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Nitin Baliga is an American biologist who is interested in mathematically modeling networks within biological systems to pave the way for predictive and personalized medicine, and a sustainable environment. Dr. Baliga is also actively engaged in HS education, bringing innovative inquiry-based curriculum on current science concepts throughout the United States. His work has been recognized internationally and he has won numerous awards including the Alvin J. Thompson award for his contributions to HS education. He is a Professor and Director of Integrative Biology at the Institute for Systems Biology, where he was one of the founding members. Dr. Baliga did all of his early schooling in Mumbai, India, where he received a B.Sc. in Microbiology (1992) from Ruia College within the Mumbai University system. In 1992, Dr. Baliga entered a national competition and won the highly coveted Central Government of India sponsored Department of Biotechnology studentship. This award supported his graduate studies in Marine Biotechnology at Goa University. After getting a M.Sc. in Marine Biotechnology in 1994, Dr. Baliga won the prestigious Council for Scientific and Industrial Research fellowship through another Central Government of India sponsored national competition. After doing his graduate research at the University of Massachusetts at Amherst, for which he was awarded a Ph.D. in Microbiology in 2000, Dr. Baliga moved cross-country to Seattle for postdoctoral training with Dr. Leroy Hood at the Institute for Systems Biology. In 2007, together with Drs. Bonneau, Reiss, and Hood, Dr. Baliga published a landmark paper1 that demonstrated for the first time that it is possible to accurately predict how an organism would respond to a new environment. This discovery was made possible by a suite of technologies2-5, experimental strategies6-11 and computational tools12-19 that Dr. Baliga and his team developed to reverse engineer the complete regulatory network of a free living organism. This study uncovered the theory and fundamental principles that underlie the evolution and predictability of biological responses1,6,20,21. Applying this approach to environmental issues, Dr. Baliga has discovered how some extremophiles might explore life in a new environment6,22,23, revised a >5 decade old “operon” paradigm of gene regulation in prokaryotes24,25, discovered diurnal anticipatory behavior in archaea26, uncovered the role of programmed cell death in unicellular chlorophytes (manuscript in review), elucidated how cells maintain copper ions at a safe concentration (Pang et al, PLOS comp Biol, in press), and discovered the role of RNases in adaptation to rapid environmental changes (manuscript in preparation). Together with collaborators at the University of Washington (Drs. John Leigh and David Stahl), and the Lawrence Berkeley Laboratories (Dr. Adam Arkin et al) he is now elucidating biological networks underlying social interactions and fuel production by microbes. Dr. Baliga has also expanded his research program to complex human disease to elucidate dysfunctional networks in cancer27 and the basis for latency in TB infections28 (with Dr. David Sherman). For instance, his work on cancer with Dr. Plaisier has identified a core set of miRNAs responsible for disease-characteristic oncogenic signatures across multiple cancers16,27,29. The predictive and actionable network models of disease have important implications for drug target discovery, drug repositioning, and combinatorial therapeutics. Having published over 50 peer-reviewed research articles in top international journals including Cell, Science and Nature, Dr. Baliga has been invited to present his work at prestigious conferences and even the Google SciFoo Camp in 2008. Dr. Baliga’s work has been profiled by The Scientist, Genome Web, Wired magazine, Genetic Engineering News, Ars Technica, Xconomy, and Nature Methods, among many others. Research in Dr. Baliga’s laboratory has been supported by the National Science Foundation, National Institutes of Health, NASA, Department of Energy, and the Department of Defense. He is the Section Editor of BMC Systems Biology, serves on scientific advisory boards of numerous academic and industrial organizations, and been instrumental in research program planning for the NSF and DOE.

1	Bonneau, R. et al., A predictive model for transcriptional control of physiology in a free living cell. Cell 131 (7), 1354-1365 (2007). 2	Bonneau, R., Baliga, N.S., Deutsch, E.W., Shannon, P., & Hood, L., Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1. Genome Biol 5 (8), R52 (2004). 3	Goo, Y.A. et al., Low-pass sequencing for microbial comparative genomics. BMC Genomics 5 (1), 3 (2004). 4	Goo, Y.A. et al., Proteomic Analysis of an Extreme Halophilic Archaeon, Halobacterium sp. NRC-1. Mol Cell Proteomics 2 (8), 506-524 (2003). 5	Van, P.T. et al., Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage. J Proteome Res 7 (9), 3755-3764 (2008). 6	Brooks, A.N., Turkarslan, S., Beer, K.D., Yin Lo, F., & Baliga, N.S., Adaptation of cells to new environments. Wiley Interdiscip Rev Syst Biol Med 3 (5), 544-561 (2011). 7	Facciotti, M.T., Bonneau, R., Hood, L., & Baliga, N.S., Systems Biology Experimental Design - Considerations for Building Predictive Gene Regulatory Network Models for Prokaryotic Systems. Current Genomics 5 (7), 527-544 (2004). 8	Facciotti, M.T. et al., Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis. BMC Syst Biol 4, 64 (2010). 9	Koide, T., Lee Pang, W., & Baliga, N.S., The role of predictive modelling in rationally re-engineering biological systems. Nat Rev Micro 7 (4), 297-305 (2009). 10	Schmid, A. & Baliga, N., Prokaryotic Systems Biology in Cell Engineering, edited by Mohammed El-Rubeai (Springer, 2006), Vol. 5. 11	Weston, A.D., Baliga, N.S., Bonneau, R., & Hood, L., Systems approaches applied to the study of Saccharomyces cerevisiae and Halobacterium sp. Cold Spring Harb Symp Quant Biol 68, 345-357 (2003). 12	Bare, J.C., Koide, T., Reiss, D., Tenenbaum, D., & Baliga, N., Integration and visualization of systems biology data in context of the genome. BMC Bioinformatics 11 (1), 382 (2010). 13	Bare, J.C., Shannon, P.T., Schmid, A.K., & Baliga, N.S., The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications. BMC Bioinformatics 8 (1), 456 (2007). 14	Bonneau, R. et al., The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7 (5), R36 (2006). 15	Gehlenborg, N. et al., Visualization of omics data for systems biology. Nat Methods 7 (3 Suppl), S56-68 (2010). 16	Plaisier, C.L., Bare, J.C., & Baliga, N.S., miRvestigator: web application to identify miRNAs responsible for co-regulated gene expression patterns discovered through transcriptome profiling. Nucleic Acids Res 39 Suppl 2, W125-131 (2011). 17	Reiss, D.J., Baliga, N.S., & Bonneau, R., Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks. BMC Bioinformatics 7 (1), 280 (2006). 18	Shannon, P. et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13 (11), 2498-2504 (2003). 19	Shannon, P., Reiss, D.J., Bonneau, R., & Baliga, N.S., Gaggle: An open-source software system for integrating bioinformatics software and data sources. BMC Bioinformatics 7, 176 (2006). 20	Ashworth, J., Wurtmann, E.J., & Baliga, N.S., Reverse engineering systems models of regulation: discovery, prediction and mechanisms. Current Opinion in Biotechnology 23 (4), 598-603 (2012). 21	Baliga, N.S., The scale of prediction. Science 320 (5880), 1297-1298 (2008). 22	Facciotti, M.T. et al., General transcription factor specified global gene regulation in archaea. Proc Natl Acad Sci U S A 104 (11), 4630-4635 (2007). 23	Turkarslan, S. et al., Niche adaptation by expansion and reprogramming of general transcription factors. Molecular systems biology 7, 554 (2011). 24	Koide, T. et al., Prevalence of transcription promoters within archaeal operons and coding sequences. Mol Syst Biol 5, 285 (2009). 25	Yoon, S.H. et al., Parallel evolution of transcriptome architecture during genome reorganization. Genome Research 21 (11), 1892-1904 (2011). 26	Whitehead, K., Pan, M., Masumura, K., Bonneau, R., & Baliga, N.S., Diurnally entrained anticipatory behavior in archaea. PLOS One 4 (5), e5485 (2009). 27	Plaisier, C.L., Pan, M., & Baliga, N.S., A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Research 22 (11), 2302-2314 (2012). 28	Rustad, T.R. et al., Global analysis of mRNA stability in Mycobacterium tuberculosis. Nucleic Acids Research 41 (1), 509-517 (2013). 29	Plaisier, C.L. & Baliga, N.S., Harnessing the power of human tumor-derived cell lines for the rational design of cancer therapies. Pigment Cell Melanoma Res 25 (4), 406-408 (2012).