Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks - Loskot, Pavel; Atitey, Komlan; Mihaylova, Lyudmila (Frontiers in Genetics, 2019)
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. Model dynamics are crucially dependent on parameter values which are often estimated from observations. Over past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. Statistical inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability and checking as well as optimum experiment design, sensitivity analysis, bifurcation analysis and other. The aim of this review paper is to explore developments of past decade to understand what BRN models are commonly used in literature, and for what inference tasks and inference methods. Initial collection of about 700 publications excluding books in computational biology and chemistry were screened to select over 260 research papers and 20 graduate theses concerning estimation problems in BRNs. The paper selection was performed as text mining using scripts to automate search for relevant keywords and terms. The outcome are tables revealing the level of interest in different inference tasks and methods for given models in literature as well as recent trends. Our findings indicate that many combinations of models, tasks and methods are still relatively sparse representing new research opportunities to explore those that have not been considered - perhaps for a good reason. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics and state space representations whereas the most common tasks in cited papers are parameter inference and model identification. The most common methods are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The paper concludes by discussing future research directions including research problems which cannot be directly deduced from presented tables.