Network Inference

Identifying Transcription Factor Influences

Transcriptional networks play an important role in the adaptation of microbes to changing environments. Using transcriptional regulation, microbes respond to stress by tuning the expression of their genes. Nonetheless, transcriptional programs driving adaptive responses are rarely defined. Importantly, even when the transcriptional network of an organism has been partially characterized, identifying the regulators that orchestrate the transition between any two cellular states is a non-trivial task.

In the Baliga lab, We are working to develop a mathematical framework that exploits network inference methods and experimental data to evaluate and rank the individual (and combined) contribution of transcriptional regulators during cellular transitions. The final aim of this work is to rationally manipulate biological systems toward desired phenotypes. As a first case of study, we plan to apply this approach to study bacterial responses to antibiotic induced stress and to design new ways to overcome antibiotic resistance.

Team

Publications

Plaisier, Christopher L., Sofie O’Brien, Brady Bernard, Sheila Reynolds, Zac Simon, Chad M. Toledo, Yu Ding, David J. Reiss, Patrick J. Paddison, and Nitin S. Baliga. “Causal Mechanistic Regulatory Network for Glioblastoma Deciphered Using Systems Genetics Network Analysis.” Cell Systems 3, no. 2 (August 2016): 172–86. https://doi.org/10.1016/j.cels.2016.06.006. Cite

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