CRII: SCH: Modeling and Analysis of Genetic Regulatory Networks under Drug Perturbation

CRII：SCH：药物扰动下遗传调控网络的建模与分析

• 批准号: 1464387
• 负责人: Xiangfang Li
• 金额: $ 17.5万
• 依托单位: Prairie View A & M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464387&HistoricalAwards=false
• 关键词: CRII; SCH; Modeling; Analysis; Genetic

CRII: Modeling and Analysis of Genetic Regulatory Networks under Drug PerturbationIn recent years, it has become increasingly clear that sophisticated computational methods and mathematical modeling will be needed to manage, interpret and understand the complexity of biological data. Considering drug discovery today is a complex, expensive, and time-consuming process with high attrition rate, a more systematic approach is needed to analyze the underlying genetic regulation in order to lead to more effective and efficient drug development. This project targets the dynamics of Genetic Regulatory Networks (GRNs) under drug perturbation using hybrid systems, in order to quantify drug effectiveness regarding different dosing regimens, optimal target(s), and combinational therapy. The interdisciplinary nature of this project promises to foster cross-fertilization of ideas between computational science and biomedical research. It is promising that such study would advance research in effective and affordable treatment of genetic diseases like cancer. Moreover, the project will take place at Prairie View A&M University, an HBCU, which historically has a strong national presence as a producer of African American engineers. Ample efforts will be carried out by the PI to involve students in research and encourage promising undergraduates to pursue graduate study. The proposed research and education activities will greatly improve African American involvement in the emerging field of computational biology.Molecularly targeted agents (MTAs) are increasingly used for the treatment of cancer in recent years to improve the efficacy and selectivity by interfering with specific targeted molecules needed for carcinogenesis and tumor growth. While the lack of specificity of the traditional cytotoxic drugs allowed a relatively straightforward approach in preclinical and clinical study, developing a paradigm to better analyze the efficacy of MTAs is substantially more complex. Moreover, complex diseases such as cancer involved the interaction of more complicated and dynamic biological systems. This proposed research investigates both deterministic and stochastic hybrid systems models to study dynamics of the underlying GRN under drug perturbations in order to provide systematic mathematical analysis for different drug perturbation scenarios. While the deterministic hybrid systems model integrates continuous and discrete dynamics of GRN, the stochastic hybrid systems model captures the inherent stochasticity in genetic regulations and uncertainties introduced by drug effects. A realistic drug pharmacology model is taken into account in the proposed model, including drug pharmacokinetics and pharmacodynamics information linked through a state-space approach. The objective is to understand how the GRN reacts when perturbed and provide suggestions for better therapeutic interventions.

CRII：药物扰动下遗传调控网络的建模和分析近年来，越来越明显的是，需要复杂的计算方法和数学模型来管理、解释和理解生物数据的复杂性。考虑到今天的药物开发是一个复杂、昂贵、耗时和高磨损率的过程，需要一种更系统的方法来分析潜在的基因调控，以导致更有效和高效的药物开发。该项目旨在利用混合系统研究药物扰动下遗传调控网络(GRN)的动态变化，以量化不同给药方案、最佳靶点(S)和联合治疗的药物有效性。这个项目的跨学科性质有望促进计算科学和生物医学研究之间的思想交流。这项研究有望推动癌症等遗传病的有效和负担得起的治疗方法的研究。此外，该项目将在HBCU下属的Prairie View A&Amp；M大学开展。作为非裔美国人工程师的培养基地，该校历来在全国拥有强大的影响力。国际学生联合会将尽最大努力让学生参与研究，并鼓励有前途的本科生攻读研究生课程。拟议的研究和教育活动将极大地促进非裔美国人参与新兴的计算生物学领域。近年来，分子靶向制剂(MTA)越来越多地用于癌症的治疗，通过干扰癌症发生和肿瘤生长所需的特定靶向分子来提高疗效和选择性。虽然传统细胞毒性药物缺乏特异性，使得在临床前和临床研究中可以采用相对简单的方法，但开发一种更好地分析MTA疗效的范例要复杂得多。此外，癌症等复杂疾病涉及更复杂和更动态的生物系统的相互作用。这项研究同时研究了确定性和随机混合系统模型，以研究药物扰动下潜在GRN的动力学，以便为不同的药物扰动情景提供系统的数学分析。确定性混杂系统模型融合了GRN的连续和离散动态，而随机混杂系统模型则捕捉到了遗传规律固有的随机性和药物效应带来的不确定性。该模型考虑了真实的药物药理学模型，通过状态空间方法将药物的药代动力学和药效学信息联系在一起。目的是了解GRN在受到干扰时的反应，并为更好的治疗干预提供建议。