Bio-ModelChecker: Using Bounded Constraint Satisfaction to Seamlessly Integrate Observed Behavior With Prior Knowledge of Biological Networks.

ABSTRACT: The in silico study and reverse engineering of regulatory networks has gained in recognition as an insightful tool for the qualitative study of biological mechanisms that underlie a broad range of complex illness. In the creation of reliable network models, the integration of prior mechanistic knowledge with experimentally observed behavior is hampered by the disparate nature and widespread sparsity of such measurements. The former challenges conventional regression-based parameter fitting while the latter leads to large sets of highly variable network models that are equally compliant with the data. In this paper, we propose a bounded Constraint Satisfaction (CS) based model checking framework for parameter set identification that readily accommodates partial records and the expone

SUBMITTER: Sedghamiz H 

PROVIDER: S-EPMC6443719 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature