Career: Multiscale Stochastic Simulation for Complex Biochemical Systems with Visualization Tools

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Career: Multiscale Stochastic Simulation for Complex Biochemical Systems with Visualization ToolsThis project aims to develop efficient simulation techniques for stochastic biochemical models, particularly the cell cycle model. Cell cycle is the sequence of events whereby a living cell replicates its components and divides them between two daughter cells, so that each daughter has the information and machinery necessary to repeat the process. Cell cycle is related to many diseases such as cardiovascular diseases and cancer. Understanding the molecular mechanisms regulating cell cycle is a major challenge of contemporary cell biology. Biologists have developed complex mathematical models of cell-cycle control in budding yeast, fission yeast, and mammalian cells. These systems are so complex that its simulation and analysis present great challenges to computational science. The career goal of the PI is to address these challenges by developing innovative computational methods and rigorous mathematical theories to integrate the full gamut of continuous, discrete, deterministic, and stochastic models, and support dynamic, seamless and automatic switching between different models and algorithms as dictated by the scales of underlying problems. This project focuses on three specific aims in this project. The primary aim is to develop innovative computational algorithms and mathematical theories about a critical multiscale challenge: stiffness. This project will develop the theory of the stiffness in discrete stochastic simulation of chemically reacting systems and an automatic stiffness detection algorithm through a running-time profile analysis. The second aim is to develop hybrid algorithms to simulate biological systems with multistate species, a special challenge in biological systems with multiple binding sites. This project will develop hybrid methods to combine particle-based methods, designed for multistate species, and population-based methods, designed for general chemical reactions. The third aim of this project is to develop algorithm and model visualization tools to introduce the algorithms and model development in computational biology to graduate and undergraduate students. The algorithms developed in this project will enable biologists to efficiently model and simulate multiscale systems and will directly benefit the whole research discipline of systems biology. Moreover, the techniques about the stiffness are also applicable to multiscale simulation of complex systems in other areas. This research project also provides learning opportunities and training for students across the disciplines of computer science, mathematics, and biology. The biological models and simulation methods will be introduced in graduate courses on computational cell biology. The algorithm visualization tool will help students understand the important computational concept of stiffness. The model visualization tool and results related to the cell cycle model will be used in undergraduate research and education in Virginia Tech and Radford University, through collaboration with a professor in the Mathematics department at Radford University. This collaboration will help to attract more women and minority students into computational science areas.