FET: AF: Small: Spatial Stochastic Modeling and Simulation with application in the Caulobacter Cell Cycle control

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In the past decade a lot of data have been accumulated about protein localization and gene-protein interactions during the asymmetric division cycle of a free-living bacterial cell (Caulobacter crescentus). An asymmetric cell division produces two daughter cells with different cellular fates, whereas symmetric cell divisions give rise to daughter cells of equivalent fates. For example, stem cells divide asymmetrically to give rise to two distinct daughter cells: one copy of the original stem cell as well as a second daughter programmed to differentiate into a non-stem cell. In this project a comprehensive computational model of this control system is built. Mathematical modeling is a well-established method for exploring hypotheses about molecular-regulatory mechanisms in living cells. These models integrate experimental evidence into a computer representation of molecular interactions. Computer simulations then serve to test the hypotheses against known experimental facts and to predict the outcome of novel experimental studies. It helps the scientific community to gain deeper insight into the molecular mechanisms controlling the asymmetric division cycle by building mathematical models of gene expression and protein localization within the cell. This project also provides training for graduate students and undergraduate researchers in computational cell biology and mathematical modeling.In this project two types of models are built: deterministic and stochastic. Deterministic models (based on differential equations) of the dynamics of protein synthesis, degradation, stoichiometric and catalytic interactions, and spatial redistribution are useful in describing the average behavior of a population of bacterial cells. To describe the precise spatial distribution and temporal dynamics of specific proteins in single cells (as measured by modern microscopic studies of fluorescently labeled proteins), detailed stochastic models of chromosome movement, gene expression, and protein dynamics are built. Simulation results of the model are compared with wet-lab experimental data. To collect simulation results, accurate yet efficient simulation methods, with corresponding numerical analysis, are also developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.