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CAREER Grant Will Help Understand Cell Cycle Model

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Yang Cao, an assistant professor in the computer science department at Virginia Tech's College of Engineering, has won a $550,000 National Science Foundation Faculty Early Career Development (CAREER) award to develop computer simulation methods that will better understand the complex, discrete, and stochastic cell cycle model.
The CAREER grant is the National Science Foundation's most prestigious award, given to creative junior faculty likely considered to become academic leaders of the future. The five-year grant funds Cao's (http://www.cs.vt.edu/user/31) research project, titled "Multi-scale Stochastic Simulation for Complex Biochemical Systems with Visualization Tools." The project will involve a multi-disciplinary tract from Virginia Tech faculty in Cao's computer science department (http://www.cs.vt.edu/), as well as those from the departments of mathematics and biology - both part of the College of Science (http://www.admiss.vt.edu/majors/cos.php).
The cell cycle is the sequence of events whereby a living cell replicates its components, reaches a target size, and then and divides itself more or less evenly between two "daughter" cells, so that each offspring receives the information and machinery necessary to repeat the process. The cycle occurs in many diseases such as cancer, and understanding the process could spur new medical-side research into possible cures of cancer, and even cardiovascular disease.
Modeling the molecular mechanisms of a cell cycle is infinitely challenging. Cell biologists previously developed complex mathematical models of cell-cycle control in budding and fission yeast, as well as mammalian cells, but the systems are so complex that computer model simulation and analysis has thus far been very time consuming.
Cao's research seeks to correct this by developing computational methods and rigorous mathematical theories to integrate the full gamut of continuous, discrete, deterministic and stochastic models. Cao also seeks to allow cell biologists the ability to switch between different models and algorithms as dictated by the scales of underlying problems, with seamless transition.
The model visualization tool and results related to the cell cycle model will be used in undergraduate research and education not only at Virginia Tech, but nearby Radford University, Cao said. This will be done through collaboration with a professor in the Radford mathematics department.
Cao heads Virginia Tech's Computational Biology Lab (http://people.cs.vt.edu/~ycao/), which focuses on the development of multi-scale, multi-physics modeling and simulation methods and tools that help biologists create complex biological model systems, simulate their dynamics and analyze their functions. Typical models include gene expression models, protein interaction networks and cell cycle models. Particular emphases focus on applications involving multiple scales and multiple physics, in which hybrid models include both discrete and continuous variables, deterministic and stochastic equations.
Among Cao's Virginia Tech collaborators is John Tyson, university distinguished professor in the biology department, and head of the Tyson Lab, which is dedicated to computational cell biology. Tyson already has helped build mathematical models of interacting genes and proteins and solved the equations on their computers. By comparing such computer simulations to a various experimental observations, Tyson has said new insights can be gleamed into the behavior of complex regulatory systems, such as cells.
Cao earned his undergraduate degree in applied math and his master degree in computational math from Tsinghua University in Beijing, China, and his doctoral degree in computer science from the University of California at Santa Barbara. He joined the Virginia Tech faculty in 2006.
Source: Steven Mackay
Virginia Tech
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Keywords:

cell, cell cycle, understand cell, cell biologists, cell replicates, challenging cell, cell biology, living cell, stochastic cell, networks cell
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