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BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments
Open accessAbstract
Summary: Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive. Availability and implementation: BioNetFit can be used on stand-alone Mac, Windows/Cygwin, and Linux platforms and on Linux-based clusters running SLURM, Torque/PBS, or SGE. The BioNetFit source code (Perl) is freely available (http://bionetfit.nau.edu). Supplementary information: Supplementary data are available at Bioinformatics online. Contact: bionetgen.help@gmail.com
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The reason for the few rule-based models might be partly because support for thorough experimentation with rule-based models including calibration and validation has only become available during the last decade [97-99].
“The reason for this might be partly because support for thorough experimentation with rule-based models including calibration and validation has only become available during the last decade [97–99].”
From Relating simulation studies by provenance—Developing a family of Wnt signaling models (2021) · cites this paper as [98]
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Cites
Numerical Recipes, The Art of Scientific Computing
Press, W. H., Flannery, B. P., Teukolsky, S. A., Vetterling, W. T., & Gould, H. (1987) · American Journal of Physics · doi:10.1119/1.14981 · Cited by 10,032
Numerical Recipes: The Art of Scientific Computing
Ziegel, E. R., Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1987) · Technometrics · doi:10.2307/1269484 · Cited by 6,704
Introduction to Evolutionary Computing
Eiben, A. E., & Smith, J. E. (2003) · Natural computing series · doi:10.1007/978-3-662-05094-1 · Cited by 4,204
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (2007) · Cambridge University Press eBooks · Cited by 3,328
Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg015 · Cited by 3,060
Efficient modeling, simulation and coarse-graining of biological complexity with NFsim
Sneddon, M. W., Faeder, J. R., & Emonet, T. (2010) · Nature Methods · doi:10.1038/nmeth.1546 · Cited by 287
Introduction to Evolutionary Computing
Eiben, A. E. (2004) · Assembly Automation · doi:10.1108/aa.2004.24.3.324.1 · Cited by 220
BioNetGen 2.2: advances in rule-based modeling
Harris, L. A., Hogg, J., Tapia, J.-J., Sekar, J. A. P., Gupta, S., Korsunsky, I., Arora, A., Barua, D., Sheehan, R. P., & Faeder, J. R. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw469 · Cited by 211
Scalable Simulation of Cellular Signaling Networks
Vincent Danos, A., Ferêt, J., Fontana, W., & Krivine, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76637-7_10 · Cited by 201
Chylek, L. A., Harris, L. A., Tung, C., Faeder, J. R., Lopez, C. F., & Hlavacek, W. S. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1245 · Cited by 132
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Monine, M. I., Posner, R. G., Savage, P. B., Faeder, J. R., & Hlavacek, W. S. (2010) · Biophysical Journal · doi:10.1016/j.bpj.2009.09.043 · Cited by 45
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- Whitley, 1994, A genetic algorithm tutorial Stat, Comput., 4, 65
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From word models to executable models of signaling networks using automated assembly
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Salazar-Cavazos, E., Nitta, C. F., Mitra, E. D., Wilson, B. S., Lidke, K. A., Hlavacek, W. S., & Lidke, D. S. (2020) · Molecular Biology of the Cell · doi:10.1091/mbc.e19-09-0548 · Cited by 32
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Harmon, B., Chylek, L. A., Liu, Y., Mitra, E. D., Mahajan, A., Saada, E. A., Schudel, B. R., Holowka, D. A., Baird, B. A., Wilson, B. S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-15568-2 · Cited by 21
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Mahajan, A., Youssef, L. A., Cleyrat, C., Grattan, R. M., Lucero, S. R., Mattison, C. P., Erasmus, M. F., Jacobson, B., Tapia, L., Hlavacek, W. S., et al. (2016) · The Journal of Immunology · doi:10.4049/jimmunol.1601334 · Cited by 16
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Neumann, J., Lin, Y. T., Mallela, A., Miller, E. F., Colvin, J. C., Duprat, A. T., Chen, Y., Hlavacek, W. S., & Posner, R. G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac004 · Cited by 10
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Metadata
- Authors
- Thomas, B. R., Chylek, L. A., Colvin, J., Sirimulla, S., Clayton, A. H. A., Hlavacek, W. S., & Posner, R. G.
- Affiliations
- Northern Arizona University (US)
- University of Health Sciences and Pharmacy (US)
- Swinburne University of Technology (AU)
- Los Alamos National Laboratory (US)
- Corresponding author
- Hlavacek, William S.
- Posner, Richard G.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-11-09
- Volume
- 32
- Issue
- 5
- Pages
- 798-800
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC4907397
- OpenAlex
- W2164554210
- Cited by
- 32
- References
- 16
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 9 · 2018: 5 · 2017: 6 · 2016: 3
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- scholarworks - UTEP (The University of Texas at El Paso) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-AC52-06NA2-5396, AC52-06NA25396)
- Northern Arizona University
- National Institutes of Health (DE-AC52-06NA25396)
- National Nuclear Security Administration (DEAC52- 06NA25396)
- National Institute of General Medical Sciences (P50GM085273, R01GM111510)
- Los Alamos National Laboratory (AC52-06NA25396, DE-AC52-06NA25396)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- parameter fitting, rule-based models, BioNetGen, distributed computing, parameter optimization, computational complexity reduction, SLURM, SGE, Perl
- MeSH terms
- Software
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