Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.

ABSTRACT: The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments

SUBMITTER: Loskot P 

PROVIDER: S-EPMC6588029 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature