Network model of cancer metabolism and histone acetylation

Network model of cancer metabolism and histone acetylation

  • Bernabé B.P., Thiele I, Galdones E, Siletz A, Chandrasekaran S, Woodruff T.K, Broadbelt L.J and Shea L.D. Dynamic genome-scale cell-specific metabolic models reveal novel inter-cellular and intra-cellular metabolic communications during ovarian follicle development. BMC bioinformatics, 2019.

  • Shen F, Cheek C, & Chandrasekaran S. Dynamic Network Modeling of Stem Cell Metabolism, Computational Stem Cell Biology, Methods in Molecular Biology, 2019. [Download supplementary data used in the tutorial here].

  • Chandrasekaran S, A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models, Microbial Metabolic Engineering, Methods in Molecular Biology, 2019

  • Cokol M, Li C and Chandrasekaran S, Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment, PLOS Computational Biology, 2018. (Download MAGENTA). Press release: A ‘decathlon’ for antibiotics puts them through more realistic testing.

Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA)

Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA)

  • Chandrasekaran S, Predicting drug interactions from chemogenomics using INDIGO, Systems Chemical Biology, Methods in Molecular Biology, 2018

  • Saul MC, Blatti C, Yang W, Bukhari SA, Shpigler HY, Troy JM, Seward CH, Sloofman L, Chandrasekaran S, Bell AM, Stubbs L. Cross‐species systems analysis of evolutionary toolkits of neurogenomic response to social challenge. Genes, Brain and Behavior, 2018

  • Shpigler HY, Saul MC, Murdoch EE, Corona F, Cash‐Ahmed AC, Seward CH, Chandrasekaran S, Stubbs LJ, Robinson GE. Honey bee neurogenomic responses to affiliative and agonistic social interactions. Genes, Brain and Behavior, 2018.

  • Dotiwala F, Santara SS, Binker-Cosen A, Li B, Chandrasekaran S**, Lieberman J**, “Granzyme B disrupts central metabolism and protein synthesis in bacteria to promote an immune cell death program”, Cell, 2017 (** - Corresponding author). Featured in Nature Reviews Microbiology and Cell Systems. Press release - Closest look yet at killer T-cell activity could yield new approach to tackling antibiotic resistance

Metabolic pathways targeted by granzyme-B

Metabolic pathways targeted by granzyme-B

  • Chandrasekaran S*, Zhang J*, Ross C, Huang Y, Asara J.M., Li H, Daley G.Q., Collins J.J. "Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling", Cell Reports, 2017. Featured in Cell Systems. [Download source code and tutorial for the dynamic metabolic modeling approach used in this study]

stem cell metabolism
  • Bukhari S.A, Saul M.C, Seward C.H, Zhang H., Bensky M., James N., Zhao S.D, Chandrasekaran S, Stubbs L., Bell A.M, “Temporal Dynamics of Neurogenomic Plasticity in Response to Social Interactions in Male Threespined Sticklebacks”, Plos Genetics, 2017.

  • Saul, M.C, Seward C, Troy J, Zhang H, Sloofman L, Lu X, Weisner P, Caetano-Anolles D, Sun H, Zhao D, Chandrasekaran S, Sinha S, and Stubbs L. “Transcriptional regulatory dynamics set the stage for a coordinated metabolic and neural response to social threat in mice”, Genome Research, 2017.

  • Shpigler H, Saul M.C, Murdoch E.E, Cash-Ahmed A, Seward C.H, Sloofman L, Chandrasekaran S, Sinha S, Stubbs L.J, and Robinson G.E. “Behavioral, transcriptomic and epigenetic responses to social challenge in honey bees”, Genes Brain and Behavior, 2017.

  • Zhang J*, Ratanasirintrawoot R*, Chandrasekaran S, [22 authors], Collins J.J, Daley G.Q, "LIN28 Regulates Stem Cell Metabolism and Conversion to Primed Pluripotency", Cell Stem Cell, 2016. Highlighted in Cell Stem Cell, "Metabolic RemodeLIN of Pluripotency"

  • Chandrasekaran S**, Cokol-Cakmak M, Sahin N, Yilancioglu K, Kazan H, Collins J.J and Cokol M**, “Chemogenomics and Orthology-based Design of Antibiotic Combination Therapies”, Molecular Systems Biology (Cover Article), 2016. Most read article in Molecular Systems Biology (as of July 2016). Featured in MedChemNet Video Interview. (Download INDIGO here) (** - Corresponding author)

  • Sobotka J, Daley M, Chandrasekaran S, Rubin B, Thompson G. “Structure and function of gene regulatory networks associated with worker sterility in honeybees", Ecology and Evolution, 2016

  • Chandrasekaran S*, Rittschof C*, Djukovic D, Gu H, Raftery D, Price N.D, and Robinson G.E, “Aggression is Associated with Aerobic Glycolysis in the Honey Bee Brain”, Genes Brain and Behavior, 2015.(Top 5 most cited articles in 2015 in Genes Brain and Behavior)

  • Brooks A.N, Reiss D.J, Allard A, Wu W, Salvanha D.M, Plaisier C, Chandrasekaran S, Pan M, Kaur A, Baliga N.S. A system‐level model for the microbial regulatory genome, Molecular Systems Biology, 2014

  • Sung J, Wang Y, Chandrasekaran S, Witten DM, and ND Price, “Molecular signatures from omics data: from chaos to consensus”. Biotechnology Journal, 2012.

  • Chandrasekaran S, Ament S.A, Eddy J.A, Rodriguez-Zas S.R, Schatz B.R, Price N.D, and Robinson G.E, "Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states", PNAS, 2011 (Cover Article). Highlighted in PNAS "Systems biology meets behavior". [Download ASTRIX algorithm and Supplementary datasets]


  • Chandrasekaran S and N.D. Price, "Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis," PNAS, 2010. [Download PROM models].