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

CRII：药物干扰下基因调控网络的建模和分析近年来，越来越清楚的是，需要复杂的计算方法和数学建模来管理、解释和理解生物数据的复杂性。考虑到今天的药物发现是一个复杂、昂贵、耗时且损耗率高的过程，需要一种更系统的方法来分析潜在的遗传调控，以导致更有效和高效的药物开发。本项目利用混合系统研究药物扰动下遗传调控网络（grn）的动态，以量化不同给药方案、最佳靶点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疾病的有效和负担得起的治疗研究。此外，该项目将在Prairie View A&amp；M大学进行，这是一所HBCU，历史上作为非裔美国工程师的生产者在全国拥有强大的影响力。学院将大力鼓励学生参与研究，鼓励有前途的本科生继续攻读研究生。拟议的研究和教育活动将大大提高非裔美国人对计算生物学这一新兴领域的参与。分子靶向药物（molecular targeted agents, mta）近年来越来越多地用于癌症的治疗，通过干扰肿瘤发生和生长所需的特异性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，使得临床前和临床研究的方法相对简单，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动态的生物系统的相互作用。本研究采用确定性和随机混合系统模型来研究药物扰动下底层GRN的动力学，为不同药物扰动情景提供系统的数学分析。确定性混合系统模型集成了GRN的连续和离散动力学，而随机混合系统模型捕获了遗传调控的固有随机性和药物效应引入的不确定性。该模型考虑了现实的药物药理学模型，包括通过状态空间方法连接的药物药代动力学和药效学信息。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。

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.