Parameter discovery for stochastic computational models in systems biology using Bayesian model checking

Faraz Hussain, Christopher J. Langmead, Qi Mi, Joyeeta Dutta-Moscato, Yoram Vodovotz, Sumit K. Jha
2014 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)  
Parameterized probabilistic complex computational (P 2 C 2 ) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P 2 C 2 models by combining prior knowledge and empirical data, given that certain system properties are unknown. These unknown components are incorporated into a model as parameters and determining their values has traditionally been a process of trial and error. We present a new algorithmic
more » ... cedure for discovering parameters in agent-based models of biological systems against behavioral specifications mined from large data-sets. Our approach uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to synthesize parameters of P 2 C 2 models. We demonstrate our algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide in a clinical agent-based model of the dynamics of acute inflammation that guarantee a set of desired clinical outcomes with high probability.
doi:10.1109/iccabs.2014.6863925 dblp:conf/iccabs/HussainLMDVJ14 fatcat:klj3qtidwnbfxaekrlllzdk7iq