ArticleScientific reports2023
Computing all persistent subspaces of a reaction-diffusion system.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Chemical organizations as a conceptual tool: from synthetic biology to interdisciplinary systems and back.Frontiers in bioengineering and biotechnology · 2026Article
- Dynamics of spindle assembly and position checkpoints: Integrating molecular mechanisms with computational models.Computational and structural biotechnology journal · 2025Review
- Kombucha-Proteinoid Crystal Bioelectric Circuits.ACS omega · 2024Article
- Cell Cycle Complexity: Exploring the Structure of Persistent Subsystems in 414 Models.Biomedicines · 2024Article
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Authors and funding
4 authors.
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Abstract
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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