Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore.
J Theor Biol. 2009 Nov 21;261(2):248-59. doi: 10.1016/j.jtbi.2009.07.037. Epub 2009 Aug 4.
The complexity of cellular networks often limits human intuition in understanding functional regulations in a cell from static network diagrams. To this end, mathematical models of ordinary differential equations (ODEs) have commonly been used to simulate dynamical behavior of cellular networks, to which a quantitative model analysis can be applied in order to gain biological insights. In this paper, we introduce a dynamical analysis based on the use of Green's function matrix (GFM) as sensitivity coefficients with respect to initial concentrations. In contrast to the classical (parametric) sensitivity analysis, the GFM analysis gives a dynamical, molecule-by-molecule insight on how system behavior is accomplished and complementarily how (impulse) signal propagates through the network. The knowledge gained will have application from model reduction and validation to drug discovery research in identifying potential drug targets, studying drug efficacy and specificity, and optimizing drug dosing and timing. The efficacy of the method is demonstrated through applications to common network motifs and a Fas-induced programmed cell death model in Jurkat T cell line.
细胞网络的复杂性常常限制了人类从静态网络图中直观理解细胞功能调控的能力。为此，常使用常微分方程（ODE）的数学模型来模拟细胞网络的动态行为，以便对其进行定量模型分析，从而获得生物学见解。在本文中，我们引入了一种基于使用格林函数矩阵（GFM）作为对初始浓度的敏感性系数的动态分析方法。与经典的（参数）敏感性分析相比，GFM 分析从分子到分子地深入了解了系统行为是如何完成的，以及（脉冲）信号如何在网络中传播。所获得的知识将可应用于模型简化和验证，以及药物发现研究，以确定潜在的药物靶点，研究药物的疗效和特异性，并优化药物剂量和时间。该方法的有效性通过应用于常见的网络基元和 Jurkat T 细胞系中 Fas 诱导的程序性细胞死亡模型得到了证明。
