Computational modeling and analysis of the dynamics of control mechanisms in molecular interaction networks of neurons

最近在测量细胞现象方面的技术创新提供了收集细胞生物学系统定量建模所需数据的手段。如今，越来越多的生物学家采用系统生物学的方法，在他们的科学努力中将计算建模和模拟与实验相结合。在这样的背景下，建模可以成为一种工具来生成可测试的假设并促进理解。在细胞中，生化信号很少通过简单、直接的路径传播，而是通过分子相互作用的网络传播。通过建模，可以了解该网络的复杂动态。从理论上讲，为了实现这一理解，需要更现实的模拟数据模型和分析方法来突出系统的特性。本研究计划旨在开发新的创新的计算方法和模型来研究神经元信息处理的细胞内机制。一种很有前途的方法是动态图，它产生动态系统的网络表示。这种方法有助于理解系统的行为是如何从称为调节模体的功能模块的时间和空间相互作用中产生的。另一个将提高建模能力的研究轴是在一个单一的建模方法中耦合所有调节神经元细胞活动的机制(例如，电、生化、形状修改)。这些计算工具随后可以应用于分析细胞的动态成像数据，以了解它们的行为。实现这一计划的长期目标将影响生物医学学术界和工业界，因为两者都越来越多地使用计算建模来实现对健康和疾病的更精确的机械理解。

The recent technical innovations in measurement of cellular phenomena provide the means to collect data required for quantitative modeling of cell biology systems. Nowadays, a growing number of biologists adopt a systems biology approach and integrate computational modeling and simulation with experiments in their scientific endeavor. In such setting, modeling can be a tool to generate testable hypotheses and to facilitate understanding. In the cell, biochemical signals are rarely propagated by simple, direct paths, but rather through a network of molecular interactions. With modeling, it is possible to understand the complex dynamics of this network. From a theoretical perspective, to achieve this understanding, one needs more realistic models and analysis methods of simulation data that highlight systemic properties.This research program aims at developing new and innovative computational methods and models to investigate the intracellular mechanisms of information processing in neurons. One promising method is the dynamic graph, which produces a network representation of a dynamical system. This method helps to understand how the behavior of a system arises from temporal and spatial interactions of functional modules known as regulatory motifs. Another axis of research that will improve modeling capabilities is the coupling within a single modeling approach of all the mechanisms (e.g. electrical, biochemical, shape modification) regulating the cellular activity of neurons. These computational tools can then be applied to the analysis of dynamic imaging data from cells to understand their behavior.Reaching this program long-term goal will impact the biomedical academic and industrial communities since both are increasingly using computational modeling to achieve a more precise mechanistic understanding of health and disease.