"Dynamics of biochemical, genetic and neuronal networks"

“生化、遗传和神经网络的动力学”

• 批准号: 217148-2012
• 负责人: Edwards, Roderick
• 金额: $ 1.53万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570103
• 关键词: Dynamics; biochemical; genetic; neuronal; networks

The recent development of technology for simultaneously measuring expression of many genes has led to a profusion of data, but also a great need for a 'systems theory' of gene regulation to provide a framework for understanding dynamics of expression patterns. Parameters (such as reaction rates and degradation rates) are typically not known with any precision, and experiments to pin them down can be difficult and expensive, so it has proven useful to develop qualitative mathematical models that do not include all physiological details, but retain interaction structure. One such approach uses piecewise-linear differential equations in which interaction terms are modelled crudely as step functions. These 'Glass networks' allow a considerable amount of analysis (by a division of phase space into 'boxes'), but lead to additional mathematical difficulties because of the discontinuities in the equations. This model framework is also the basis for computational tools to carry out automated qualitative analysis of gene networks, which are already being used, but which suffer from an inadequate handling of some of these mathematical difficulties. One main thrust of my research is to improve our understanding of these issues, and the computational tools that depend on them. Gene regulation is carried out by proteins in cells, which are themselves influenced by external signals via 'pathways' of biochemical reactions. Detailed mathematical models of these signalling pathways can help to make quantitative predictions about effects of interventions, such as the use of 'chimeric proteins' to link cell proliferation pathways to cell death pathways, with the aim of killing cancer cells. Yet another context where I use network theory to understand cellular processes is in networks of neurons with particular architectures and how they respond to various types of electrical stimuli. The equations are different but principles are similar. This is relevant, for example, to the effects of stimulators used to treat Parkinson's disease. Mathematical techniques sometimes bridge problems or even fields, and I also propose to explore applications of the piecewise-linear approach to problems in Physics relating to sustained oscillations called 'breathers' in chains of particles.

最近发展的同时测量多个基因表达的技术导致了大量的数据，但也非常需要基因调控的系统理论来提供一个框架来理解表达模式的动态。参数(如反应速率和降解速率)通常是不精确的，确定它们的实验可能是困难和昂贵的，所以事实证明，开发不包括所有生理细节但保留相互作用结构的定性数学模型是有用的。其中一种方法使用分段线性微分方程，其中相互作用项被粗略地建模为阶跃函数。这些“玻璃网络”允许进行大量的分析(通过将相空间划分为“盒子”)，但由于方程中的不连续性，导致了额外的数学困难。这一模型框架也是计算工具对基因网络进行自动化定性分析的基础，基因网络已经在使用，但在处理其中一些数学困难方面存在不足。我的研究的一个主要目的是提高我们对这些问题的理解，以及依赖于它们的计算工具。基因调控是由细胞中的蛋白质执行的，这些蛋白质本身受到外部信号的影响，通过生化反应的“路径”。这些信号通路的详细数学模型可以帮助对干预措施的效果做出定量预测，例如使用“嵌合蛋白”将细胞增殖途径与细胞死亡途径联系起来，以达到杀死癌细胞的目的。我使用网络理论来理解细胞过程的另一个背景是具有特定结构的神经元网络，以及它们如何对各种类型的电刺激做出反应。方程式不同，但原理是相似的。例如，这与用于治疗帕金森氏症的刺激剂的效果有关。数学技巧有时会在问题甚至领域之间架起桥梁，我还建议探索分段线性方法在与粒子链中的持续振荡有关的问题上的应用。