Rule-Based Modelling of Cellular Signalling

  • Vincent Danos
  • Jérôme Feret
  • Walter Fontana
  • Russell Harmer
  • Jean Krivine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4703)


Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combinatorial explosion makes the traditional modelling paradigm based on systems of differential equations impractical. In contrast, agent-based or concurrent languages, such as κ [1,2,3] or the closely related BioNetGen language [4,5,6,7,8,9,10], describe biological interactions in terms of rules, thereby avoiding the combinatorial explosion besetting differential equations. Rules are expressed in an intuitive graphical form that transparently represents biological knowledge. In this way, rules become a natural unit of model building, modification, and discussion. We illustrate this with a sizeable example obtained from refactoring two models of EGF receptor signalling that are based on differential equations [11,12]. An exciting aspect of the agent-based approach is that it naturally lends itself to the identification and analysis of the causal structures that deeply shape the dynamical, and perhaps even evolutionary, characteristics of complex distributed biological systems. In particular, one can adapt the notions of causality and conflict, familiar from concurrency theory, to κ, our representation language of choice. Using the EGF receptor model as an example, we show how causality enables the formalization of the colloquial concept of pathway and, perhaps more surprisingly, how conflict can be used to dissect the signalling dynamics to obtain a qualitative handle on the range of system behaviours. By taming the combinatorial explosion, and exposing the causal structures and key kinetic junctures in a model, agent- and rule-based representations hold promise for making modelling more powerful, more perspicuous, and of appeal to a wider audience.


Cellular Signalling Causal Structure Epidermal Growth Factor Receptor Signaling Combinatorial Explosion Slow Recovery Rate 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Vincent Danos
    • 1
    • 3
    • 4
  • Jérôme Feret
    • 2
  • Walter Fontana
    • 3
  • Russell Harmer
    • 3
    • 4
  • Jean Krivine
    • 5
  1. 1.Plectix Biosystems 
  2. 2.École Normale Supérieure 
  3. 3.Harvard Medical School 
  4. 4.CNRS, Université Denis Diderot 
  5. 5.École Polytechnique 

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