Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

  • Author(s): J. Yang 1 ; X. Meng 1 ; W.S. Hlavacek 2, 3
    • View affiliations
    • Affiliations: 1: Chinese Academy of Sciences, Max Plank Society Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Shanghai, People's Republic of China
      2: Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, USA
      3: Department of Biology, University of New Mexico, Albuquerque, USA
  • Source: Volume 4, Issue 6, November 2010, p. 453 – 466
    DOI:  10.1049/iet-syb.2010.0015 , Print ISSN 1751-8849, Online ISSN 1751-8857
© The Institution of Engineering and Technology
Published

The authors propose a theoretical formalism, molecular finite automata (MFA), to describe individual proteins as rule-based computing machines. The MFA formalism provides a framework for modelling individual protein behaviours and systems-level dynamics via construction of programmable and executable machines. Models specified within this formalism explicitly represent the context-sensitive dynamics of individual proteins driven by external inputs and represent protein–protein interactions as synchronised machine reconfigurations. Both deterministic and stochastic simulations can be applied to quantitatively compute the dynamics of MFA models. They apply the MFA formalism to model and simulate a simple example of a signal-transduction system that involves an MAP kinase cascade and a scaffold protein.

Inspec keywords: molecular biophysics; biological techniques; proteins; biology computing; biochemistry; stochastic processes

Other keywords: synchronised machine reconfigurations; molecular finite automata; rule-based simulation; individual proteins; systems-level dynamics; biochemical systems; protein-protein interactions; signal-transduction system; rule-based computing machines; scaffold protein; rule-based modelling; stochastic simulations

Subjects: Model reactions in molecular biophysics; Biophysical instrumentation and techniques; Biology and medical computing; Biomolecular interactions, charge transfer complexes

References

    1. 1)
    2. 2)
      • S.S. Andrews , N.J. Addy , R. Brent , A.P. Arkin , H.M. Sauro . Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput. Biol.
    3. 3)
    4. 4)
    5. 5)
      • T. Pawson , P. Nash . Assembly of cell regulatory systems through protein interaction domains. Science , 445 - 452
    6. 6)
      • N.M. Borisov , A.S. Chistopolsky , J.R. Faeder , B.N. Kholodenko . Domain- oriented reduction of rule-based network models. IET Syst. Biol , 342 - 351
    7. 7)
      • M. Lis , M.N. Artyomov , S. Devadas , A.K. Chakraborty . Efficient stochastic simulation of reaction–diffusion processes via direct compilation. Bioinformatics , 2289 - 2291
    8. 8)
      • M. Sipser . (2005) Introduction to the theory of computation.
    9. 9)
      • A. Phillips , L. Cardelli . Efficient, correct simulation of biological processes in the stochastic pi-calculus. Lect. Notes Comput. Sci. , 184 - 199
    10. 10)
      • Yoshimi, M., Iwaoka, Y., Nishikawa, Y.: `FPGA implementation of a data-driven stochastic biochemical simulator with the next reaction method', Int. Conf. on Field Programmable Logic and Applications, 2007, p. 254–259.
    11. 11)
      • J. Fisher , D. Harel , M.S. Iyengar . (2010) On statecharts for biology, Symbolic systems biology: theory and methods.
    12. 12)
      • I.I. Moraru , J.C. Schaff , B.M. Slepchenko . Virtual cell modelling and simulation software environment. IET Syst. Biol. , 352 - 362
    13. 13)
      • M. Meier-Schellersheim , X. Xu , B. Angermann , E.J. Kunkel , T. Jin , R.N. Germain . Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method. PLoS Comput. Biol.
    14. 14)
    15. 15)
      • W.S. Hlavacek , J.R. Faeder . The complexity of cell signaling and the need for a new mechanics. Sci. Signal.
    16. 16)
      • T.P. Garrington , G.L. Johnson . Organization and regulation of mitogen- activated protein kinase signaling pathways. Curr. Opin. Cell Biol. , 211 - 218
    17. 17)
      • M. Mohri . Finite-state transducers in language and speech processing. Comput. Linguist. , 269 - 311
    18. 18)
      • J. Fisher , T.A. Henzinger . Executable cell biology. Nat. Biotechnol , 1239 - 1250
    19. 19)
      • M.L. Blinov , J. Yang , J.R. Faeder , W.S. Hlavacek . Graph theory for rule-based modeling of biochemical networks. Lect. Notes Comput. Sci. , 89 - 106
    20. 20)
    21. 21)
    22. 22)
      • H. Conzelmann , J. Saez-Rodriguez , T. Sauter , B.N. Kholodenko , E. Gilles . A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics
    23. 23)
    24. 24)
      • Andrei, O., Kirchner, H.: `Graph rewriting and strategies for modeling biochemical networks', Proc. Ninth Int. Symp. on Symbolic and Numeric Algorithms for Scientific Computing, 2007, p. 407–414.
    25. 25)
      • D.T. Gillespie . Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem , 35 - 55
    26. 26)
      • J.R. Faeder , W.S. Hlavacek , I. Reischl . Investigation of early events in FcɛRI-mediated signaling using a detailed mathematical model. J. Immunol. , 3769 - 3781
    27. 27)
      • V. Danos , J. Feret , W. Fontana , R. Harmer , J. Krivine . Rule-based modelling of cellular signalling. Lect. Notes Comput. Sci. , 17 - 41
    28. 28)
      • A.F. Voter . Introduction to the kinetic Monte Carlo method. Radiat. Effects Solids , 1 - 23
    29. 29)
      • C. Priami , P. Quaglia . Beta binders for biological interactions. Lect. Notes Comput. Sci. , 20 - 33
    30. 30)
      • J. Colvin , M.I. Monine , J.R. Faeder , W.S. Hlavacek , D.D. Von Hoff , R.G. Posner . Simulation of large-scale rule-based models. Bioinformatics , 910 - 917
    31. 31)
      • W.S. Hlavacek , J.R. Faeder , M.L. Blinov , R.G. Posner , M. Hucka , W. Fontana . Rules for modeling signal-transduction systems. Sci. STKE
    32. 32)
      • J. Colvin , M.I. Monine , R.N. Gutenkunst , W.S. Hlavacek , D.D. Von Hoff , R.G. Posner . RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinf.
    33. 33)
      • N.M. Borisov , N.I. Markevich , J.B. Hoek , B.N. Kholodenko . Trading the micro-world of combinatorial complexity for the macro-world of protein interaction domains. BioSystems , 152 - 166
    34. 34)
      • L. Lok , R. Brent . Automatic generation of cellular reaction networks with moleculizer 1.0. Nat. Biotechnol. , 131 - 136
    35. 35)
      • J.E. Hopcroft , J.D. Ullman . (1979) Introduction to automata theory, languages, and computation.
    36. 36)
      • A. Regev , W. Silverman , E. Shapiro . Representation and simulation of biochemical processes using the π-calculus process algebra. Pacific Symp. on Biocomputing , 459 - 470
    37. 37)
      • W.S. Hlavacek , J.R. Faeder , M.L. Blinov , A.S. Perelson , B. Goldstein . The complexity of complexes in signal transduction. Biotechnol. Bioeng. , 783 - 794
    38. 38)
      • L. Cardelli , A. Condon , D. Harel , J.N. Kok , A. Salomaa , E. Winfree . (2009) Artificial biochemistry, Algorithmic bioprocesses.
    39. 39)
      • B.J. Mayer , M.L. Blinov , L.M. Loew . Molecular machines or pleiomorphic ensembles: signaling complexes revisited. J. Biol.
    40. 40)
      • J. Yang , W.S. Hlavacek . Rejection-free kinetic Monte Carlo simulation of multivalent biomolecular interactions.
    41. 41)
      • R. Breitling , D. Hoeller . Current challenges in quantitative modeling of epidermal growth factor signaling. FEBS Lett. , 6289 - 6294
    42. 42)
      • J.R. Faeder , M.L. Blinov , W.S. Hlavacek . Rule-based modeling of biochemical systems with BioNetGen. Methods Mol. Biol , 113 - 167
    43. 43)
      • Keane, J.F., Bradley, C., Ebeling, C.: `A compiled accelerator for biological cell signaling simulations', Proc. 2004 ACM/SIGDA 12th Int. Symp. on Field Programmable Gate Arrays, 2004, p. 233–241.
    44. 44)
      • A. Mallavarapu , M. Thomson , B. Ullian , J. Gunawardena . Programming with models: modularity and abstraction provide powerful capabilities for systems biology. J. Roy. Soc. Interface , 257 - 270
    45. 45)
      • N. van Kampen . (2001) Stochastic processes in physics and chemistry.
    46. 46)
      • B. Goldstein , J.R. Faeder , W.S. Hlavacek , M.L. Blinov , A. Redondo , C. Wofsy . Modeling the early signaling events mediated by FcɛRI. Mol. Immunol. , 1213 - 1219
    47. 47)
      • M.L. Blinov , J.R. Faeder , B. Goldstein , W.S. Hlavacek . BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics , 3289 - 3291
    48. 48)
      • C.J. Morton-Firth , D. Bray . Predicting temporal fluctuations in an intracellular signalling pathway. J. Theor. Biol. , 117 - 128
    49. 49)
      • A. Regev , E. Shapiro . Cellular abstractions: cells as computation. Nature
    50. 50)
      • B.B. Aldridge , J.M. Burke , D.A. Lauffenburger , P.K. Sorger . Physico-chemical modelling of cell signalling pathways. Nat. Cell Biol. , 1195 - 1203
    51. 51)
      • V. Danos , J. Feret , W. Fontana , J. Krivine . Scalable simulation of cellular signaling networks. Lect. Notes Comput. Sci. , 139 - 157
    52. 52)
      • D. Brand , P. Zafiropulo . On communicating finite-state machines. J. ACM , 323 - 342
    53. 53)
      • T. Hunter . Signaling – 2000 and beyond. Cell , 113 - 127
    54. 54)
      • B.N. Kholodenko . Cell signalling dynamics in time and space. Nat. Rev. Mol. Cell Biol , 165 - 176
    55. 55)
    56. 56)
      • E. Börger , R.F. Stärk . (2003) Abstract state machines: a method for high-level system design and analysis.
    57. 57)
      • J. Yang , M.I. Monine , J.R. Faeder , W.S. Hlavacek . Kinetic Monte Carlo method for rule-based modeling of biochemical networks. Phys. Rev. E
    58. 58)
      • X.J. Yang . Multisite protein modification and intramolecular signaling. Oncogene , 1653 - 1662
    59. 59)
      • L. Salwinski , D. Eisenberg . In silico simulation of biological network dynamics. Nat. Biotechnol. , 1017 - 1019
    60. 60)
      • R.P. Bhattacharyya , A. Reményi , B.J. Yeh , W.A. Lim . Domains, motifs, and scaffolds: the role of modular interactions in the evolution and wiring of cell signaling circuits. Annu. Rev. Biochem. , 655 - 680
    61. 61)
      • M.A. Gibson , J. Bruck . Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A , 1876 - 1889
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2010.0015
Loading

Related content

content/journals/10.1049/iet-syb.2010.0015
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address