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

点击翻译按钮获取中文摘要

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.