title: cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems.
abstract: Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae.
authors.affiliation: Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy; SYSBIO Centre for Systems Biology, Milano, Italy.
authors.forename: Marco S
authors.initials: MS
authors.lastname: Nobile
authors.affiliation: Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Bergamo, Italy; Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Roma, Italy; SYSBIO Centre for Systems Biology, Milano, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Cazzaniga
authors.affiliation: Dipartimento di Informatica, Università degli Studi di Milano, Milano, Italy; Istituto di Analisi dei Sistemi ed Informatica "Antonio Ruberti", Consiglio Nazionale delle Ricerche, Roma, Italy; SYSBIO Centre for Systems Biology, Milano, Italy.
authors.forename: Daniela
authors.initials: D
authors.lastname: Besozzi
authors.affiliation: Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Milano, Italy; SYSBIO Centre for Systems Biology, Milano, Italy.
authors.forename: Dario
authors.initials: D
authors.lastname: Pescini
authors.affiliation: Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy; SYSBIO Centre for Systems Biology, Milano, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Mauri
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003205:Computing Methodologies; D000242:Cyclic AMP / Q000378:metabolism; D017868:Cyclic AMP-Dependent Protein Kinases / Q000378:metabolism; D053263:Gene Regulatory Networks; D007700:Kinetics; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000378:metabolism; D015398:Signal Transduction; D013269:Stochastic Processes; D018631:ras Proteins / Q000378:metabolism
chemical_list: D000242:Cyclic AMP; D017868:Cyclic AMP-Dependent Protein Kinases; D018631:ras Proteins