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Verifiable biology
The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, comp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169095/ https://www.ncbi.nlm.nih.gov/pubmed/37160165 http://dx.doi.org/10.1098/rsif.2023.0019 |
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author | Konur, Savas Gheorghe, Marian Krasnogor, Natalio |
author_facet | Konur, Savas Gheorghe, Marian Krasnogor, Natalio |
author_sort | Konur, Savas |
collection | PubMed |
description | The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest. |
format | Online Article Text |
id | pubmed-10169095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-101690952023-05-10 Verifiable biology Konur, Savas Gheorghe, Marian Krasnogor, Natalio J R Soc Interface Review Articles The formalization of biological systems using computational modelling approaches as an alternative to mathematical-based methods has recently received much interest because computational models provide a deeper mechanistic understanding of biological systems. In particular, formal verification, complementary approach to standard computational techniques such as simulation, is used to validate the system correctness and obtain critical information about system behaviour. In this study, we survey the most frequently used computational modelling approaches and formal verification techniques for computational biology. We compare a number of verification tools and software suites used to analyse biological systems and biochemical networks, and to verify a wide range of biological properties. For users who have no expertise in formal verification, we present a novel methodology that allows them to easily apply formal verification techniques to analyse their biological or biochemical system of interest. The Royal Society 2023-05-10 /pmc/articles/PMC10169095/ /pubmed/37160165 http://dx.doi.org/10.1098/rsif.2023.0019 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Review Articles Konur, Savas Gheorghe, Marian Krasnogor, Natalio Verifiable biology |
title | Verifiable biology |
title_full | Verifiable biology |
title_fullStr | Verifiable biology |
title_full_unstemmed | Verifiable biology |
title_short | Verifiable biology |
title_sort | verifiable biology |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169095/ https://www.ncbi.nlm.nih.gov/pubmed/37160165 http://dx.doi.org/10.1098/rsif.2023.0019 |
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