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Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent |
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| Authors: | Yuanfeng Wang Scott Christley Eric Mjolsness Xiaohui Xie |
| Affiliation: | (1) Department of Physics and Astronomy, University of California, 92617 Irvine, CA, USA;(2) Department of Mathematics, University of California, 92617 Irvine, CA, USA;(3) Department of Computer Science, University of California, 92617 Irvine, CA, USA;(4) Center for Complex Biological Systems, University of California, 92617 Irvine, CA, USA;(5) Institute for Genomics and Bioinformatics, University of California, 92617 Irvine, CA, USA |
| Abstract: | BackgroundStochastic effects can be important for the behavior of processes involving small population numbers, so the study of stochastic models has become an important topic in the burgeoning field of computational systems biology. However analysis techniques for stochastic models have tended to lag behind their deterministic cousins due to the heavier computational demands of the statistical approaches for fitting the models to experimental data. There is a continuing need for more effective and efficient algorithms. In this article we focus on the parameter inference problem for stochastic kinetic models of biochemical reactions given discrete time-course observations of either some or all of the molecular species. |
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