Parameter inference for biochemical systems that undergo a Hopf bifurcation
Parameter inference for biochemical systems that undergo a Hopf bifurcation
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DOI：
10.1529/biophysj.107.126086
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发表时间：
2008-07-15
影响因子：
3.4
通讯作者：
Stumpf, Michael P. H.
中科院分区：
文献类型：
--
作者：
Kirk, Paul D. W.;Toni, Tina;Stumpf, Michael P. H.
参数数学模型越来越广泛地用于描述生物系统，这意味着推断模型参数的能力是非常重要的。在这项研究中，我们考虑了经历分叉的非线性常微分方程模型的参数不可推测性，重点研究了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的形状，分析了模型经历Hopf分叉时发生的变化。我们证明了推理与参数对建模系统动态稳定性的影响之间存在着内在的联系，希望这将推动这一领域的进一步研究。
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.