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 standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of biochemical reaction networks that, by construction, are characterized by both features (e.g. system connectivity, reaction discreteness) and emergent dynamics resembling real biological networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting in a single connected component, which generally results in a computationally demanding task. To avoid any burden in the execution time, SMGen exploits a Main-Worker paradigm to speed up the overall process. SMGen is also provided with a user-friendly Graphical User Interface that allows the user to easily set up all the parameters required to generate a set of synthetic models with any used-defined number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric 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 high numbers of errors during the creation process of the RBMs, a circumstance that increases the overall running time. Though, SMGen can create synthetic models with 512 species and reactions in less than 7 seconds. The open-source code of SMGen is available on GitLab: https://gitlab.com/sgr34/smgen.

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SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks

Symmetry ◽

10.3390/sym14010119 ◽

2022 ◽

Vol 14 (1) ◽

pp. 119

Author(s):

Simone G. Riva ◽

Paolo Cazzaniga ◽

Marco S. Nobile ◽

Simone Spolaor ◽

Leonardo Rundo ◽

...

Keyword(s):

Biochemical Networks ◽

Biochemical Reaction ◽

Reaction Networks ◽

Computational Systems Biology ◽

Biochemical Reaction Networks ◽

Asymmetric Reaction ◽

Computational Performance ◽

Set Up ◽

Definition Of ◽

Synthetic Models

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.

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Finding pattern in biochemical reaction networks - a sub-graph mining approach

10.7287/peerj.preprints.1479v1 ◽

2015 ◽

Author(s):

Ron Henkel ◽

Fabienne Lambusch ◽

Dagmar Waltemath

Keyword(s):

Biological Networks ◽

Graph Mining ◽

Simulation Models ◽

Biochemical Reaction ◽

Reaction Networks ◽

Large Set ◽

Biological Processes ◽

Biochemical Reaction Networks ◽

Biologically Relevant ◽

Complex Models

Biological questions today are often answered with the help of simulation models. Many of these models encode biological processes as biochemical reaction networks. The increasing amount of published models and the growing size of encoded reaction networks demand methods to analyse models. Specifically, researchers need to identify reoccurring and biologically relevant patterns. However, pattern recognition in large networks is a hard problem, and only partial solutions for very specific biological networks exist until now. In addition, while such patterns where already postulated, identifying them manually is barley feasible given a large set of complex models. This paper examines automatic methods to find reoccurring patterns structural similarities in models represented as bipartite graphs. An approach is presented to find the most frequent structures within the models. Appropriate patterns were found, which occur in a major part of the 575 input models. The occurrences of the resulting structures can provide insight into the encoding of certain biological processes, evaluate the postulated structures and serve as a reasonable similarity measure for grouping models that share many common structures.

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