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.
Highlights
Nowadays, the use of computational methods represents a valuable and integrative tool to conventional experimental biology, thanks to the promising capability to gain a global-level understanding of the emergent dynamics of biological systems and to elucidate the mechanisms governing their functionality, that most of the times can be hardly determined by laboratory experiments only
We introduce our Graphics Processing Units (GPU)-oriented design of tau-leaping, consisting in a four phases workflow, and present the data structures, the memory allocation strategies and the advanced functions exploited on the Fermi architecture
The numerical values of each varied parameter were determined with a linear sampling for the amounts of molecular species; a logarithmic sampling was instead considered for stochastic constants, in order to uniformly span over many orders of magnitude
Summary
The use of computational methods represents a valuable and integrative tool to conventional experimental biology, thanks to the promising capability to gain a global-level understanding of the emergent dynamics of biological systems and to elucidate the mechanisms governing their functionality, that most of the times can be hardly determined by laboratory experiments only. The shift from the reproduction of the experimental observations to the capability of making predictions on the behavior of the system in unexplored conditions can be limited by the lack or the inaccuracy of available quantitative data (e.g., reaction rates, intracellular concentrations, etc.), which are indispensable to settle a good model parameterization To cope with these problems, several computational methods can be exploited [1], such as parameter estimation (PE) [5,6,7,8,9], sensitivity analysis (SA) [10,11,12], parameter identifiability (PI) [13,14,15], parameter sweep analysis (PSA) [6], reverse engineering (RE) [7,16], etc. These methods usually require the execution of many simulations to explore the high-dimensional search space of possible model parameterizations, resulting in prohibitive computational costs
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