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

复杂网络控制算法及其在生物分子网络药物靶标识别中的应用

• 批准号: RGPIN-2016-05214
• 负责人: WU, FANGXIANG
• 金额: $ 2.26万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668954
• 关键词: Algorithms; Complex; Network; Control; Their

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.

许多具有科学意义的系统可以被建模为复杂网络。我们理解这样一个复杂网络的最终证据，反映在我们能够将它从一个不希望的状态引导到一个希望的状态。另一方面，众所周知，复杂疾病源于控制其发病机制的一些复杂生物分子网络的故障。药物对于将生物分子网络从疾病表型（状态）转向健康表型（状态）至关重要。药物靶标是生物分子，其参与控制特定疾病的发病机理的生物分子网络，并且通过与合适的药物组合可以改变网络的状态。在生物分子网络中，某些药物靶点可能比其他靶点更有效或更低效。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一步，因为在该过程中，晚期失败的成本明显高于早期失败的成本。将复杂网络映射为动态系统，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点视为受控动态系统中的转向节点。通过将控制信号（药物）应用于转向节点（药物靶标），我们希望将故障生物分子网络从疾病状态转向健康状态。因此，我们可以将生物分子网络中的药物靶点识别表述为动力学系统的一些控制问题。我提出的研究的长期目标是开发先进的算法来解决复杂网络的一些控制问题，同时为从生物分子网络中识别药物靶点提供生物信息学工具。为了达到我的长远目标，我在这份建议书中设计了三个具体目标。目标一：目标2：开发复杂网络的输出可控性/可传递性的算法，并将其应用于从生物分子网络中识别药物靶标;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点鉴定产生相当大的影响。拟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在上述学科多学科领域的知识、研究技能和专业知识。