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Accelerated Analysis of Biological Parameters Space Using GPUs

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Parallel Computing Technologies (PaCT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10421))

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Abstract

Mathematical modeling and computer simulation represent a valuable mean to integrate experimental research for the study of biological systems. However, many computational methods—e.g., sensitivity analysis—require the execution of a massive number of simulations to investigate the model behavior in physiological or perturbed conditions, which can be a computationally challenging task. This huge amount of simulations is necessary to collect data in the vast space of kinetic parameters. This paper provides the state-of-the-art of biochemical simulators relying on Graphics Processing Units (GPUs) in the context of Systems Biology. Moreover, we discuss two examples of integration of such simulators into computational methods for parameter sweep and sensitivity analysis, both implemented using the Python language.

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Notes

  1. 1.

    Simulations can also be extended to keep track of the spatial positioning of species, which is mandatory in the case of biological system whose components are not uniformly distributed in the reaction volume or its compartments. Spatial simulation will not be discussed in this paper.

  2. 2.

    Notably, the creation of the figure required approximatively 3 min.

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Author information

Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Marco S. Nobile & Giancarlo Mauri

  2. SYSBIO.IT Centre of Systems Biology, Milano, Italy

    Marco S. Nobile & Giancarlo Mauri

Authors
  1. Marco S. Nobile
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  2. Giancarlo Mauri
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Correspondence to Marco S. Nobile .

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Editors and Affiliations

  1. Russian Academy of Sciences, Novosibirsk, Russia

    Victor Malyshkin

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Nobile, M.S., Mauri, G. (2017). Accelerated Analysis of Biological Parameters Space Using GPUs. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-62932-2_6

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