SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks
Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardiz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/1/119 |
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| author | Simone G. Riva Paolo Cazzaniga Marco S. Nobile Simone Spolaor Leonardo Rundo Daniela Besozzi Andrea Tangherloni |
| author_facet | Simone G. Riva Paolo Cazzaniga Marco S. Nobile Simone Spolaor Leonardo Rundo Daniela Besozzi Andrea Tangherloni |
| author_sort | Simone G. Riva |
| collection | DOAJ |
| description | Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by relevant features (e.g., system connectivity and reaction discreteness) and non-trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component that, generally, results in a computationally demanding task; to speed up the overall process, SMGen exploits a main–worker paradigm. SMGen is also provided with a user-friendly graphical user interface, which allows the user to easily set up all the parameters required to generate a set of synthetic models with any number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric reaction-based models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct a large number of errors during the creation process of the RBMs, a circumstance that increases the running time. Still, SMGen can generate synthetic models with hundreds of species and reactions in less than 7 s. |
| first_indexed | 2024-03-10T00:25:41Z |
| format | Article |
| id | doaj.art-61b07ef3e8eb4af69b7291435e77f4b0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T00:25:41Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-61b07ef3e8eb4af69b7291435e77f4b02023-11-23T15:33:43ZengMDPI AGSymmetry2073-89942022-01-0114111910.3390/sym14010119SMGen: A Generator of Synthetic Models of Biochemical Reaction NetworksSimone G. Riva0Paolo Cazzaniga1Marco S. Nobile2Simone Spolaor3Leonardo Rundo4Daniela Besozzi5Andrea Tangherloni6Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, ItalyDepartment of Human and Social Sciences, University of Bergamo, 24129 Bergamo, ItalyBicocca Bioinformatics Biostatistics and Bioimaging Centre (B4), 20854 Vedano al Lambro, ItalyDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, ItalyDepartment of Radiology, University of Cambridge, Cambridge CB2 0QQ, UKDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, ItalyDepartment of Human and Social Sciences, University of Bergamo, 24129 Bergamo, ItalySeveral software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by relevant features (e.g., system connectivity and reaction discreteness) and non-trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component that, generally, results in a computationally demanding task; to speed up the overall process, SMGen exploits a main–worker paradigm. SMGen is also provided with a user-friendly graphical user interface, which allows the user to easily set up all the parameters required to generate a set of synthetic models with any number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric reaction-based models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct a large number of errors during the creation process of the RBMs, a circumstance that increases the running time. Still, SMGen can generate synthetic models with hundreds of species and reactions in less than 7 s.https://www.mdpi.com/2073-8994/14/1/119synthetic modelsreaction-based modelsbiochemical networkssystems biology |
| spellingShingle | Simone G. Riva Paolo Cazzaniga Marco S. Nobile Simone Spolaor Leonardo Rundo Daniela Besozzi Andrea Tangherloni SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks Symmetry synthetic models reaction-based models biochemical networks systems biology |
| title | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks |
| title_full | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks |
| title_fullStr | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks |
| title_full_unstemmed | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks |
| title_short | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks |
| title_sort | smgen a generator of synthetic models of biochemical reaction networks |
| topic | synthetic models reaction-based models biochemical networks systems biology |
| url | https://www.mdpi.com/2073-8994/14/1/119 |
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