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

Pavel Loskot Orcid Logo, Komlan Atitey, Lyudmila Mihaylova

Frontiers in Genetics, Volume: 10

Swansea University Author: Pavel Loskot Orcid Logo

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DOI (Published version): 10.3389/fgene.2019.00549

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 dependen...

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Published in: Frontiers in Genetics
ISSN: 1664-8021
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa50551
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first_indexed 2019-06-05T11:07:47Z
last_indexed 2019-09-23T14:15:48Z
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spelling 2019-09-23T04:17:29.7366971 v2 50551 2019-05-28 Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks bc7cba9ef306864239b9348c3aea4c3e 0000-0002-2773-2186 Pavel Loskot Pavel Loskot true false 2019-05-28 EEN 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 in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. 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 are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed. Journal Article Frontiers in Genetics 10 1664-8021 31 12 2019 2019-12-31 10.3389/fgene.2019.00549 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2019-09-23T04:17:29.7366971 2019-05-28T13:08:18.1628352 Pavel Loskot 0000-0002-2773-2186 1 Komlan Atitey 2 Lyudmila Mihaylova 3 0050551-25072019092222.pdf loskot2019(2).pdf 2019-07-25T09:22:22.1070000 Output 1191468 application/pdf Version of Record true 2019-07-25T00:00:00.0000000 false eng
title Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
spellingShingle Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
Pavel Loskot
title_short Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
title_full Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
title_fullStr Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
title_full_unstemmed Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
title_sort Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
author_id_str_mv bc7cba9ef306864239b9348c3aea4c3e
author_id_fullname_str_mv bc7cba9ef306864239b9348c3aea4c3e_***_Pavel Loskot
author Pavel Loskot
author2 Pavel Loskot
Komlan Atitey
Lyudmila Mihaylova
format Journal article
container_title Frontiers in Genetics
container_volume 10
publishDate 2019
institution Swansea University
issn 1664-8021
doi_str_mv 10.3389/fgene.2019.00549
document_store_str 1
active_str 0
description 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 in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. 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 are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.
published_date 2019-12-31T04:02:02Z
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score 11.012924

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