FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks

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 be tested by means of 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 in rule-based modeling. 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-grained 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.

SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks

“…We simulated the dynamics of this RBM for 50 time steps (arbitrary units) using FiCoS [19], and the achieved dynamics are shown in Figure 4. These plots evidence that, although the RBM was randomly generated by SMGen, the simulated behaviour is nontrivial (e.g., the reactants were not instantly exhausted, resulting in flat dynamics).…”

“…The capability of generating synthetic RBMs that exhibit non-trivial dynamics is fundamental to perform in-depth computational analyses and comparisons among any existing and novel simulators. Indeed, in the case of stable or flat dynamics, or when the overall behaviour of the network is extremely fast and all the reactants are immediately depleted, some of the most advanced integration algorithms can simulate the emergent dynamics in just one computation step [19]. In such a case, the computational performance of the simulation tools is only partially assessed, thus hindering a fair comparison among the tools.…”

“…Unfortunately, these computational tasks require the execution of huge amounts of simulations, so that the capabilities of biochemical simulators running on central processing units (CPUs) (see, e.g., [8][9][10]) can be easily overtaken. Thus, several simulators exploiting graphics processing units (GPUs) have been lately introduced to reduce the running times (see, e.g., [11][12][13][14][15][16][17][18][19]).…”

“…The computational performance of several GPU-powered tools has already been assessed using randomly generated synthetic RBMs [13,18,19]. However, only a few generators of biochemical models have been proposed so far, hindering the possibility of having a common and well-defined benchmarking approach.…”

“…The outcome of such analyses showed that some well-known and widely used meta-heuristics, generally able to outperform all competitors in the optimization of benchmark functions, obtained poor performance when used to solve the PE problem. Moreover, SMGen was exploited to generate symmetric and asymmetric RBMs required for the in-depth analyses and comparisons of the computational performance of different biochemical simulators, and to highlight their peculiarities [19]. These analyses allowed for determining the best simulator to employ under specific conditions, such as the size of the RBM and the total number of simulations to perform.…”