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

中环填海第二期：药物扰动下基因调控网络的建模与分析近年来，人们越来越清楚地认识到，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，昂贵和耗时的过程，高损耗率，需要一个更系统的方法来分析潜在的遗传调控，以导致更有效和更高效的药物开发。该项目的目标是使用混合系统在药物扰动下遗传调控网络（GRNs）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。该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。这项研究有望推动有效和负担得起的治疗癌症等遗传疾病的研究。此外，该项目将在Prairie View A M大学（HBCU）进行，该大学历史上作为非洲裔美国工程师的生产者在全国具有很强的影响力。研究所将作出充分努力，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大大提高非裔美国人参与新兴领域的计算生物学。近年来，分子靶向药物（MTA）越来越多地用于癌症治疗，通过干扰致癌和肿瘤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，允许在临床前和临床研究中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动态的生物系统的相互作用。本研究探讨了确定性和随机混合系统模型，以研究药物扰动下的基本GRN的动态，以提供系统的数学分析，为不同的药物扰动情况。虽然确定性混合系统模型集成了GRN的连续和离散动态，随机混合系统模型捕捉到遗传调控和药物效应引入的不确定性的固有随机性。一个现实的药物药理学模型被考虑在拟议的模型，包括药物的药代动力学和药效学信息通过状态空间的方法。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.