EMT/BSSE: A Computational Framework for Inferring Self-Regulatory Properties from High-Dimensional Dynamic Models of Biological Systems

EMT/BSSE：从生物系统高维动态模型推断自我调节特性的计算框架

• 批准号: 0829742
• 负责人: Robert Clewley
• 金额: $ 10万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0829742&HistoricalAwards=false
• 关键词: EMT; BSSE; Computational; Framework; Inferring

Biological forms of computation present some of the most promising?yet challenging?examples of adaptive mechanisms that we would like to understand well enough to engineer ourselves. However, models for biological processes are becoming increasingly detailed and unwieldy in an effort to reproduce ever-more detailed experimental observations. This research involves the development of mathematical theory, algorithms, and software for efficiently constraining models to data and to analyze their properties mathematically. A sufficiently detailed understanding through mathematical analysis permits the generalization of operating principles that provide biological insights and a basis for engineering similar mechanisms. In particular, the investigators apply these methods to infer adaptive and self-governing properties from detailed dynamical models of excitable neural and cardiac tissue. Although detailed models of physical systems may involve many variables and parameters, mathematical analysis often demonstrates effective lower dimensionality in their operating principles. A decomposition of a complex model to approximate lower-dimensional sub-regimes facilitates analysis by standard techniques from dynamical systems and optimization theory. In contrast to a priori reductions to ?toy? models, software tools monitor and control the sources of error in the approximations, in particular the assumptions underlying the decomposition are validated against global constraints to ensure consistency with the behavior of the full physical system. To study abstract properties of the system such as adaptiveness, decompositions can be made in terms of measurements of qualitative features in the dynamics. These features may be simple or complex according to the needs of the problem. Their formalized definition in software structures enables existing techniques for model optimization and inference to be applied more intelligently, particularly in the context of model behavior that may resemble experimental data only in qualitative terms.

生物形式的计算呈现出一些最有前途的？但具有挑战性？我们希望能够充分理解这些适应机制的例子，以便我们自己进行工程设计。然而，生物过程的模型正变得越来越详细和笨拙，以再现越来越详细的实验观察。该研究涉及数学理论，算法和软件的发展，用于有效地将模型约束到数据并以数学方式分析其属性。通过数学分析得到的足够详细的理解，可以使操作原理的一般化，从而提供生物学见解，并为工程设计类似的机制提供基础。特别是，研究人员应用这些方法从可兴奋的神经和心脏组织的详细动力学模型中推断出自适应和自我管理的特性。虽然物理系统的详细模型可能涉及许多变量和参数，但数学分析通常表明其操作原理具有有效的低维性。将复杂模型分解为近似的低维子区域有助于通过动力系统和优化理论的标准技术进行分析。与先验减少相比？玩具？模型、软件工具监视和控制近似中的误差源，特别是针对全局约束验证分解的基本假设，以确保与整个物理系统的行为一致。为了研究系统的抽象属性，如自适应性，可以根据动态中定性特征的测量进行分解。根据问题的需要，这些特征可以是简单的或复杂的。它们在软件结构中的形式化定义使现有的模型优化和推理技术能够更智能地应用，特别是在模型行为可能仅在定性方面类似于实验数据的情况下。