AUTHOR=Loskot Pavel , Atitey Komlan , Mihaylova Lyudmila 

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

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00549

DOI=10.3389/fgene.2019.00549

ISSN=1664-8021

ABSTRACT=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.