Review Article | Published:

Executable cell biology

Nature Biotechnology volume 25, pages 12391249 (2007) | Download Citation

Abstract

Computational modeling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviors. In this review, we distinguish between two types of biological models—mathematical and computational—which differ in their representations of biological phenomena. We call the approach of constructing computational models of biological systems 'executable biology', as it focuses on the design of executable computer algorithms that mimic biological phenomena. We survey the main modeling efforts in this direction, emphasize the applicability and benefits of executable models in biological research and highlight some of the challenges that executable biology poses for biology and computer science. We claim that for executable biology to reach its full potential as a mainstream biological technique, formal and algorithmic approaches must be integrated into biological research. This will drive biology toward a more precise engineering discipline.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , & A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).

  2. 2.

    Systems biology: a brief overview. Science 295, 1662–1664 (2002).

  3. 3.

    & Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

  4. 4.

    , , & Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inf. Process. Lett. 80, 25–31 (2001).

  5. 5.

    , & Representation and simulation of biochemical processes using the pi-calculus process algebra. Pac. Symp. Biocomput. 459–470 (2001).

  6. 6.

    , & A formal language for computational systems biology. OMICS 8, 370–380 (2004).

  7. 7.

    Abstract machines of systems biology. Transactions on Computational Systems Biology III. LNCS 3737, 145–168, (2005).

  8. 8.

    , & in Visual Languages and Formal Methods Stresa, Italy, September 5-7, 2001 (IEEE, 2001).

  9. 9.

    , & Toward rigorous comprehension of biological complexity: modeling, execution and visualization of thymic T-cell maturation. Genome Res. 13, 2485–2497 (2003).

  10. 10.

    et al. in First International Workshop on Computational Methods in Systems Biology (CMSB), Roverto, Italy, February 24–26, 2003 (ed. Priami, C.) LNCS 2602, 4–20 (2003).

  11. 11.

    , , , & Computational insights into Caenorhabditis elegans vulval development. Proc. Natl. Acad. Sci. USA 102, 1951–1956 (2005).

  12. 12.

    , & Emergent dynamics of thymocyte development and lineage determination. PLoS Comput. Biol. 3, e13 (2007).

  13. 13.

    , , & Predictive modeling of signaling croostalk during C. elegans vulval development. PLoS Comput. Biol. 3, e92 (2007).

  14. 14.

    et al. Towards verified biological models. in Transactions on Computational Biology and Bioinformatics (in the press).

  15. 15.

    Statecharts: a visual formalism for complex systems. Sci. Comput. Program. 8, 231–274 (1987).

  16. 16.

    & Reactive Modules. Form. Methods Syst. Des. 15, 7–48 (1999).

  17. 17.

    , & Intercellular coupling amplifies fate segregation during Caenorhabditis elegans vulval development. Proc. Natl. Acad. Sci. USA 103, 1331–1336 (2006).

  18. 18.

    & Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 7, 820–828 (2006).

  19. 19.

    , , & Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195–1203 (2006).

  20. 20.

    & A biological approach to computational models of proteomic networks. Curr. Opin. Chem. Biol. 10, 73–80 (2006).

  21. 21.

    , , & Integrated mechanistic and data-driven modelling for multivariate analysis of signalling pathways. J. R. Soc. Interface 3, 515–526 (2006).

  22. 22.

    , , , & Robustness of cellular functions. Cell 118, 675–685 (2004).

  23. 23.

    Mathematical models in microbial systems biology. Curr. Opin. Microbiol. 7, 513–518 (2004).

  24. 24.

    Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007).

  25. 25.

    et al. A genomic regulatory network for development. Science 295, 1669–1678 (2002).

  26. 26.

    & Modeling transcriptional regulatory networks. Bioessays 24, 1118–1129 (2002).

  27. 27.

    & Transcriptional regulatory cascades in development: initial rates, not steady state, determine network kinetics. Proc. Natl. Acad. Sci. USA 100, 9371–9376 (2003).

  28. 28.

    , & Model Checking (MIT Press, Cambridge, Massachusetts, 1999).

  29. 29.

    , & Qualitative networks: a symbolic approach to analyze biological signaling networks. BMC Syst. Biol. 1 (2007).

  30. 30.

    Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467 (1969).

  31. 31.

    & The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol. 39, 103–129 (1973).

  32. 32.

    & Binary analysis and optimization-based normalization of gene expression data. Bioinformatics 18, 555–565 (2002).

  33. 33.

    , , , & The role of certain Post classes in Boolean network models of genetic networks. Proc. Natl. Acad. Sci. USA 100, 10734–10739 (2003).

  34. 34.

    , , , & The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101, 4781–4786 (2004).

  35. 35.

    & The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J. Theor. Biol. 223, 1–18 (2003).

  36. 36.

    , & Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput., 17–28 (1999).

  37. 37.

    , , & Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000).

  38. 38.

    , & Discovery of regulatory interactions through perturbation: inference and experimental design. Pac. Symp. Biocomput., 305–316 (2000). [AU: Please provide the missing volume number in this journal reference. (in reference 38 “Ideker, Thorsson, Karp, 2000”). ]

  39. 39.

    , , , & Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).

  40. 40.

    , & Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16, 707–726 (2000).

  41. 41.

    Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103 (2002).

  42. 42.

    Petri net modelling of biological networks. Brief. Bioinform. 8, 210–219 (2007).

  43. 43.

    , , , & Modelling and simulation of signal transductions in an apoptosis pathway by using timed Petri nets. J. Biosci. 32, 113–127 (2007).

  44. 44.

    , & in 1st ISMB , Bethesda, Maryland, July 1993 (eds. Hunter, L., Searls, D. & Shavlik, J.) 328–336 (AAAI, 1993).

  45. 45.

    & in Conference on Information Intelligence and Systems (ICIIS), Bethesda, Maryland, October 31–November 3, 1999, 4–9 (IEEE, 1999).

  46. 46.

    , , & Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E. coli. Bioinformatics 21 Suppl 2, ii190–ii196 (2005).

  47. 47.

    , & in 4th International Conference on Computational Methods in Systems Biology (CMSB), Trento, Italy, October 18–19, 2006 (ed. Priami, C.) LNCS 4210, 127–142 (2006).

  48. 48.

    , & Executable Petri net models for the analysis of metabolic pathways. Int. J. Softw. Tools Tech. Transf. 3, 394–404 (2001).

  49. 49.

    & Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc. Natl. Acad. Sci. USA 95, 6750–6755 (1998).

  50. 50.

    , & Stochastic kinetic analysis of the Escherichia coli stress circuit using sigma(32)-targeted antisense. Biotechnol. Bioeng. 75, 120–129 (2001).

  51. 51.

    , , & Stochastic vs. deterministic modeling of intracellular viral kinetics. J. Theor. Biol. 218, 309–321 (2002).

  52. 52.

    et al. The Pathalyzer: a tool for analysis of signal transduction pathways. [in Biology], San Diego, December 2–4, 2005, LNCS 4023 (2005).

  53. 53.

    , & Reactive animation: Realistic modeling of complex dynamic systems. Computer 38, 38–47 (2005).

  54. 54.

    & LSCs: Breathing life into message sequence charts. Form. Methods Syst. Des. 19, 45–80 (2001).

  55. 55.

    & The combined action of two intercellular signaling pathways specifies three cell fates during vulval induction in C. elegans. Cell 58, 679–693 (1989).

  56. 56.

    Communicating and Mobile Systems: The pi-Calculus (Cambridge University Press, Cambridge, UK, 1999).

  57. 57.

    The stochastic pi-calculus. Comp. J. 38, 578–589 (1995).

  58. 58.

    , , , & Bioambients: An abstraction for biological compartments. Theor. Comput. Sci. 325, 141–167 (2004).

  59. 59.

    Brane caluli. in Computational Methods in Systems Biology (CMSB) Paris, May 26, 2004 (eds. Danos, V. & Schächter) LNCS 3082, 257 (2004).

  60. 60.

    , , & Modeling biochemical pathways through enhanced pi-calculus. Theor. Comput. Sci. 325, 111–140 (2004).

  61. 61.

    , , & Analysis of signalling pathways using the prism model checker. in 3rd International Conference on Computational Methods in Systems Biology (CMSB) 179–190, (ed. G. Plotkin) Edinburgh, Scotland (2005).

  62. 62.

    , , & Stronger computational modelling of signalling pathways using both continuous and discrete-state methods. in 4th International Conference on Computational Methods in Systems Biology (CMSB), Trento, Italy, October 18–19 (ed. C. Priami) LNCS 4210, 63–78 (2006).

  63. 63.

    , , , & Probabilistic model checking of complex biological pathways. in 4th International Conference on Computational Methods in Systems Biology (CMSB), Trento, Italy, October 18–19 (ed. C. Priami) LNCS 4210, 32–48 (2006).

  64. 64.

    The theory of hybrid automata in Proceedings 11th IEEE Symposium on Logic in Computer Science 278–292 (1996).

  65. 65.

    & Lateral inhibition through delta-notch signalling: A piecewise affine hybrid model. in 4th International Workshop on Hybrid Systems Computation and Control, Rome, Italy, LNCS 2034, 232–246 (2001).

  66. 66.

    & Symbolic reachable set computation of piecewise affine hybrid automata and its application to biological modeling: Delta-Notch protein signaling. in IEE Transactions on Systems Biology, volume 1, 170–183, June 2004.

  67. 67.

    , , & Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

  68. 68.

    & Reconstruction of genetic circuits. Nature 438, 443–448 (2005).

  69. 69.

    , , , & A fluctuation method to quantify in vivo fluorescence data. Biophys. J. 91, 759–766 (2006).

  70. 70.

    & Genetic “code”: representations and dynamical models of genetic components and networks. Annu. Rev. Genomics Hum. Genet. 3, 341–369 (2002).

  71. 71.

    Can a biologist fix a radio?–Or, what I learned while studying apoptosis. Cancer Cell 2, 179–182 (2002).

  72. 72.

    et al. Simulation and verification for computational modeling of signaling pathways. in Proceedings of Winter Simulation Conference, Monterey, California, December 2–6, 2006, 1666–1674 (IEEE, 2006).

  73. 73.

    et al. Hybrid modeling and simulation of biomolecular networks. in Fourth International Workshop on Hybrid Systems: Computation and Control, Rome, Italy, March 28–30, 2001 (eds. Di Benedetto, M.D. & Sangiovanni-Vincentelli, A.L.) LNCS 2034, 19–32 (2001).

Download references

Acknowledgements

We apologize to colleagues whose work was not reviewed due to lack of space. We thank John K. Heath, Alex Hajnal, Freddy Radtke and Nir Piterman for helpful discussions and critical readings of the manuscript and the anonymous referees for valuable comments. J.F. is particularly grateful to David Harel for introducing her to this line of research and for many fruitful discussions over the years. Our research is supported in part by the Swiss National Science Foundation under grant 205321-111840.

Author information

Affiliations

  1. Microsoft Research, Cambridge CB3 0FB, UK.

    • Jasmin Fisher
  2. School of Computer and Communication Sciences, EPFL, Lausanne CH-1015, Switzerland.

    • Jasmin Fisher
    •  & Thomas A Henzinger
  3. Electrical Engineering & Computer Sciences, University of California at Berkeley, California 94720-1770, USA.

    • Thomas A Henzinger

Authors

  1. Search for Jasmin Fisher in:

  2. Search for Thomas A Henzinger in:

Corresponding authors

Correspondence to Jasmin Fisher or Thomas A Henzinger.

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nbt1356

Further reading