COSYS: A Computational Infrastructure for Systems Biology
Computational models are essential in order to integrate and extract knowledge from the large amount of -omics data that are increasingly being collected thanks to high-throughput technologies. Unfortunately, the definition of an appropriate mathematical model is typically inaccessible to scientists with a poor computational background, whereas expert users often lack the proficiency required for biologically grounded models. Although many efforts have been put in software packages intended to bridge the gap between the two communities, once a model is defined, the problem of simulating and analyzing it within a reasonable time still persists. We here present COSYS, a web-based infrastructure for Systems Biology that guides the user through the definition, simulation and analysis of reaction-based models, including the deterministic and stochastic description of the temporal dynamics, and the Flux Balance Analysis. In the case of computationally demanding analyses, COSYS can exploit GPU-accelerated algorithms to speed up the computation, thereby making critical tasks, as for instance an exhaustive scan of parameter values, attainable to a large audience.
KeywordsSystems biology Modeling and simulation Flux Balance Analysis GPGPU computing High-performance computing
This work has been supported by SYSBIO Centre of Systems Biology, through the MIUR grant SysBioNet—Italian Roadmap for ESFRI Research Infrastructures.
- 4.Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Comput. Phys. 81(25), 2340–2361 (1977)Google Scholar
- 8.Klipp, E., Liebermeister, W., Wierling, C., et al.: Systems Biology: A Textbook. Wiley, San Diego (2009)Google Scholar
- 9.Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 74–85. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29066-4_7 CrossRefGoogle Scholar
- 10.Nobile, M.S., Besozzi, D., Cazzaniga, P., et al.: Reverse engineering of kinetic reaction networks by means of cartesian genetic programming and particle swarm optimization. In: Proceedings of CEC 2013, vol. 1, p. 1594–1601 (2013)Google Scholar