Team abstraction

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Section: New Results

Analysis of Biological Pathways

We have introduced a framework to design and analyze biological networks. We focus on protein-protein interaction networks described as graph rewriting systems. Such networks can be used to model some signaling pathways that control the cell cycle. The task is made difficult due to the combinatorial blow up in the number of reachable species (i.e., non-isomorphic connected components of proteins).

Automatic Reduction of Differential Semantics

Participants : Vincent Danos [University of Edinburgh] , Jérôme Feret, Walter Fontana [Harvard Medical School] , Russ Harmer [Harvard Medical School] , Jean Krivine [Paris VII] .

We have developed an abstract interpretation-based framework that enables the reduction of the differential semantics for protein-protein interaction networks. Results are sound since trajectories in the abstract system are projections of the trajectories in the concrete system.

This framework has been fully formalized in [15] , whereas more conceptual descriptions addressed to a broader audience have been proposed in [7] and [13] .

Several talks have also been given on this topic in international workshops [40] , and conference [42] .

Automatic Reduction of Stochastic Semantics

Participants : Ferdinanda Camporesi, Jérôme Feret, Thomas Henzinger [Institute of Science and Technology, Austria] , Heinz Koeppl [École Polytechnique Fédérale de Lausanne] , Tatjana Petrov [École Polytechnique Fédérale de Lausanne] .

We have proposed an abstract interpretation-based framework for reducing the state-space of stochastic semantics for protein-protein interaction networks.

In [16] , we show several examples so as to illustrate why the model reduction that is proposed in [15] for the differential semantics is not sound, in general, for the stochastic semantics.

The model reduction framework for the stochastic semantics is formalized in [25] , and the relationships with the notions of lumpability, and bisimulation is established. This framework is explained to a broader audience in [18] .

Combining Model Reduction

Participants : Ferdinanda Camporesi, Jérôme Feret, Heinz Koeppl [École Polytechnique Fédérale de Lausanne] , Tatjana Petrov [École Polytechnique Fédérale de Lausanne] .

In [19] , we propose two frameworks for combining model reduction for differential and stochastic semantics.

Rule Refinements

Participants : Vincent Danos [University of Edinburgh] , Jérôme Feret, Russ Harmer [Paris VII] , Jean Krivine [Harvard Medical School] , Elaine Murphy [University of Edinburgh] .

We have proposed a formal framework to refine rule-based protein-protein interaction networks while preserving their stochastic and their differential semantics. Refinements is a key process in rule-based modeling. Refining an interaction allows tuning the kinetics of an interaction according to some constraints in the context of the interacting proteins.

In [57] , we had proposed a framework to make homogeneous refinements. In such a homogeneous refinement, the accuracy of the refinement is the same for each protein of a given type. In [36] , we have extended this framework in order to make heterogeneous refinements, where each agent in a given pattern can be refined independently.


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