Kirk Paul D W, Toni Tina, Stumpf Michael P H
Division of Molecular Biosciences, Imperial College, London, England.
Biophys J. 2008 Jul;95(2):540-9. doi: 10.1529/biophysj.107.126086. Epub 2008 May 2.
The increasingly widespread use of parametric mathematical models to describe biological systems means that the ability to infer model parameters is of great importance. In this study, we consider parameter inferability in nonlinear ordinary differential equation models that undergo a bifurcation, focusing on a simple but generic biochemical reaction model. We systematically investigate the shape of the likelihood function for the model's parameters, analyzing the changes that occur as the model undergoes a Hopf bifurcation. We demonstrate that there exists an intrinsic link between inference and the parameters' impact on the modeled system's dynamical stability, which we hope will motivate further research in this area.
使用参数数学模型来描述生物系统的情况日益普遍，这意味着推断模型参数的能力至关重要。在本研究中，我们考虑经历分岔的非线性常微分方程模型中的参数可推断性，重点关注一个简单但通用的生化反应模型。我们系统地研究了模型参数的似然函数形状，分析了模型经历霍普夫分岔时发生的变化。我们证明，推断与参数对建模系统动态稳定性的影响之间存在内在联系，希望这将推动该领域的进一步研究。
