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

生物形式的计算提供了一些最有希望、但具有挑战性的自适应机制的例子，我们希望充分了解这些例子，以便自己进行工程设计。然而，为了重现更详细的实验观察，生物过程的模型正变得越来越详细和笨拙。这项研究涉及到数学理论、算法和软件的开发，以便有效地将模型约束到数据并对其性质进行数学分析。通过数学分析进行充分详细的理解，可以概括出提供生物学见解的工作原理，并为设计类似的机制奠定基础。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适应和自我管理的特性。尽管物理系统的详细模型可能涉及许多变量和参数，但数学分析往往在其工作原理中证明了有效的低维。将复杂模型分解为近似的低维子区域，便于采用动力系统和最优化理论中的标准技术进行分析。而不是先验地减少到？玩具？模型、软件工具监测和控制近似中的误差来源，特别是根据全局约束对分解所依据的假设进行验证，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，如适应性，可以根据动力学中定性特征的测量进行分解。根据问题的需要，这些功能可能很简单，也可能很复杂。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，特别是在可能仅在定性方面类似实验数据的模型行为的背景下。

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.