Statistical inference in ensemble modeling of cellular metabolism.

ABSTRACT: Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusi

SUBMITTER: Hameri T 

PROVIDER: S-EPMC6922442 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature