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dc.contributor.author
Manica, Matteo
dc.contributor.author
Polig, Raphael
dc.contributor.author
Purandare, Mitra
dc.contributor.author
Mathis, Roland
dc.contributor.author
Hagleitner, Christoph
dc.contributor.author
Rodriguez Martinez, María
dc.date.accessioned
2021-01-11T08:57:37Z
dc.date.available
2020-12-24T03:40:50Z
dc.date.available
2021-01-11T08:57:37Z
dc.date.issued
2020-11
dc.identifier.issn
1545-5963
dc.identifier.issn
1557-9964
dc.identifier.other
10.1109/TCBB.2019.2936836
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/458486
dc.identifier.doi
10.3929/ethz-b-000458486
dc.description.abstract
Boolean models are a powerful abstraction for qualitative modeling of gene regulatory networks. With the recent availability of advanced high-throughput technologies, Boolean models have increasingly grown in size and complexity, posing a challenge for existing software simulation tools that have not scaled at the same speed. Field Programmable Gate Arrays (FPGAs) are powerful reconfigurable integrated circuits that can offer massive performance improvements. Due to their highly parallel nature, FPGAs are well suited to simulate complex molecular networks. We present here a new simulation framework for Boolean models, which first converts the model to Verilog, a standardized hardware description language, and then connects it to an execution core that runs on an FPGA coherently attached to a POWER8 processor. We report an order of magnitude speedup over a multi-threaded software simulation tool running on the same processor on a selection of Boolean models. Analysis on a T-cell large granular lymphocyte leukemia (T-LGL) demonstrates that our framework achieves consistent performance improvements resulting in new biological insights. In addition, we show that our solution allows to perform attractor detection at an unprecedented speed, exhibiting a speedup ranging from one to three orders of magnitude compared to alternative software solutions. © 2004-2012 IEEE.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Institute of Electrical and Electronics Engineers
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Field programmable gate arrays
en_US
dc.subject
accelerator architectures
en_US
dc.subject
mathematical model
en_US
dc.subject
biological system modeling
en_US
dc.subject
biological systems
en_US
dc.subject
biological control systems
en_US
dc.subject
biological processes
en_US
dc.subject
computer simulation
en_US
dc.subject
systems simulation
en_US
dc.subject
computational biology
en_US
dc.subject
computational systems biology
en_US
dc.subject
bioinformatics
en_US
dc.subject
systems biology
en_US
dc.title
FPGA Accelerated Analysis of Boolean Gene Regulatory Networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-09-03
ethz.journal.title
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ethz.journal.volume
17
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
IEEE/ACM trans. comput. biol. bioinform.
ethz.pages.start
2141
en_US
ethz.pages.end
2147
en_US
ethz.size
7 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION, a tool for cancer patient risk-stratification and pers. drug selection through multi-omic data integration.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Los Alamitos, CA
en_US
ethz.publication.status
published
en_US
ethz.grant.agreementno
668858
ethz.grant.fundername
SBFI
ethz.grant.funderDoi
10.13039/501100007352
ethz.grant.program
H2020
ethz.date.deposited
2020-12-24T03:40:54Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-11T08:57:46Z
ethz.rosetta.lastUpdated
2021-02-15T23:04:47Z
ethz.rosetta.versionExported
true
ethz.COinS
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