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.
(2022). SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks [journal article - articolo]. In SYMMETRY. Retrieved from http://hdl.handle.net/10446/202023
Citazione: | (2022). SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks [journal article - articolo]. In SYMMETRY. Retrieved from http://hdl.handle.net/10446/202023 | |
Titolo: | SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks | |
Tipologia specifica: | articolo | |
Tutti gli autori: | Riva, Simone G.; Cazzaniga, Paolo; Nobile, Marco S.; Spolaor, Simone; Rundo, Leonardo; Besozzi, D...aniele; Tangherloni, Andrea | |
Data di pubblicazione: | 2022 | |
Abstract (eng): | 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. | |
Rivista: | ||
Nelle collezioni: | 1.1.01 Articoli/Saggi in rivista - Journal Articles/Essays |
File allegato/i alla scheda:
File | Descrizione | Tipologia | Licenza | |
---|---|---|---|---|
symmetry-14-00119 (1).pdf | publisher's version - versione editoriale | ![]() | Open AccessVisualizza/Apri |