TY - GEN
T1 - Parameter discovery for stochastic computational models in systems biology using Bayesian model checking
AU - Hussain, Faraz
AU - Langmead, Christopher J.
AU - Mi, Qi
AU - Dutta-Moscato, Joyeeta
AU - Vodovotz, Yoram
AU - Jha, Sumit K.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/24
Y1 - 2014/7/24
N2 - Parameterized probabilistic complex computational (P2C2) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P2C2 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 procedure 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 P2C2 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.
AB - Parameterized probabilistic complex computational (P2C2) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P2C2 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 procedure 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 P2C2 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.
UR - http://www.scopus.com/inward/record.url?scp=84908577796&partnerID=8YFLogxK
U2 - 10.1109/ICCABS.2014.6863925
DO - 10.1109/ICCABS.2014.6863925
M3 - Conference contribution
AN - SCOPUS:84908577796
T3 - 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014
BT - 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014
Y2 - 2 June 2014 through 4 June 2014
ER -