Information transfer from extracellular environment to cellular response is fundamental for cellular fitness and regulation in stochastic environments. As a part of NIST’s Engineering Biology program this project combines information theory and machine learning with large omics data to design optimal genetic sensors. We are developing computational methods to evaluate:
(1) The selection criteria for biochemical design parameters, reaction pathways and rates, based on the information transfer from the environmental input to the transcript or protein expressions.
(2) The impact of augmenting new information processing pathways on cellular fitness and when such augmentations are necessary.
Complex biological processes like differentiation, apoptosis, and homeostasis, emerge from hierarchical and modular regulatory interactions among hundreds of genes. The topology of regulatory interactions determines the phenotypic microstates that exist in these biological processes. We are developing computational methods to evaluate the efficacy of communication in gene regulatory networks (GRNs) and thermodynamic properties that determine phenotypic transitions, like the inducible free energy change due to master regulators.
Sarkar, S., Hubbard, J. B., Halter, M., & Plant, A. L. (2021). Information Thermodynamics and Reducibility of Large Gene Networks. Entropy, 23(1), 63.
Hubbard, J. B., Halter, M., Sarkar, S., & Plant, A. L. (2020). The role of fluctuations in determining cellular network thermodynamics. PloS one, 15(3), e0230076.
Sarkar, S., Tack, D., & Ross, D. (2020). Sparse estimation of mutual information landscapes quantifies information transmission through cellular biochemical reaction networks. Communications Biology, 3(1), 1-8.
Sarkar, S., & Lin‐Gibson, S. (2018). Computational Design of Photocured Polymers Using Stochastic Reaction–Diffusion Simulation (Adv. Theory Simul. 7/2018). Advanced Theory and Simulations, 1(7), 1870016. (Cover article)
Sarkar, S., Baker, P. J., Chan, E. P., Lin-Gibson, S., & Chiang, M. Y. (2017). Quantifying the sensitivity of the network structure and properties from simultaneous measurements during photopolymerization. Soft matter, 13(21), 3975-3983.
Sarkar, S., Warner, J. E., Aquino, W., & Grigoriu, M. D. (2014). Stochastic reduced order models for uncertainty quantification of intergranular corrosion rates. Corrosion Science, 80, 257-268.
Sarkar, S., & Aquino, W. (2013). Changes in electrodic reaction rates due to elastic stress and stress-induced surface patterns. Electrochimica Acta, 111, 814-822.
Sarkar, S., Warner, J. E., & Aquino, W. (2012). A numerical framework for the modeling of corrosive dissolution. Corrosion Science, 65, 502-511.
Sarkar, S., & Aquino, W. (2011). Electroneutrality and ionic interactions in the modeling of mass transport in dilute electrochemical systems. Electrochimica Acta, 56(24), 8969-8978.