Mathematical Modeling - Biochemical Networks in Yeast

描述(由申请人提供)：这个项目的目标是开发数学工具，从实验数据中建立生化网络的模型。然后，这些工具将被应用于转录组学、蛋白质组学和代谢组学数据，这些数据是从模式生物酿酒酵母的10个参与氧化应激反应的基因网络中收集的。 提出的方法是开发一个两阶段的建模过程。将生化网络看作一个由一组常微分方程组描述的连续动态系统，首先用数值方法将系统近似为离散时间系统。然后使用几种工具来分析这个离散系统。其中一个工具是进一步的简化，通过将变量的状态值分类为有限的状态集来实现。由此得到的框架允许使用计算代数中的成熟方法来研究有限状态集上离散时间系统的可能模型的整个空间。 为了演示和测试数学方法，将用面包师的酵母酿酒酵母进行实验。野生型酵母的细胞培养会受到过氧化氢异丙苯和氧化应激的干扰，样品将在一段时间内进行测量。这项实验将重复10次，每次都使用不同的基因缺失突变体。突变体将根据数学模型产生的假设进行选择。除了支持数学上的发展，这些实验还有望揭示谷胱甘肽新陈代谢和相关抗氧化剂分子(如抗坏血酸)的调节细节。

DESCRIPTION (provided by applicant): The goal of this project is to develop mathematical tools to model biochemical networks from experimental data. These tools will then be applied to transcriptomics, proteomics, and metabolomics data collected from a network of ten genes of the model organism Saccharomyces cerevisiae that are involved in oxidative stress response. The proposed approach is to develop a two-stage modeling process. Viewing a biochemical network as a continuous dynamical system, described by a collection of ordinary differential equations, we first approximate the system by a discrete-time system, using numerical methods. Several tools are then used to analyze this discrete system. One of these tools is a further reduction, achieved by categorizing the state values of the variables into a finite set of states. The resulting framework allows the use of well-developed methods from computational algebra to study the whole space of possible models for the discrete-time system on a finite state set. To demonstrate and test the mathematical methodologies, experiments will be carded out with the baker's yeast Saccharomyces cerevisiae. Cell cultures of wild type yeast will be perturbed by addition of cumene hydroperoxide, and oxidative stress, and samples will be measured in a time course. The experiment will be repeated ten times, each time with a different gene deletion mutant. Mutants will be selected according to the hypotheses generated by the mathematical models. In addition to supporting the mathematical developments, the experiments are expected to reveal details about the regulation of glutathione metabolism and related antioxidant molecules (such as Lascorbic acid).