For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
56
views
38
references
 Top references
cited by
4
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      63
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      LASSIE: simulating large-scale models of biochemical systems on GPUs.

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Stochastic simulation of chemical kinetics.

          Daniel Gillespie (2007)
          Stochastic chemical kinetics describes the time evolution of a well-stirred chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to chemical kinetics in the analysis of cellular systems in biology, where the small molecular populations of only a few reactant species can lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. After reviewing the supporting theory of stochastic chemical kinetics, I discuss some recent advances in methods for using that theory to make numerical simulations. These include improvements to the exact stochastic simulation algorithm (SSA) and the approximate explicit tau-leaping procedure, as well as the development of two approximate strategies for simulating systems that are dynamically stiff: implicit tau-leaping and the slow-scale SSA.
          Article 0 comments Cited 506 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Stochastic modelling for quantitative description of heterogeneous biological systems.

            Darren J. Wilkinson (2009)
            Two related developments are currently changing traditional approaches to computational systems biology modelling. First, stochastic models are being used increasingly in preference to deterministic models to describe biochemical network dynamics at the single-cell level. Second, sophisticated statistical methods and algorithms are being used to fit both deterministic and stochastic models to time course and other experimental data. Both frameworks are needed to adequately describe observed noise, variability and heterogeneity of biological systems over a range of scales of biological organization.
            Article 0 comments Cited 170 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Quantitative and logic modelling of molecular and gene networks.

              Nicolas Le Novère (2015)
              Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
              Article 0 comments Cited 156 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (iso-abbrev): BMC Bioinformatics
                Title: BMC bioinformatics
                Publisher: Springer Nature
                ISSN (Electronic): 1471-2105
                ISSN (Print): 1471-2105
                Publication date (Electronic): May 10 2017
                Volume: 18
                Issue: 1
                Affiliations
                [1 ] Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.
                [2 ] SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126, Italy.
                [3 ] Department of Human and Social Sciences, University of Bergamo, Piazzale Sant'Agostino 2, Bergamo, 24129, Italy. paolo.cazzaniga@unibg.it.
                [4 ] SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126, Italy. paolo.cazzaniga@unibg.it.
                Article
                Publisher Item ID: 10.1186/s12859-017-1666-0
                DOI: 10.1186/s12859-017-1666-0
                PMC ID: 5424297
                PubMed ID: 28486952
                SO-VID: 669890d5-224c-4724-a30a-ae73ec3b4f10
                History

                Keywords: Deterministic simulation,Fine-grained parallelization,GPU computing,Graphics Processing Unit,LSODA,Numerical integration method,Nvidia CUDA,Reaction-based model,Rule-based model,Systems biology
                Data availability:
                Keywords: Deterministic simulation, Fine-grained parallelization, GPU computing, Graphics Processing Unit, LSODA, Numerical integration method, Nvidia CUDA, Reaction-based model, Rule-based model, Systems biology

                Comments

                Comment on this article

                scite_

                Similar content63

                See all similar

                Cited by9

                See all cited by

                Most referenced authors554

                See all reference authors