Gireg Desmeulles

Multi-Agents System to Model Cell Signalling by Using Fuzzy Cognitive Maps. Application to Computer Simulation of Multiple Myeloma

In order to simulate biological processes, we use multi-agents system. However, modelling cell behavior in systems biology is complex and may be based on intracellular biochemical pathway. So, we have developed in this study a Fuzzy Influence Graph to model MAPK pathway. A Fuzzy Influence Graph is also called Fuzzy Cognitive Map.This model can be integrated in agents representing cells. Results indicate that despite individual variations, the average behavior of MAPK pathway in a cells group is close to results obtained by ordinary differential equations. So, we have also modelled multiple myeloma cells signalling by using this approach.

Formal Validation of Asynchronous Interaction-Agents Algorithms for Reaction-Diffusion Problems

In the context of biological complex systems multi-agent simulation, we present an interaction-agent model for reaction-diffusion problems that enables interaction with the simulation during the execution, and we establish a mathematical validation for our model. We use two types of interaction-agents: on one hand, in a chemical reactor with no spatial dimension -e.g. a cell-, a reaction-agent represents an autonomous chemical reaction between several reactants, and modifies the concentration of reaction products. On the other hand, we use interface-agents in order to take into account the spatial dimension that appears with diffusion : interface-agents achieve the matching transfer of reactants between cells. This approach, where the simulation engine makes agents intervene in a chaotic and asynchronous way, is an alternative to the classical model - which is not relevant when the limits conditions are frequently modified- based on partial derivative equations. We enounciate convergence results for our interaction-agent methods, and illustrate our model with an example about coagulation inside a blood vessel.

Data Consistency in Distributed Virtual Reality Simulations Applied to Biology

We perform biological simulations in the virtual reality context. In order to run large simulations, we choose to put together a set of standard computers and create a grid in charge of distributing the biological simulations. We propose to make the distribution on to the ReISCOP generic model, developed in our laboratory, which allows us to easily design biological simulations. This method is also based on the replication of passive elements. It is a spatial distribution in which local simulations are periodically synchronized and the consistency of replicated data is checked. This synchronization is not a strong synchronization but a weak one. The consistency method is built on the transmission between nodes of the grid of the state variations of the data. The software used for distributing simulations is named DIVA, and is an individual based software located on each node of the grid. DIVA confers a peer to peer architecture upon the grid.