Abstraction-based segmental simulation of reaction networks using adaptive memoization
Abstract Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffn...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-05966-5 |
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| author | Martin Helfrich Roman Andriushchenko Milan Češka Jan Křetínský Štefan Martiček David Šafránek |
| author_facet | Martin Helfrich Roman Andriushchenko Milan Češka Jan Křetínský Štefan Martiček David Šafránek |
| author_sort | Martin Helfrich |
| collection | DOAJ |
| description | Abstract Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time. Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems. |
| format | Article |
| id | doaj-art-9c5f251963bf4bff8d0f2c71e710a3f9 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-9c5f251963bf4bff8d0f2c71e710a3f92025-08-20T02:50:04ZengBMCBMC Bioinformatics1471-21052024-11-0125112410.1186/s12859-024-05966-5Abstraction-based segmental simulation of reaction networks using adaptive memoizationMartin Helfrich0Roman Andriushchenko1Milan Češka2Jan Křetínský3Štefan Martiček4David Šafránek5Department of Computer Science, Technical University of MunichFaculty of Information Technology, Brno University of TechnologyFaculty of Information Technology, Brno University of TechnologyDepartment of Computer Science, Technical University of MunichFaculty of Information Technology, Brno University of TechnologyFaculty of Informatics, Masaryk UniversityAbstract Background Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model’s dynamics may take a prohibitively long time. Results We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system’s dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. Conclusion We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.https://doi.org/10.1186/s12859-024-05966-5Reaction networksStochastic simulationPopulation abstractionMemoization |
| spellingShingle | Martin Helfrich Roman Andriushchenko Milan Češka Jan Křetínský Štefan Martiček David Šafránek Abstraction-based segmental simulation of reaction networks using adaptive memoization BMC Bioinformatics Reaction networks Stochastic simulation Population abstraction Memoization |
| title | Abstraction-based segmental simulation of reaction networks using adaptive memoization |
| title_full | Abstraction-based segmental simulation of reaction networks using adaptive memoization |
| title_fullStr | Abstraction-based segmental simulation of reaction networks using adaptive memoization |
| title_full_unstemmed | Abstraction-based segmental simulation of reaction networks using adaptive memoization |
| title_short | Abstraction-based segmental simulation of reaction networks using adaptive memoization |
| title_sort | abstraction based segmental simulation of reaction networks using adaptive memoization |
| topic | Reaction networks Stochastic simulation Population abstraction Memoization |
| url | https://doi.org/10.1186/s12859-024-05966-5 |
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