Publications

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Articles (62)

  • L. Geistlinger, C. Mirzayi, F. Zohra, R. Azhar, S. Elsafoury, C. Grieve, J. Wokaty, S. D. Gamboa-Tuz, P. Sengupta, I. Hecht, A. Ravikrishnan, R. S. Gonc calves, E. Franzosa, K. Raman, V. Carey, J. B. Dowd, H. E. Jones, S. Davis, N. Segata, C. Huttenhower, and L. Waldron (2023) BugSigDB Captures Patterns of Differential Abundance across a Broad Range of Host-Associated Microbial Signatures Nature Biotechnology:1-13 , Nature Publishing Group
  • S. Murali, M. Ibrahim, H. Rajendran, S. Shagun, S. K. Masakapalli, K. Raman, and S. Srivastava (2023) Genome-scale metabolic model led engineering of Nothapodytes nimmoniana plant cells for high camptothecin production Frontiers in Plant Science 14:1207218
  • M. Pradeep and K. Raman (2023) COWAVE: A labelled COVID-19 wave dataset for building predictive models PLoS ONE 18(7):e0284076
  • D. K. Kuppa Baskaran, S. Umale, Z. Zhou, K. Raman, and K. Anantharaman (2023) Metagenome-Based Metabolic Modelling Predicts Unique Microbial Interactions in Deep-Sea Hydrothermal Plume Microbiomes ISME Communications 3(1):1-14 , Nature Publishing Group
  • P. Sengupta, S. K. M. Sivabalan, A. Mahesh, I. Palanikumar, D. K. Kuppa Baskaran, and K. Raman (2023) Big Data for a Small World: A Review on Databases and Resources for Studying Microbiomes Journal of the Indian Institute of Science
  • P. Jagadeesan, K. Raman, and A. K. Tangirala (2023) Sloppiness: Fundamental study, new formalism and its application in model assessment PLoS ONE 18(3):e0282609
  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2023) On Biological Networks Capable of Robust Adaptation in the Presence of Uncertainties: A Linear Systems-Theoretic Approach Mathematical Biosciences 358:108984
  • P. Jagadeesan, K. Raman, and A. K. Tangirala (2022) Bayesian Optimal Experiment Design for Sloppy Systems IFAC-PapersOnLine 55(23):121-126
  • S. Venkatraghavan, S. Anantakrishnan, and K. Raman (2022) Probing patterning in microbial consortia with a cellular automaton for spatial organisation Scientific Reports 12(1):17159
  • A. Anilkumar Sithara, D. Maripuri, K. Moorthy, S. Amirtha Ganesh, P. Philip, S. Banerjee, M. Sudhakar, and K. Raman (2022) iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data NAR Genomics and Bioinformatics 4(3):lqac053
  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2022) Discovering design principles for biological functionalities: Perspectives from systems biology Journal of Biosciences 47(4):56
  • 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
  • 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
  • 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
  • D. Chakraborty, R. Rengaswamy, and K. Raman (2022) Designing Biological Circuits: From Principles to Applications ACS Synthetic Biology 11(4):1377-1388
  • 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
  • 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
  • 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
  • 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
  • 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
  • M. Ibrahim, L. Raajaraam, and K. Raman (2021) Modelling Microbial Communities: Harnessing Consortia for Biotechnological Applications Computational and Structural Biotechnology Journal 19:3892-3907
  • S. Gangadharan and K. Raman (2021) The Art of Molecular Computing: Whence and Whither BioEssays 43(8):2100051
  • S. Banerjee, K. Raman, and B. Ravindran (2021) Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes Cancers 13(10):2366
  • S. M. Keating, D. Waltemath, M. König, F. Zhang, A. Dräger, C. Chaouiya, F. T. Bergmann, A. Finney, C. S. Gillespie, T. Helikar, S. Hoops, R. S. Malik-Sheriff, S. L. Moodie, I. I. Moraru, C. J. Myers, A. Naldi, B. G. Olivier, S. Sahle, J. C. Schaff, L. P. Smith, M. J. Swat, D. Thieffry, L. Watanabe, D. J. Wilkinson, M. L. Blinov, K. Begley, J. R. Faeder, H. F. Gómez, T. M. Hamm, Y. Inagaki, W. Liebermeister, A. L. Lister, D. Lucio, E. Mjolsness, C. J. Proctor, K. Raman, N. Rodriguez, C. A. Shaffer, B. E. Shapiro, J. Stelling, N. Swainston, N. Tanimura, J. Wagner, M. Meier-Schellersheim, H. M. Sauro, B. Palsson, H. Bolouri, H. Kitano, A. Funahashi, H. Hermjakob, J. C. Doyle, M. Hucka, and S. L. C. 3. members (2020) SBML Level 3: An Extensible Format for the Exchange and Reuse of Biological Models Molecular Systems Biology 16(8):e9110
  • 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
  • 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
  • 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
  • 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
  • G. Sambamoorthy and K. Raman (2020) MinReact: A Systematic Approach for Identifying Minimal Metabolic Networks Bioinformatics (Oxford, England) 36(15):4309-4315
  • 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
  • N. T. Devika and K. Raman (2019) Deciphering the Metabolic Capabilities of Bifidobacteria Using Genome-Scale Metabolic Models Scientific Reports 9(1):18222
  • 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 (2019) Assessment of network module identification across complex diseases Nature Methods 16(9):843-852
  • 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
  • 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
  • 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
  • G. Sambamoorthy, H. Sinha, and K. Raman (2019) Evolutionary design principles in metabolism Proc Biol Sci 286(1898):20190098
  • 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
  • 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
  • G. Sambamoorthy and K. Raman (2018) Understanding the evolution of functional redundancy in metabolic networks Bioinformatics 34(17):i981–i987
  • A. Ravikrishnan, M. Nasre, and K. Raman (2018) Enumerating all possible biosynthetic pathways in metabolic networks. Scientific reports 8:9932+
  • 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
  • A. Sankar, S. Ranu, and K. Raman (2017) Predicting novel metabolic pathways through subgraph mining Bioinformatics 33(24):3955-3963
  • 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
  • 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
  • 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
  • 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
  • R. Partha and K. Raman (2014) Revisiting Robustness and Evolvability: Evolution in Weighted Genotype Spaces PLoS ONE 9(11):e112792+
  • 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
  • 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
  • K. Raman and A. Wagner (2011) Evolvability and robustness in a complex signalling circuit Molecular BioSystems 7(4):1081-1092
  • K. Raman and A. Wagner (2010) The evolvability of programmable hardware Journal of The Royal Society Interface 8(55):269-281
  • 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
  • K. Raman (2010) Construction and analysis of protein-protein interaction networks Automated Experimentation 2(1):2+
  • K. Raman and N. Chandra (2010) Systems biology Resonance 15(2):131-153
  • 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
  • K. Raman and N. Chandra (2009) Flux balance analysis of biological systems: applications and challenges. Briefings in bioinformatics 10(4):435-449
  • 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+
  • K. Raman and N. Chandra (2008) Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance BMC Microbiology 8:234
  • 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+
  • 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
  • K. Raman, P. Rajagopalan, and N. Chandra (2006) Principles and Practices of Pathway Modelling Current Bioinformatics 1(2):147-160
  • 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+

Books (4)

  • K. Raman (2021) An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks , Chapman and Hall/CRC , Boca Raton, FL
  • A. Ravikrishnan and K. Raman (2018) Systems-level modelling of microbial communities : theory and practice , CRC Press
  • 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
  • V. V. Kulkarni, K. Raman, and G. Stan (2014) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations , Springer

In Books (5)

  • P. Bhattacharya, K. Raman, and A. K. Tangirala (2021) Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities
  • K. Raman, A. Pratapa, O. Mohite, and S. Balachandran (2018) Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL.
  • A. Badri, A. Srinivasan, and K. Raman (2016) In Silico Approaches to Metabolic Engineering
  • K. Raman, Y. Kalidas, and N. Chandra (2007) Model Driven Drug Discovery: Principles and Practices , Artech House Publishers
  • K. Raman and N. Chandra (2011) Systems Biology of Tuberculosis: Insights for Drug Discovery , New York, NY

Miscellaneous (1)

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

PhD Thesis

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