Algorithms for Complex Network Control and Their Applications for Drug Target Identification from Biomolecular Networks

许多科学研究领域的系统可以被建模为复杂的网络。我们对这样一个复杂网络的理解的最终证据反映在我们将其从不希望看到的状态引导到期望状态的能力上。另一方面，众所周知，一种复杂的疾病源于控制其发病机制的一些复杂的生物分子网络的故障。药物是引导生物分子网络从疾病表型(状态)转向健康表型(状态)的关键。药物靶点是一种生物分子，它参与控制特定疾病的发病机制的生物分子网络，通过与适当的药物结合可以改变网络的状态。在生物分子网络中，一些药物靶标的效率可能比其他药物更高或更低。确定最有效的药物靶点是药物设计和开发过程中非常早期和关键的步骤，因为在这一过程中，后期失败的成本远远高于早期失败的成本。复杂网络可以映射到动态系统，从而可以研究复杂网络的控制问题。因此，我们可以将生物分子网络中的药物靶标视为受控动态系统中的转向节点。通过将控制信号(药物)应用于控制节点(药物靶标)，我们希望将发生故障的生物分子网络从疾病状态引导到健康状态。因此，我们可以将生物分子网络中的药物靶标识别描述为动态系统的一些控制问题。我提出的研究的长期目标是为复杂网络的一些控制问题开发先进的算法，同时提供生物信息学工具，从生物分子网络中识别药物靶标。为了实现我的长期目标，这份提案设计了三个具体目标。目标1：开发复杂网络的传递性算法并将其应用于从生物分子网络中识别药物靶标；目标2：开发复杂网络的输出可控性/传递性算法并将其应用于从生物分子网络中识别药物靶标；以及目标3：开发复杂网络的最优控制方法并将其应用于从生物分子网络中识别药物靶标。拟议研究的成功结果将对复杂疾病的药物靶标识别产生相当大的影响。建议的研究计划可以促进计算机科学、复杂网络科学、控制系统和药理学等学科的多学科研究环境。拟议的研究计划还将在协作研究环境中对高素质人员(HQP)进行有效的跨学科培训，以增加HQP在上述主题的多学科领域的知识、研究技能和专业知识。

Many systems of scientific interests can be modeled as complex networks. The ultimate proof of our understanding of such a complex network is reflected by our ability to steer it from an undesired state to a desired state. On the other hand, it is well acknowledged that a complex disease stems from the malfunction of some complex biomolecular networks that control its pathogenesis. Drugs are essential for steering the biomolecular networks from a disease phenotype (state) to a healthy phenotype (state). A drug target is a biomolecule which is involved in a biomolecular network that controls the pathogenesis of a specific disease and via which states of the network can be changed by combining with suitable drugs. Some drug targets may be more or less efficient than others in biomolecular networks. Identifying the most efficient drug targets is a very early and critical step in the drug design and development process as the costs of late failure are significantly higher than those of early failure in that process. A complex network can be mapped to a dynamic system and thus control issues of the complex network can be studied. As a result, we can view drug targets in a biomolecular network as steering nodes in a controlled dynamic system. By applying control signals (drugs) to steering nodes (drug targets) we desire to steer a malfunctioning biomolecular network from a disease state to a healthy state. We can thus formulate drug target identifications from biomolecular networks as some control issues of dynamic systems. The long-term goal of my proposed research is to develop advanced algorithms for some control issues of complex networks while providing bioinformatics tools for identifying drug targets from biomolecular networks. To achieve my long-term goal, three specific objectives are designed in this proposal. Objective 1: developing algorithms for transittability of complex networks and applying them for identifying drug targets from biomolecular networks; Objective 2: developing algorithms for output controllability/transittability of complex networks and applying them for identifying drug targets from biomolecular networks; and Objective 3: developing optimal control methods for complex networks and applying them for identifying drug targets from biomolecular networks. The successful outcomes of the proposed research will have considerable ramifications for drug target identifications of complex diseases. The proposed research program can foster the multidisciplinary research environment in subjects of computer science, complex network science, control systems, and pharmacology. The proposed research program will also enable effective cross-disciplinary training of high qualified personnel (HQP) in a collaborative research environment to increase knowledge, research skills, and expertise of HQP in multidisciplinary areas of the aforementioned subjects.