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

在过去的十年里，关于自由生活的细菌细胞(Caulbacter Crescentus)在不对称分裂周期中的蛋白质定位和基因-蛋白质相互作用已经积累了大量的数据。不对称的细胞分裂产生两个具有不同细胞命运的子细胞，而对称的细胞分裂产生相同命运的子细胞。例如，干细胞不对称分裂，产生两个不同的子细胞：一个是原始干细胞的副本，另一个是被编程分化为非干细胞的第二个子细胞。在本项目中，建立了该控制系统的综合计算模型。数学建模是探索活细胞中分子调控机制假说的成熟方法。这些模型将实验证据整合到分子相互作用的计算机表示中。然后，计算机模拟用于对照已知的实验事实来检验这些假说，并预测新的实验研究的结果。它通过建立基因表达和蛋白质在细胞内定位的数学模型，帮助科学界更深入地了解控制不对称分裂周期的分子机制。该项目还为研究生和本科生提供计算细胞生物学和数学建模方面的培训。在该项目中，建立了两种类型的模型：确定性模型和随机模型。蛋白质合成、降解、化学计量和催化相互作用以及空间重新分布的动力学的确定性模型(基于微分方程式)在描述细菌细胞种群的平均行为方面很有用。为了描述单个细胞中特定蛋白质的精确空间分布和时间动力学(通过现代荧光标记蛋白质的显微研究来测量)，建立了详细的染色体运动、基因表达和蛋白质动力学的随机模型。将该模型的模拟结果与湿法实验室实验数据进行了比较。为了收集模拟结果，还开发了准确而高效的模拟方法，并进行了相应的数值分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.