Development of methodologies for modeling and design molecular biological systems

分子生物系统建模和设计方法的开发

• 批准号: 327746-2010
• 负责人: Wu, FangXiang
• 金额: $ 1.46万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=549199
• 关键词: Development; methodologies; modeling; design; molecular

Genomic-alerted diseases (e.g., obesity, cancer, HIV, H1N1) stem from the dysfunction of molecular biological systems, not only their isolated components (e.g., genes, proteins). With advances in high throughput measurement techniques such as microarray, ChIP-chip, and mass spectrometry, large-scale biological data have been and will continuously be produced. Such data contain insightful information for understanding the mechanism of molecular biological systems and have proved useful in diagnosis, treatment, and drug design for genomic-alerted diseases. However, even if our ability were adequate for the task to measure the functional states and interactions among biological network components, computational limitations alone would prohibit us to understand the behaviours of molecular biological systems because their complexity exponentially grows with the number of network components and interactions among them. With such various types of biological data, my research has been focused on developments of advanced methodologies for understanding the details and principles of molecular biological systems while aiming at designing (or controlling) them to have a desired behaviour (e.g., robustness ot diseases). The long-term objective of my proposed research is to advance methodologies for modeling, analyzing and designing molecular biological systems (networks). To achieve the long-term objective, three specific short-term objectives are designed for my proposed research: 1) inferring gene regulatory networks from various types of biological data; 2) molecular biological system identification and parameter estimation based on various types of biological data; and 3) analyzing and simulating molecular biological systems for designing them. While ambitious, the successful outcome of the proposed research will have considerable ramifications for designing and engineering molecular biological systems with the potential to benefit many areas of industry such as personalized pharmaceutical development and crop yield enhancement.

基因组引起的疾病（如肥胖、癌症、艾滋病毒、H1N1）源于分子生物系统的功能障碍，而不仅仅是它们的孤立组成部分（如基因、蛋白质）。随着微阵列、ChIP-chip和质谱等高通量测量技术的进步，大规模的生物数据已经并将继续产生。这些数据包含了理解分子生物学系统机制的深刻信息，并已被证明对基因组预警疾病的诊断、治疗和药物设计有用。然而，即使我们的能力足以完成测量生物网络组件之间的功能状态和相互作用的任务，单独的计算限制也会阻止我们理解分子生物系统的行为，因为它们的复杂性随着网络组件的数量和它们之间的相互作用呈指数级增长。有了这些不同类型的生物数据，我的研究一直集中在先进方法的发展上，以理解分子生物系统的细节和原理，同时旨在设计（或控制）它们具有期望的行为（例如，对疾病的稳健性）。我提出的研究的长期目标是推进建模、分析和设计分子生物系统（网络）的方法。为了实现长期目标，我为我提出的研究设计了三个具体的短期目标：1)从各种类型的生物数据推断基因调控网络；2)基于各类生物数据的分子生物系统辨识与参数估计；3)分析和模拟分子生物系统，为设计分子生物系统提供依据。虽然雄心勃勃，但拟议研究的成功结果将对设计和工程分子生物系统产生相当大的影响，并有可能使许多工业领域受益，如个性化药物开发和作物产量提高。