Cells as Machines: Towards Deciphering Biochemical Programs in the Cell

  • François Fages
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8337)


Systems biology aims at understanding complex biological processes in terms of their basic mechanisms at the molecular level in cells. The bet of applying theoretical computer science concepts and software engineering methods to the analysis of distributed biochemical reaction systems in the cell, designed by natural evolution, has led to interesting challenges in computer science, and new model-based insights in biology. In this paper, we review the development over the last decade of the biochemical abstract machine (Biocham) software environment for modeling cell biology molecular reaction systems, reasoning about them at different levels of abstraction, formalizing biological behaviors in temporal logic with numerical constraints, and using them to infer non-measurable kinetic parameter values, evaluate robustness, decipher natural biochemical processes and implement new programs in synthetic biology.


Synthetic Biology System Biology Markup Language Covariance Matrix Adaptation Evolution Strategy Chemical Master Equation Order Binary Decision Diagram 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angeli, D., Leenheer, P.D., Sontag, E.D.: A petri net approach to persistence analysis in chemical reaction networks. In: Biology and Control Theory: Current Challenges. LNCIS, vol. 357, pp. 181–216. Springer (2007)Google Scholar
  2. 2.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Michael Cherry, J., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Ballesta, A., Dulong, S., Abbara, C., Cohen, B., Okyar, A., Clairambault, J., Levi, F.: A combined experimental and mathematical approach for molecular-based optimization of irinotecan circadian delivery. PLOS Computational Biology 7(9) (2011)Google Scholar
  4. 4.
    Banâtre, J.-P., Le Métayer, D.: Chemical reaction as a computational model. Functional Programmming, 103–117 (1989)Google Scholar
  5. 5.
    Banâtre, J.-P., Priol, T.: Chemical programming of future service-oriented architectures. Jounral of Software 4, 738–746 (2009)Google Scholar
  6. 6.
    Bernot, G., Comet, J.-P., Richard, A., Guespin, J.: A fruitful application of formal methods to biological regulatory networks: Extending thomas’ asynchronous logical approach with temporal logic. Journal of Theoretical Biology 229(3), 339–347 (2004)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Berry, G., Boudol, G.: The chemical abstract machine. Theoretical Computer Science 96 (1992)Google Scholar
  8. 8.
    Cardelli, L.: Brane calculi - interactions of biological membranes. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 257–278. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Chabrier-Rivier, N., Chiaverini, M., Danos, V., Fages, F., Schächter, V.: Modeling and querying biochemical interaction networks. Theoretical Computer Science 325(1), 25–44 (2004)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Chaouiya, C.: Petri net modelling of biological networks. Briefings in Bioinformatics (2007)Google Scholar
  11. 11.
    Chelliah, V., Laibe, C., Novère, N.: Biomodels database: A repository of mathematical models of biological processes. In: Schneider, M.V. (ed.) Silico Systems Biology. Methods in Molecular Biology, vol. 1021, pp. 189–199. Humana Press (2013)Google Scholar
  12. 12.
    Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: NuSMV 2: An openSource tool for symbolic model checking. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, p. 359. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press (1999)Google Scholar
  14. 14.
    Cousot, P., Cousot, R.: Abstract interpretation: A unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: POPL 1977: Proceedings of the 6th ACM Symposium on Principles of Programming Languages, pp. 238–252. ACM Press, New York (1977)Google Scholar
  15. 15.
    Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325(1), 69–110 (2004)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    De Maria, E., Fages, F., Rizk, A., Soliman, S.: Design, optimization, and predictions of a coupled model of the cell cycle, circadian clock, dna repair system, irinotecan metabolism and exposure control under temporal logic constraints. Theoretical Computer Science 412(21), 2108–2127 (2011)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Fages, F., Rizk, A.: On temporal logic constraint solving for the analysis of numerical data time series. Theoretical Computer Science 408(1), 55–65 (2008)CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Fages, F., Soliman, S.: Abstract interpretation and types for systems biology. Theoretical Computer Science 403(1), 52–70 (2008)CrossRefMATHMathSciNetGoogle Scholar
  19. 19.
    Funahashi, A., Matsuoka, Y., Jouraku, A., Morohashi, M., Kikuchi, N., Kitano, H.: Celldesigner 3.5: A versatile modeling tool for biochemical networks. Proceedings of the IEEE 96(8), 1254–1265 (2008)CrossRefGoogle Scholar
  20. 20.
    Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Grafahrend-Belau, E., Schreiber, F., Heiner, M., Sackmann, A., Junker, B.H., Grunwald, S., Speer, A., Winder, K., Koch, I.: Modularization of biochemical networks based on a classification of petri net by T-invariants. BMC Bioinformatics 9(90) (February 2008)Google Scholar
  22. 22.
    Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)CrossRefGoogle Scholar
  23. 23.
    Heitzler, D., Durand, G., Gallay, N., Rizk, A., Ahn, S., Kim, J., Violin, J.D., Dupuy, L., Gauthier, C., Piketty, V., Crépieux, P., Poupon, A., Clément, F., Fages, F., Lefkowitz, R.J., Reiter, E.: Competing g protein-coupled receptor kinases balance g protein and β-arrestin signaling. Molecular Systems Biology 8(590) (June 2012)Google Scholar
  24. 24.
    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 22(24), 3067–3074 (2006)CrossRefGoogle Scholar
  25. 25.
    Huang, D.-A., Jiang, J.-H., Huang, R.-Y., Cheng, C.-Y.: Compiling program control flows into biochemical reactions. In: ICCAD 2012: IEEE/ACM International Conference on Computer-Aided Design, San Jose, USA (November 2012)Google Scholar
  26. 26.
    Huang, R.-Y., Huang, D.-A., Chiang, H.-J.K., Jiang, J.-H., Fages, F.: Species minimization in computation with biochemical reactions. In: IWBDA 2013: Proceedings of the Fifth International Workshop on Bio-Design Automation. Imperial College, London (2013)Google Scholar
  27. 27.
    Hucka, M.: et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)CrossRefGoogle Scholar
  28. 28.
    Hucka, M., Hoops, S., Keating, S.M., Nicolas, L.N., Sahle, S., Wilkinson, D.: Systems biology markup language (SBML) level 2: Structures and facilities for model definitions. In: Nature Precedings (December 2008)Google Scholar
  29. 29.
    Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival Jr., B., Assad-Garcia, N., Glass, J.I., Covert, M.W.: A whole-cell computational model predicts phenotype from genotype. Cell 150(2), 389–401 (2012)CrossRefGoogle Scholar
  30. 30.
    Kitano, H.: Towards a theory of biological robustness. Molecular Systems Biology 3, 137 (2007)CrossRefGoogle Scholar
  31. 31.
    Kitano, H.: Systems biology: A brief overview. Science 295(5560), 1662–1664 (2002)CrossRefGoogle Scholar
  32. 32.
    Kohn, K.W.: Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Molecular Biology of the Cell 10(8), 2703–2734 (1999)CrossRefGoogle Scholar
  33. 33.
    le Novere, N., Hucka, M., Mi, H., Moodie, S., Schreiber, F., Sorokin, A., Demir, E., Wegner, K., Aladjem, M.I., Wimalaratne, S.M., Bergman, F.T., Gauges, R., Ghazal, P., Kawaji, H., Li, L., Matsuoka, Y., Villeger, A., Boyd, S.E., Calzone, L., Courtot, M., Dogrusoz, U., Freeman, T.C., Funahashi, A., Ghosh, S., Jouraku, A., Kim, S., Kolpakov, F., Luna, A., Sahle, S., Schmidt, E., Watterson, S., Wu, G., Goryanin, I., Kell, D.B., Sander, C., Sauro, H., Snoep, J.L., Kohn, K., Kitano, H.: The systems biology graphical notation. Nature Biotechnology 27(8), 735–741 (2009)CrossRefGoogle Scholar
  34. 34.
    Matsuno, H., Doi, A., Nagasaki, M., Miyano, S.: Hybrid petri net representation of gene regulatory network. In: Proceedings of the 5th Pacific Symposium on Biocomputing, pp. 338–349 (2000)Google Scholar
  35. 35.
    Matsuno, H., Tanaka, Y., Aoshima, H., Doi, A., Matsui, M., Miyano, S.: Biopathways representation and simulation on hybrid functional petri net. Silico Biology 3, 32 (2003)Google Scholar
  36. 36.
    Nabli, F., Fages, F., Martinez, T., Soliman, S.: A boolean model for enumerating minimal siphons and traps in petri nets. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 798–814. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  37. 37.
    Nagasaki, M., Onami, S., Miyano, S., Kitano, H.: Bio-calculus: Its concept, and an application for molecular interaction. In: Currents in Computational Molecular Biology. Frontiers Science Series, vol. 30, Universal Academy Press, Inc. (2000) This book is a collection of poster papers presented at the RECOMB 2000 Poster Session.Google Scholar
  38. 38.
    Priami, C., Regev, A., Silverman, W., Shapiro, E.: Application of a stochastic name passing calculus to representation and simulation of molecular processes. Information Processing Letters 80, 25–31 (2001)CrossRefMATHMathSciNetGoogle Scholar
  39. 39.
    Reddy, V.N., Mavrovouniotis, M.L., Liebman, M.N.: Petri net representations in metabolic pathways. In: Hunter, L., Searls, D.B., Shavlik, J.W. (eds.) Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology (ISMB), pp. 328–336. AAAI Press (1993)Google Scholar
  40. 40.
    Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: Bioambients: An abstraction for biological compartments. Theoretical Computer Science 325(1), 141–167 (2004)CrossRefMATHMathSciNetGoogle Scholar
  41. 41.
    Regev, A., Silverman, W., Shapiro, E.Y.: Representation and simulation of biochemical processes using the pi-calculus process algebra. In: Proceedings of the sixth Pacific Symposium of Biocomputing, pp. 459–470 (2001)Google Scholar
  42. 42.
    Rizk, A., Batt, G., Fages, F., Soliman, S.: A general computational method for robustness analysis with applications to synthetic gene networks. Bioinformatics 12(25), il69–il78 (2009)Google Scholar
  43. 43.
    Rizk, A., Batt, G., Fages, F., Soliman, S.: Continuous valuations of temporal logic specifications with applications to parameter optimization and robustness measures. Theoretical Computer Science 412(26), 2827–2839 (2011)CrossRefMATHMathSciNetGoogle Scholar
  44. 44.
    Rohr, C., Marwan, W., Heiner, M.: Snoopy - a unifying petri net framework to investigate biomolecular networks. Bioinformatics 26(7), 974–975 (2010)CrossRefGoogle Scholar
  45. 45.
    Segel, L.A.: Modeling dynamic phenomena in molecular and cellular biology. Cambridge University Press, Cambridge (1984)MATHGoogle Scholar
  46. 46.
    Senum, P., Riedel, M.: Rate-independent constructs for chemical computation. PLOS One 6(6) (2011)Google Scholar
  47. 47.
    Soliman, S.: Invariants and other structural properties of biochemical models as a constraint satisfaction problem. Algorithms for Molecular Biology 7(15) (May 2012)Google Scholar
  48. 48.
    Tyson, J.J.: Modeling the cell division cycle: cdc2 and cyclin interactions. Proceedings of the National Academy of Sciences 88(16), 7328–7332 (1991)CrossRefGoogle Scholar
  49. 49.
    Uhlendorf, J., Miermont, A., Delaveau, T., Charvin, G., Fages, F., Bottani, S., Batt, G., Hersen, P.: Long-term model predictive control of gene expression at the population and single-cell levels. Proceedings of the National Academy of Sciences 109(35), 14271–14276 (2012)CrossRefGoogle Scholar
  50. 50.
    Zevedei-Oancea, I., Schuster, S.: Topological analysis of metabolic networks based on petri net theory. Silico Biology, 3(29) (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • François Fages
    • 1
  1. 1.Inria Paris-RocquencourtEPI ContraintesFrance

Personalised recommendations