Skip to main content

Process Calculi Abstractions for Biology

  • Chapter
  • First Online:
Algorithmic Bioprocesses

Part of the book series: Natural Computing Series ((NCS))

Abstract

Several approaches have been proposed to model biological systems by means of the formal techniques and tools available in computer science. To mention just a few of them, some representations are inspired by Petri nets theory and others by stochastic processes.

A most recent approach consists in interpreting living entities as terms of process calculi, by composition of a few behavioural abstractions. This paper comparatively surveys the state of the art of the process calculi approach to biological modelling.

The modelling features of a set of calculi are tested against a simple biological scenario, and available extensions and tools are briefly commented upon.

This work has been partially sponsored by the PRIN 2006 Project BISCA—Sistemi e calcoli di ispirazione biologica e loro applicazioni and by the FIRB project RBPR0523C3.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akutsu T, Miyano S, Kuhara S (2000) Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J Comput Biol 7(3):331–343

    Article  Google Scholar 

  2. Alves R, Savageau MA (2000) Extending the method of mathematically controlled comparison to include numerical comparisons. Bioinformatics 16(9):786–798

    Article  Google Scholar 

  3. Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW (2001) BIND—the biomolecular interaction network database. Nucleic Acids Res 29(1):242–245

    Article  Google Scholar 

  4. van Bakel S, Kahn I, Vigliotti M, Heath J (2007) Modelling intarcellular fate of FGF receptors with BioAmbients. In: ENTCS

    Google Scholar 

  5. Baldan P, Bracciali A, Brodo L, Bruni R (2007) Deducing interactions in partially unspecified biological systems. In: Proceedings of the second international conference on algebraic biology (AB 2007). Lecture notes in computer science, vol 4545. Springer, Berlin, pp 262–276

    Google Scholar 

  6. The Beta workbench home page. http://www.cosbi.eu/Rpty_Soft_BetaWB.php

  7. BioSpi home page. http://www.wisdom.weizmann.ac.il/~biospi

  8. Busi N (2007) Towards a causal semantics for brane calculi. In: Proceedings of the fifth brainstorming week on membrane computing, pp 97–111

    Google Scholar 

  9. Busi N, Zandron C (2006) Modeling and analysis of biological processes by mem(brane) calculi and systems. In: Proceedings of the winter simulation conference (WSC 2006), Monterey, CA, USA, December 3–6, 2006. WSC, Monterey, pp 1646–1655

    Chapter  Google Scholar 

  10. Calder M, Gilmore S, Hillston J (2005) Automatically deriving ODEs from process algebra models of signalling pathways. In: Plotkin G (ed) Proceedings of computational methods in systems biology (CMSB 2005), Edinburgh, Scotland, pp 204–215

    Google Scholar 

  11. Calder M, Gilmore S, Hillston J (2006) Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. Trans Comput Syst Biol VII(4230):1–23; also appeared in: Proc BioCONCUR’04

    MathSciNet  Google Scholar 

  12. Calder M, Gilmore S, Hillston J, Vyshemirsky V (2009) Formal methods for biochemical signalling pathways. In: Formal methods: state of art and new directions, BCS FACS. Springer, Berlin (in press)

    Google Scholar 

  13. Calder M, Vyshemirsky V, Orton R, Gilbert D (2005) Analysis of signalling pathways using the PRISM model checker. In: Plotkin G (ed) Third international workshop on computational methods in systems biology (CMSB’05)

    Google Scholar 

  14. Cardelli L (2005) Brane calculi—interactions of biological membranes. In: Computational methods in systems biology, international conference CMSB 2004, revised selected papers, Paris, France, May 26–28, 2004. Lecture notes in computer science, vol 3082. Springer, Berlin, pp 257–278

    Google Scholar 

  15. Cardelli L, Gardner P, Kahramanoğulları O (2008) A process model of rho GTP-binding proteins in the context of phagocytosis. In: Proc. of FBTC’07. Electron Notes Theor Comput Sci 194(3):87–102

    Article  Google Scholar 

  16. Chang R (2005) Physical chemistry for the biosciences. University Science

    Google Scholar 

  17. Chiarugi D, Degano P, Marangoni R (2007) A computational approach to the functional screening of genomes. PLoS Comput Biol 3(9):1801–1806

    Article  MathSciNet  Google Scholar 

  18. Ciocchetta F, Hillston J (2007) Bio-PEPA: an extension of the process algebra PEPA for biochemical networks. In: From biology to concurrency and back (FBTC 07), ENTCS

    Google Scholar 

  19. Curti M, Degano P, Priami C, Baldari C (2004) Modelling biochemical pathways through enhanced π-calculus. Theor Comput Sci 325(1):111–140

    Article  MATH  MathSciNet  Google Scholar 

  20. Danos V, Feret J, Fontana W, Harmer R, Krivine J (2007) Rule-based modelling of cellular signalling. In: Proceedings CONCUR’07. Lecture notes in computer science. Springer, Berlin

    Google Scholar 

  21. Danos V, Feret J, Fontana W, Krivine J (2007) Scalable simulation of cellular signaling networks. In: Proceedings APLAS’07

    Google Scholar 

  22. Danos V, Feret J, Fontana W, Krivine J (2008) Abstract interpretation of reachable complexes in biological signalling networks. In: Proceedings VMCAI’08. Lecture notes in computer science. Springer, Berlin

    Google Scholar 

  23. Danos V, Fontana W, Harmer R, Krivine J (2007) Biological signalling and causality

    Google Scholar 

  24. Danos V, Krivine J (2004) Reversible communicating systems. In: Proceedings CONCUR’04. Lecture notes in computer science, vol 3170. Springer, Berlin, pp 292–307

    Google Scholar 

  25. Danos V, Krivine J (2007) Formal molecular biology done in CCS-R. Electron Notes Theor Comput Sci 180(3):31–49

    Article  Google Scholar 

  26. Danos V, Laneve C (2004) Formal molecular biology. Theor Comput Sci 325(1)

    Google Scholar 

  27. Danos V, Pradalier S (2005) Projective brane calculus. In: Computational methods in systems biology, international conference CMSB 2004, revised selected papers, Paris, France, May 26–28, 2004. Lecture notes in computer science, vol 3082. Springer, Berlin, pp 134–148

    Google Scholar 

  28. Degano P, Prandi D, Priami C, Quaglia P (2006) Beta-binders for biological quantitative experiments. Electron Notes Theor Comput Sci 164(3):101–117

    Article  Google Scholar 

  29. Dematte L, Prandi D, Priami C, Romanel A (2007) Effective index: a formal measure of drug effects. In: Proceedings FOSBE 2007, pp 485–490

    Google Scholar 

  30. Dhaeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8):707–726

    Article  Google Scholar 

  31. Efroni S, Harel D, Cohen IR (2003) Towards rigorous comprehension of biological complexity: modeling, execution, and visualization of thymic T-cell maturation Genome Res 13(11):2485–2497

    Article  Google Scholar 

  32. Fontana W, Buss L (1996) The barrier of objects: from dynamical systems to bounded organizations. In: Boundaries and barriers: on the limits to scientific knowledge. Addison–Wesley, Reading

    Google Scholar 

  33. Gillespie D (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361

    Article  Google Scholar 

  34. Gilmore S, Hillston J (1994) The PEPA workbench: a tool to support a process algebra-based approach to performance modelling. In: Proceedings of the seventh international conference on modelling techniques and tools for computer performance evaluation. Lecture notes in computer science, vol 794. Springer, Vienna, pp 353–368

    Google Scholar 

  35. Guerriero M, Priami C, Romanel A (2007) Beta-binders with static compartments. In: Proceedings of the second international conference on algebraic biology (AB07). Lecture notes in computer science. Springer, Berlin

    Google Scholar 

  36. Guerriero ML, Priami C (2006) Causality and concurrency in beta-binders. Cosbi Technical Report TR-01-2006. Available at http://www.cosbi.eu/templates/cosbi/php/get_paper.php?id=1

  37. Heath J, Kwiatkowska M, Norman G, Parker D, Tymchyshyn O (2008) Probabilistic model checking of complex biological pathways. Theor Comput Sci 319:239–257

    Article  MathSciNet  Google Scholar 

  38. van Helden J, Naim A, Mancuso R, Eldridge M, Wernisch L, D DG, Wodak S (2000) Representing and analysing molecular and cellular function using the computer. Biol Chem 381(9–10):921–935

    Article  Google Scholar 

  39. Hillston J (1996) A compositional approach to performance modelling. Cambridge University Press, Cambridge

    Google Scholar 

  40. Hoare CAR (1985) Communicating sequential processes. Prentice–Hall, New York

    MATH  Google Scholar 

  41. Kam N, Harel D, Kugler H, Marelly R, Pnueli A, Hubbard EJA, Stern MJ (2003) Formal modeling of C. elegans development: a scenario-based approach. In: Computational methods in systems biology, proceedings of the first international workshop, CMSB 2003, Roverto, Italy, February 24–26, 2003. Lecture notes in computer science, vol 2602. Springer, Berlin

    Google Scholar 

  42. The kappa factory. http://www.lix.polytechnique.fr/~krivine/kappaFactory.html

  43. Karp PD, Riley M, Saier M, Paulsen I, Collado-Vides J, Paley S, Pellegrini-Toole A, Bonavides C, Gama-Castro S (2002) The EcoCyc database. Nucleic Acids Res 30(1):56–58

    Article  Google Scholar 

  44. Kazic T (2000) Semiotes: a semantics for sharing. Bioinformatics 16(12):1129–1144

    Article  Google Scholar 

  45. Koshland D (1958) Application of a theory of enzyme specificity to protein synthesis. Proc Natl Acad Sci USA 44(2):98–104

    Article  Google Scholar 

  46. Kuttler C, Lhoussaine C, Niehren C (2007) A stochastic pi calculus for concurrent objects. In: Algebraic biology, second international conference, AB 2007, Castle of Hagenberg, Austria, July 2–4, 2007. Lecture notes in computer science, vol 4545. Springer, Berlin, pp 232–246

    Google Scholar 

  47. Kuttler C, Niehren J (2006) Gene regulation in the pi calculus: simulating cooperativity at the lambda switch. Trans Comput Syst Biol VII(4230):24–55

    MathSciNet  Google Scholar 

  48. Kwiatkowska M, Norman G, Parker D (2002) PRISM: probabilistic symbolic model checker. In: Proceedings TOOLS 2002. Lecture notes in computer science, vol 2324. Springer, Berlin, pp 200–204

    Google Scholar 

  49. Laneve C, Tarissan F (2007) A simple calculus for proteins and cells. Electron Notes Theor Comput Sci 171:139–154

    Article  Google Scholar 

  50. Lecca P, Priami C (2007) Cell cycle control in eukaryotes: a biospi model. Electron Notes Theor Comput Sci 180(3):51–63

    Article  Google Scholar 

  51. Lecca P, Priami C, Quaglia P, Rossi B, Laudanna C, Costantin G (2004) A stochastic process algebra approach to simulation of autoreactive lymphocyte recruitment. SIMULATION: Trans Soc Mod Simul Int 80(6):273–288

    Article  Google Scholar 

  52. Leye S, Priami C, Uhrmacher A (2007) A parallel beta-binders simulator. Tech Rep TR-17-2007, The Microsoft Research, University of Trento CoSBi

    Google Scholar 

  53. Miculan M, Bacci G (2006) Modal logics for brane calculus. In: Proceedings of the computational methods in systems biology, international conference (CMSB 2006). Lecture notes in computer science, vol 4210. Springer, Berlin, pp 1–16

    Google Scholar 

  54. Milner R (1989) Communication and concurrency. Prentice–Hall, New York

    MATH  Google Scholar 

  55. Milner R (1999) Communicating and mobile systems: the π-calculus. Cambridge University Press, Cambridge

    Google Scholar 

  56. Möbius home page. http://www.mobius.uiuc.edu/

  57. Nielson F, Nielson H, Priami C, Rosa D (2007) Control flow analysis for BioAmbients. Electron Notes Theor Comput Sci 180(3):65–79

    Article  Google Scholar 

  58. Nielson HR, Nielson F, Pilegaard H (2004) Spatial analysis of bioambients. In: Giacobazzi R (ed) Proceedings of the static analysis, 11th international symposium (SAS’04). Lecture notes in computer science, vol 3148. Springer, Berlin, pp 69–83

    Google Scholar 

  59. Păun G (2002) Membrane computing. An introduction. Springer, Berlin

    MATH  Google Scholar 

  60. Peleg M, Yeh I, Altman R (2002) Modeling biological processes using workflow and Petri net models. Bioinformatics 18:825–837

    Article  Google Scholar 

  61. The PEPA plug-in project. http://www.dcs.ed.ac.uk/pepa/tools/plugin/

  62. Phillips A, Cardelli L (2004) A correct abstract machine for the stochastic pi-calculus. In: BioConcur ’04, workshop on concurrent models in molecular biology

    Google Scholar 

  63. Phillips A, Cardelli L, Castagna G (2006) A graphical representation for biological processes in the stochastic pi-calculus. Trans Comput Syst Biol 4230:123–152

    MathSciNet  Google Scholar 

  64. Pilegaard H, Nielson F, Nielson HR (2005) Static analysis of a model of the LDL degradation pathway. In: Plotkin G (ed) Third international workshop on computational methods in systems biology (CMSB’05)

    Google Scholar 

  65. Plotkin GD (2004) A structural approach to operational semantics. J Log Algebr Program 60–61:17–139

    MathSciNet  Google Scholar 

  66. Prandi D, Priami C, Quaglia P (2006) Shape spaces in formal interactions. ComPlexUS 2(3–4):128–139

    Google Scholar 

  67. Priami C, Quaglia P (2005) Beta binders for biological interactions. In: Danos V, Schächter V (eds) Proceedings of the 2nd international workshop on computational methods in systems biology (CMSB ’04). Lecture notes in bioinformatics, vol 3082. Springer, Berlin, pp 21–34

    Google Scholar 

  68. Priami C, Regev A, Silverman W, Shapiro E (2001) Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inf Process Lett 80(1):25–31

    Article  MATH  MathSciNet  Google Scholar 

  69. Regev A, Panina EM, Silverman W, Cardelli L, Shapiro EY (2004) BioAmbients: an abstraction for biological compartments. Theor Comput Sci 325(1):141–167

    Article  MATH  MathSciNet  Google Scholar 

  70. Regev A, Shapiro E (2002) Cells as computation. Nature 419:343

    Article  Google Scholar 

  71. Regev A, Silverman W, Shapiro E (2001) Representation and simulation of biochemical processes using the π-calculus process algebra. In: Proceedings of pacific symposium on biocomputing (PSB’01), vol 6, pp 459–470

    Google Scholar 

  72. Romanel A, Dematté L, Priami C (2007) The beta workbench. Tech rep TR-03-2007, The Microsoft Research, University of Trento CoSBi

    Google Scholar 

  73. Sangiorgi D, Walker D (2001) The π-calculus: a theory of mobile processes. Cambridge University Press, Cambridge

    Google Scholar 

  74. Sauro HM, Hucka M, Finney A, Wellock C, Bolouri H, Doyle J, Kitano H (2003) Next generation simulation tools: the systems biology workbench and biospice integration. OMICS: J Integr Biol 7(4):355–372

    Article  Google Scholar 

  75. Schuster S, Fell DA, Dandekar T (2000) A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol 18(3):326–332

    Article  Google Scholar 

  76. SPiM home page. http://research.microsoft.com/en-us/projects/spim/

  77. Stenesh J (1998) Biochemistry. Springer, Berlin

    Google Scholar 

  78. Szallasi Z (1999) Genetic network analysis in light of massively parallel biological data. In: Altman R, Dunker A, Hunter L, Klein T (eds) Pacific symposium on biocomputing, vol 4. World Scientific, Singapore, pp 5–16

    Google Scholar 

  79. Versari C, Busi N (2007) Efficient stochastic simulation of biological systems with multiple variable volumes. In: Proceedings FBTC 07

    Google Scholar 

  80. Wingender E, Chen X, Fricke E, Geffers R, Hehl R, Liebich I, M MK, Matys V, Michael H, Ohnhauser R, Pruss M, Schacherer F, Thiele S, Urbach S (2001) The TRANSFAC system on gene expression regulation. Nucleic Acids Res 29(1):281–283

    Article  Google Scholar 

  81. Yi TM, Huang Y, Simon MI, Doyle J (2000) Robust perfect adaptation in bacterial chemotaxis through integral feedback control. Proc Natl Acad Sci USA 97(9):4649–4653

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Luisa Guerriero .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Guerriero, M.L., Prandi, D., Priami, C., Quaglia, P. (2009). Process Calculi Abstractions for Biology. In: Condon, A., Harel, D., Kok, J., Salomaa, A., Winfree, E. (eds) Algorithmic Bioprocesses. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88869-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88869-7_23

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88868-0

  • Online ISBN: 978-3-540-88869-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics