MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks

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MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks. / Mizeranschi, Alexandru E.; Swain, Martin T.; Scona, Raluca; Fazilleau, Quentin; Bosak, Bartosz; Piontek, Tomasz; Kopta, Piotr; Thompson, Paul; Dubitzky, Werner.

In: Future Generation Computer Systems, Vol. 63, 01.10.2016, p. 1-14.

Research output: Contribution to journalArticlepeer-review

Harvard

Mizeranschi, AE, Swain, MT, Scona, R, Fazilleau, Q, Bosak, B, Piontek, T, Kopta, P, Thompson, P & Dubitzky, W 2016, 'MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks', Future Generation Computer Systems, vol. 63, pp. 1-14. https://doi.org/10.1016/j.future.2016.04.002

APA

Mizeranschi, A. E., Swain, M. T., Scona, R., Fazilleau, Q., Bosak, B., Piontek, T., Kopta, P., Thompson, P., & Dubitzky, W. (2016). MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks. Future Generation Computer Systems, 63, 1-14. https://doi.org/10.1016/j.future.2016.04.002

Vancouver

Mizeranschi AE, Swain MT, Scona R, Fazilleau Q, Bosak B, Piontek T et al. MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks. Future Generation Computer Systems. 2016 Oct 1;63:1-14. https://doi.org/10.1016/j.future.2016.04.002

Author

Mizeranschi, Alexandru E. ; Swain, Martin T. ; Scona, Raluca ; Fazilleau, Quentin ; Bosak, Bartosz ; Piontek, Tomasz ; Kopta, Piotr ; Thompson, Paul ; Dubitzky, Werner. / MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks. In: Future Generation Computer Systems. 2016 ; Vol. 63. pp. 1-14.

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@article{5327460b33d8415cb46e00328612d678,
title = "MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks",
abstract = "Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using both real and artificial gene expression data.",
keywords = "gene-regulatory networks, reverse-engineering of gene-regulation models, distributed multiscale computing",
author = "Mizeranschi, {Alexandru E.} and Swain, {Martin T.} and Raluca Scona and Quentin Fazilleau and Bartosz Bosak and Tomasz Piontek and Piotr Kopta and Paul Thompson and Werner Dubitzky",
note = "This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.future.2016.04.002",
year = "2016",
month = oct,
day = "1",
doi = "10.1016/j.future.2016.04.002",
language = "English",
volume = "63",
pages = "1--14",
journal = "Future Generation Computer Systems",
issn = "0167-739X",
publisher = "Elsevier",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - MultiGrain/MAPPER: A distributed multiscale computing approach to modeling and simulating gene regulation networks

AU - Mizeranschi, Alexandru E.

AU - Swain, Martin T.

AU - Scona, Raluca

AU - Fazilleau, Quentin

AU - Bosak, Bartosz

AU - Piontek, Tomasz

AU - Kopta, Piotr

AU - Thompson, Paul

AU - Dubitzky, Werner

N1 - This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.future.2016.04.002

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using both real and artificial gene expression data.

AB - Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key task in this area is the automated inference or reverse-engineering of dynamic mechanistic GRN models from gene expression time-course data. Besides a lack of suitable data (in particular multi-condition data from the same system), one of the key challenges of this task is the computational complexity involved. The more genes in the GRN system and the more parameters a GRN model has, the higher the computational load. The computational challenge is likely to increase substantially in the near future when we tackle larger GRN systems. The goal of this study was to develop a distributed computing framework and system for reverse-engineering of GRN models. We present the resulting software called MultiGrain/MAPPER. This software is based on a new architecture and tools supporting multiscale computing in a distributed computing environment. A key feature of MultiGrain/MAPPER is the realization of GRN reverse-engineering based on the underlying distributed computing framework and multi-swarm particle swarm optimization. We demonstrate some of the features of MultiGrain/MAPPER and evaluate its performance using both real and artificial gene expression data.

KW - gene-regulatory networks

KW - reverse-engineering of gene-regulation models

KW - distributed multiscale computing

UR - http://hdl.handle.net/2160/42631

U2 - 10.1016/j.future.2016.04.002

DO - 10.1016/j.future.2016.04.002

M3 - Article

VL - 63

SP - 1

EP - 14

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

SN - 0167-739X

ER -

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