逻辑网络作为一种有限状态离散时间动态系统，在基因调控网络的研究中起到重要的推动作用。牵制控制策略是逻辑网络研究领域的一个前沿课题，探索其分布式策略以及低计算复杂度是网络控制和复杂生物系统领域的重要研究内容。逻辑网络的网络拓扑结构与系统动力学行为密切相关，结合网络拓扑结构和节点逻辑运算，采用信息论和网络控制的分析方法可极大降低计算复杂度。为此，本项目拟利用矩阵半张量积、图论和矩阵表示，设计基于系统网络拓扑结构而非全局状态空间的分布式牵制控制，从而极大降低大规模逻辑网络的计算复杂度；借鉴复杂网络理论，提出大规模逻辑网络模型降阶算法，探索并建立大规模逻辑网络拓扑结构与分布式牵制控制、系统模型降阶之间的联系。申请者丰富的研究积累和前期探索性研究，为本项目的顺利完成提供了扎实的基础，最终研究成果将丰富大规模逻辑网络的牵制控制理论，发展其研究方法，为研究大规模逻辑网络提供理论支持。

As a finite-state discrete-time dynamic system, logical networks play an important role in the research of genetic regulatory networks. Pinning control is a frontier topic in the field of logical network researches, exploring its distributed form and low computational complexity are important research contents in networked control and complex biological systems. The network structure of logical networks has a strong relationship with dynamical behaviours. Combining network structure with nodes’ logical information, it will dramatically reduce computational complexity in large-scale logical networks using analytical method of information theory and networked control. Thus, the main objectives of this proposal are to design distributed pinning control schemes based on network structure but not on overall state transition space for large-scale logical networks using algebraic state space method and matrix representations, while reducing computational complexity; to propose algorithms of model reduction using the complex network theory; to explore and establish the relationship between network structure and distributed pinning control and model reduction. The rich research accumulation and preliminary exploratory research of the applicant provide a solid foundation for successful completion of the project, which will contribute to enrich pinning control theory of large-scale logical networks, develop research methods, and provide an important theoretical basis for large-scale logical networks.