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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Autores principales:	Andrea Tangherloni, Marco S Nobile, Paolo Cazzaniga, Giulia Capitoli, Simone Spolaor, Leonardo Rundo, Giancarlo Mauri, Daniela Besozzi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c68e42988e454d1fb93c38388c684beb
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spelling	oai:doaj.org-article:c68e42988e454d1fb93c38388c684beb2021-12-02T19:57:49ZFiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.1553-734X1553-735810.1371/journal.pcbi.1009410https://doaj.org/article/c68e42988e454d1fb93c38388c684beb2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009410https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Mathematical 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.Andrea TangherloniMarco S NobilePaolo CazzanigaGiulia CapitoliSimone SpolaorLeonardo RundoGiancarlo MauriDaniela BesozziPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009410 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Andrea Tangherloni Marco S Nobile Paolo Cazzaniga Giulia Capitoli Simone Spolaor Leonardo Rundo Giancarlo Mauri Daniela Besozzi FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
description	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.
format	article
author	Andrea Tangherloni Marco S Nobile Paolo Cazzaniga Giulia Capitoli Simone Spolaor Leonardo Rundo Giancarlo Mauri Daniela Besozzi
author_facet	Andrea Tangherloni Marco S Nobile Paolo Cazzaniga Giulia Capitoli Simone Spolaor Leonardo Rundo Giancarlo Mauri Daniela Besozzi
author_sort	Andrea Tangherloni
title	FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
title_short	FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
title_full	FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
title_fullStr	FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
title_full_unstemmed	FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.
title_sort	ficos: a fine-grained and coarse-grained gpu-powered deterministic simulator for biochemical networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c68e42988e454d1fb93c38388c684beb
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FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.

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