The advent of the computer and computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study : the in virtuo experiment.This work lies on the framework of multi-agent systems. We propose a generic model for systems biology based on reification of the interactions, on a concept of organization and on a multi-model approach. By ``reification'' we understand that interactions are considered as autonomous agents. The aim has been to combine the systemic paradigm and the virtual reality to provide an application able to collect, simulate, experiment and understand the knowledge owned by different biologists working around an interdisciplinary subject. In that case, we have been focused on the urticaria disease understanding.The method permits to integrate different natures of model. We have modeled biochemical reactions, molecular diffusion, cell organisations and mechanical interactions. It also permits to embed different expert system modeling methods like fuzzy cognitive maps.
Abstract-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.
In the context of biological complex systems multiagent 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 modelwhich 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.
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