Stochastic Models of Cell Cycle Regulation in Eukaryotes

描述（由申请人提供）：细胞生长、DNA合成、有丝分裂和细胞分裂的周期是细胞（以及所有生物体）生长、发育和繁殖的基本过程。因此，了解真核细胞中控制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，以至于需要数学和计算方法来可靠地跟踪所有相关基因、mrna、蛋白质和多蛋白复合物的相互作用。确定性模型（常微分方程）足以理解细胞群的平均行为，但要理解单个细胞的远非平均行为，需要随机模型来准确解释单个细胞内少量参与分子和分裂过程的不可预测性（即子细胞之间分子成分的不平等分配）所产生的噪声。准确地模拟一个群体中细胞之间的可变反应可能是理解异常和病变细胞增殖的关键。提出的更新目标是：1)建立一个现实和准确的芽殖酵母细胞周期控制的随机模型，并将该模型扩展到哺乳动物细胞增殖的控制；2)测量单个酵母细胞的随机效应，以便为该模型提供实验约束和测试；3)开发有效的算法和软件来支持随机建模和模拟，并使这些工具易于科学界使用。我们在弗吉尼亚理工大学的多学科团队在项目的各个方面都有成熟的专业知识，并与随机模拟、敏感性分析、分岔理论、建模软件和酵母遗传学领域的顶尖研究人员密切合作。由于所有真核细胞似乎都采用相同的基本分子机制来调节酵母的细胞周期，因此成功模拟单个酵母细胞的生长和分裂将有助于更好地理解哺乳动物细胞分裂在对人类健康具有重要意义的基本生物过程中的作用：例如胚胎发育、组织再生、伤口愈合和致癌。

DESCRIPTION (provided by applicant): The cycle of cell growth, DNA synthesis, mitosis and cell division is the 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 all the relevant 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 noise stemming 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). Accurately modeling the variable responses among cells in a population may be critical to understanding abnormal and diseased cell proliferation. The goals of the proposed renewal are to 1) develop a realistic and accurate stochastic model of cell cycle control in budding yeast and to extend this model to the control of mammalian cell proliferation, 2) measure stochastic effects in single yeast cells in order to provide experimental constraints on and tests of the model, and 3) develop effective algorithms and software to support stochastic modeling and simulation, and to make these tools readily available to the scientific community. Our multi-disciplinary team at Virginia Tech has proven expertise in all aspects of the project and close collaborations with top researchers in the areas of stochastic simulation, sensitivity analysis, bifurcation theory, modeling software, 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 role of mammalian cell division in basic biological processes of significance to human health: e.g., embyronic development, tissue regeneration, wound healing, and carcinogenesis. PUBLIC HEALTH RELEVANCE: The cell division cycle is the fundamental process of biological growth and reproduction, and mistakes in this process underlie many serious health problems, especially cancer. An integrative understanding of the cellular basis of health and disease will require, among other things, a description of the cell cycle by computational models that account accurately for the reliability of DNA replication and inheritance despite the molecular fluctuations that inevitably occur in the small confines of a living cell. Hence, a validated stochastic model of the eukaryotic cell cycle is essential to progress in the field of molecular systems biology.