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Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-s...

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Autores principales: Wysocka, Emilia M., Page, Matthew, Snowden, James, Simpson, T. Ian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760030/
https://www.ncbi.nlm.nih.gov/pubmed/36540795
http://dx.doi.org/10.7717/peerj.14516
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author Wysocka, Emilia M.
Page, Matthew
Snowden, James
Simpson, T. Ian
author_facet Wysocka, Emilia M.
Page, Matthew
Snowden, James
Simpson, T. Ian
author_sort Wysocka, Emilia M.
collection PubMed
description Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.
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spelling pubmed-97600302022-12-19 Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network Wysocka, Emilia M. Page, Matthew Snowden, James Simpson, T. Ian PeerJ Bioinformatics Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system. PeerJ Inc. 2022-12-15 /pmc/articles/PMC9760030/ /pubmed/36540795 http://dx.doi.org/10.7717/peerj.14516 Text en © 2022 Wysocka et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Wysocka, Emilia M.
Page, Matthew
Snowden, James
Simpson, T. Ian
Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title_full Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title_fullStr Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title_full_unstemmed Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title_short Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network
title_sort comparison of rule- and ordinary differential equation-based dynamic model of darpp-32 signalling network
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760030/
https://www.ncbi.nlm.nih.gov/pubmed/36540795
http://dx.doi.org/10.7717/peerj.14516
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