Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843-4466, USA.
Curr Genomics. 2009 Sep;10(6):375-87. doi: 10.2174/138920209789177584.
Computational modeling of genomic regulation has become an important focus of systems biology and genomic signal processing for the past several years. It holds the promise to uncover both the structure and dynamical properties of the complex gene, protein or metabolic networks responsible for the cell functioning in various contexts and regimes. This, in turn, will lead to the development of optimal intervention strategies for prevention and control of disease. At the same time, constructing such computational models faces several challenges. High complexity is one of the major impediments for the practical applications of the models. Thus, reducing the size/complexity of a model becomes a critical issue in problems such as model selection, construction of tractable subnetwork models, and control of its dynamical behavior. We focus on the reduction problem in the context of two specific models of genomic regulation: Boolean networks with perturbation (BN(P)) and probabilistic Boolean networks (PBN). We also compare and draw a parallel between the reduction problem and two other important problems of computational modeling of genomic networks: the problem of network inference and the problem of designing external control policies for intervention/altering the dynamics of the model.
在过去几年中，基因组调控的计算建模已成为系统生物学和基因组信号处理的一个重要焦点。它有望揭示负责细胞在各种环境和状态下运作的复杂基因、蛋白质或代谢网络的结构和动态特性。这反过来又将为预防和控制疾病制定最佳干预策略。同时，构建这样的计算模型面临着几个挑战。高复杂性是模型实际应用的主要障碍之一。因此，减小模型的大小/复杂性成为模型选择、可处理子网模型构建以及控制其动态行为等问题的关键问题。我们专注于两个特定的基因组调控模型的约简问题：带扰动的布尔网络（BN(P)）和概率布尔网络（PBN）。我们还比较并绘制了约简问题与基因组网络计算建模的另外两个重要问题之间的平行关系：网络推断问题和设计外部控制策略以干预/改变模型动态的问题。
