Chemical Kinetics

化学动力学

• 批准号: RGPIN-2014-06158
• 负责人: Siegel, David
• 金额: $ 1.02万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665169
• 关键词: Chemical; Kinetics

The subject of study is chemical reaction networks. A set of reactions gives rise to a system of ordinary differential equations in the well--mixed*case and a set of reaction-diffusion equations in the spatially inhomogeneous case. The main area of applications is biochemical reaction networks which*are commonly used in systems biology. There are two major goals: (1) to determine when these systems have regular dynamics: every solution tends to*an equilibrium (homogeneous equilibrium in the case of reaction-diffusion systems); (2) to determine when it is possible to reduce to a smaller system *that captures the dynamics of the original system. Under (1), we seek to finish the proof of the global attractor conjecture, study systems with*different kinds of kinetics and consider reaction-diffusion systems with different diffusion constants. The new approach of reducing a chemical*reaction network to a compartmental system will be explored. Under (2), we seek to determine when two new systematic ways of reducing a system*provide good approximations and explore globally defined slow manifolds in higher dimensions. This research is complementary to other investigations*of more complicated dynamics such as multistability, oscillations and pattern formation. There will be implications for biological modeling*as we attempt to understand large-scale biochemical reaction networks.

研究主题是化学反应网络。一组反应引起一个系统的常微分方程在良好的混合 * 的情况下和一组反应扩散方程在空间不均匀的情况下。 其主要应用领域是系统生物学中常用的生化反应网络。 有两个主要目标：（1）确定这些系统何时具有规则的动力学：每个解都趋向于 * 一个平衡（在反应扩散系统的情况下是齐次平衡）;（2）确定何时可以简化为一个更小的系统 *，它捕获了原始系统的动力学。 在（1）中，我们试图完成全局吸引子猜想的证明，研究具有不同动力学的系统，并考虑具有不同扩散常数的反应扩散系统。 将化学反应网络简化为一个分室系统的新方法将被探索。 在（2）中，我们试图确定何时两种新的系统化方法减少系统 * 提供良好的近似，并探索更高维度中的全局定义的慢流形。 这项研究是补充其他调查 * 更复杂的动力学，如多稳定性，振荡和图案的形成。 当我们试图理解大规模的生化反应网络时，这将对生物建模产生影响。