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Abstract：
The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.
摘要：
控制的概念是理解和应用生物网络模型的核心。它们的一些关键结构特征与控制功能有关，通过基因调控，信令,或代谢机制，和计算模型需要编码这些。模型的应用通常集中在基于模型的控制上，例如在生物医学或代谢工程中。本文提出了一种基于模型的控制方法，该方法利用了生物网络的两个共同特征，即它们的模块化结构和监管机制的规范特征。本文的重点是细胞内调控网络，由布尔网络模型表示。本文的主要结果是，可以通过一次关注一个模块来识别控制策略。本文还提出了一种基于规范功能的监管规则，以识别无助于网络控制并且可以排除的模块。即使是中等规模的网络，找到全局控制输入在计算上非常具有挑战性。这里提出的模块化方法导致了解决这个问题的高效方法。将该方法应用于已发布的血癌大颗粒淋巴细胞（T-LGL）白血病的布尔网络模型，以识别实现所需控制目标的最小控制集。
