Innovations of the Rule-Based Modeling Approach

  • Lily A. Chylek
  • Edward C. Stites
  • Richard G. Posner
  • William S. Hlavacek

Abstract

New modeling approaches are needed to tackle the complexity of cell signaling systems. An emerging approach is rule-based modeling, in which protein-protein interactions are represented at the level of functional components. By using rules to represent interactions, a modeler can avoid enumerating the reachable chemical species in a system, which is a necessity in traditional modeling approaches. A set of rules can be used to generate a reaction network, or to perform simulations with or without network generation. Although the rule-based approach is a relatively recent development in biology, it is based on concepts that have proven useful in other fields. In this chapter, we discuss innovations of the rule-based modeling approach, relative to traditional approaches for modeling chemical kinetics. These innovations include the use of rules to concisely capture the dynamics of molecular interactions, the view of models as programs, and agent-based computational approaches that can be applied to simulate the chemical kinetics of a system characterized by a large traditional model. These innovations should enable the development of models that can relate the molecular state of a cell to its phenotype, even though vast and complex networks bridge perturbations at the molecular level to fates and activities at the cellular level. In the future, we expect that validated rule-based models will be useful for model-guided studies of cell signaling mechanisms, interpretation of temporal phosphoproteomic data, and cell engineering applications.

Keywords

Computational modeling Combinatorial complexity Protein-protein interactions Cell signaling Rule-based modeling Formal languages Simulation algorithms Chemical kinetics 

Acronyms

BEM

Bond electron matrix

BNGL

BioNetGen Language

ODE

Ordinary differential equation

SBGN

Systems Biology Graphical Notation

SBML

Systems Biology Markup Language

Notes

Acknowledgments

We thank Michael L. Blinov, James R. Faeder, David J. Klinke II, Jean Krivine, and Carlos F. Lopez for helpful discussions.

References

  1. 1.
    Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664PubMedGoogle Scholar
  2. 2.
    Kitano H (2002) Computational systems biology. Nature 420:206–210PubMedGoogle Scholar
  3. 3.
    Lazebnik Y (2002) Can a biologist fix a radio?–or, what I learned while studying apoptosis. Cancer Cell 2:179–182PubMedGoogle Scholar
  4. 4.
    Kreeger PK, Lauffenburger DA (2010) Cancer systems biology: a network modeling perspective. Carcinogenesis 31:2–8PubMedPubMedCentralGoogle Scholar
  5. 5.
    Chakraborty AK, Das J (2010) Pairing computation with experimentation: a powerful coupling for under-standing T cell signalling. Nat Rev Immunol 10:59–71PubMedGoogle Scholar
  6. 6.
    Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser IDC (2011) Systems biology in immunology–a computational modeling merspective. Annu Rev Immunol 29:527–585PubMedPubMedCentralGoogle Scholar
  7. 7.
    Lander AD (2010) The edges of understanding. BMC Biol 8:40PubMedPubMedCentralGoogle Scholar
  8. 8.
    Downward J (2011) Targeting RAF: trials and tribulations. Nat Med 17:286–288PubMedGoogle Scholar
  9. 9.
    Kholodenko BN (2006) Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol 7:165–176PubMedPubMedCentralGoogle Scholar
  10. 10.
    Kholodenko BN, Hancock JF, Kolch W (2010) Signalling ballet in space and time. Nat Rev Mol Cell Biol 11:414–426PubMedPubMedCentralGoogle Scholar
  11. 11.
    Hunter T (2000) Signaling–2000 and beyond. Cell 100:113–127PubMedGoogle Scholar
  12. 12.
    Pawson T, Nash P (2003) Assembly of cell regulatory systems through protein interaction domains. Science 300:445–452PubMedGoogle Scholar
  13. 13.
    Kandasamy K, Mohan SS, Raju R, Keerthikumar S, Kumar G, Venugopal A, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, Gollapudi S, Tattikota S, Mohan S, Padhukasahasram H, Subbannayya Y, Goel R, Jacob H, Zhong J, Sekhar R, Nanjappa V, Balakrishnan L, Subbaiah R, Ramachandra Y, Rahiman BA, Prasad TK, Lin JX, Houtman J, Desiderio S, Renauld JC, Constantinescu S (2010) NetPath: a public resource of curated signal transduction pathways. Genome Biol 11:R3PubMedPubMedCentralGoogle Scholar
  14. 14.
    Hlavacek WS, Faeder JR, Blinov ML, Perelson AS, Goldstein B (2003) The complexity of complexes in signal transduction. Biotechnol Bioeng 84:783–794PubMedGoogle Scholar
  15. 15.
    Mayer BJ, Blinov ML, Loew LM (2009) Molecular machines or pleiomorphic ensembles: signaling complexes revisited. J Biol 8:81PubMedPubMedCentralGoogle Scholar
  16. 16.
    Faeder JR, Blinov ML, Hlavacek WS (2009) Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 500:113–167PubMedGoogle Scholar
  17. 17.
    Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with smoldyn 2.1. PLoS Comput Biol 6:e1000705PubMedPubMedCentralGoogle Scholar
  18. 18.
    Colvin J, Monine MI, Faeder JR, Hlavacek WS, Von Hoff DD, Posner RG (2009) Simulation of large-scale rule-based models. Bioinformatics 25:910–917PubMedPubMedCentralGoogle Scholar
  19. 19.
    Website about Kappa and Kappa-based software tools [http://kappalanguage.org/]
  20. 20.
    Sneddon MW, Faeder JR, Emonet T (2011) Efficient modeling, simulation, and coarse-graining of biological complexity with NFsim. Nat Methods 8:177–183PubMedGoogle Scholar
  21. 21.
    Colvin J, Monine MI, Gutenkunst R, Hlavacek WS, Von Hoff DD, Posner RG (2010) RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinf 11:404Google Scholar
  22. 22.
    Moraru II, Schaff JC, Slepchenko BM, Blinov ML, Morgan F, Lakshminarayana A, Gao F, Li Y, Loew LM (2008) Virtual Cell modelling and simulation software environment. IET Syst Biol 2:352–362PubMedPubMedCentralGoogle Scholar
  23. 23.
    Meier-Schellersheim M, Xu X, Angermann B, Kunkel E, Jin T, Germain RN (2006) Key role of local regulation chemosensing revealed by a new molecular interaction-based modeling method. PLoS Comput Biol 2:e82PubMedPubMedCentralGoogle Scholar
  24. 24.
    Angermann BR, Klauschen F, Garcia AD, Prustel T, Zhang F, Germain RN, Meier-Schellersheim M (2012) Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat MethodsGoogle Scholar
  25. 25.
    Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielse UB, Lauffenburger D, Sorger PK (2009) Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 5:239PubMedPubMedCentralGoogle Scholar
  26. 26.
    Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F (2011) Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acid Res 39:D261PubMedPubMedCentralGoogle Scholar
  27. 27.
    Bateman A, Coin L, Durbin R, Finn RD, Hollich V, GriffithsJones S, Khanna A, Marshall M, Moxon S, Sonnhammer ELL, Studholme DJ, Yeats C, Eddy SR (2004) The Pfam protein families database. Nucleic Acids Res 32:D138–D141PubMedPubMedCentralGoogle Scholar
  28. 28.
    Dinkel H, Michael S, Weatheritt RJ, Davey NE, Van Roey K, Altenberg B, Toedt G, Uyar B, Seiler M, Budd A, Jdicke L, Dammert MA, Schroeter C, Hammer M, Schmidt T, Jehl P, McGuigan C, Dymecka M, Chica C, Luck K, Via A, Chatr-aryamontri A, Haslam N, Grebnev G, Edwards RJ, Steinmetz MO, Meiselbach H, Diella F, Gibson TJ (2012) ELM—the database of eukaryotic linear motifs. Nucleic Acids Res 40:D242–D251PubMedPubMedCentralGoogle Scholar
  29. 29.
    Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A (2009) Human protein reference database—2009 update. Nucleic Acids Res 37:D767–D772Google Scholar
  30. 30.
    Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W (2006) Rules for modeling signal transduction systems. Sci STKE, 2006:re6Google Scholar
  31. 31.
    Danos V, Feret J, Fontana W, Harmer R, Krivine J (2007) Rule-based modelling of cellular signalling. Lect Notes Comput Sci 4703:17–41Google Scholar
  32. 32.
    Fisher J, Harel D, Henzinger TA (2011) Biology as reactivity. Commun ACM 54:72–82Google Scholar
  33. 33.
    Lim WA, Pawson T (2010) Phosphotyrosine signaling: evolving a new cellular communication system. Cell 142:661–667PubMedPubMedCentralGoogle Scholar
  34. 34.
    Endy D, Brent R (2001) Modeling cellular behavior. Nature 409:391–395PubMedGoogle Scholar
  35. 35.
    Bray D (2003) Molecular prodigality. Science 299:1189–1190PubMedGoogle Scholar
  36. 36.
    Le Novere N, Hucka M, Mi H, Moodie S, Schreiber F,Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villeger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H (2009) The systems biology graphical notation. Nat Biotechnol 27:735–741Google Scholar
  37. 37.
    Bray D, Lay S (1997) Computer-based analysis of the binding steps in protein complex formation. Proc Natl Acad Sci 94:13493–13498PubMedPubMedCentralGoogle Scholar
  38. 38.
    Morton-Firth CJ, Bray D (1998) Predicting temporal fluctuations in an intracellular signalling pathway. J Theor Biol 192:117–128PubMedGoogle Scholar
  39. 39.
    Le Novere N, Shimizu TS (2001) StochSim: modelling of stochastic biomolecular processes. Bioinformatics 17:575–576Google Scholar
  40. 40.
    Regev A, Silverman W, Shapiro E (2001) Representation and simulation of biochemical processes using the π-calculus process algebra. Pac Symp Biocomput 2001:459–470Google Scholar
  41. 41.
    Priami C, Regev A, Shapiro E, Silverman W (2001) Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inf Process Lett 80:25–31Google Scholar
  42. 42.
    The BioSPI project homepage [http://www.wisdom.weizmann.ac.il/∼biospi/]
  43. 43.
    Dematte L, Priami C, Romanel A (2008) The BlenX language: a tutorial. Lect Notes Comput Sci 5016:313–365Google Scholar
  44. 44.
    Kahramanogullari O, Cardelli L, Caron E: An Intuitive Automated Modelling Interface for Systems Biology. In DCM 2009:73–86Google Scholar
  45. 45.
    Phillips A, Cardelli L (2007) Efficient, correct simulation of biological processes in the stochastic pi-calculus. Lect Notes Comput Sci 4695:184–199Google Scholar
  46. 46.
    Goldstein B, Faeder JR, Hlavacek WS, Blinov ML, Redondo A, Wofsy C (2002) Modeling the early signaling events mediated by FcεRI. Mol Immunol 38:1213–1219PubMedGoogle Scholar
  47. 47.
    Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, Wofsy C, Goldstein B (2003) Investigation of early events in FcεRI-mediated signaling using a detailed mathematical model. J Immunol 170:3769–3781PubMedGoogle Scholar
  48. 48.
    Nag A, Monine MI, Faeder JR, Goldstein B (2009) Aggregation of membrane proteins by cytosolic cross-linkers: theory and simulation of the LAT-Grb2-SOS1 system. Biophys J 96:2604–2623PubMedPubMedCentralGoogle Scholar
  49. 49.
    Nag A, Monine MI, Blinov ML, Goldstein B (2010) A detailed mathematical model predicts that serial engagement of IgE-FcεRI complexes can enhance Syk activation in mast cells. J Immunol 185:3268–3276PubMedPubMedCentralGoogle Scholar
  50. 50.
    Nag A, Blinov ML, Goldstein B (2010) Shaping the response: the role of FcεRI and Syk expression levels in mast cell signaling. IET Syst Biol 4:334–347PubMedPubMedCentralGoogle Scholar
  51. 51.
    Monine MI, Posner RG, Savage PB, Faeder JR, Hlavacek WS (2010) Modeling multivalent ligand-receptor interactions with steric constraints on configurations of cell-surface receptor aggregates. Biophys J 98:48–56PubMedPubMedCentralGoogle Scholar
  52. 52.
    Lee KH, Dinner AR, Tu C, Campi G, Raychaudhuri S, Varma R, Sims TN, Burack WR, Wu H, Wang J, Kanagawa O, Markiewicz M, Allen PM, Dustin ML, Chakraborty AK, Shaw AS (2003) The immunological synapse balances T cell receptor signaling and degradation. Science 302:1218–1222PubMedGoogle Scholar
  53. 53.
    Li QJ, Dinner AR, Qi S, Irvine DJ, Huppa JB, Davis MM, Chakraborty AK (2004) CD4 enhances T cell sensitivity to antigen by coordinating Lck accumulation at the immunological synapse. Nat Immunol 5:791–799Google Scholar
  54. 54.
    Altan-Bonnet G, Germain RN (2005) Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol 3:e356PubMedPubMedCentralGoogle Scholar
  55. 55.
    Nag A, Monine M, Perelson AS, Goldstein B (2012) Modeling and simulation of aggregation of membrane protein LAT with molecular variability in the number of binding sites for cytosolic Grb2-SOS1- Grb2. PLoS ONE 7:e28758PubMedPubMedCentralGoogle Scholar
  56. 56.
    Faeder JR, Blinov ML, Hlavacek WS (2005) Graphical rule-based representation of signal transduction net- works. In: Liebrock L (ed.) Proceedings 2005 ACM Symposium on Applied Computing, ACM Press, New York, pp 133–140Google Scholar
  57. 57.
    Chylek LA, Hu B, Blinov ML, Emonet T, Faeder JR, Goldstein B, Gutenkunst RN, Haugh JM, Lipniacki T, Posner RG, Yang J, Hlavacek WS (2011) Guidelines for visualizing and annotating rule-based models. Mol BioSyst 7:2779–2795PubMedPubMedCentralGoogle Scholar
  58. 58.
    Xu W, Smith AM, Faeder JR, Marai GE (2011) RuleBender: a visual interface for rule-based modeling. Bioinformatics 27:1721–1722PubMedPubMedCentralGoogle Scholar
  59. 59.
    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 20:3289–3291PubMedGoogle Scholar
  60. 60.
    Danos V, Laneve C (2004) Formal molecular biology. Theoret Comput Sci 325:69–110Google Scholar
  61. 61.
    Feret J, Danos V, Krivine J, Harmer R, Fontana W (2009) Internal coarse-graining of molecular systems. Proc Natl Acad Sci USA 106:6453–6458Google Scholar
  62. 62.
    Barua D, Faeder JR, Haugh JM (2009) A bipolar clamp mechanism for activation of Jak-family protein tyrosine kinases. PLoS Comput Biol 5:e1000364PubMedPubMedCentralGoogle Scholar
  63. 63.
    Dushek O, Das R, Coombs D (2009) A role for rebinding in rapid and reliable T cell responses to antigen. PLoS Comput Biol 5:e1000578PubMedPubMedCentralGoogle Scholar
  64. 64.
    Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092Google Scholar
  65. 65.
    Bortz AB, Kalos M, Lebowitz J (1975) A new algorithm for Monte Carlo simulations of Ising spin systems. J Comput Phys 17:10–18Google Scholar
  66. 66.
    Voter AF (2007) Introduction to the kinetic Monte Carlo method. In: Sickafus KE, Sickafus KE (eds) Radiation Effects in Solids. Springer, Kotomin, pp 1–21Google Scholar
  67. 67.
    Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22:403–434Google Scholar
  68. 68.
    Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361Google Scholar
  69. 69.
    Gillespie DT (2007) Stochastic simulation of chemical kinetics. Annu Rev Phys ChemGoogle Scholar
  70. 70.
    Dugundji J, Ugi I (1973) An algebraic model of constitutional chemistry as a basis for chemical computer programs. Top Curr Chem 39:19–64Google Scholar
  71. 71.
    Ugi I, Bauer J, Bley K, Dengler A, Dietz A, Fontain E, Gruber B, Herges R, Knauer M, Reitsman K, Stein N (1993) Computer-assisted solution of chemical problems—the historic development and the present state of the art of a new discipline of chemistry. Agnew Chem Int Ed Engl 32:201–227Google Scholar
  72. 72.
    Green WH Jr (2007) Predictive kinetics: a new approach for the 21st century. Adv Chem Eng 32:1–50Google Scholar
  73. 73.
    Faulon JL, Carbonell P (2010) Reaction network generation. In: Faulon JL, Bender A (eds.) Handbook of Chemoinformatics Algorithms, Chapman & Hall/CRC Press, Boca Raton, pp 317–341Google Scholar
  74. 74.
    Rangarajan S, Bahn A, Daoutidis P (2010) Rule-based generation of thermochemical routes to biomass conversion. Ind Eng Chem Res 49:10459–10470Google Scholar
  75. 75.
    Klinke DJ II, Finley SD (2012) Timescale analysis of rule-based biochemical reaction networks. Biotechnol ProgrGoogle Scholar
  76. 76.
    Klinke DJ II, Broadbelt LJ (1999) Construction of a mechanistic model of Fischer-Tropsch synthesis on Ni(1 1 1) and Co (0 0 0 1) surfaces. Chem Eng Sci 54:3379–3389Google Scholar
  77. 77.
    Broadbelt LJ, Pfaendtner J (2005) Lexicography of kinetic modeling of complex reaction networks. AIChE J 51:2112–2121Google Scholar
  78. 78.
    Hatzimanikatis V, Li C, Ionita JA, Henry CS, Jankowski MD, Broadbelt LJ (2005) Exploring the diversity of complex metabolic networks. Bioinformatics 21:1603–1609PubMedGoogle Scholar
  79. 79.
    Mu F, Unkefer CJ, Unkefer PJ, Hlavacek WS (2011) Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 27:1537–1545PubMedPubMedCentralGoogle Scholar
  80. 80.
    Milner R, Parrow J, Walker D (1992) A calculus of mobile processes, I. Inform Comput 100:1–40Google Scholar
  81. 81.
    Fokkink W (2000) Introduction to process algebra. Springer, BerlinGoogle Scholar
  82. 82.
    Fisher J, Henzinger TA (2007) Executable cell biology. Nat Biotechnol 25:1239–1249PubMedGoogle Scholar
  83. 83.
    Harmer R (2009) Rule-based modeling and tunable resolution. EPTCS 9:65–72Google Scholar
  84. 84.
    Harmer R, Danos V, Feret J, Krivine J, Fontana W (2010) Intrinsic information carriers in combinatorial dynamical systems. Chaos 20:037108PubMedGoogle Scholar
  85. 85.
    PySB Python framework for Systems Biology modeling [http://pysb.org/]
  86. 86.
    Lopez CF, Muhlich JL, Bachman JA, Sorger PK (2013) Programming biological models in Python with PySB. Mol Syst Biol 9:646Google Scholar
  87. 87.
    Faeder JR, Blinov ML, Goldstein B, Hlavacek WS (2005) Rule-based modeling of biochemical networks. Complexity 10:22–41Google Scholar
  88. 88.
    Lok L, Brent R (2005) Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat Biotechnol 23:131–136PubMedGoogle Scholar
  89. 89.
    Harris LA, Hogg JS, Faeder JR (2009) Compartmental rule-based modeling of biochemical systems. In: Rossetti M, Hill R, Johansson B, Dunkin A, Ingallls R (eds.) Proceedings of the 2009 Winter Simulation Conference. pp 908–919Google Scholar
  90. 90.
    Maus C, Rybacki S, Uhrmacher AM (2011) Rule-based multi-level modeling of cell biological systems. BMC Syst Biol 5:166PubMedPubMedCentralGoogle Scholar
  91. 91.
    Mallavarapu A, Thomson M, Ullian B, Gunawardena J (2009) Programming with models: modularity and abstraction provide powerful capabilities for systems biology. J R Soc Interf 6:257Google Scholar
  92. 92.
    Lis M, Artyomov MN, Devadas S, Chakraborty AK (2009) Efficient stochastic simulation of reaction-diffusion processes via direct compilation. Bioinformatics 25:2289–2291PubMedPubMedCentralGoogle Scholar
  93. 93.
    Yang J, Monine MI, Faeder JR, Hlavacek WS (2008) Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Phys Rev E 78:031910Google Scholar
  94. 94.
    Gruenert G, Ibrahim B, Lenser T, Lohel M, Hinze T, Dittrich P (2010) Rule-based spatial modeling with diffusing, geometrically constrained molecules. BMC Bioinf 11:307Google Scholar
  95. 95.
  96. 96.
    Hu B, Fricke GM, Faeder JR, Posner RG, Hlavacek WS (2009) GetBonNie for building, analyzing and sharing rule-based models. Bioinformatics 25:1457–1460PubMedPubMedCentralGoogle Scholar
  97. 97.
    Clarke EM, Faeder JR, Harris LA, Langmead CJ, Legay A, Jha SK (2008) Statistical model checking in BioLab: applications to the automated analysis of T-cell receptor signaling pathway. Lect Notes Comput Sci 5307:231–250Google Scholar
  98. 98.
    Koschorreck M, Gilles E (2008) ALC: automated reduction of rule-based models. BMC Syst Biol 2:91PubMedPubMedCentralGoogle Scholar
  99. 99.
    Ollivier JF, Shahrezaei V, Swain P (2010) Scalable rule-based modeling of allosteric proteins and biochemical networks. PLoS Comput Biol 6:e1000975PubMedPubMedCentralGoogle Scholar
  100. 100.
    Fages F, Soliman S, Chabrier-Rivier N (2004) Modelling and querying interaction networks in the biochemical abstract machine BIOCHAM. J Biol Phys Chem 4:64–73Google Scholar
  101. 101.
    Priami C, Ballarini P, Quaglia P (2009) BlenX4Bio–BlenX for Biologists. In: Computational Methods in Systems Biology, Springer, pp 26–51Google Scholar
  102. 102.
    Vilar JMG, Saiz L (2010) CplexA: a Mathematica package to study macromolecular-assembly control of gene expression. Bioinformatics 26:2060–2061PubMedGoogle Scholar
  103. 103.
    Tolle D, Nov`ere L (2010) Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes. BMC Syst Biol 4:24PubMedPubMedCentralGoogle Scholar
  104. 104.
    Eker S, Knapp M, Laderoute K, Lincoln P, Talcott C (2004) Pathway Logic: Executable models of biological networks. Electron Notes Theor Comput Sci. 71:125–142Google Scholar
  105. 105.
    Maiwald T, Timmer J (2008) Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24:2037–2043PubMedPubMedCentralGoogle Scholar
  106. 106.
  107. 107.
    Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci USA, 99:7280–7287Google Scholar
  108. 108.
    Yang J, Hlavacek WS (2011) Efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems. Phys Biol 8:055009PubMedPubMedCentralGoogle Scholar
  109. 109.
    Goldstein B (1988) Desensitization, histamine release and the aggregation of IgE on human basophils. In: Perelson AS (ed.) Theoretical immunology, part one, SFI studies in the sciences of complexity. Addison-Wesley, Reading, MA, pp 3–40Google Scholar
  110. 110.
    Blue JL, Beichl I, Sullivan F (1995) Faster Monte Carlo simulations. Phys Rev E 51:R867–R868Google Scholar
  111. 111.
    Gibson MA, Bruck J (2000) Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A 104:1876–1889Google Scholar
  112. 112.
    Slepoy A, Thompson AP, Plimpton SJ (2008) A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks. J Chem Phys 128:205101PubMedGoogle Scholar
  113. 113.
    Danos V, Feret J, Fontana W, Krivine J (2007) Scalable simulation of cellular signalling networks. Lect Notes Comput Sci 4807:139–157Google Scholar
  114. 114.
    Shimizu TS, Bray D (2001) Computational cell biology—the stochastic approach. In: Kitano H (ed.) Foundations of systems biology. MIT PressGoogle Scholar
  115. 115.
    Houtman JCD, Barda-Saad M, Samelson LE (2005) Examining multiprotein signaling complexes from all angles. FEBS J 500:5426–5435Google Scholar
  116. 116.
    Schulze WX, Deng L, Mann M (2005) Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol Syst Biol 2005(1):0008Google Scholar
  117. 117.
    Zhang Y, Wolf-Yadlin A, Ross PL, Pappin DJ, Rush J, Lauffenburger DA (2005) Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol Cell Proteomics 4:1240–1250PubMedGoogle Scholar
  118. 118.
    Jones RB, Gordus A, Krall JA, MacBeath G (2006) A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439:168–174PubMedGoogle Scholar
  119. 119.
    VanMeter AJ, Rodriguez AS, Bowman ED, Jen J, Harris CC, Deng J, Calvert VS, Silvestri A, Fredolini C, Chandhoke V, Petricoin EF, Liotta LA, Espina V (2008) Laser capture microdissection and protein microar- ray analysis of human non-small cell lung cancer: differential epidermal growth factor receptor (EGPR) phosphorylation events associated with mutated EGFR compared with wild type. Mol Cell Proteomics 7:1902–1924PubMedPubMedCentralGoogle Scholar
  120. 120.
    Ciaccio MF, Wagner JP, Chuu CP, Lauffenburger DA, Jones RB (2010) Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods 7:148–155PubMedPubMedCentralGoogle Scholar
  121. 121.
    Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299PubMedGoogle Scholar
  122. 122.
    Blinov ML, Faeder JR, Golstein B, Hlavacek WS (2006) A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. Biosystems 83:136–151PubMedGoogle Scholar
  123. 123.
    Houtman JCD, Houghtling RA, Barda-Saad M, Toda Y, Samelson LE (2005) Early phosphorylation kinetics of proteins involved in proximal TCR-mediated signaling pathways. J Immunol 175:2449PubMedPubMedCentralGoogle Scholar
  124. 124.
    Sawyers C (2004) Targeted cancer therapy. Nature 432:294–297PubMedGoogle Scholar
  125. 125.
    Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN (2007) Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 3:144PubMedPubMedCentralGoogle Scholar
  126. 126.
    Thomson T, Benjamin KR, Bush A, Love T, Pincus D, Resnekov O, Yu RC, Gordon A, Colman-Lerner A, Endy D, Brent R (2011) Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range. Proc Natl Acad Sci USA 13:20265–20270Google Scholar
  127. 127.
    Calzone L, Fages F, Soliman S (2006) BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22:1805–1807PubMedGoogle Scholar
  128. 128.
    Gong H, Zuliani P, Komuravelli A, Faeder JR, Clarke EM (2010) Analysis and verification of the HMGB1 signaling pathway. BMC Bioinf 11:S10Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Lily A. Chylek
    • 1
  • Edward C. Stites
    • 2
  • Richard G. Posner
    • 2
    • 3
  • William S. Hlavacek
    • 4
    • 5
  1. 1.Department of Chemistry and Chemical BiologyCornell UniversityIthacaUSA
  2. 2.Clinical Translational Research DivisionTranslational Genomics Research InstitutePhoenixUSA
  3. 3.Department of Biological SciencesNorthern Arizona UniversityFlagstaffUSA
  4. 4.Department of BiologyUniversity of New MexicoAlbuquerqueUSA
  5. 5.Theoretical Biology and Biophysics Group, Theoretical DivisionLos Alamos National LaboratoryLos Alamos USA

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