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Modelling with ANIMO: between fuzzy logic and differential equations

BACKGROUND: Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events oc...

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Autores principales: Schivo, Stefano, Scholma, Jetse, van der Vet, Paul E., Karperien, Marcel, Post, Janine N., van de Pol, Jaco, Langerak, Rom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4962523/
https://www.ncbi.nlm.nih.gov/pubmed/27460034
http://dx.doi.org/10.1186/s12918-016-0286-z
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author Schivo, Stefano
Scholma, Jetse
van der Vet, Paul E.
Karperien, Marcel
Post, Janine N.
van de Pol, Jaco
Langerak, Rom
author_facet Schivo, Stefano
Scholma, Jetse
van der Vet, Paul E.
Karperien, Marcel
Post, Janine N.
van de Pol, Jaco
Langerak, Rom
author_sort Schivo, Stefano
collection PubMed
description BACKGROUND: Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. RESULTS: ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. CONCLUSIONS: Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0286-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-49625232016-07-28 Modelling with ANIMO: between fuzzy logic and differential equations Schivo, Stefano Scholma, Jetse van der Vet, Paul E. Karperien, Marcel Post, Janine N. van de Pol, Jaco Langerak, Rom BMC Syst Biol Methodology Article BACKGROUND: Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. RESULTS: ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. CONCLUSIONS: Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0286-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-07-27 /pmc/articles/PMC4962523/ /pubmed/27460034 http://dx.doi.org/10.1186/s12918-016-0286-z Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Schivo, Stefano
Scholma, Jetse
van der Vet, Paul E.
Karperien, Marcel
Post, Janine N.
van de Pol, Jaco
Langerak, Rom
Modelling with ANIMO: between fuzzy logic and differential equations
title Modelling with ANIMO: between fuzzy logic and differential equations
title_full Modelling with ANIMO: between fuzzy logic and differential equations
title_fullStr Modelling with ANIMO: between fuzzy logic and differential equations
title_full_unstemmed Modelling with ANIMO: between fuzzy logic and differential equations
title_short Modelling with ANIMO: between fuzzy logic and differential equations
title_sort modelling with animo: between fuzzy logic and differential equations
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4962523/
https://www.ncbi.nlm.nih.gov/pubmed/27460034
http://dx.doi.org/10.1186/s12918-016-0286-z
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