FiCoS: a fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks
| Autor: | Simone Spolaor, Paolo Cazzaniga, Leonardo Rundo, Giulia Capitoli, Marco S. Nobile, Giancarlo Mauri, Andrea Tangherloni, Daniela Besozzi |
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| Přispěvatelé: | Tangherloni, Andrea [0000-0002-5856-4453], Nobile, Marco S [0000-0002-7692-7203], Cazzaniga, Paolo [0000-0001-7780-0434], Spolaor, Simone [0000-0002-3383-367X], Rundo, Leonardo [0000-0003-3341-5483], Mauri, Giancarlo [0000-0003-3520-4022], Besozzi, Daniela [0000-0001-5532-3059], Apollo - University of Cambridge Repository, Tangherloni, A, Nobile, M, Cazzaniga, P, Capitoli, G, Spolaor, S, Rundo, L, Mauri, G, Besozzi, D, Information Systems IE&IS, Nobile, Marco S. [0000-0002-7692-7203] |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Speedup Computer science Biochemistry Systems Science Stiffness Models Biochemical Simulations Biology (General) Graphics Settore INF/01 - Informatica Ecology Basis (linear algebra) Mathematical model Simulation and Modeling Physics Systems Biology Stoichiometry Chemistry Computational Theory and Mathematics Modeling and Simulation Ordinary differential equation Physical Sciences Synthetic Biology Metabolic Pathways Algorithms Metabolic Networks and Pathways Research Article Computer and Information Sciences Biophysical Simulations Ecological Metrics Exploit Biochemical Phenomena QH301-705.5 Systems biology Computation Materials Science Material Properties Biophysics FOS: Physical sciences Autophagy Computational Biology Computer Graphics Computer Simulation Humans Mathematical Concepts Models Biological Protein Biosynthesis Software Context (language use) Research and Analysis Methods gpu computing biochemical simulation Cellular and Molecular Neuroscience Genetics Mechanical Properties Molecular Biology Ecology Evolution Behavior and Systematics Simulation Ecology and Environmental Sciences Biology and Life Sciences Species Diversity Biological Metabolism General-purpose computing on graphics processing units Mathematics Biological network |
| Zdroj: | PLoS Computational Biology, Vol 17, Iss 9, p e1009410 (2021) PLoS Computational Biology PLoS Computational Biology, 17(9):e1009410. Public Library of Science |
| ISSN: | 1553-734X |
| DOI: | 10.1101/2021.01.15.426855 |
| Popis: | 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. Author summary Systems Biology is an interdisciplinary research area focusing on the integration of biological data with mathematical and computational methods in order 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 by means of laboratory experiments. In such a context, mechanistic models can be used to describe and investigate biochemical reaction networks by taking into account all the details related to their stoichiometry and kinetics. Unfortunately, these models can be characterized by hundreds or thousands of molecular species and biochemical reactions, making their simulation unfeasible with classic simulators running on Central Processing Units (CPUs). In addition, a large number of simulations might be required to calibrate the models and/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 models. We thus designed and developed a novel GPU-based tool, called FiCoS, to speed-up the computational analyses typically required in Systems Biology. |
| Databáze: | OpenAIRE |
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