Advertisement

A Step-by-Step Guide to Using BioNetFit

  • William S. Hlavacek
  • Jennifer A. Csicsery-Ronay
  • Lewis R. Baker
  • María del Carmen Ramos Álamo
  • Alexander Ionkov
  • Eshan D. Mitra
  • Ryan Suderman
  • Keesha E. Erickson
  • Raquel Dias
  • Joshua Colvin
  • Brandon R. Thomas
  • Richard G. PosnerEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)

Abstract

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

Key words

Rule-based modeling Model calibration Parameter estimation Parameter uncertainty Confidence level Nonlinear least squares fitting Genetic algorithm (GA) Stochastic simulation algorithm (SSA) Network-free simulation Ordinary differential equations (ODEs) 

Notes

Acknowledgments

Development of BioNetFit has been supported by National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) grant R01GM111510 (RGP and WSH). EDM acknowledges support from the Laboratory-Directed Research and Development (LDRD) program at Los Alamos National Laboratory (LANL), which is operated for the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) under contract DE-AC52-06NA25396. RS is grateful for support from the Los Alamos Center for Nonlinear Studies (CNLS). KEE is supported by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by DOE and the National Cancer Institute (NCI) of NIH. JL acknowledges support from the Science, Technology, Engineering, and Mathematics Talent Expansion Program (STEP) of the National Science Foundation (NSF). M del C RÁ acknowledges support from the DOE/NNSA-sponsored Minority Serving Institutions (MSI) Internship program. The Monsoon cluster at Northern Arizona University (NAU) is supported by the Arizona Board of Regents Technology and Research Initiative Fund (TRIF). The Darwin cluster at Los Alamos National Laboratory is supported by the Computational Systems and Software Environment (CSSE) subprogram of the Advanced Simulation and Computing (ASC) program at LANL, which is funded by DOE/NNSA.

References

  1. 1.
    Zi Z (2011) Sensitivity analysis approaches applied to systems biology models. IET Syst Biol 5:336–346CrossRefGoogle Scholar
  2. 2.
    Gutenkunst RN, Waterfall JJ, Casey FP, Brown KS, Myers CR, Sethna JP (2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput Biol 3:e189CrossRefGoogle Scholar
  3. 3.
    Mitra ED, Dias R, Posner RG, Hlavacek WS (2018) Using both qualitative and quantitative data in parameter identification for systems biology models. Nat Commun 9:3901CrossRefGoogle Scholar
  4. 4.
    Raue A, Steiert B, Schelker M, Kreutz C, Maiwald T et al (2015) Data 2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems. Bioinformatics 31:3558–3560CrossRefGoogle Scholar
  5. 5.
    Blinov ML, Faeder JR, Goldstein B, Hlavacek WS (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289–3291CrossRefGoogle Scholar
  6. 6.
    Harris LA, Hogg JS, Tapia JJ, Sekar JA, Gupta S et al (2016) BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32:3366–3368CrossRefGoogle Scholar
  7. 7.
    Sneddon MW, Faeder JR, Emonet T (2011) Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 8:177–183CrossRefGoogle Scholar
  8. 8.
    Xu W, Smith AM, Faeder JR, Marai GE (2011) RuleBender: a visual interface for rule-based modeling. Bioinformatics 27:1721–1722CrossRefGoogle Scholar
  9. 9.
    Smith AM, Xu W, Sun Y, Faeder JR, Marai GE (2012) RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics 13:S3CrossRefGoogle Scholar
  10. 10.
    Boutillier P, Maasha M, Li X, Medina-Abarca HF, Krivine J et al (2018) The Kappa platform for rule-based modeling. Bioinformatics 34:i583–i592CrossRefGoogle Scholar
  11. 11.
    Sorokin A, Sorokina O, Armstrong JD (2015) RKappa: statistical sampling suite for Kappa models. Lect Notes Comput Sci 7699:128–142CrossRefGoogle Scholar
  12. 12.
    Thomas BR, Chylek LA, Colvin J, Sirimulla S, Clayton AHA et al (2016) BioNetFit: a fitting tool compatible with BioNetGen, NFsim, and distributed computing environments. Bioinformatics 32:798–800CrossRefGoogle Scholar
  13. 13.
    Faeder JR, Blinov ML, Hlavacek WS (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 500:113–167CrossRefGoogle Scholar
  14. 14.
    Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR (2014) Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLoS Comput Biol 10:e1003544CrossRefGoogle Scholar
  15. 15.
    Chylek LA, Harris LA, Tung CS, Faeder JR, Lopez CF, Hlavacek WS (2014) Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdiscip Rev Syst Biol Med 6:13–36CrossRefGoogle Scholar
  16. 16.
    Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117CrossRefGoogle Scholar
  17. 17.
    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New YorkCrossRefGoogle Scholar
  18. 18.
    Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes: the art of scientific computing, 3rd edn. Cambridge University Press, Cambridge, pp 809–810Google Scholar
  19. 19.
    Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R et al (2005) SUNDIALS: suite of nonlinear and differential/algebraic equation solvers. ACM Trans Math Softw 31:363–396CrossRefGoogle Scholar
  20. 20.
    Munsky B, Neuert G, van Oudenaarden A (2012) Using gene expression noise to understand gene regulation. Science 336:183–187CrossRefGoogle Scholar
  21. 21.
    Suderman R, Mitra ED, Lin YT, Erickson KE, Feng S, Hlavacek WS (2018) Generalizing Gillespie’s direct method to enable network-free simulations. Bull Math Biol.  https://doi.org/10.1007/s11538-018-0418-2
  22. 22.
    Hlavacek WS, Faeder JR, Blinov ML, Perelson AS, Goldstein B (2003) The complexity of complexes in signal transduction. Biotechnol Bioeng 84:783–794CrossRefGoogle Scholar
  23. 23.
    Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W (2006) Rules for modeling signal-transduction systems. Sci STKE 2006:re6PubMedGoogle Scholar
  24. 24.
    Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 58:35–55CrossRefGoogle Scholar
  25. 25.
    Danos V, Feret J, Fontana W, Krivine J (2007) Scalable simulation of cellular signaling networks. Lect Notes Comput Sci 4807:139–157CrossRefGoogle Scholar
  26. 26.
    Yang J, Monine MI, Faeder JR, Hlavacek WS (2008) Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Phys Rev E 78:031910CrossRefGoogle Scholar
  27. 27.
    Faeder JR, Blinov ML, Goldtstein B, Hlavacek WS (2005) Rule-based modeling of biochemical networks. Complexity 10:22–41CrossRefGoogle Scholar
  28. 28.
    Blinov ML, Yang J, Faeder JR, Hlavacek WS (2006) Graph theory for rule-based modeling of biochemical networks. Lect Notes Comput Sci 4230:89–106CrossRefGoogle Scholar
  29. 29.
    GitHub page for the BioNetFit source code. https://github.com/RuleWorld/BioNetFit Accessed 6 Sep 2018
  30. 30.
    GNU General Publica License v3.0. https://www.gnu.org/licenses/gpl-3.0.en.html. Accessed 6 Sep 2018
  31. 31.
    GitHub page for the BioNetGen source code. https://github.com/RuleWorld/bionetgen Accessed 6 Sep 2018
  32. 32.
    MIT License. https://opensource.org/licenses/MIT. Accessed 6 Sep 2018
  33. 33.
    Faeder Lab web site. https://www.csb.pitt.edu/Faculty/Faeder/. Accessed 6 Sep 2018
  34. 34.
    BioNetFit user manual. http://bionetfit.nau.edu/files/BioNetFit_User_Manual.pdf. Accessed 6 Sep 2018
  35. 35.
    GitHub page where the files of the egg fitting and bootstrapping problems can be found. https://github.com/RuleWorld/RuleHub/tree/master/Contributed/Hlavacek2018Egg. Accessed 11 Sep 2018
  36. 36.
    GitHub page where the files of the elephant fitting problem can be found. https://github.com/RuleWorld/RuleHub/tree/master/Contributed/Hlavacek2018Elephant. Accessed 11 Sep 2018
  37. 37.
    Kuhl FP, Giardina CR (1982) Elliptic Fourier features of a closed contour. Comput Gr Image Process 18:236–258CrossRefGoogle Scholar
  38. 38.
    Sekar JA, Faeder JR (2012) Rule-based modeling of signal transduction: a primer. Methods Mol Biol 880:139–218CrossRefGoogle Scholar
  39. 39.
    Chylek LA, Harris LA, Faeder JR, Hlavacek WS (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 12:045007CrossRefGoogle Scholar
  40. 40.
    Apgar JF, Witmer DK, White FM, Tidor B (2010) Sloppy models, parameter uncertainty, and the role of experimental design. Mol BioSyst 6:1890–1900CrossRefGoogle Scholar
  41. 41.
    Tönsing C, Timmer J, Kreutz C (2014) Cause and cure of sloppiness in ordinary differential equation models. Phys Rev E 90:023303CrossRefGoogle Scholar
  42. 42.
    Kozer N, Barua D, Orchard S, Nice EC, Burgess AW et al (2013) Exploring higher-order EGFR oligomerisation and phosphorylation—a combined experimental and theoretical approach. Mol BioSyst 9:1849–1863CrossRefGoogle Scholar
  43. 43.
    Chylek LA, Akimov V, Dengjel J, Rigbolt KT, Hu B et al (2014) Phosphorylation site dynamics of early T-cell receptor signaling. PLoS One 9:e104240CrossRefGoogle Scholar
  44. 44.
    Mahajan A, Youssef LA, Cleyrat C, Grattan R, Lucero SR et al (2017) Allergen valency, dose, and FcϵRI occupancy set thresholds for secretory responses to Pen a 1 and motivate design of hypoallergens. J Immunol 198:1034–1046CrossRefGoogle Scholar
  45. 45.
    Harmon B, Chylek LA, Liu Y, Mitra ED, Mahajan A et al (2017) Timescale separation of positive and negative signaling creates history-dependent responses to IgE receptor stimulation. Sci Rep 7:15586CrossRefGoogle Scholar
  46. 46.
    Erickson KE, Rukhlenko OS, Shahinuzzaman M, Slavkova KP, Lin YT, et al. (2018) Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. Preprint. https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-18-29001. Accessed 26 Oct 2018
  47. 47.
    Resnekov O, Munsky B, Hlavacek WS (2014) Perspective on the q-bio Summer School and Conference: 2007–2014 and beyond. Quant Biol 2:54–58CrossRefGoogle Scholar
  48. 48.
    The web site of the Annual q-bio Conference and Summer School. http://q-bio.org/wp/. Accessed 11 Sep 2018
  49. 49.
    RuleHub, a repository for BioNetGen and BioNetFit files that define models and fitting jobs. https://github.com/RuleWorld/RuleHub. Accessed 11 Sep 2018
  50. 50.
    Zhang F, Meier-Schellersheim M (2018) SBML Level 3 package: multistate, multicomponent and multicompartment species, version 1, release 1. J Integr Bioinform 15:1.  https://doi.org/10.1515/jib-2017-0077 CrossRefGoogle Scholar
  51. 51.
    Egea JA, Balsa-Canto E, García MSG, Banga JR (2009) Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind Eng Chem Res 48:4388–4401CrossRefGoogle Scholar
  52. 52.
    Penas DR, Banga JR, González P, Doallo R (2015) Enhanced parallel differential evolution algorithm for problems in computational systems biology. Appl Soft Comput 33:86–99CrossRefGoogle Scholar
  53. 53.
    Somogyi ET, Bouteiller JM, Glazier JA, König M, Medley JK et al (2015) libRoadRunner: a high performance SBML simulation and analysis library. Bioinformatics 31:3315–3321CrossRefGoogle Scholar
  54. 54.
    Hucka M, Bergmann FT, Drager A, Hoops S, Keating SM et al (2018) The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core. J Integr Bioinform 15:1.  https://doi.org/10.1515/jib-2017-0081 CrossRefGoogle Scholar
  55. 55.
    Miskov-Zivanov N, Turner MS, Lane LP, Morel PA, Faeder JR (2013) The duration of T cell stimulation is a critical determinant of cell fate and plasticity. Sci Signal 6:ra97CrossRefGoogle Scholar
  56. 56.
    Hause RJ Jr, Leung KK, Barkinge JL, Ciaccio MF, Chuu CP, Jones RB (2012) Comprehensive binary interaction mapping of SH2 domains via fluorescence polarization reveals novel functional diversification of ErbB receptors. PLoS One 7:e44471CrossRefGoogle Scholar
  57. 57.
    Koytiger G, Kaushansky A, Gordus A, Rush J, Sorger PK, MacBeath G (2013) Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected. Mol Cell Proteomics 12:1204–1213CrossRefGoogle Scholar
  58. 58.
    Kulak NA, Pichler G, Paron I, Nagaraj N, Mann M (2014) Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods 11:319–324CrossRefGoogle Scholar
  59. 59.
    Hein MY, Hubner NC, Poser I, Cox J, Nagaraj N et al (2015) A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163:712–723CrossRefGoogle Scholar
  60. 60.
    Shi T, Niepel M, McDermott JE, Gao Y, Nicora CD et al (2016) Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway. Sci Signal 9:rs6CrossRefGoogle Scholar
  61. 61.
    Yi L, Shi T, Gritsenko MA, X’avia Chan CY, Fillmore TL et al (2018) Targeted quantification of phosphorylation dynamics in the context of EGFR-MAPK pathway. Anal Chem 90:5256–5263CrossRefGoogle Scholar
  62. 62.
    Stites EC, Aziz M, Creamer MS, Von Hoff DD, Posner RG, Hlavacek WS (2015) Use of mechanistic models to integrate and analyze multiple proteomic datasets. Biophys J 108:1819–1829CrossRefGoogle Scholar
  63. 63.
    Shiraishi F, Savageau MA (1992) The tricarboxylic acid cycle in Dictyostelium discoideum. I. Formulation of alternative kinetic representations. J Biol Chem 267:22912–22918PubMedGoogle Scholar
  64. 64.
    Shiraishi F, Savageau MA (1992) The tricarboxylic acid cycle in Dictyostelium discoideum. II. Evaluation of model consistency and robustness. J Biol Chem 267:22919–22925PubMedGoogle Scholar
  65. 65.
    Shiraishi F, Savageau MA (1992) The tricarboxylic acid cycle in Dictyostelium discoideum. III. Analysis of steady state and dynamic behavior. J Biol Chem 267:22926–22933PubMedGoogle Scholar
  66. 66.
    Ni TC, Savageau MA (1996) Application of biochemical systems theory to metabolism in human red blood cells: signal propagation and accuracy of representation. J Biol Chem 271:7927–7941CrossRefGoogle Scholar
  67. 67.
    Ni TC, Savageau MA (1996) Model assessment and refinement using strategies from biochemical systems theory: application to metabolism in human red blood cells. J Theor Biol 179:329–368CrossRefGoogle Scholar
  68. 68.
    Lopez CF, Muhlich JL, Bachman JA, Sorger PK (2013) Programming biological models in Python using PySB. Mol Syst Biol 9:646CrossRefGoogle Scholar
  69. 69.
    Chylek LA, Stites EC, Posner RG, Hlavacek WS (2013) Innovations of the rule-based modeling approach. In: Prokop A, Csukás B (eds) Systems biology. Springer, DordrechtGoogle Scholar
  70. 70.
    Chylek LA, Wilson BS, Hlavacek WS (2014) Modeling biomolecular site dynamics in immunoreceptor signaling systems. Adv Exp Med Biol 844:245–262CrossRefGoogle Scholar
  71. 71.
    Chylek LA, Holowka DA, Baird BA, Hlavacek WS (2018) Ch 13: Quantitative modeling of mast cell signaling. In: Das J, Jayaprakas C (eds) Systems immunology: an introduction to modeling methods for scientists. CRC Press, Boca Raton, FL, pp 213–226CrossRefGoogle Scholar
  72. 72.
    Stefan MI, Bartol TM, Sejnowski TJ, Kennedy MB (2014) Multi-state modeling of biomolecules. PLoS Comput Biol 10:e1003844CrossRefGoogle Scholar
  73. 73.
    Lipniacki T, Hat B, Faeder JR, Hlavacek WS (2008) Stochastic effects and bistability in T cell receptor signaling. J Theor Biol 254:110–122CrossRefGoogle Scholar
  74. 74.
    Suderman R, Hlavacek WS (2017) TRuML: a translator for rule-based modeling. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB ‘17), Boston, 2017. ACM Press, New YorkGoogle Scholar
  75. 75.
    GitHub page for TRuML source code. https://github.com/lanl/TRuML. Accessed 6 Sep 2018
  76. 76.
    Lemons NW, Hu B, Hlavacek WS (2011) Hierarchical graphs for rule-based modeling of biochemical systems. BMC Bioinformatics 12:45CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • William S. Hlavacek
    • 4
  • Jennifer A. Csicsery-Ronay
    • 1
  • Lewis R. Baker
    • 2
    • 3
  • María del Carmen Ramos Álamo
    • 4
  • Alexander Ionkov
    • 1
  • Eshan D. Mitra
    • 4
  • Ryan Suderman
    • 1
    • 5
  • Keesha E. Erickson
    • 4
  • Raquel Dias
    • 6
  • Joshua Colvin
    • 6
  • Brandon R. Thomas
    • 6
  • Richard G. Posner
    • 6
    Email author
  1. 1.Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear StudiesLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Department of Applied MathematicsUniversity of ColoradoBoulderUSA
  4. 4.Theoretical Biology and Biophysics Group, Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  5. 5.Immunetrics, Inc.PittsburghUSA
  6. 6.Department of Biological SciencesNorthern Arizona UniversityFlagstaffUSA

Personalised recommendations