Models of spatio-temporal reaction systems with applications to systems and synthetic biology

时空反应系统模型及其在系统和合成生物学中的应用

基本信息

批准号：
EP/K041096/1
负责人：
Heather Harrington
金额：
$ 35.65万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2014
资助国家：
英国
起止时间：
2014 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FK041096%2F1
关键词：
Models spatio temporal reaction systems

项目摘要

The applicant plans to combine mathematical tools with multi-resolution data to develop models and methods in a framework that considers both space and time. Due to the nonlinear nature of the real-world, understanding behaviors of the physical, biological and social fields require general and widely applicable mathematical frameworks. To do this, the applicant will focus on a particular biological problem of widespread interest: protein interactions and their ability to regulate cellular decision making. This is of paramount importance; for example, in cancer, a cell is unable to transmit a death signal through protein interactions, causing the cell to continue to proliferate when it should arrest. Often these signaling processes involve many agents, all interacting in nontrivial ways, in different locations and at different levels of organization. The development and analysis of mathematical models describing protein interactions will, crucially, allow us to understand their dynamics, predict molecular mechanisms, reveal their function, and guide cell decisions. This is an ongoing biologically and medically relevant problem of fundamental importance and mathematical/statistical approaches may provide insights into the role and dynamics in spatial organization in cells, but more generally, to other systems. Dynamical approaches will be used for model development since these consider the temporal deterministic evolution of the system and may provide mechanistic information about the system. Throughout the fellowship, analysis and methods will be developed with chemically resolved data of the protein interaction system. Multi-resolution data of these protein interactions will be collected from Supporting Partners at the Weizmann Institute, Princeton University and University of Tokyo to test our predictions and methods. A range of mathematical/statistical approaches will be used for analysis and method development. One particular problem we will focus on is how to determine which model could describe data generated from a system. This question of model selection, or model discrmination, has led the applicant to work and develop a range of methods to distinguish between models. Understanding the mechanisms responsible for the behavior of protein signaling in a spatio-temporal framework would advance the fields of mathematics and biology. The Supporting Partners and applicant have already contributed to this arena of studying complex systems; moreover, they share a mutual interest to advance the field through the construction of spatio-temporal models of protein interactions and develop novel mathematical techniques that may be applicable to other inherently spatial systems.More generally, the research has direct biological implications-the work may provide insights for dysfunction of protein signaling resulting in diseases such as cancer, and we can extend our framework to analyze other biological processes inside living organism. These types of investigations will benefit from the mathematical, statistical and computational protocols that are developed in this project. Furthermore, the nonparametric and statistical methods developed for different types of spatial models can be applied to other contexts.

申请人计划将数学工具与多分辨率数据相结合，以在考虑空间和时间的框架中开发模型和方法。由于现实世界的非线性性质，了解物理，生物学和社会领域的行为需要一般且广泛适用的数学框架。为此，申请人将重点关注广泛兴趣的特定生物学问题：蛋白质相互作用及其调节细胞决策的能力。这至关重要；例如，在癌症中，细胞无法通过蛋白质相互作用传输死亡信号，导致细胞在应停止时继续增殖。通常，这些信号过程涉及许多代理，所有代理都以非平凡的方式，不同的位置和不同的组织级别进行交互。描述蛋白质相互作用的数学模型的开发和分析将使我们能够理解其动力学，预测分子机制，揭示其功能并指导细胞决策。这是一个持续存在的生物学和医学上相关的问题，即基本重要性和数学/统计方法可能会提供有关细胞空间组织中的作用和动态的见解，但更普遍地对其他系统提供了见解。动态方法将用于模型开发，因为这些方法考虑了系统的时间确定性演变，并可能提供有关系统的机械信息。在整个奖学金中，分析和方法将使用蛋白质相互作用系统的化学分辨数据开发。这些蛋白质相互作用的多分辨率数据将从魏兹曼学院，普林斯顿大学和东京大学的支持伙伴中收集，以测试我们的预测和方法。一系列数学/统计方法将用于分析和方法开发。我们将重点关注的一个特定问题是如何确定哪种模型可以描述从系统生成的数据。模型选择或模型离散的问题使申请人能够工作并开发了一系列方法来区分模型。了解负责蛋白质信号在时空框架中行为的机制将推进数学和生物学领域。支持伙伴和申请人已经为研究复杂系统的领域做出了贡献。此外，他们具有共同的兴趣，通过构建蛋白质相互作用的时空模型，并开发出可能适用于其他固有的空间系统的新型数学技术，并开发出新的数学技术。更一般而言，这项研究具有直接的生物学意义 - 工作可能会为诸如癌症和其他癌症的生命范围内的蛋白质传播提供洞察力，我们可以扩展诸如癌症和其他疾病的疾病，我们可以扩展其他疾病，我们分析了其他疾病的疾病，并分析了与其他疾病的疾病相关性，并分析了与生物群体的相关性，以分析。这些类型的研究将受益于本项目中开发的数学，统计和计算协议。此外，可以将针对不同类型的空间模型开发的非参数和统计方法应用于其他情况。