Predicting Simulation Parameters of Biological Systems Using a Gaussian Process Model

• Activity Number: 285
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Xiangxin Zhu*+ and Max Welling and John Lowengrub
• Companies: University of California at Irvine and University of California at Irvine and University of California at Irvine
• Keywords: Gaussian process regression ; biological system simulation

Abstract

Finding optimal simulation parameter values of biological systems is a very difficult and expensive task in systems biology. Brute force searching is infeasible in practice due to the huge search space. In this paper, we propose predicting the parameters efficiently by learning the relationship between system outputs and parameters using regression. However, the conventional parametric regression models suffer from two issues. First, restricting the regression function as a certain fixed type introduces too strong assumptions that reduce the model flexibility. Second, conventional regression models fail to take into account the fact that a fixed parameter value may correspond to multiple different outputs due to the stochastic nature of most biological simulations, and the existence of a potentially large number of other factors that affect the simulation outputs. We propose a novel approach based on a Gaussian process model that addresses the two issues jointly. We apply our approach on a tumor vessel growth model and the feedback Wright-Fisher model. The experimental results show that our method can predicts the parameter values of both of the two models with high accuracy.