PUBLICATIONS
[** - corresponding author]
2024
59. Lin D, Zhang L, Zhang J**, Chandrasekaran S**, Inferring Metabolic Objectives and Tradeoffs in Single Cells During Embryogenesis, Biorxiv.
58. Sambarey A, Smith K, Chung CH, Arora HS, Yang Z, Agarwal P, Chandrasekaran S**, Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis, iScience. Press release: Multimodal AI to guide personalized treatments for TB
57. Eames A, Chandrasekaran S**, Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth, PNAS Nexus.
2023
56. Scott Campit, Rupa Bhowmick, Taoan Lu, Aaditi Saoji, Ran Jin, Aaron Robida, Chandrasekaran S**, Data-Driven Screening to Infer Metabolic Modulators of the Cancer Epigenome. bioRxiv [preprint]
55. Mohammad Askandar Iqbal**, Shumaila Siddiqui, Kirk Smith, Prithvi Singh, Bhupender Kumar, Salem Chouaib, and Chandrasekaran S**, Pathway-Based Analysis of Breast Tumors Reveal Metabolic Subtypes of Clinical Relevance. iScience.
2022
54. Smith K, Rhoads N, Chandrasekaran S**, Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures, STAR Protocols.
53. Campit, S., Keshamouni, V. G., & Chandrasekaran, S**. Constraint-based modeling identifies metabolic vulnerabilities during the epithelial to mesenchymal transition. bioRxiv. [preprint]
52. Orbach SM, Brooks MD, Zhang Y, Campit SE, Bushnell GG, Decker JT, Rebernick RJ, Chandrasekaran S, Wicha MS, Jeruss JS, Shea LD. Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer. Clinical & Experimental Metastasis.
51. Chung, C. H., & Chandrasekaran, S**. A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions. PNAS Nexus.
50. Bakshi U, Gupta V, Lee AR, Davis JM, Chandrasekaran S., Jin YS, Freeman MF. and Sung J. TaxiBGC: a Taxonomy-guided Approach for the Identification of Experimentally Verified Microbial Biosynthetic Gene Clusters in Shotgun Metagenomic Data. mSystems.
CARAMeL combines machine learning with metabolic modeling to predict how drug interactions are impacted by both extrinsic and intrinsic metabolic heterogeneity
49. Cantrell J, Chung CH, Chandrasekaran S**, Machine learning to design antimicrobial combination therapies: promises and pitfalls, Drug Discovery Today [review article].
48. Assante G, Chandrasekaran S., Ng S, Tourna A, Chung CH, Isse, KA, ... & Youngson, NA. Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease. Genome Medicine.
47. Yadav S, Virk R, Chung CH, Eduardo MB, VanDerway D, Chen D, Burdett K, Gao H, Zeng Z, Ranjan M, Cottone G, Xuei X, Chandrasekaran S, Backman V, Chatterton R, Khan SA, Clare SE. Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer. NPJ Breast Cancer.
2021
46. King J, Patel M, Chandrasekaran S**. Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective. Metabolites [review article].
45. Lee HJ**, Shen F, Eames A, Jedrychowski MP, Chandrasekaran S**. Dynamic metabolic network modeling of a mammalian cell cycle using time-course multi-omics data, bioRxiv. [Preprint]
44. Smith K, Shen F, Lee HJ, Chandrasekaran S**. Metabolic signatures of regulation by phosphorylation and acetylation. iScience. [Download CAROM]
Predicting PTM regulation using CAROM
43. Zhao J,…, Lin D, Chandrasekaran S, Fu X, Zhang D, Fan H, Xie W, Li H, Hu Z, Zhang J, Metabolic remodeling during murine early embryo development. Nature Metabolism.
42. Chung CH, Lin D-W, Eames A, Chandrasekaran S**. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites. [review article].
Overview of Next-generation GEMs that incorporate multiple regulatory mechanisms
41. Chandrasekaran S**, Danos N, George UZ, Han JP, Quon G, Müller R, Tsang Y, Wolgemuth C, “The Axes of Life: A roadmap for understanding dynamic multiscale systems”, Integrative and Comparative Biology. [review article] [Editor’s Choice]
40. Müller R, Han JP, Chandrasekaran S, Bogdan P. “Deep Learning for Reintegrating Biology”. Integrative & Comparative Biology. [review article]
39. Giblin W, … Chung C…, Chandrasekaran S, Nikolovska-Coleska Z, Verhaegen M, Snyder NW, Rivera MN, Osterman AL, Lyssiotis CA, Lombard DB, ”The deacylase SIRT5 supports melanoma viability by regulating chromatin dynamics”. Journal of Clinical Investigation.
38. Cicchese J, Sambarey A, Kirschner DE, Linderman JJ**, Chandrasekaran S**. "A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs." Scientific Reports.
Simulating efficacy of Tuberculosis drug regimens at the molecular, cellular and granuloma scale
2020
37. Jones, BM, Rao VD, Gernat T, Jagla T, Cash-Ahmed A, Rubin BE, Middendorf M, Sinha S, Chandrasekaran S**, Robinson GE**. “Individual differences in honey bee (Apis mellifera) behavior enabled by plasticity in brain gene regulatory networks”. eLife. Press release: Brain gene expression patterns predict behavior of individual honey bees
Predicting regulators of bee behavior using AI
36. Rhodes JS, Rendeiro C, Mun JG, Du K, Thaman P, Snyder A, Pinardo H, Drnevich J, Chandrasekaran S, Lai C, Schimpf KJ, Kuchan MJ. Brain α-tocopherol concentration and stereoisomer profile alter hippocampal gene expression in weanling mice. Journal of Nutrition.
35. Shcherbina A, …Chung C, … Chandrasekaran S, Jang YC, Brooks SV, Aguilar CA. Dissecting Murine Muscle Stem Cell Aging Through Regeneration Using Integrative Genomic Analysis, Cell Reports.
34. Zhou W, [24 authors], Chandrasekaran S, Lawrence TS, Lyssiotis CA, Wahl DR. Purine metabolism regulates DNA repair and therapy resistance in glioblastoma, Nature Communications.
33. Campit S, Meliki A, Youngson N, Chandrasekaran S**, Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. BioEssays. [review article]
Studying metabolic-epigenetic interactions using systems biology
32. Oruganty K, Campit S, Mamde S, Lyssiotis C, Chandrasekaran S**, Common biochemical properties of metabolic genes recurrently dysregulated in tumors, Cancer and Metabolism.
31. Campit S and Chandrasekaran S**, Inferring Metabolic Flux from Time-Course Metabolomics, Methods in Molecular Biology: Metabolic Flux Analysis in Eukaryotic Cells, [Preprint PDF, Supplement]. [review article]
30. Nelson B, …Campit S,…, Chandrasekaran S, … Lyssiotis C, Tissue of origin dictates GOT1 dependence and confers synthetic lethality to radiotherapy, Cancer and Metabolism.
29. Chandrasekaran S, Deep learning AI discovers surprising new antibiotics, The Conversation. [magazine article]
Harnessing AI to find antibiotics against pathogens like MRSA (Source: NIAID)
2019
28. Ma S, Jaipalli J, Larkins-Ford J, Lohmiller J, Aldridge B, Sherman D**, and Chandrasekaran S**, Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis, mBio. Summary: Designing new TB drugs using AI. Press release: How an AI solution designs new tuberculosis drug regimens. (** - Corresponding authors)
Predicting both drug interactions and regulators of drug interactions using INDIGO
27. Chandrasekaran S. Tying Metabolic Branches With Histone Tails Using Systems Biology. Epigenetics insights. [review article]
26. Shen F, Boccuto L, Pauly R, Srikanth S, Chandrasekaran S**, Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors, Genome Biology. (Download Metabolism-Epigenome model). Summary: How metabolism affects gene activity via the ‘Histone code’.
Network model of cancer metabolism and histone acetylation
25. 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.
24. Shen F, Cheek C, & Chandrasekaran S**. Dynamic Network Modeling of Stem Cell Metabolism, Methods in Molecular Biology: Computational Stem Cell Biology. [Download protocol and Preprint]. [review article]
23. Chandrasekaran S, A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models, Methods in Molecular Biology: Microbial Metabolic Engineering.[Preprint PDF]. [review article]
2018
22. Cokol M**, Li C and Chandrasekaran S**, Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment, PLOS Computational Biology. (Download MAGENTA). Press release: A ‘decathlon’ for antibiotics puts them through more realistic testing.
Overview of MAGENTA to predict the impact of environment on drug interactions
21. Chandrasekaran S, Predicting drug interactions from chemogenomics using INDIGO, Methods in Molecular Biology: Systems Chemical Biology, [Preprint PDF]. [review article]
20. 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.
19. 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.
2017
18. 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 (** - 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
17. 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. Featured in Cell Systems. [Download source code and tutorial for the dynamic metabolic modeling approach used in this study]
16. 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.
15. 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.
14. 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.
2016
13. 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. Highlighted in Cell Stem Cell, "Metabolic RemodeLIN of Pluripotency"
12. 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). Featured in MedChemNet. (Download INDIGO here) (** - Corresponding author)
11. 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.
2010-2015
10. 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)
9. 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
8. Chandrasekaran S, “Predicting Phenotype from Genotype through Reconstruction and Integrative Modeling of Metabolic and Regulatory Networks”, Systems and Synthetic Biology: A Systematic Approach (ed V.V.Kulkarni/G.B.Stan/K.S.Raman), 2014 [review article]
7. Chandrasekaran S, “Transcriptional Regulation Of Metabolism And Behavior: Insights From Reconstruction And Modeling Of Complex Biochemical Networks” (dissertation), 2013
6. Chandrasekaran S and N.D. Price, “Metabolic Constraint-based Refinement of Transcriptional Regulatory Networks”, PLOS Computational Biology, 2013. Highlighted in Nature Genetics and Genomeweb.
5. Simeonidis E, Chandrasekaran S, Price ND. “A guide to integrating transcriptional regulatory and metabolic networks using PROM (Probabilistic Regulation of Metabolism)”, Methods in Molecular Biology: Systems Metabolic Engineering (ed: Hal Alper), 2012. [review article]
4. Sung J, Wang Y, Chandrasekaran S, Witten DM, and ND Price, “Molecular signatures from omics data: from chaos to consensus”. Biotechnology Journal, 2012. [review article]
3. 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]
2. 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].
1. LB. Edelman, Chandrasekaran S, and N.D. Price, "Systems biology of embryogenesis”, Reproduction, Fertility, and Development, 2010. [review article]