Rule-based modelling and simulation of biochemical systems with molecular finite automata

IET Systems Biology

Volumn 4, Issue 6, 2010, Pages 453-466

  Yang, J ^a   Meng, X ^a   Hlavacek, W S ^b,c  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b CENTER FOR NONLINEAR STUDIES   (United States)

^c UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMICAL SYSTEMS; COMPUTING MACHINES; CONTEXT-SENSITIVE; EXTERNAL INPUT; INDIVIDUAL PROTEINS; MAP KINASE; MODELLING AND SIMULATIONS; PROTEIN-PROTEIN INTERACTIONS; RULE BASED; SCAFFOLD PROTEIN; STOCHASTIC SIMULATIONS; TRANSDUCTION SYSTEM;

COMPUTER SIMULATION; PROTEINS; SIGNAL TRANSDUCTION;

STOCHASTIC MODELS;

PROTEIN;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BIOLOGY; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER SIMULATION; METABOLISM; METHODOLOGY; MONTE CARLO METHOD; SIGNAL TRANSDUCTION; STATISTICS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; MAP KINASE SIGNALING SYSTEM; MODELS, BIOLOGICAL; MODELS, CHEMICAL; MODELS, MOLECULAR; MONTE CARLO METHOD; PROTEINS; STOCHASTIC PROCESSES;

EID: 78149452287     PISSN: 17518849     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-syb.2010.0015     Document Type: Article

References (61)

1. KitanoH.: 'Computational systems biology'Nature2002420p. 206-21010.1038/nature012540028-0836
2. KholodenkoB.N.: 'Cell signalling dynamics in time and space'Nat. Rev. Mol. Cell Biol20067p. 165-176
3. AldridgeB.B. BurkeJ.M. LauffenburgerD.A. SorgerP.K.: 'Physico-chemical modelling of cell signalling pathways'Nat. Cell Biol.20068p. 1195-1203
4. BreitlingR. HoellerD.: 'Current challenges in quantitative modeling of epidermal growth factor signaling'FEBS Lett.2005579p. 6289-6294
5. HlavacekW.S. FaederJ.R. BlinovM.L. PerelsonA.S. GoldsteinB.: 'The complexity of complexes in signal transduction'Biotechnol. Bioeng.200384p. 783-794
6. YangX.J.: 'Multisite protein modification and intramolecular signaling'Oncogene200524p. 1653-1662
7. HunterT.: 'Signaling - 2000 and beyond'Cell2000100p. 113-127
8. PawsonT. NashP.: 'Assembly of cell regulatory systems through protein interaction domains'Science2003300p. 445-4520036-8075
9. BhattacharyyaR.P. ReményiA. YehB.J. LimW.A.: 'Domainsmotifsand scaffolds: the role of modular interactions in the evolution and wiring of cell signaling circuits'Annu. Rev. Biochem.200675p. 655-680
10. HlavacekW.S. FaederJ.R.: 'The complexity of cell signaling and the need for a new mechanics'Sci. Signal.20092p. pe46
11. MayerB.J. BlinovM.L. LoewL.M.: 'Molecular machines or pleiomorphic ensembles: signaling complexes revisited'J. Biol.20098p. 81
12. HlavacekW.S. FaederJ.R. BlinovM.L. PosnerR.G. HuckaM. FontanaW.: 'Rules for modeling signal-transduction systems'Sci. STKE20062006p. re6
13. DanosV. FeretJ. FontanaW. HarmerR. KrivineJ.: 'Rule-based modelling of cellular signalling'Lect. Notes Comput. Sci.20074703p. 17-41
14. DanosV. LaneveC.: 'Formal molecular biology'Theor. Comput. Sci.2004325p. 69-11010.1016/j.tcs.2004.03.0650304-3975
15. PriamiC. QuagliaP.: 'Beta binders for biological interactions'Lect. Notes Comput. Sci.20053082p. 20-33
16. FaederJ.R. BlinovM.L. GoldsteinB. HlavacekW.S.: 'Rule-based modeling of biochemical networks'Complexity200510p. 22-4110.1002/cplx.200741076-2787
17. BlinovM.L. YangJ. FaederJ.R. HlavacekW.S.: 'Graph theory for rule-based modeling of biochemical networks'Lect. Notes Comput. Sci.20064230p. 89-106
18. AndreiO. KirchnerH.: 'Graph rewriting and strategies for modeling biochemical networks'Proc. Ninth Int. Symp. on Symbolic and Numeric Algorithms for Scientific Computing2007p. 407-414
19. FeretJ. DanosV. KrivineJ. HarmerR. FontanaW.: 'Internal coarse-graining of molecular systems'Proc. Natl. Acad. Sci. USA2009106p. 6453-645810.1073/pnas.08099081060027-8424
20. BlinovM.L. FaederJ.R. GoldsteinB. HlavacekW.S.: 'BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains'Bioinformatics200420p. 3289-3291
21. MoraruI.I. SchaffJ.C. SlepchenkoB.M.: et al. 'Virtual cell modelling and simulation software environment'IET Syst. Biol.20082p. 352-362
22. FaederJ.R. BlinovM.L. HlavacekW.S.: 'Rule-based modeling of biochemical systems with BioNetGen'Methods Mol. Biol2009500p. 113-167
23. MallavarapuA. ThomsonM. UllianB. GunawardenaJ.: 'Programming with models: modularity and abstraction provide powerful capabilities for systems biology'J. Roy. Soc. Interface20096p. 257-270
24. LokL. BrentR.: 'Automatic generation of cellular reaction networks with moleculizer 1.0'Nat. Biotechnol.200523p. 131-136
25. AndrewsS.S. AddyN.J. BrentR. ArkinA.P. SauroH.M.: 'Detailed simulations of cell biology with Smoldyn 2.1'PLoS Comput. Biol.20106p. e1000705
26. Meier-SchellersheimM. XuX. AngermannB. KunkelE.J. JinT. GermainR.N.: 'Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method'PLoS Comput. Biol.20062p. e82
27. LisM. ArtyomovM.N. DevadasS. ChakrabortyA.K.: 'Efficient stochastic simulation of reaction-diffusion processes via direct compilation'Bioinformatics200925p. 2289-2291
28. Morton-FirthC.J. BrayD.: 'Predicting temporal fluctuations in an intracellular signalling pathway'J. Theor. Biol.1998192p. 117-128
29. RegevA. SilvermanW. ShapiroE.: 'Representation and simulation of biochemical processes using the π-calculus process algebra'Pacific Symp. on Biocomputing2001vol. 6p. 459-470
30. PriamiC. RegevA. ShapiroE. SilvermanW.: 'Application of a stochastic name-passing calculus to representation and simulation of molecular processes'Inf. Process. Lett.200180p. 25-3110.1016/S0020-0190(01)00214-90020-0190
31. RegevA. ShapiroE.: 'Cellular abstractions: cells as computation'Nature2002419p. 3430028-0836
32. FisherJ. HenzingerT.A.: 'Executable cell biology'Nat. Biotechnol200725p. 1239-1250
33. GillespieD.T.: 'Stochastic simulation of chemical kinetics'Annu. Rev. Phys. Chem200758p. 35-55
34. HopcroftJ.E. MotwaniR. UllmanJ.D.: 'Introduction to automata theorylanguagesand computation'2nd(Addison-WesleyNew York 2000)
35. SipserM.: 'Introduction to the theory of computation'2nd(Course Technology 2005)
36. LeeD. YannakakisM.: 'Principles and methods of testing finite state machines - a survey'Proc. IEEE199684p. 1090-112310.1109/5.5339560018-9219
37. BörgerE. StärkR.F.: 'Abstract state machines: a method for high-level system design and analysis'(Springer VerlagNew York 2003)
38. MohriM.: 'Finite-state transducers in language and speech processing'Comput. Linguist.199723p. 269-3110891-2017
39. BrandD. ZafiropuloP.: 'On communicating finite-state machines'J. ACM198330p. 323-3420004-5411
40. GoldsteinB. FaederJ.R. HlavacekW.S. BlinovM.L. RedondoA. WofsyC.: 'Modeling the early signaling events mediated by Fc RI'Mol. Immunol.200238p. 1213-1219
41. FaederJ.R. HlavacekW.S. ReischlI.: et al. 'Investigation of early events in FcRI-mediated signaling using a detailed mathematical model'J. Immunol.2003170p. 3769-3781
42. BorisovN.M. MarkevichN.I. HoekJ.B. KholodenkoB.N.: 'Signaling through receptors and scaffolds: independent interactions reduce combinatorial complexity'Biophys. J.200589p. 951-96610.1529/biophysj.105.0605330006-3495
43. ConzelmannH. Saez-RodriguezJ. SauterT. KholodenkoB.N. GillesE.: 'A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks'BMC Bioinformatics20067p. 34
44. BorisovN.M. MarkevichN.I. HoekJ.B. KholodenkoB.N.: 'Trading the micro-world of combinatorial complexity for the macro-world of protein interaction domains'BioSystems200683p. 152-166
45. BorisovN.M. ChistopolskyA.S. FaederJ.R. KholodenkoB.N.: 'Domain- oriented reduction of rule-based network models'IET Syst. Biol20082p. 342-351
46. van KampenN.G.: 'Stochastic processes in physics and chemistry'3rd(North-HollandBoston 2007)
47. VoterA.F.: 'Introduction to the kinetic Monte Carlo method'Radiat. Effects Solids2007235p. 1-23
48. YangJ. MonineM.I. FaederJ.R. HlavacekW.S.: 'Kinetic Monte Carlo method for rule-based modeling of biochemical networks'Phys. Rev. E200878p. 031910
49. YangJ. HlavacekW.S.: 'Rejection-free kinetic Monte Carlo simulation of multivalent biomolecular interactions'Arxiv preprint arXiv:0812.4619v52010
50. DanosV. FeretJ. FontanaW. KrivineJ.: 'Scalable simulation of cellular signaling networks'Lect. Notes Comput. Sci.20074807p. 139-157
51. ColvinJ. MonineM.I. FaederJ.R. HlavacekW.S. Von HoffD.D. PosnerR.G.: 'Simulation of large-scale rule-based models'Bioinformatics200925p. 910-917
52. ColvinJ. MonineM.I. GutenkunstR.N. HlavacekW.S. Von HoffD.D. PosnerR.G.: 'RuleMonkey: software for stochastic simulation of rule-based models'BMC Bioinf.201011p. 404
53. GarringtonT.P. JohnsonG.L.: 'Organization and regulation of mitogen- activated protein kinase signaling pathways'Curr. Opin. Cell Biol.199911p. 211-218
54. GibsonM.A. BruckJ.: 'Efficient exact stochastic simulation of chemical systems with many species and many channels'J. Phys. Chem. A2000104p. 1876-18891089-5639
55. BrayD.: 'Protein molecules as computational elements in living cells'Nature1995376p. 307-31210.1038/376307a00028-0836
56. FisherJ. HarelD.: 'On statecharts for biology'IyengarM.S.Symbolic systems biology: theory and methods (Jones and Bartlett 2010)
57. CardelliL.: 'Artificial biochemistry'CondonA.HarelD.KokJ.N.SalomaaA.WinfreeE.Algorithmic bioprocesses (Natural Computing SeriesSpringerBerlin 2009)p. 429-462
58. PhillipsA. CardelliL.: 'Efficientcorrect simulation of biological processes in the stochastic pi-calculus'Lect. Notes Comput. Sci.20074695p. 184-199
59. KeaneJ.F. BradleyC. EbelingC.: 'A compiled accelerator for biological cell signaling simulations'Proc. 2004 ACM/SIGDA 12th Int. Symp. on Field Programmable Gate Arrays2004p. 233-241
60. SalwinskiL. EisenbergD.: 'In silico simulation of biological network dynamics'Nat. Biotechnol.200422p. 1017-1019
61. YoshimiM. IwaokaY. NishikawaY.: et al. 'FPGA implementation of a data-driven stochastic biochemical simulator with the next reaction method'Int. Conf. on Field Programmable Logic and Applications2007p. 254-259