High-performance computing in Systems Biology: accelerating the simulation and analysis of large and complex biological systems
- Marco S. Nobile, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy
- Paolo Cazzaniga, Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy
- Andrea Tangherloni, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
- Simone Spolaor, Department of Informatics, University of Milano, Milan, Italy
- Daniela Besozzi, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy
- Giancarlo Mauri, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; SYSBIO.IT Centre of Systems Biology, Milan, Italy - email@example.com
Mathematical modeling and simulation algorithms nowadays provide solid grounds for quantitative investigations of biological systems, in a synergistic way with traditional experimental research.
Despite the possibility offered by various computational methods to achieve an in-depth understanding of cells functioning, typical tasks for model definition, calibration and analysis (e.g., reverse engineering, parameter estimation, sensitivity analysis, etc.) are still computationally challenging. Indeed, these problems require to execute a large number of simulations of the same model, each one corresponding to a different physiological or perturbed system condition. In addition, in the case of large-scale systems, characterized by hundreds or thousands species and reactions, even a single simulation can be unfeasible if executed on conventional computing architectures like Central Processing Units (CPUs).
To overcome these drawbacks, parallel infrastructures can be used to strongly reduce the prohibitive running times of computational methods in Systems Biology, by distributing the workload over multiple independent computing units. In particular, General-Purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, since they are pervasive, cheap and extremely efficient parallel multi-core co-processors, which give access to low-cost, energy-efficient means to achieve tera-scale performances on common workstations.
We present the GPU-powered simulators that we designed and implemented to accelerate simulation and the analysis of reaction-based models of biological systems (cuTauLeaping, cupSODA, LASSIE). In particular, we present both coarse-grain and fine-grain methods, which allow to execute a large number of parallel simulations of the same model, and to parallelize all calculations required by a single simulation run, respectively.
These methods were developed to carry out either stochastic or deterministic simulations of both small and large-scale models, providing a relevant reduction of the running time, up to two orders of magnitude with respect to a classic CPU-bound execution.
- Nobile M.S., Cazzaniga P., Besozzi D., Pescini D., Mauri G.: cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems, PLoS ONE, 9(3): e91963, 2014
- Nobile M.S., Cazzaniga P., Besozzi D., Mauri G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA, Journal of Supercomputing, vol. 69, issue 1, pp.17–24, 2014
- Tangherloni A., Cazzaniga P., Nobile M.S., Besozzi D., Mauri G.: Deterministic simulations of large-scale models of cellular processes accelerated on Graphics Processing Units, 12th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB2015), Naples (Italy), 2015
- Nobile M.S., Besozzi D., Cazzaniga P., Mauri G., Pescini D.: Reverse engineering of kinetic reaction networks by means of cartesian genetic programming and particle swarm optimization, Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC2013), Cancun (Mexico), Vol. 1, pp. 1594–1601, 2013
- 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 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Computational Biology, EvoBIO 2012, Malaga (Spain), Proceedings. Mario Giacobini, Leonardo Vanneschi, and William Bush (Eds.). Lecture Notes in Computer Science. Vol. 7264, pp. 74–85, 2012
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