Radulescu O, Gorban A N, Zinovyev A, Noel V
DIMNP UMR CNRS, University of Montpellier 2 Montpellier, France.
Front Genet. 2012 Jul 19;3:131. doi: 10.3389/fgene.2012.00131. eCollection 2012.
Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to simpler models, in a way that depends only on the orders of magnitude and not on the exact values of the kinetic parameters. The main idea used for such robust simplifications of networks is the concept of dominance among model elements, allowing hierarchical organization of these elements according to their effects on the network dynamics. This concept finds a natural formulation in tropical geometry. We revisit, in the light of these new ideas, the main approaches to model reduction of reaction networks, such as quasi-steady state (QSS) and quasi-equilibrium approximations (QE), and provide practical recipes for model reduction of linear and non-linear networks. We also discuss the application of model reduction to the problem of parameter identification, via backward pruning machine learning techniques.
生化网络在计算生物学中用于对细胞信号传导、新陈代谢和基因表达调控等系统的机制细节进行建模。参数和结构的不确定性以及组合爆炸是分析此类大型模型动态的巨大障碍。多尺度性作为这些网络的一个重要特性，可用于克服其中一些障碍。具有许多明显分离时间尺度的网络可以简化为更简单的模型，其方式仅取决于数量级，而不取决于动力学参数的精确值。用于此类网络稳健简化的主要思想是模型元素之间的主导概念，允许根据这些元素对网络动态的影响对其进行层次组织。这个概念在热带几何中有自然的表述。鉴于这些新思想，我们重新审视反应网络模型简化的主要方法，如准稳态（QSS）和准平衡近似（QE），并提供线性和非线性网络模型简化的实用方法。我们还讨论了通过反向剪枝机器学习技术将模型简化应用于参数识别问题。
