The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists.In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.

On Designing Multicore-Aware Simulators for Systems Biology Endowed with OnLine Statistics

ALDINUCCI, MARCO;CALCAGNO, CRISTINA;COPPO, Mario;DAMIANI, Ferruccio;DROCCO, MAURIZIO;SCIACCA, EVA;SPINELLA, SALVATORE;TROINA, ANGELO
2014-01-01

Abstract

The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists.In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.
2014
1
14
http://www.hindawi.com/journals/bmri/2014/207041/
bioinformatics; biological model; computer interface; computer program; Monte Carlo method; statistics; process design; online analysis; stochastic model; systems biology
Marco Aldinucci;Cristina Calcagno;Mario Coppo;Ferruccio Damiani;Maurizio Drocco;Eva Sciacca;Salvatore Spinella;Massimo Torquati;Angelo Troina
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/150051
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