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

Prairie View A & M University, Prairie View TX

Investigators

Abstract

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