Probabilistic Approximation Techniques for Biopathway Modeling PDF
Bing Liu, Department of Computer Science, Carnegie Mellon University

11/12/2012, 11am, GHC 8102


Quantitative modeling of biopathway dynamics is crucial to the system-level understanding of cellular functions and behavior. A common method of representing biopathways is through a system of ordinary differential equations (ODEs). However, large ODE-based pathway models are often difficult to calibrate and analyze. In this talk, I will present a recently developed probabilistic approximation technique using which the above problems can be considerably simplified. We approximate the ODE dynamics as a dynamic Bayesian network and use Bayesian inference techniques to perform tasks such as parameter estimation, global sensitivity analysis and probabilistic verification. We have tested our methods on a number of existing pathway models. We have also carried out a combined computational and experimental study of the human complement system. Apart from improved performance, the crucial insights we have gained crucial insights from the study of complement system could contribute to the development of immunomodulation therapies. Finally, I will show that the scalability of our approach can be extended via a GPU implementation.


Bing Liu received his B.Comp from School of Computing, National University of Singapore (NUS), Singapore (2006) and his Ph.D in Computational Biology from NUS Graduate School for Integrative Sciences and Engineering, NUS (2011). He is currently a Postdoctoral Fellow at the Computer Science Department, Carnegie Mellon University. His research interests include computational systems biology and high-performance computing.


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nsfSupported by an Expeditions in Computing award from the National Science Foundation