Stochastic Models of Cell Cycle Regulation in Eukaryotes

描述(申请人提供)：细胞生长、DNA合成、有丝分裂和细胞分裂的周期是细胞(和所有活着的生物体)生长、发育和繁殖的基本过程。因此，了解控制真核细胞这些过程的分子机制对科学和人类健康至关重要。控制系统是如此复杂，需要数学和计算方法来可靠地跟踪数十个基因、mRNAs、蛋白质和多蛋白质复合体的相互作用。确定性模型(常微分方程式)足以理解一组细胞的平均行为，但要理解单个细胞的远离平均行为，需要能准确解释生长-分裂周期中噪声事件的随机模型。噪音来自单个细胞内的少量参与分子，以及分裂过程的反复无常(即，子细胞之间分子成分的不平等分配)。该项目的目标是在发芽酵母中建立一个现实和准确的细胞周期控制的随机模型。为了实现这一目标，研究人员将：1)根据基本生化反应制定适合于精确随机模拟的分子调控系统。2)采用适当的方法对这些随机过程进行近似模拟，以便有效地计算适合于与实验进行比较的概率。3)发展了随机动力系统的参数估计、灵敏度分析和分叉理论方法。4)创造一个软硬件环境，支持任何现实的基因/mRNA/蛋白质调控网络的随机模型所需的苛刻计算。5)将这些方法和工具应用于单个酵母细胞生长和分裂的已知变异性。弗吉尼亚理工大学的多学科团队在该项目的所有方面都拥有成熟的专业知识，并将得到外部顾问的支持，这些顾问是随机模拟、敏感性分析、分叉理论和酵母遗传学领域的顶级研究人员。由于所有真核细胞似乎都使用相同的基本分子机制来调节酵母的细胞周期，因此成功地模拟单个酵母细胞的生长和分裂将转化为更好地理解细胞分裂在与人类健康密切相关的基本生物学过程中的作用：例如，胚胎发育、组织再生、伤口愈合和癌症发生。

DESCRIPTION (provided by applicant): The cycle of cell growth, DNA synthesis, mitosis and cell division is fundamental process by which cells (and all living organisms) grow, develop and reproduce. Hence, it is of crucial importance to science and human health to understand the molecular mechanisms that control these processes in eukaryotic cells. The control system is so complex that mathematical and computational methods are needed to reliably track the interactions of dozens of genes, mRNAs, proteins, and multiprotein complexes. Deterministic models (ordinary differential equations) are adequate for understanding the average behavior of groups of cells, but to understand the far-from-average behavior of individual cells requires stochastic models that accurately account for noisy events in the growth-division cycle. Noise stems from small numbers of participating molecules within a single cell, and from vagaries of the division process (i.e., unequal partitioning of molecular components between daughter cells). The goal of the proposed project is to create a realistic and accurate stochastic model of cell cycle control in budding yeast. To accomplish this goal the investigators will: 1) Formulate the molecular regulatory system in terms of elementary biochemical reactions, suitable for exact stochastic simulation. 2) Employ appropriate methods for approximate simulation of these stochastic process, in order to efficiently compute probabilities suitable for comparison to experiments. 3) Develop methods for parameter estimation, sensitivity analysis and bifurcation theory of stochastic dynamical systems. 4) Create a software/hardware environment that supports the demanding computations required of stochastic models of any realistic gene/mRNA/protein regulatory network. 5) Apply the methods and tools to known variability in growth and division of single yeast cells. The multi-disciplinary team at Virginia Tech has proven expertise in all aspects of the project and will be supported by external advisors who are top researchers in the areas of stochastic simulation, sensitivity analysis, bifurcation theory and yeast genetics. Because all eukaryotic cells seem to employ the same fundamental molecular machinery that regulates the cell cycle of yeast, success in modeling growth and division of single yeast cells will translate into better understanding of the roles of cell division in basic biological processes of significant relevance to human health: e.g., embyronic development, tissue regeneration, wound healing, and carcinogenesis.