Title

Parameter Discovery For Stochastic Computational Models In Systems Biology Using Bayesian Model Checking

Abstract

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.

Publication Date

7-24-2014

Publication Title

2014 IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2014

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCABS.2014.6863925

Socpus ID

84908577796 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84908577796

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