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Computing all persistent subspaces of a reaction-diffusion system

An algorithm is presented for computing a reaction-diffusion partial differential equation (PDE) system for all possible subspaces that can hold a persistent solution of the equation. For this, all possible sub-networks of the underlying reaction network that are distributed organizations (DOs) are...

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Autores principales: Peter, Stephan, Woitke, Linus, Dittrich, Peter, Ibrahim, Bashar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567720/
https://www.ncbi.nlm.nih.gov/pubmed/37821664
http://dx.doi.org/10.1038/s41598-023-44244-x
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author Peter, Stephan
Woitke, Linus
Dittrich, Peter
Ibrahim, Bashar
author_facet Peter, Stephan
Woitke, Linus
Dittrich, Peter
Ibrahim, Bashar
author_sort Peter, Stephan
collection PubMed
description An algorithm is presented for computing a reaction-diffusion partial differential equation (PDE) system for all possible subspaces that can hold a persistent solution of the equation. For this, all possible sub-networks of the underlying reaction network that are distributed organizations (DOs) are identified. Recently it has been shown that a persistent subspace must be a DO. The algorithm computes the hierarchy of DOs starting from the largest by a linear programming approach using integer cuts. The underlying constraints use elementary reaction closures as minimal building blocks to guarantee local closedness and global self-maintenance, required for a DO. Additionally, the algorithm delivers for each subspace an affiliated set of organizational reactions and minimal compartmentalization that is necessary for this subspace to persist. It is proved that all sets of organizational reactions of a reaction network, as already DOs, form a lattice. This lattice contains all potentially persistent sets of reactions of all constrained solutions of reaction-diffusion PDEs. This provides a hierarchical structure of all persistent subspaces with regard to the species and also to the reactions of the reaction-diffusion PDE system. Here, the algorithm is described and the corresponding Python source code is provided. Furthermore, an analysis of its run time is performed and all models from the BioModels database as well as further examples are examined. Apart from the practical implications of the algorithm the results also give insights into the complexity of solving reaction-diffusion PDEs.
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spelling pubmed-105677202023-10-13 Computing all persistent subspaces of a reaction-diffusion system Peter, Stephan Woitke, Linus Dittrich, Peter Ibrahim, Bashar Sci Rep Article An algorithm is presented for computing a reaction-diffusion partial differential equation (PDE) system for all possible subspaces that can hold a persistent solution of the equation. For this, all possible sub-networks of the underlying reaction network that are distributed organizations (DOs) are identified. Recently it has been shown that a persistent subspace must be a DO. The algorithm computes the hierarchy of DOs starting from the largest by a linear programming approach using integer cuts. The underlying constraints use elementary reaction closures as minimal building blocks to guarantee local closedness and global self-maintenance, required for a DO. Additionally, the algorithm delivers for each subspace an affiliated set of organizational reactions and minimal compartmentalization that is necessary for this subspace to persist. It is proved that all sets of organizational reactions of a reaction network, as already DOs, form a lattice. This lattice contains all potentially persistent sets of reactions of all constrained solutions of reaction-diffusion PDEs. This provides a hierarchical structure of all persistent subspaces with regard to the species and also to the reactions of the reaction-diffusion PDE system. Here, the algorithm is described and the corresponding Python source code is provided. Furthermore, an analysis of its run time is performed and all models from the BioModels database as well as further examples are examined. Apart from the practical implications of the algorithm the results also give insights into the complexity of solving reaction-diffusion PDEs. Nature Publishing Group UK 2023-10-11 /pmc/articles/PMC10567720/ /pubmed/37821664 http://dx.doi.org/10.1038/s41598-023-44244-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Peter, Stephan
Woitke, Linus
Dittrich, Peter
Ibrahim, Bashar
Computing all persistent subspaces of a reaction-diffusion system
title Computing all persistent subspaces of a reaction-diffusion system
title_full Computing all persistent subspaces of a reaction-diffusion system
title_fullStr Computing all persistent subspaces of a reaction-diffusion system
title_full_unstemmed Computing all persistent subspaces of a reaction-diffusion system
title_short Computing all persistent subspaces of a reaction-diffusion system
title_sort computing all persistent subspaces of a reaction-diffusion system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567720/
https://www.ncbi.nlm.nih.gov/pubmed/37821664
http://dx.doi.org/10.1038/s41598-023-44244-x
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