Integrated Dynamics of Temporal and Spatial Controls in the Cell Division of Caulobacter crescentus

该项目的目的是通过建立细菌细胞内基因表达和蛋白质定位的数学模型，更深入地了解控制细菌生长，分裂和分化的分子机制。自由生活的水生细菌Caulobacter crescentus是解决这些问题的合适生物体，因为它不对称地分裂成两种不同类型的细胞（茎细胞和群集细胞），并且因为其不对称分裂过程的分子基础很容易在实验室中通过现代遗传、生物化学和显微镜方法进行研究。鉴于目前控制这一过程的基因和蛋白质的丰富实验结果，该资助项目将解决对精确和预测数学模型的迫切需求，这些模型将详细的分子机制与所观察到的柄杆菌繁殖和分化特性联系起来。将考虑两种类型的数学模型。基于非线性偏微分方程系统的确定性模型将用于描述柄杆菌细胞群体的平均行为，这是许多实验方案中收集的数据类型。随机模型-基于单个细胞内单个分子的反应和扩散-将用于描述细胞内特定蛋白质的精确空间分布和时间动态，如通过荧光标记蛋白质的显微镜研究所测量的。该项目将为活细胞基因表达和蛋白质动力学的时空建模提供新的思路、方法、算法和软件。它还将为两名研究生（一名生命科学家和一名计算机科学家）提供计算细胞生物学、数学建模和空间随机模拟现代方法方面的培训。细菌生长、分裂和分化的过程对人类福祉至关重要，因为我们与许多类型的细菌有着密切的关系。有益细菌在我们的消化道中定植，为我们的作物固氮，并在我们的生物反应器中生产有价值的产品。病原菌会在我们、我们的农作物和家畜身上引起疾病。为了控制有益菌和致病菌，我们需要了解控制细菌繁殖和分化的分子机制。数学模型是探索关于这些机制的假设的有用工具，因为它们将大量的实验证据整合到分子相互作用的现实和准确的计算机表示中。然后，计算机模拟用于以全面的方式针对已知的实验事实来测试假设，并预测新的实验研究的结果。柄杆菌属的细胞分裂周期是测试分子细胞生物学中数学建模的效用的有利案例。此外，由于柄杆菌与固氮细菌和致病细菌密切相关，并且由于其不对称分裂模式类似于人类干细胞的繁殖和分化，因此该项目预期的数学方法和生物学结果最终可能导致农业和医学的实际发展。

The purpose of this project is to gain deeper insight into the molecular mechanisms controlling the growth, division and differentiation of bacteria by building mathematical models of gene expression and protein localization within a bacterial cell. The free-living aquatic bacterium, Caulobacter crescentus, is a suitable organism for addressing such questions because it divides asymmetrically into two different types of cells (a stalked cell and a swarmer cell) and because the molecular basis of its asymmetric division process is easily studied in the laboratory by modern genetic, biochemical and microscopic methods. In light of the wealth of experimental results now available on the genes and proteins controlling this process, the funded project will address the pressing need for accurate and predictive mathematical models that connect detailed molecular mechanisms with the observed properties of Caulobacter reproduction and differentiation. Two types of mathematical models will be considered. Deterministic models--based on systems of nonlinear partial differential equations--will be used to describe the average behavior of a population of Caulobacter cells, which is the type of data collected in many experimental protocols. Stochastic models--based on the reaction and diffusion of individual molecules within a single cell--will be used to describe the precise spatial distribution and temporal dynamics of specific proteins within cells, as measured by microscopic studies of fluorescently labeled proteins. This project will provide new ideas, methods, algorithms and software for spatiotemporal modeling of gene expression and protein dynamics in living cells. It will also provide training for two graduate students, a life scientist and a computer scientist, in modern methods of computational cell biology, mathematical modeling and spatial stochastic simulations.The processes by which bacteria grow, divide and differentiate are of great importance to human welfare because we live in intimate relationships with many types of bacteria. Beneficial bacteria colonize our digestive tract, fix nitrogen for our crops, and produce valuable products in our bioreactors. Pathogenic bacteria cause diseases in us and in our crops and domestic animals. To gain control over both beneficial and pathogenic bacteria, we need to understand the molecular mechanisms governing bacterial reproduction and differentiation. Mathematical models are useful tools for exploring hypotheses about these mechanisms because they integrate a wealth of experimental evidence into a realistic and accurate computer representation of molecular interactions. Computer simulations then serve to test the hypotheses against known experimental facts in a comprehensive fashion and to predict the outcome of novel experimental studies. The cell division cycle of Caulobacter is a favorable case for testing the utility of mathematical modeling in molecular cell biology. In addition, because Caulobacter is closely related to both nitrogen-fixing and disease-causing bacteria and because its asymmetric mode of division is analogous to the reproduction and differentiation of human stem cells, the mathematical methods and biological results expected from this project may ultimately lead to practical developments in agriculture and medicine.