Efficiently Encoding Complex Biochemical Models with the Multistate Model Builder (MSMB)

  • Alida Palmisano
  • Stefan Hoops
  • Layne T. Watson
  • Thomas C. JonesJr
  • John J. Tyson
  • Clifford A. Shaffer
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)


Biologists seek to create increasingly complex molecular regulatory network models. Writing such a model is a creative effort that requires flexible analysis tools and better modeling languages than offered by many of today’s biochemical model editors. Our Multistate Model Builder (MSMB) supports multistate models created using different modeling styles that suit the modeler rather than the software. MSMB defines a simple but powerful syntax to describe multistate species. Our syntax reduces the number of reactions needed to encode the model, thereby reducing the cognitive load involved with model creation. MSMB gives extensive feedback during all stages of model creation. Users can activate error notifications, and use these notifications as a guide toward a consistent, syntactically correct model. Any consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB’s multistate syntax through realistic models of cell cycle regulation and mRNA transcription. MSMB is an open-source project implemented in Java and it uses the COPASI API. Complete information and the installation package can be found at

Key words

Systems biology Biological networks Mathematical modeling Chemical reaction systems COPASI SBML Software Model editor Multistate 



This manuscript was prepared by Alida Palmisano as a follow-up of the work done while employed at Virginia Tech. The opinions expressed in this manuscript are the authors’ own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States Government.


  1. 1.
    Wikipedia. Editing –Wikipedia, The Free Encyclopedia (2016) [cited 2016 July].
  2. 2.
    Palmisano A, Hoops S, Watson LT et al (2014) Multistate Model Builder (MSMB): a flexible editor for compact biochemical models. BMC Syst Biol 8:42CrossRefGoogle Scholar
  3. 3.
    Palmisano A, Hoops S, Watson LT et al (2015) JigCell Run Manager (JC-RM): a tool for managing large sets of biochemical model parametrizations. BMC Syst Biol 9:95CrossRefGoogle Scholar
  4. 4.
    Hoops S, Sahle S, Gauges R et al (2006) COPASI--a COmplex PAthway SImulator. Bioinformatics 22:3067–3074CrossRefGoogle Scholar
  5. 5.
    Funahashi A, Matsuoka Y, Jouraku A et al (2008) CellDesigner 3.5: A versatile modeling tool for biochemical networks. Proc IEEE 96:1254–1265CrossRefGoogle Scholar
  6. 6.
    Moraru II, Schaff JC, Slepchenko BM et al (2008) Virtual cell modelling and simulation software environment. IET Syst Biol 2:352–362CrossRefGoogle Scholar
  7. 7.
    Smith LP, Bergmann FT, Chandran D et al (2009) Antimony: a modular model definition language. Bioinformatics 25:2452–2454CrossRefGoogle Scholar
  8. 8.
    Blinov ML, Faeder JR, Goldstein B et al (2004) BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20:3289–3291CrossRefGoogle Scholar
  9. 9.
    Hlavacek WS, Faeder JR, Blinov ML et al (2006) Rules for modeling signal-transduction systems. Sci STKE 2006(344):re6PubMedGoogle Scholar
  10. 10.
    Smith AM, Xu W, Sun Y et al (2012) RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics 13(Suppl 8):S3Google Scholar
  11. 11.
    Zhang F, Angermann BR, Meier-Schellersheim M (2013) The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions. Bioinformatics 29:1229–1230CrossRefGoogle Scholar
  12. 12.
    Barik D, Baumann WT, Paul MR et al (2010) A model of yeast cell-cycle regulation based on multisite phosphorylation. Mol Syst Biol 6:405CrossRefGoogle Scholar
  13. 13.
    Chen KC, Calzone L, Csikasz-Nagy A et al (2004) Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell 15:3841–3862CrossRefGoogle Scholar
  14. 14.
    Li C, Donizelli M, Rodriguez N et al (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92CrossRefGoogle Scholar
  15. 15.
    Firczuk H, Kannambath S, Pahle J et al (2013) An in vivo control map for the eukaryotic mRNA translation machinery. Mol Syst Biol 9:635CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Alida Palmisano
    • 1
    • 2
  • Stefan Hoops
    • 3
  • Layne T. Watson
    • 1
    • 4
    • 5
  • Thomas C. JonesJr
    • 1
  • John J. Tyson
    • 2
  • Clifford A. Shaffer
    • 1
  1. 1.Department of Computer ScienceVirginia TechBlacksburgUSA
  2. 2.Department of Biological SciencesVirginia TechBlacksburgUSA
  3. 3.Biocomplexity Institute of Virginia TechBlacksburgUSA
  4. 4.Department of MathematicsVirginia TechBlacksburgUSA
  5. 5.Department of Aerospace and Ocean EngineeringVirginia TechBlacksburgUSA

Personalised recommendations