» Articles » PMID: 38789933

Maboss for HPC Environments: Implementations of the Continuous Time Boolean Model Simulator for Large CPU Clusters and GPU Accelerators

Overview
Publisher Biomed Central
Specialty Biology
Date 2024 May 24
PMID 38789933
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial.

Results: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.

Conclusion: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Citing Articles

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?.

Bongrand P Int J Mol Sci. 2025; 25(24.

PMID: 39769135 PMC: 11676049. DOI: 10.3390/ijms252413371.

References
1.
Fumia H, Martins M . Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes. PLoS One. 2013; 8(7):e69008. PMC: 3724878. DOI: 10.1371/journal.pone.0069008. View

2.
Stoll G, Viara E, Barillot E, Calzone L . Continuous time Boolean modeling for biological signaling: application of Gillespie algorithm. BMC Syst Biol. 2012; 6:116. PMC: 3517402. DOI: 10.1186/1752-0509-6-116. View

3.
Montagud A, Beal J, Tobalina L, Traynard P, Subramanian V, Szalai B . Patient-specific Boolean models of signalling networks guide personalised treatments. Elife. 2022; 11. PMC: 9018074. DOI: 10.7554/eLife.72626. View

4.
Sizek H, Hamel A, Deritei D, Campbell S, Regan E . Boolean model of growth signaling, cell cycle and apoptosis predicts the molecular mechanism of aberrant cell cycle progression driven by hyperactive PI3K. PLoS Comput Biol. 2019; 15(3):e1006402. PMC: 6436762. DOI: 10.1371/journal.pcbi.1006402. View

5.
Licata L, Lo Surdo P, Iannuccelli M, Palma A, Micarelli E, Perfetto L . SIGNOR 2.0, the SIGnaling Network Open Resource 2.0: 2019 update. Nucleic Acids Res. 2019; 48(D1):D504-D510. PMC: 7145695. DOI: 10.1093/nar/gkz949. View