Abstract
Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can then be tested with targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring when rule-based models are analysed. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel “black-box” deterministic simulator that effectively realizes both a fine- and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely the Dormand–Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855 ×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes.
Author summary Systems Biology is an interdisciplinary research area focusing on the integration of biology and in-silico simulation of mathematical models to unravel and predict the emergent behavior of complex biological systems. The ultimate goal is the understanding of the complex mechanisms at the basis of biological processes together with the formulation of novel hypotheses that can be then tested with laboratory experiments. In such a context, detailed mechanistic models can be used to describe biological networks. Unfortunately, these models can be characterized by hundreds or thousands of molecular species and chemical reactions, making their simulation unfeasible using classic simulators running on modern Central Processing Units (CPUs). In addition, a large number of simulations might be required to calibrate the models or to test the effect of perturbations. In order to overcome the limitations imposed by CPUs, Graphics Processing Units (GPUs) can be effectively used to accelerate the simulations of these detailed models. We thus designed and developed a novel GPU-based tool, called FiCoS, to speed-up the computational analyses typically required in Systems Biology.
Competing Interest Statement
The authors have declared no competing interest.
