Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks
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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-06-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00549/full |
| _version_ | 1818991004721610752 |
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| author | Pavel Loskot Komlan Atitey Lyudmila Mihaylova |
| author_facet | Pavel Loskot Komlan Atitey Lyudmila Mihaylova |
| author_sort | Pavel Loskot |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T20:03:22Z |
| format | Article |
| id | doaj.art-8ad2631f55294660bd787e0efe1e0bbb |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-20T20:03:22Z |
| publishDate | 2019-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-8ad2631f55294660bd787e0efe1e0bbb2022-12-21T19:28:00ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-06-011010.3389/fgene.2019.00549453395Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction NetworksPavel Loskot0Komlan Atitey1Lyudmila Mihaylova2College of Engineering, Swansea University, Swansea, United KingdomCollege of Engineering, Swansea University, Swansea, United KingdomDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United KingdomThe 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.https://www.frontiersin.org/article/10.3389/fgene.2019.00549/fullautomationBayesian analysisbiochemical reaction networkestimationinferencemodeling |
| spellingShingle | Pavel Loskot Komlan Atitey Lyudmila Mihaylova Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks Frontiers in Genetics automation Bayesian analysis biochemical reaction network estimation inference modeling |
| title | 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_short | 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 |
| topic | automation Bayesian analysis biochemical reaction network estimation inference modeling |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00549/full |
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