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Improving our Mechanistic Understanding of Cell Cycle Dynamics

提高我们对细胞周期动力学的机制理解

基本信息

批准号：
2105279
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2105279
关键词：
Improving our Mechanistic Understanding Cell

项目摘要

This project falls within the EPSRC "Synthetic Biology" and "Systems Biology" research areas. The past decades saw enormous advances in biological sciences, resulting in a staggering amount of detailed knowledge about the biochemical reactions and mechanisms in a cell. This knowledge enabled rational design of biological systems, giving rise to synthetic biology as a new engineering discipline. Despite synthetic biology can provide solutions to pressing environmental, medical and ethical problems, intricate interactions of the designed system with its (cellular) environment limit the complexity of synthetic biological systems. An important strategy to overcome this limitation, is to integrate the ever-increasing amount of biological knowledge to a mechanistic, systems level understanding of the processes in a cell. In this project we focus on the processes that regulate cell cycle progression using the following a four-step strategy. (1) We cast knowledge about biochemical reaction networks into ordinary differential equations (ODEs). (2) We obtain highly multiplexed snapshot measurements of cell cycle regulators in asynchronous cell populations. (3) We computationally reconstruct cell cycle regulator time-courses from our snapshot measurements. (4) We develop optimisation and model reduction/selection toolboxes to fit unknown kinetic parameters of the ordinary differential equations from (1) to predict experimental data from (2, 3). By building a human cell cycle model as described above, we want to improve our mechanistic understanding of cell cycle dynamics. The cell cycle model shall help synthetic biologists, researchers and medical practitioners alike, for high throughput design and hypotheses testing in silico.

该项目属于EPSRC“合成生物学”和“系统生物学”研究领域的福尔斯。在过去的几十年里，生物科学取得了巨大的进步，导致了关于细胞中生化反应和机制的惊人的详细知识。这些知识使生物系统的合理设计成为可能，从而使合成生物学成为一门新的工程学科。尽管合成生物学可以为紧迫的环境，医学和伦理问题提供解决方案，但设计系统与其（细胞）环境的复杂相互作用限制了合成生物系统的复杂性。克服这一限制的一个重要策略是将不断增加的生物学知识整合到对细胞过程的机械的、系统水平的理解中。在这个项目中，我们专注于使用以下四步策略调节细胞周期进程的过程。(1)我们将生化反应网络的知识转化为常微分方程（ODE）。(2)我们获得了高度多路复用的快照测量细胞周期调节器在异步细胞群。(3)我们计算重建细胞周期调节剂的时间进程，从我们的快照测量。(4)我们开发了优化和模型简化/选择工具箱来拟合来自（1）的常微分方程的未知动力学参数，以预测来自（2，3）的实验数据。通过构建如上所述的人类细胞周期模型，我们希望提高我们对细胞周期动力学的机械理解。细胞周期模型将帮助合成生物学家，研究人员和医疗从业者，高通量设计和假设测试在硅片。