Literature DB >> 20123941

High performance cellular level agent-based simulation with FLAME for the GPU.

Paul Richmond1, Dawn Walker, Simon Coakley, Daniela Romano.   

Abstract

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

Mesh:

Year:  2010        PMID: 20123941     DOI: 10.1093/bib/bbp073

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  26 in total

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Review 3.  Computational approaches for translational clinical research in disease progression.

Authors:  Mary F McGuire; Madurai Sriram Iyengar; David W Mercer
Journal:  J Investig Med       Date:  2011-08       Impact factor: 2.895

4.  PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

Authors:  Luis Martí-Bonmatí; Ángel Alberich-Bayarri; Ruth Ladenstein; Ignacio Blanquer; J Damian Segrelles; Leonor Cerdá-Alberich; Polyxeni Gkontra; Barbara Hero; J M García-Aznar; Daniel Keim; Wolfgang Jentner; Karine Seymour; Ana Jiménez-Pastor; Ismael González-Valverde; Blanca Martínez de Las Heras; Samira Essiaf; Dawn Walker; Michel Rochette; Marian Bubak; Jordi Mestres; Marco Viceconti; Gracia Martí-Besa; Adela Cañete; Paul Richmond; Kenneth Y Wertheim; Tomasz Gubala; Marek Kasztelnik; Jan Meizner; Piotr Nowakowski; Salvador Gilpérez; Amelia Suárez; Mario Aznar; Giuliana Restante; Emanuele Neri
Journal:  Eur Radiol Exp       Date:  2020-04-03

5.  Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms.

Authors:  Scott Christley; Briana Lee; Xing Dai; Qing Nie
Journal:  BMC Syst Biol       Date:  2010-08-09

6.  Real-Time Agent-Based Modeling Simulation with in-situ Visualization of Complex Biological Systems: A Case Study on Vocal Fold Inflammation and Healing.

Authors:  Nuttiiya Seekhao; Caroline Shung; Joseph JaJa; Luc Mongeau; Nicole Y K Li-Jessen
Journal:  IEEE Int Symp Parallel Distrib Process Workshops Phd Forum       Date:  2016-05

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Authors:  Gianluca Ascolani; Timothy M Skerry; Damien Lacroix; Enrico Dall'Ara; Aban Shuaib
Journal:  Interface Focus       Date:  2020-12-11       Impact factor: 3.906

8.  Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

Authors:  Paul Richmond; Lars Buesing; Michele Giugliano; Eleni Vasilaki
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

Review 9.  Systems approaches to influenza-virus host interactions and the pathogenesis of highly virulent and pandemic viruses.

Authors:  Marcus J Korth; Nicolas Tchitchek; Arndt G Benecke; Michael G Katze
Journal:  Semin Immunol       Date:  2012-12-05       Impact factor: 11.130

10.  A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor.

Authors:  Himanshu Kaul; Zhanfeng Cui; Yiannis Ventikos
Journal:  PLoS One       Date:  2013-03-29       Impact factor: 3.240

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