Rule-based modeling: A computational approach for studying biomolecular site dynamics in cell signaling systems

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volumn 6, Issue 1, 2014, Pages 13-36

  Chylek, Lily A ^a   Harris, Leonard A ^b   Tung, Chang Shung ^c   Faeder, James R ^b   Lopez, Carlos F ^d   Hlavacek, William S ^e  

^a Department of Obstetrics Gynecology   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c LOS ALAMOS NATIONAL LABORATORY   (United States)

^d VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e CENTER FOR NONLINEAR STUDIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ALLOSTERISM; AUTOPHOSPHORYLATION; BAYES THEOREM; BIOLOGICAL PHENOMENA AND FUNCTIONS CONCERNING THE ENTIRE ORGANISM; CHEMICAL REACTION KINETICS; COMPARTMENT MODEL; COMPUTER LANGUAGE; COMPUTER PROGRAM; CONCEPTUAL FRAMEWORK; CONFORMATIONAL TRANSITION; DIMERIZATION; ENZYME SUBSTRATE COMPLEX; GENE CONTROL; GENE DUPLICATION; GENE EXPRESSION; INFORMATION PROCESSING; INFORMATION SCIENCE; INTRACELLULAR SIGNALING; LIGAND BINDING; MARKUP LANGUAGE; MATHEMATICAL ANALYSIS; MOLECULAR DYNAMICS; MOLECULAR INTERACTION; MONTE CARLO METHOD; OLIGOMERIZATION; PHYSICAL CHEMISTRY; PROTEIN CONFORMATION; PROTEIN CROSS LINKING; PROTEIN DOMAIN; PROTEIN MOTIF; PROTEIN PHOSPHORYLATION; PROTEIN PROCESSING; PROTEIN PROTEIN INTERACTION; REVIEW; RULE BASED MODEL; SYSTEMS BIOLOGY;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; KINETICS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; MODELS, MOLECULAR; SIGNAL TRANSDUCTION;

EID: 84890408904     PISSN: 19395094     EISSN: 1939005X     Source Type: Journal    
DOI: 10.1002/wsbm.1245     Document Type: Review

References (222)

1. Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science 1999, 283:381-387.
2. Kitano H. Systems biology: a brief overview. Science 2002, 295:1662-1664.
3. Nurse P. Life, logic and information. Nature 2008, 454:424-426.
4. Hlavacek WS, Faeder JR, Blinov ML, Posner RG, Hucka M, Fontana W. Rules for modeling signal-transduction systems. Sci STKE 2006, 2006:re6.
5. Kholodenko B, Yaffe MB, Kolch W. Computational approaches for analyzing information flow in biological networks. Sci Signal 2012, 5:re1.
6. Artyomov MN, Lis M, Devadas S, Davis MM, Chakraborty AK. CD4 and CD8 binding to MHC molecules primarily acts to enhance Lck delivery. Proc Natl Acad Sci U S A 2010, 107:16916-16921.
7. Gong H, Zuliani P, Komuravelli A, Faeder JR, Clarke EM. Analysis and verification of the HMGB1 signaling pathway. BMC Bioinformatics 2010, 11(suppl 7):S10.
8. Nag A, Faeder JR, Goldstein B. Shaping the response: the role of FcεRI and Syk expression levels in mast cell signaling. IET Syst Biol 2010, 4:334-347.
9. Nag A, Monine MI, Blinov ML, Goldstein B. A detailed mathematical model predicts that serial engagement of IgE-FcεRI complexes can enhance Syk activation in mast cells. J Immunol 2010, 185:3268-3276.
10. Dushek O, van der Merwe PA, Shahrezaei V. Ultrasensitivity in multisite phosphorylation of membrane-anchored proteins. Biophys J 2011, 100:1189-1197.
11. Geier F, Fengos G, Iber D. A computational analysis of the dynamic roles of Talin, Dok1, and PIPKI for integrin activation. PLoS One 2011, 6:e24808.
12. Thomson TM, Benjamin KR, Bush A, Love T, Pincus D, Resnekov O, Yu RC, Gordon A, Colman-Lerner A, Endy D, et al. Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range. Proc Natl Acad Sci U S A 2011, 108:20265-20270.
13. Barua D, Goldstein B. A mechanistic model of early FcεRI signaling: lipid rafts and the question of protection from dephosphorylation. PLoS One 2012, 7:e51669.
14. Barua D, Hlavacek WS, Lipniacki T. A computational model for early events in B cell antigen receptor signaling: analysis of the roles of Lyn and Fyn. J Immunol 2012, 189:646-658.
15. Creamer MS, Stites EC, Aziz M, Cahill JA, Tan CW, Berens ME, Han H, Bussey KJ, Von Hoff DD, Hlavacek WS, et al. Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling. BMC Syst Biol 2012, 6:107.
16. Deeds EJ, Krivine J, Feret J, Danos V, Fontana W. Combinatorial complexity and compositional drift in protein interaction networks. PLoS One 2012, 7:e32032.
17. Kocieniewski P, Faeder JR, Lipniacki T. The interplay of double phosphorylation and scaffolding in MAPK pathways. J Theor Biol 2012, 295:116-124.
18. Michalski PJ, Loew LM. CaMKII activation and dynamics are independent of the holoenzyme structure: an infinite subunit holoenzyme approximation. Phys Biol 2012, 9:036010.
19. Nag A, Monine M, Perelson AS, Goldstein B. 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 2012, 7:e28758.
20. Falkenberg CV, Loew LM. Computational analysis of Rho GTPase cycling. PLOS Comput Biol 2013, 9:e1002831.
21. Kesseler KJ, Blinov ML, Elston TC, Kaufmann WK, Simpson DA. A predictive mathematical model of the DNA damage G2 checkpoint. J Theor Biol 2013, 320:159-169.
22. Kozer N, Barua D, Orchard S, Nice EC, Burgess AW, Hlavacek WS, Clayton AH. Exploring higher-order EGFR oligomerisation and phosphorylation--a combined experimental and theoretical approach. Mol BioSyst 2013, 9:1849-1863.
23. Liu Y, Barua D, Liu P, Wilson BS, Oliver JM, Hlavacek WS, Singh AK. Single-cell measurements of IgE-mediated FcεRI signaling using an integrated microfluidic platform. PLoS One 2013, 8:e60159.
24. Mukherjee S, Zhu J, Zikherman J, Parameswaran R, Kadlecek TA, Wang Q, Au-Yeung B, Ploegh H, Kuriyan J, Das J, et al. Monovalent and multivalent ligation of the B cell receptor exhibit differential dependence upon Syk and Src family kinases. Sci Signal 2013, 6:ra1.
25. Mukhopadhyay H, Cordoba SP, Maini PK, van der Merwe PA, Dushek O. Systems model of T cell receptor proximal signaling reveals emergent ultrasensitivity. PLOS Comput Biol 2013, 9:e1003004.
26. Barua D, Hlavacek WS. Modeling the effect of APC truncation on destruction complex function in colorectal cancer cells. PLOS Comput Biol, In press.
27. Scott JD, Pawson T. Cell signaling in space and time: where proteins come together and when they're apart. Science 2009, 326:1220-1224.
28. Kuzu G, Keskin O, Gursoy A, Nussinov R. Constructing structural networks of signaling pathways on the proteome scale. Curr Opin Struct Biol 2012, 22:367-377.
29. Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, et al. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 2012, 490:556-560.
30. Zhang QC, Petrey D, Garzon JI, Deng L, Honig B. PrePPI: a structure-informed database of protein-protein interactions. Nucleic Acids Res 2013, 41:D828-D833.
31. Johnson ME, Hummer G. Interface-resolved network of protein-protein interactions. PLOS Comput Biol 2013, 9:e1003065.
32. Kholodenko BN, Hancock JF, Kolch W. Signalling ballet in space and time. Nat Rev Mol Cell Biol 2010, 11:414-426.
33. Ozbabacan SE, Engin HB, Gursoy A, Keskin O. Transient protein-protein interactions. Protein Eng Des Sel 2011, 24:635-648.
34. Weng G, Bhalla US, Iyengar R. Complexity in biological signaling systems. Science 1999, 284:92-96.
35. Dang CV. MYC on the path to cancer. Cell 2012, 149:22-35.
36. Kirouac DC, Saez-Rodriguez J, Swantek J, Burke JM, Lauffenburger DA, Sorger PK. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol 2012, 6:29.
37. Nobeli I, Favia AD, Thornton JM. Protein promiscuity and its implications for biotechnology. Nat Biotechnol 2009, 27:157-167.
38. Rowland MA, Fontana W, Deeds EJ. Crosstalk and competition in signaling networks. Biophys J 2012, 103:2389-2398.
39. Hunter T. Signaling--2000 and beyond. Cell 2000, 100:113-127.
40. Kandasamy K, Mohan SS, Raju R, Keerthikumar S, Kumar GS, Venugopal AK, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol 2010, 11:R3.
41. McLaughlin RN Jr, Poelwijk FJ, Raman A, Gosal WS, Ranganathan R. The spatial architecture of protein function and adaptation. Nature 2012, 491:138-142.
42. Clune J, Mouret JB, Lipson H. The evolutionary origins of modularity. Proc R Soc B 2013, 280:20122863.
43. Bornberg-Bauer E, Albà MM. Dynamics and adaptive benefits of modular protein evolution. Curr Opin Struct Biol 2013, 23:459-466.
44. Peisajovich SG, Garbarino JE, Wei P, Lim WA. Rapid diversification of cell signaling phenotypes by modular domain recombination. Science 2010, 328:368-372.
45. Jin J, Pawson T. Modular evolution of phosphorylation-based signalling systems. Philos Trans R Soc Lond B Biol Sci 2012, 367:2540-2555.
46. Yang XJ. Multisite protein modification and intramolecular signaling. Oncogene 2005, 24:1653-1662.
47. Prabakaran S, Lippens G, Steen H, Gunawardena J. Post-translational modification: nature's escape from genetic imprisonment and the basis for dynamic information encoding. WIREs: Syst Biol Med 2012, 4:565-583.
48. Hlavacek WS, Faeder JR, Blinov ML, Perelson AS, Goldstein B. The complexity of complexes in signal transduction. Biotechnol Bioeng 2003, 84:783-794.
49. Perkins JR, Diboun I, Dessailly BH, Lees JG, Orengo C. Transient protein-protein interactions: structural, functional, and network properties. Structure 2010, 18:1233-1243.
50. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006, 127:635-648.
51. Daub H, Olsen JV, Bairlein M, Gnad F, Oppermann FS, Korner R, Greff Z, Keri G, Stemmann O, Mann M. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol Cell 2008, 31:438-448.
52. Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, et al. The systems biology graphical notation. Nat Biotechnol 2009, 27:735-741.
53. Kholodenko BN, Demin OV, Moehren G, Hoek JB. Quantification of short term signaling by the epidermal growth factor receptor. J Biol Chem 1999, 274:30169-30181.
54. Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002, 20:370-375.
55. Wiley HS, Shvartsman SY, Lauffenburger DA. Computational modeling of the EGF-receptor system: a paradigm for systems biology. Trends Cell Biol 2003, 13:43-50.
56. Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. A network model of early events in epidermal growth factor receptor signaling that accounts for combinatorial complexity. BioSystems 2006, 83:136-151.
57. Kiyatkin A, Aksamitiene E, Markevich NI, Borisov NM, Hoek JB, Kholodenko BN. Scaffolding protein Grb2-associated binder 1 sustains epidermal growth factor-induced mitogenic and survival signaling by multiple positive feedback loops. J Biol Chem 2006, 281:19925-19938.
58. Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 2007, 3:144.
59. Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielsen UB, Lauffenburger DA, Sorger PK. Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 2009, 5:239.
60. Nakakuki T, Birtwistle MR, Saeki Y, Yumoto N, Ide K, Nagashima T, Brusch L, Ogunnaike BA, Okada-Hatakeyama M, Kholodenko BN. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 2010, 141:884-896.
61. Lemmon MA, Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell 2010, 141:1117-1134.
62. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, Beijersbergen RL, Bardelli A, Bernards R. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 2012, 483:100-103.
63. Stites EC. The response of cancers to BRAF inhibition underscores the importance of cancer systems biology. Sci Signal 2012, 5:pe46.
64. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.
65. Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A 2003, 100:11980-11985.
66. Das J, Ho M, Zikherman J, Govern C, Yang M, Weiss A, Chakraborty AK, Roose JP. Digital signaling and hysteresis characterize Ras activation in lymphoid cells. Cell 2009, 136:337-351.
67. Burgess AW, Cho HS, Eigenbrot C, Ferguson KM, Garrett TP, Leahy DJ, Lemmon MA, Sliwkowski MX, Ward CW, Yokoyama S. An open-and-shut case? Recent insights into the activation of EGF/ErbB receptors. Mol Cell 2003, 12:541-552.
68. Walton GM, Chen WS, Rosenfeld MG, Gill GN. Analysis of deletions of the carboxyl terminus of the epidermal growth factor receptor reveals self-phosphorylation at tyrosine 992 and enhanced in vivo tyrosine phosphorylation of cell substrates. J Biol Chem 1990, 265:1750-1754.
69. Downward J, Parker P, Waterfield MD. Autophosphorylation sites on the epidermal growth factor receptor. Nature 1984, 311:483-485.
70. Margolis BL, Lax I, Kris R, Dombalagian M, Honegger AM, Howk R, Givol D, Ullrich A, Schlessinger J. All autophosphorylation sites of epidermal growth factor (EGF) receptor and HER2/neu are located in their carboxyl-terminal tails. Identification of a novel site in EGF receptor. J Biol Chem 1989, 264:10667-10671.
71. Salcini AE, McGlade J, Pelicci G, Nicoletti I, Pawson T, Pelicci PG. Formation of Shc-Grb2 complexes is necessary to induce neoplastic transformation by overexpression of Shc proteins. Oncogene 1994, 9:2827-2836.
72. Lehr S, Kotzka J, Herkner A, Klein E, Siethoff C, Knebel B, Noelle V, Bruning JC, Klein HW, Meyer HE, et al. Identification of tyrosine phosphorylation sites in human Gab-1 protein by EGF receptor kinase in vitro. Biochemistry 1999, 38:151-159.
73. Schulze WX, Deng L, Mann M. Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol Syst Biol 2005, 1:2005.0008.
74. Hause RJ Jr, Leung KK, Barkinge JL, Ciaccio MF, Chuu CP, Jones RB. Comprehensive binary interaction mapping of SH2 domains via fluorescence polarization reveals novel functional diversification of ErbB receptors. PLoS One 2012, 7:e44471.
75. Agazie YM, Hayman MJ. Molecular mechanism for a role of SHP2 in epidermal growth factor receptor signaling. Mol Cell Biol 2003, 23:7875-7886.
76. Okutani T, Okabayashi Y, Kido Y, Sugimoto Y, Sakaguchi K, Matuoka K, Takenawa T, Kasuga M. Grb2/Ash binds directly to tyrosines 1068 and 1086 and indirectly to tyrosine 1148 of activated human epidermal growth factor receptors in intact cells. J Biol Chem 1994, 269:31310-31314.
77. Batzer AG, Rotin D, Urena JM, Skolnik EY, Schlessinger J. Hierarchy of binding sites for Grb2 and Shc on the epidermal growth factor receptor. Mol Cell Biol 1994, 14:5192-5201.
78. Jorissen RN, Walker F, Pouliot N, Garrett TP, Ward CW, Burgess AW. Epidermal growth factor receptor: mechanisms of activation and signalling. Exp Cell Res 2003, 284:31-53.
79. Sakaguchi K, Okabayashi Y, Kido Y, Kimura S, Matsumura Y, Inushima K, Kasuga M. Shc phosphotyrosine-binding domain dominantly interacts with epidermal growth factor receptors and mediates Ras activation in intact cells. Mol Endocrinol 1998, 12:536-543.
80. Cunnick JM, Mei L, Doupnik CA, Wu J. Phosphotyrosines 627 and 659 of Gab1 constitute a bisphosphoryl tyrosine-based activation motif (BTAM) conferring binding and activation of SHP2. J Biol Chem 2001, 276:24380-24387.
81. Pamonsinlapatham P, Hadj-Slimane R, Lepelletier Y, Allain B, Toccafondi M, Garbay C, Raynaud F. p120-Ras GTPase activating protein (RasGAP): a multi-interacting protein in downstream signaling. Biochimie 2009, 91:320-328.
82. McDonald CB, Seldeen KL, Deegan BJ, Bhat V, Farooq A. Assembly of the Sos1-Grb2-Gab1 ternary signaling complex is under allosteric control. Arch Biochem Biophys 2010, 494:216-225.
83. McDonald CB, Balke JE, Bhat V, Mikles DC, Deegan BJ, Seldeen KL, Farooq A. Multivalent binding and facilitated diffusion account for the formation of the Grb2-Sos1 signaling complex in a cooperative manner. Biochemistry 2012, 51:2122-2135.
84. Liu Y, Rohrschneider LR. The gift of Gab. FEBS Lett 2002, 515:1-7.
85. Margarit SM, Sondermann H, Hall BE, Nagar B, Hoelz A, Pirruccello M, Bar-Sagi D, Kuriyan J. Structural evidence for feedback activation by Ras·GTP of the Ras-specific nucleotide exchange factor SOS. Cell 2003, 112:685-695.
86. Boykevisch S, Zhao C, Sondermann H, Philippidou P, Halegoua S, Kuriyan J, Bar-Sagi D. Regulation of Ras signaling dynamics by Sos-mediated positive feedback. Curr Biol 2006, 16:2173-2179.
87. Bos JL, Rehmann H, Wittinghofer A. GEFs and GAPs: critical elements in the control of small G proteins. Cell 2007, 129:865-877.
88. Kitano H. Computational systems biology. Nature 2002, 420:206-210.
89. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K. Scalable molecular dynamics with NAMD. J Comput Chem 2005, 26:1781-1802.
90. Brooks BR, Brooks CL III, Mackerell AD Jr, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, et al. CHARMM: the biomolecular simulation program. J Comput Chem 2009, 30:1545-1614.
91. Sethi A, Goldstein B, Gnanakaran S. Quantifying intramolecular binding in multivalent interactions: a structure-based synergistic study on Grb2-Sos1 complex. PLoS Comput Biol 2011, 7:e1002192.
92. Saunders MG, Voth GA. Coarse-graining of multiprotein assemblies. Curr Opin Struct Biol 2012, 22:144-150.
93. Lopez CF, Nielsen SO, Moore PB, Klein ML. Understanding nature's design for a nanosyringe. Proc Natl Acad Sci U S A 2004, 101:4431-4434.
94. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005, 308:523-529.
95. Janes KA, Yaffe MB. Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 2006, 7:820-828.
96. Cheong R, Rhee A, Wang CJ, Nemenman I, Levchenko A. Information transduction capacity of noisy biochemical signaling networks. Science 2011, 334:354-358.
97. Covert MW, Knight EM, Reed JL, Herrgard MJ, Palsson BØ. Integrating high-throughput and computational data elucidates bacterial networks. Nature 2004, 429:92-96.
98. Papin JA, Palsson BØ. The JAK-STAT signaling network in the human B-cell: an extreme signaling pathway analysis. Biophys J 2004, 87:37-46.
99. Wynn ML, Consul N, Merajver SD, Schnell S. Logic-based models in systems biology: a predictive and parameter-free network analysis method. Integr Biol 2012, 4:1323-1337.
100. Low-Nam ST, Lidke KA, Cutler PJ, Roovers RC, van Bergen en Henegouwen PMP, Wilson BS, Lidke DS. ErbB1 dimerization is promoted by domain co-confinement and stabilized by ligand binding. Nat Struct Mol Biol 2011, 18:1244-1249.
101. Kleiman LB, Maiwald T, Conzelmann H, Lauffenburger DA, Sorger PK. Rapid phospho-turnover by receptor tyrosine kinases impacts downstream signaling and drug binding. Mol Cell 2011, 43:723-737.
102. Koytiger G, Kaushansky A, Gordus A, Rush J, Sorger PK, MacBeath G. Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected. Mol Cell Proteomics 2013, 12:1204-1213.
103. Linding R, Jensen LJ, Ostheimer GJ, van Vugt MA, Jorgensen C, Miron IM, Diella F, Colwill K, Taylor L, Elder K, et al. Systematic discovery of in vivo phosphorylation networks. Cell 2007, 129:1415-1426.
104. Lefebvre C, Rieckhof G, Califano A. Reverse-engineering human regulatory networks. WIREs: Syst Biol Med 2012, 4:311-325.
105. Yang J, Bruno WJ, Hlavacek WS, Pearson JE. On imposing detailed balance in complex reaction mechanisms. Biophys J 2006, 91:1136-1141.
106. Ederer M, Gilles ED. Thermodynamically feasible kinetic models of reaction networks. Biophys J 2007, 92:1846-1857.
107. Danos V, Oury N. Equilibrium and termination II: the case of Petri nets. Math Struct Comp Sci 2013, 23:290-307.
108. Kerr RA, Bartol TM, Kaminsky B, Dittrich M, Chang JCJ, Baden SB, Sejnowski TJ, Stiles JR. Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J Sci Comput 2008, 30:3126-3149.
109. Andrews SS, Addy NJ, Brent R, Arkin AP. Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput Biol 2010, 6:e1000705.
110. Tyson JJ, Novák B. Functional motifs in biochemical reaction networks. Annu Rev Phys Chem 2010, 61:219-240.
111. Spencer SL, Sorger PK. Measuring and modeling apoptosis in single cells. Cell 2011, 144:926-939.
112. Kolitz SE, Lauffenburger DA. Measurement and modeling of signaling at the single-cell level. Biochemistry 2012, 51:7433-7443.
113. Purvis JE, Lahav G. Encoding and decoding cellular information through signaling dynamics. Cell 2013, 152:945-956.
114. Le Novère N, Endler L. Using chemical kinetics to model biochemical pathways. Methods Mol Biol 2013, 1021:147-167.
115. Harcourt AV, Esson W. On the laws of connexion between the conditions of a chemical change and its amount. Proc R Soc Lond 1865, 14:470-474.
116. Gillespie DT, Hellander A, Petzold LR. Perspective: stochastic algorithms for chemical kinetics. J Chem Phys 2013, 138:170901.
117. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U. COPASI--a COmplex PAthway SImulator. Bioinformatics 2006, 22:3067-3074.
118. Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U. Computational modeling of biochemical networks using COPASI. Methods Mol Biol 2009, 500:17-59.
119. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 2003, 19:524-531.
120. Keating SM, Le Novère N. Supporting SBML as a model exchange format in software applications. Methods Mol Biol 2013, 1021:201-225.
121. Chylek LA, Stites EC, Posner RG, Hlavacek WS. Innovations of the rule-based modeling approach. In: Prokop A, Csukás B, eds. Systems Biology: Integrative Biology and Simulation Tools, Springer; Dordrecht 2013, 1:273-300.
122. Chylek LA. Decoding the language of phosphorylation site dynamics. Sci Signal 2013, 6:jc2.
123. Bonabeau E. Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A 2002, 99(suppl 3):7280-7287.
124. Regev A, Silverman W, Shapiro E. Representation and simulation of biochemical processes using the pi-calculus process algebra. Proc Pac Symp Biocomput 2001, 6:459-470.
125. Priami C, Regev A, Shapiro E, Silverman W. Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inform Process Lett 2001, 80:25-31.
126. Danos V, Laneve C. Formal molecular biology. Theor Comput Sci 2004, 325:69-110.
127. Cardelli L. Artificial biochemistry. Nat Comp Ser In: Algorithmic Bioprocesses. LNCS. Springer; Heidelberg 2009:429-462.
128. Yang J, Meng X, Hlavacek WS. Rule-based modelling and simulation of biochemical systems with molecular finite automata. IET Syst Biol 2010, 4:453-466.
129. Faeder JR, Blinov ML, Goldstein B, Hlavacek WS. Rule-based modeling of biochemical networks. Complexity 2005, 10:22-41.
130. Blinov ML, Yang J, Faeder JR, Hlavacek WS. Graph theory for rule-based modeling of biochemical networks. Lect Notes Comput Sci 2006, 4230:89-106.
131. Danos V, Harmer R, Winskel G. Constraining rule-based dynamics with types. Math Struct Comp Sci 2013, 23:272-289.
132. Hayman J, Heindel T. Pattern graphs and rule-based models: the semantics of Kappa. Lect Notes Comput Sci 2013, 7794:1-16.
133. Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 2004, 20:3289-3291.
134. Faeder JR, Blinov ML, Hlavacek WS. Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 2009, 500:113-167.
135. Löwe M. Algebraic approach to single-pushout graph transformation. Theor Comput Sci 1993, 109:181-224.
136. Mu F, Williams RF, Unkefer CJ, Unkefer PJ, Faeder JR, Hlavacek WS. Carbon-fate maps for metabolic reactions. Bioinformatics 2007, 23:3193-3199.
137. Lemons NW, Hu B, Hlavacek WS. Hierarchical graphs for rule-based modeling of biochemical systems. BMC Bioinformatics 2011, 12:45.
138. Feret J, Danos V, Krivine J, Harmer R, Fontana W. Internal coarse-graining of molecular systems. Proc Natl Acad Sci U S A 2009, 106:6453-6458.
139. Jamalyaria F, Rohlfs R, Schwartz R. Queue-based method for efficient simulation of biological self-assembly systems. J Comput Phys 2005, 204:100-120.
140. Zhang T, Rohlfs R, Schwartz R. Implementation of a discrete event simulator for biological self-assembly systems. In: Kuhl ME, Steiger NM, Armstrong FB, Joines JA, eds. Proceedings of the 2005 Winter Simulation Conference, Orlando, FL. 2005, 2223-2231.
141. Monine MI, Posner RG, Savage PB, Faeder JR, Hlavacek WS. Modeling multivalent ligand-receptor interactions with steric constraints on configurations of cell-surface receptor aggregates. Biophys J 2010, 98:48-56.
142. Gruenert G, Ibrahim B, Lenser T, Lohel M, Hinze T, Dittrich P. Rule-based spatial modeling with diffusing, geometrically constrained molecules. BMC Bioinformatics 2010, 11:307.
143. Tschernyschkow S, Herda S, Gruenert G, Doring V, Gorlich D, Hofmeister A, Hoischen C, Dittrich P, Diekmann S, Ibrahim B. Rule-based modeling and simulations of the inner kinetochore structure. Prog Biophys Mol Biol 2013.
144. Lu C, Mi LZ, Grey MJ, Zhu J, Graef E, Yokoyama S, Springer TA. Structural evidence for loose linkage between ligand binding and kinase activation in the epidermal growth factor receptor. Mol Cell Biol 2010, 30:5432-5443.
145. Endres NF, Das R, Smith AW, Arkhipov A, Kovacs E, Huang Y, Pelton JG, Shan Y, Shaw DE, Wemmer DE, et al. Conformational coupling across the plasma membrane in activation of the EGF receptor. Cell 2013, 152:543-556.
146. Yun CH, Mengwasser KE, Toms AV, Woo MS, Greulich H, Wong KK, Meyerson M, Eck MJ. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proc Natl Acad Sci U S A 2008, 105:2070-2075.
147. Endres NF, Engel K, Das R, Kovacs E, Kuriyan J. Regulation of the catalytic activity of the EGF receptor. Curr Opin Struct Biol 2011, 21:777-784.
148. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 2004, 25:1605-1612.
149. Sekar JA, Faeder JR. Rule-based modeling of signal transduction: a primer. Methods Mol Biol 2012, 880:139-218.
150. Xu W, Smith AM, Faeder JR, Marai GE. RuleBender: a visual interface for rule-based modeling. Bioinformatics 2011, 27:1721-1722.
151. Smith AM, Xu W, Sun Y, Faeder JR, Marai GE. RuleBender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC Bioinformatics 2012, 13(suppl 8):S3.
152. Lopez CF, Muhlich JL, Bachman JA, Sorger PK. Programming biological models in Python using PySB. Mol Syst Biol 2013, 9:646.
153. Kiselyov VV, Versteyhe S, Gauguin L, De Meyts P. Harmonic oscillator model of the insulin and IGF1 receptors' allosteric binding and activation. Mol Syst Biol 2009, 5:243.
154. Faeder JR, Blinov ML, Hlavacek WS. Graphical rule-based representation of signal-transduction networks. In: Proceedings of the 2005 ACM Symposium on Applied Computing. Santa Fe, NM: ACM Press; 2005, 133-140.
155. Danos V, Feret J, Fontana W, Krivine J. Scalable simulation of cellular signaling networks. Lect Notes Comput Sci 2007, 4807:139-157.
156. Yang J, Monine MI, Faeder JR, Hlavacek WS. Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Phys Rev E 2008, 78:031910.
157. Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Von Hoff DD, Posner RG. RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinformatics 2010, 11:404.
158. Meier-Schellersheim M, Xu X, Angermann B, Kunkel EJ, Jin T, Germain RN. Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method. PLoS Comput Biol 2006, 2:e82.
159. Meier-Schellersheim M, Klauschen F, Angermann B. Computational modeling of signaling networks for eukaryotic chemosensing. Methods Mol Biol 2009, 571:507-526.
160. Angermann BR, Klauschen F, Garcia AD, Prustel T, Zhang F, Germain RN, Meier-Schellersheim M. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods 2012, 9:283-289.
161. Zhang F, Angermann BR, Meier-Schellersheim M. The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions. Bioinformatics 2013, 29:1229-1230.
162. Lis M, Artyomov MN, Devadas S, Chakraborty AK. Efficient stochastic simulation of reaction-diffusion processes via direct compilation. Bioinformatics 2009, 25:2289-2291.
163. Chylek LA, Wilson BS, Hlavacek WS. Modeling biomolecular site dynamics in immunoreceptor signaling sytems. In: Corey SJ, Kimmel M, Leonard JN, eds. A Systems Biology Approach to Blood. Springer; In press.
164. Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 2007, 58:35-55.
165. Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R, Shumaker DE, Woodward CS. SUNDIALS: suite of nonlinear and differential/algebraic equation solvers. ACM T Math Software 2005, 31:363-396.
166. Fricke T, Wendt D. The Markoff-automaton: a new algorithm for simulating the time-evolution of large stochastic dynamic systems. Int J Mod Phys C 1995, 6:277-306.
167. Harris LA, Clancy P. A "partitioned leaping" approach for multiscale modeling of chemical reaction dynamics. J Chem Phys 2006, 125:144107.
168. Lok L, Brent R. Automatic generation of cellular reaction networks with Moleculizer 1.0. Nat Biotechnol 2005, 23:131-136.
169. Bornstein BJ, Keating SM, Jouraku A, Hucka M. LibSBML: an API library for SBML. Bioinformatics 2008, 24:880-881.
170. Andrews SS. Spatial and stochastic cellular modeling with the Smoldyn simulator. Methods Mol Biol 2012, 804:519-542.
171. Moraru II, Schaff JC, Slepchenko BM, Blinov ML, Morgan F, Lakshminarayana A, Gao F, Li Y, Loew LM. Virtual Cell modelling and simulation software environment. IET Syst Biol 2008, 2:352-362.
172. Dembo M, Goldstein B. Theory of equilibrium binding of symmetric bivalent haptens to cell surface antibody: application to histamine release from basophils. J Immunol 1978, 121:345-353.
173. Perelson AS, DeLisi C. Receptor clustering on a cell surface. I. Theory of receptor cross-Linking by ligands bearing two chemically identical functional groups. Math Biosci 1980, 48:71-110.
174. Conzelmann H, Fey D, Gilles ED. Exact model reduction of combinatorial reaction networks. BMC Syst Biol 2008, 2:78.
175. Conzelmann H, Gilles ED. Dynamic pathway modeling of signal transduction networks: a domain-oriented approach. Methods Mol Biol 2008, 484:559-578.
176. Borisov NM, Chistopolsky AS, Faeder JR, Kholodenko BN. Domain-oriented reduction of rule-based network models. IET Syst Biol 2008, 2:342-351.
177. Harmer R, Danos V, Feret J, Krivine J, Fontana W. Intrinsic information carriers in combinatorial dynamical systems. Chaos 2010, 20(3):037108.
178. Bugenhagen SM, Beard DA. Specification, construction, and exact reduction of state transition system models of biochemical processes. J Chem Phys 2012, 137:154108.
179. Klinke DJ II, Finley SD. Timescale analysis of rule-based biochemical reaction networks. Biotechnol Prog 2012, 28:33-44.
180. Morton-Firth CJ, Bray D. Predicting temporal fluctuations in an intracellular signalling pathway. J Theor Biol 1998, 192:117-128.
181. Shimizu TS, Bray D. Computational biology--the stochastic approach. In: Kitano H, ed. Foundations of Systems Biology, Chapter 10. Cambridge, MA: MIT Press; 2001.
182. Colvin J, Monine MI, Faeder JR, Hlavacek WS, Von Hoff DD, Posner RG. Simulation of large-scale rule-based models. Bioinformatics 2009, 25:910-917.
183. Sneddon MW, Faeder JR, Emonet T. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 2011, 8:177-183.
184. Yang J, Hlavacek WS. The efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems. Phys Biol 2011, 8:055009.
185. Feret J, Krivine J. KaSim3 reference manual. 2013. Available at: http://kappalanguage.org. (Accessed September 03, 2013).
186. Mjolsness E. Time-ordered product expansions for computational stochastic system biology. Phys Biol 2013, 10:035009.
187. Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR. Exact hybrid particle/population simulation of rule-based models of biochemical systems. Available at: http://arxiv.org/abs/1301.6854. (Accessed September 3, 2013).aq
188. Anderson DF, Higham DJ. Multilevel Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics. Multiscale Model Sim 2012, 10:146-179.
189. Daigle BJ Jr, Roh MK, Petzold LR, Niemi J. Accelerated maximum likelihood parameter estimation for stochastic biochemical systems. BMC Bioinformatics 2012, 13:68.
190. Goutsias J, Jenkinson G. Markovian dynamics on complex reaction networks. Phys Rep 2013, 529:199-264.
191. Chylek LA, Hu B, Blinov ML, Emonet T, Faeder JR, Goldstein B, Gutenkunst RN, Haugh JM, Lipniacki T, Posner RG, et al. Guidelines for visualizing and annotating rule-based models. Mol BioSyst 2011, 7:2779-2795.
192. Tiger CF, Krause F, Cedersund G, Palmer R, Klipp E, Hohmann S, Kitano H, Krantz M. A framework for mapping, visualisation and automatic model creation of signal-transduction networks. Mol Syst Biol 2012, 8:578.
193. Danos V, Feret J, Fontana W, Harmer R, Krivine J. Rule-based modelling of cellular signalling. Lect Notes Comput Sci 2007, 4703:17-41.
194. Faeder JR. Toward a comprehensive language for biological systems. BMC Biol 2011, 9:68.
195. Hlavacek WS. How to deal with large models? Mol Syst Biol 2009, 5:240.
196. Wang Z, Sagotsky J, Taylor T, Shironoshita P, Deisboeck TS. Accelerating cancer systems biology research through Semantic Web technology. WIREs: Syst Biol Med 2013, 5:135-151.
197. Pedersen M, Phillips A, Plotkin G. A high-level language for rule-based modeling. Available at: http://people.ds.cam.ac.uk/mdp40/papers/lbs-kappa.pdf. (Accessed August 16, 2013).
198. Danos V, Feret J, Fontana W, Harmer R, Krivine J. Rule-based modelling and model perturbation. Lect Notes Comput Sci 2009, 5750:116-137.
199. Lim WA, Pawson T. Phosphotyrosine signaling: evolving a new cellular communication system. Cell 2010, 142:661-667.
200. Le Novère N, Finney A, Hucka M, Bhalla US, Campagne F, Collado-Vides J, Crampin EJ, Halstead M, Klipp E, Mendes P, et al. Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 2005, 23:1509-1515.
201. Bray D. Protein molecules as computational elements in living cells. Nature 1995, 376:307-312.
202. Fisher J, Henzinger TA. Executable cell biology. Nat Biotechnol 2007, 25:1239-1249.
203. Regev A, Shapiro E. Cells as computation. Nature 2002, 419:343.
204. Asztalos A, Daniels M, Sethi A, Shen T, Langan P, Redondo A, Gnanakaran S. A coarse-grained model for synergistic action of multiple enzymes on cellulose. Biotechnol Biofuels 2012, 5:55.
205. Daoutidis P, Marvin WA, Rangarajan S, Torres AI. Engineering biomass conversion processes: a systems perspective. AIChE J 2013, 59:3-18.
206. Scotti M, Stella L, Shearer EJ, Stover PJ. Modeling cellular compartmentation in one-carbon metabolism. WIREs: Syst Biol Med 2013, 5:343-365.
207. Ray JCJ, Igoshin OA. Adaptable functionality of transcriptional feedback in bacterial two-component systems. PLoS Comput Biol 2010, 6:e1000676.
208. Marchisio MA, Colaiacovo M, Whitehead E, Stelling J. Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits. BMC Syst Biol 2013, 7:42.
209. Vilar JM, Saiz L. Reliable prediction of complex phenotypes from a modular design in free energy space: an extensive exploration of the lac operon. ACS Synth Biol 2013.
210. Le Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B, et al. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 2006, 34:D689-D691.
211. Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, et al. BioModels Database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 2010, 4:92.
212. Chelliah V, Laibe C, Le Novère N. BioModels Database: a repository of mathematical models of biological processes. Methods Mol Biol 2013, 1021:189-199.
213. Cox J, Mann M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 2011, 80:273-299.
214. Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, White FM. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc Natl Acad Sci U S A 2007, 104:5860-5865.
215. VanMeter AJ, Rodriguez AS, Bowman ED, Jen J, Harris CC, Deng J, Calvert VS, Silvestri A, Fredolini C, Chandhoke V, et al. Laser capture microdissection and protein microarray 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 2008, 7:1902-1924.
216. Ciaccio MF, Wagner JP, Chuu CP, Lauffenburger DA, Jones RB. Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods 2010, 7:148-155.
217. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B Jr, Assad-Garcia N, Glass JI, Covert MW. A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150:389-401.
218. Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol 2013, 31:419-425.
219. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet 2007, 8:450-461.
220. Hlavacek WS. Two challenges of systems biology. In: Stumpf MPH, Balding DJ, Girolami MA, eds. Handbook of Statistical Systems Biology: Wiley; Chichester, UK. 2011, 3-14.
221. http://bionetgen.org
222. http://kappalanguage.org