QSPcc reduces bottlenecks in computational model simulations

Danilo Tomasoni; Alessio Paris; Stefano Giampiccolo; Federico Reali; Giulia Simoni; Luca Marchetti; Chanchala Kaddi; Susana Neves-Zaph; Corrado Priami; Karim Azer; Rosario Lombardo

doi:10.1038/s42003-021-02553-9

Communications Biology (Sep 2021)

QSPcc reduces bottlenecks in computational model simulations

Danilo Tomasoni,
Alessio Paris,
Stefano Giampiccolo,
Federico Reali,
Giulia Simoni,
Luca Marchetti,
Chanchala Kaddi,
Susana Neves-Zaph,
Corrado Priami,
Karim Azer,
Rosario Lombardo

Affiliations

Danilo Tomasoni: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Alessio Paris: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Stefano Giampiccolo: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Federico Reali: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Giulia Simoni: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Luca Marchetti: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Chanchala Kaddi: Data and Data Science – Translational Disease Modeling, Sanofi
Susana Neves-Zaph: Data and Data Science – Translational Disease Modeling, Sanofi
Corrado Priami: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology
Karim Azer: Data and Data Science – Translational Disease Modeling, Sanofi
Rosario Lombardo: Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology

DOI: https://doi.org/10.1038/s42003-021-02553-9
Journal volume & issue: Vol. 4, no. 1
pp. 1 – 10

Abstract

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Lombardo and colleagues present QSPcc, a computational code compiler designed to convert code from popular scientific programming languages, such as MATLAB or R, into fast-running C code. This reduces the computational load required for complex modelling approaches and reduces user investment learning additional complex languages.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal