. Sundaramurthy (2020) Mycobacterium Tuberculosis (Mtb) Lipid Mediated Lysosomal Rewiring in Infected Macrophages Modulates Intracellular Mtb Trafficking and Survival Journal of Biological Chemistry 295:9192-9210 PMID DOI

  • G. Sambamoorthy and K. Raman (2020) MinReact: A Systematic Approach for Identifying Minimal Metabolic Networks Bioinformatics (Oxford, England) 36(15):4309-4315 PMID DOI

  • A. Ravikrishnan, L. M. Blank, S. Srivastava, and K. Raman (2020) Investigating Metabolic Interactions in a Microbial Co-Culture through Integrated Modelling and Experiments Computational and Structural Biotechnology Journal 18:1249-1258 DOI

  • N. T. Devika and K. Raman (2019) Deciphering the Metabolic Capabilities of Bifidobacteria Using Genome-Scale Metabolic Models Scientific Reports 9(1):18222 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. Gu noche, S. G mez, 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. M ller, R. Narayan, O. Narykov, T. Natoli, T. Norman, S. Park, L. Perfetto, D. Perrin, S. Pirr , T. M. Przytycka, X. Qian, K. Raman, D. Ramazzotti, E. Ramsahai, B. Ravindran, P. Rennert, J. Saez-Rodriguez, C. Schrfe, 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. T rei, 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 (2019) Assessment of network module identification across complex diseases Nature Methods 16(9):843-852 DOI

  • A. Srinivasan, V. S, K. Raman, and S. Srivastava (2019) Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture Biochemical Engineering Journal 151:107256**DOI**

  • A. Badri, K. Raman, and G. Jayaraman (2019) Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation Processes 7(6):343 DOI

  • B. Tripathi, S. Parthasarathy, H. Sinha, K. Raman, and B. Ravindran (2019) Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks Frontiers in Genetics 10:164 DOI

  • G. Sambamoorthy, H. Sinha, and K. Raman (2019) Evolutionary design principles in metabolism Proc Biol Sci 286(1898):20190098-20190098 PMID,DOI

  • B. Tripathi, S. Parthasarathy, H. Sinha, K. Raman, and B. Ravindran (2019) Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks Frontiers in Genetics 10 , Frontiers Media SA DOI

  • S. Sahoo, R. K. Ravi Kumar, B. Nicolay, O. Mohite, K. Sivaraman, V. Khetan, P. Rishi, S. Ganesan, K. Subramanyan, K. Raman, W. Miles, and S. V. Elchuri (2019) Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma FEBS Lett 593(1):23-41 PMID,DOI

  • A. Srinivasan, V. S, K. Raman, and S. Srivastava (2019) Rational metabolic engineering for enhanced alpha-tocopherol production in Helianthus annuus cell culture Biochemical Engineering Journal:107256 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. Gu-noche, S. G-mez, 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. M-ller, R. Narayan, O. Narykov, T. Natoli, T. Norman, S. Park, L. Perfetto, D. Perrin, S. Pirr-, T. M. Przytycka, X. Qian, K. Raman, D. Ramazzotti, E. Ramsahai, B. Ravindran, P. Rennert, J. Saez-Rodriguez, C. Sch-rfe, 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. T-rei, 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 (2019) Assessment of network module identification across complex diseases Nature Methods 16(9):843-852 DOI

  • A. Badri, K. Raman, and G. Jayaraman (2019) Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation Processes 7(6) DOI

  • K. Azhagesan, B. Ravindran, and K. Raman (2018) Network-based features enable prediction of essential genes across diverse organisms PLOS ONE 13(12):1-13 , Public Library of Science DOI

  • G. Sambamoorthy and K. Raman (2018) Understanding the evolution of functional redundancy in metabolic networks Bioinformatics 34(17):i981-i987 DOI

  • A. Ravikrishnan, M. Nasre, and K. Raman (2018) Enumerating all possible biosynthetic pathways in metabolic networks. Scientific reports 8:9932+ PMID

  • K. Raman, A. Pratapa, O. Mohite, and S. Balachandran (2018) Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL. Methods in molecular biology (Clifton, N.J.) 1716:315-336 PMID

  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2018) A systems-theoretic approach towards designing biological networks for perfect adaptation IFAC-PapersOnLine 51(1):307-312 DOI

  • A. Sankar, S. Ranu, and K. Raman (2017) Predicting novel metabolic pathways through subgraph mining Bioinformatics 33(24):3955-3963 DOI

  • P. Bhatter, K. Raman, and V. Janakiraman (2017) Elucidating the biosynthetic pathways of volatile organic compounds in Mycobacterium tuberculosis through a computational approach Mol. BioSyst. 13(4):750-755**PMID**,**DOI**

  • N. Rajasekaran, S. Suresh, S. Gopi, K. Raman, and A. N. Naganathan (2017) A General Mechanism for the Propagation of Mutational Effects in Proteins. Biochemistry 56(1):294-305 PMID,DOI

  • A. Pratapa, S. Balachandran, and K. Raman (2015) Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks Bioinformatics 31(20):3299-3305 , Oxford University Press PMID,DOI

  • A. Ravikrishnan and K. Raman (2015) Critical assessment of genome-scale metabolic networks: the need for a unified standard Briefings in Bioinformatics 16(6):1057-1068 , Oxford University Press PMID,** DOI**

  • R. Partha and K. Raman (2014) Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces PLoS ONE 9(11):e112792+ , Public Library of Science DOI

  • K. Raman, N. Damaraju, and G. K. Joshi (2014) The organisational structure of protein networks: revisiting the centrality-lethality hypothesis Systems and Synthetic Biology 8(1):73-81 , Springer Netherlands DOI

  • A. Kulkarni, L. Ananthanarayan, and K. Raman (2013) Identification of putative and potential cross-reactive chickpea (Cicer arietinum) allergens through an in silico approach Computational Biology and Chemistry 47:149-155 DOI

  • K. Raman and A. Wagner (2011) Evolvability and robustness in a complex signalling circuit Molecular BioSystems 7(4):1081-1092 , The Royal Society of Chemistry DOI

  • K. Raman and A. Wagner (2010) The evolvability of programmable hardware Journal of The Royal Society Interface 8(55):269-281 PMID,DOI

  • K. Raman, A. G. Bhat, and N. Chandra (2010) A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis Molecular BioSystems 6(3):516-530 , The Royal Society of Chemistry PMID,DOI

  • K. Raman (2010) Construction and analysis of protein-protein interaction networks Automated Experimentation 2(1):2+ PMID,DOI

  • K. Raman and N. Chandra (2009) Flux balance analysis of biological systems: applications and challenges. Briefings in bioinformatics 10(4):435-449 PMID,DOI

  • K. Raman, R. Vashisht, and N. Chandra (2009) Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis Mol. BioSyst. 5(12):1740-1751 , The Royal Society of Chemistry DOI

  • K. Raman and N. Chandra (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance BMC Microbiology 8:234 DOI

  • K. Raman, K. Yeturu, and N. Chandra (2008) targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC systems biology 2(1):109+ PMID,DOI

  • K. D. Verkhedkar, K. Raman, N. R. Chandra, and S. Vishveshwara (2007) Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis PLoS ONE 2(9):e881+ , Public Library of Science , Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India. PMID,DOI

  • K. Raman, P. Rajagopalan, and N. Chandra (2007) Hallmarks of mycolic acid biosynthesis: A comparative genomics study Proteins: Structure, Function, and Bioinformatics 69(2):358-368 , Bioinformatics Centre and Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India DOI

  • K. Raman, P. Rajagopalan, and N. Chandra (2006) Principles and Practices of Pathway Modelling Current Bioinformatics 1(2):147-160 , Bentham Science Publishers DOI

  • K. Raman, P. Rajagopalan, and N. Chandra (2005) Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-Tubercular Drugs PLoS Computational Biology 1(5):e46+ , Public Library of Science PMID,DOI

    Books (3)

  • A. Ravikrishnan and K. Raman (2018) Systems-level modelling of microbial communities : theory and practice , CRC Press

  • V. V. Kulkarni, K. Raman, and G. Stan (2014) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations , Springer DOI

  • V. V. Kulkarni, G. Stan, and K. Raman (2014) A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular Systems , Springer DOI

    Books (3)

  • A. Badri, A. Srinivasan, and K. Raman (2016) In Silico Approaches to Metabolic Engineering DOI

  • K. Raman and N. Chandra (2011) Systems Biology of Tuberculosis: Insights for Drug Discovery , Springer New York , New York, NY DOI

  • K. Raman, Y. Kalidas, and N. Chandra (2007) Model Driven Drug Discovery: Principles and Practices , Artech House Publishers

    Miscellaneous (3)

  • A. Pratapa, S. Balachandran, and K. Raman (2014) Fast-SL: An efficient algorithm to identify synthetic lethal reaction sets in metabolic networks

  • K. Raman and N. Chandra (2010) Systems biology Resonance 15(2):131-153 DOI

  • K. Raman, N. Chandra, K. Raman, and N. Chandra (2008) PathwayAnalyser: A Systems Biology Tool for Flux Analysis of Metabolic Pathways Nature Precedings , Nature Publishing Group DOI

Teaching

  • BT 1010: Module on -Big Data in Biology- (Jul 2017)

  • BT 2020: Numerical Methods for Biology (Jan 2018, 2019)

  • BT 3051: Data Structures and Algorithms for Biology (Jul 2014-2019)

  • BT 3240: Metabolic Regulation (Jul 2011-2013)

  • BT 4110: Computational Biology Lab (Jul 2015-2017)

  • BT 4310: Current Topics in Synthetic Biology (Jul 2014, 2019)

  • BT 5240: Computational Systems Biology (Jan 2013-2019, Winter 2017)

    Honours And Awards

  • Young Faculty Recognition Award (IIT Madras), for excellence in teaching and research (2015)

  • Sir Vithal N. Chandavarkar Memorial Medal for the best Ph.D. thesis in the Supercomputer Education and Research Centre, Indian Institute of Science (2011)