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

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 frui...

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Veröffentlicht in:	PLoS computational biology Jg. 17; H. 9; S. e1009410
Hauptverfasser:	Tangherloni, Andrea, Nobile, Marco S., Cazzaniga, Paolo, Capitoli, Giulia, Spolaor, Simone, Rundo, Leonardo, Mauri, Giancarlo, Besozzi, Daniela
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.09.2021 Public Library of Science (PLoS)
Schlagworte:	Algorithms Autophagy Biochemical Phenomena Biochemistry Biological activity Biological complexity Biological models (mathematics) Biology Biology and Life Sciences Calibration Central processing units Chemical reactions Computational Biology Computer and Information Sciences Computer applications Computer Graphics Computer Simulation Computer-generated environments CPUs Differential equations Ecology and Environmental Sciences Graphics processing units Humans Libraries Mathematical Concepts Mathematical models Metabolic Networks and Pathways Metabolism Methods Models, Biological Networks Ordinary differential equations Parallel processing Parameter estimation Perturbation Physical Sciences Protein Biosynthesis Research and Analysis Methods Scale models Simulation Simulators Software Synthetic Biology Systems Biology Italy
ISSN:	1553-7358, 1553-734X, 1553-7358
Online-Zugang:	Volltext
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Zusammenfassung:	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.
Bibliographie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1009410