Recent research advances in Discrete EVent system Specification (DEVS) as well as cellular space modeling emphasized the need for high performance modeling methodologies and environments. The growing demand for cellular space models has directed researchers to use different implementation formalisms. Many efforts were dedicated to develop cellular space models in DEVS in order to employ the advantage of discrete event systems. Unfortunately, the conventional implementations degrade the performance in large scale cellular models because of the huge volume of inter-cell messages generated during simulation. This work introduces a new multi-layer formalism for cellular DEVS models that assures high performance and ease of user specification. It starts with the parallel DEVS specification layer and derives a high performance cellular DEVS layer using the property of closure under coupling. This is done through converting the parallel DEVS into its equivalent non-modular form which involves computational and communication overhead tradeoffs. The new specification layer, in contrast to multi-component DEVS, is identical to the modular parallel DEVS in the sense of state trajectories which are updated according to the modular message passing methodology. The equivalency of the two forms is verified using simulation methods. Once the equivalency has been ensured, analysis of the models becomes a decisive factor in employing modularity in cellular DEVS models. Non-modular models show significant speedup in simulation runs given that their event list handler is implemented based on analytical and experimental survey that involve actual operation counts. However, the new high performance non-modular specification layer is complicated to implement. Therefore, a third layer of specification is proposed to provide a simple user specification that is automatically converted into the fast complex cellular DEVS specification, which is finally put in the standard parallel DEVS specification. A tool was implemented to automatically accept user's model specification via GUI and generate the models using the new specifications. The generated models are then required to be tested and verified using some automatic DEVS verification methods. As a result, the model development and verification processes are made easier and faster.

Cellular space modeling is becoming an increasingly important modeling paradigm for modeling complex systems with spatial-temporal behaviors. The growing demand for cellular space models has directed researchers to use different modeling formalisms, among which Discrete Event System Specification (DEVS) is widely used due to its formal modeling and simulation framework. The increasing complexity of systems to be modeled asks for cellular space models with large number of cells for modeling the systems¡¯ spatial-temporal behavior. Improving simulation performance becomes crucial for simulating large scale cellular space models. In this dissertation, we proposed a framework for improving simulation performance for large scale DEVS-based cellular space models. The framework has a layered structure, which includes modeling, simulation, and network layers corresponding to the DEVS-based modeling and simulation architecture. Based on this framework, we developed methods at each layer to overcome performance issues for simulating large scale cellular space models. Specifically, to increase the runtime and memory efficiency for simulating large number of cells, we applied Dynamic Structure DEVS (DSDEVS) to cellular space modeling and carried out comprehensive performance measurement. DSDEVS improves simulation performance by making the simulation focus only on those active models, and thus be more efficient than when the entire cellular space is loaded. To reduce the number of simulation cycles caused by extensive message passing among cells, we developed a pre-schedule modeling approach that exploits the model behavior for improving simulation performance. At the network layer, we developed a modified time-warp algorithm that supports parallel simulation of DEVS-based cellular space models. The developed methods have been applied to large scale wildfire spread simulations based on the DEVS-FIRE simulation environment and have achieved significant performance results.

Le cerveau est composé de neurones et de cellules gliales qui jouent un rôle de soutien et de protection du réseau cellulaire. L’espace extra-cellulaire (ECS) correspond à l’espace qui existe entre ces cellules. Les modifications de sa structure peuvent dépendre de plusieurs paramètres comme l’âge, l’apprentissage ou les maladies neuro-dégénératives. Le volume de l’ECS correspond à environ 20$%$ du volume total du cerveau et les neurotransmetteurs et autres molécules circulent dans cet espace pour assurer une communication neuronale optimale. Cependant, les dimensions et la viscosité locale de cet espace restent encore mal-connues. L’ECS est composé entre autres de protéoglycans, de glycoaminoglycans (acide hyaluronique…) et de fluide cérébrospinal. Nous avons proposé dans cette thèse une stratégie pour mesurer les dimensions et les propriétés rhéologiques de l’espace extra-cellulaire de tranches de cerveaux de rats maintenue en vie à l’aide du suivi de nanotubes de carbone individuels luminescents. Pour ces applications, nous avons étudier la biocompatibilité et le rapport signal sur bruit de nos échantillons de nanotubes afin de les détecter en profondeur dans les tranches de cerveaux et de pouvoir mesurer leurs propriétés de diffusion. / The brain is mainly composed of neurons which ensure neuronal communication and glialcells which play a role in supporting and protecting the neural network. The extracellular space corresponds to the space that exists between all these cells and represents around 20 %of the whole brain volume. In this space, neurotransmitters and other molecules circulate into ensure optimal neuronal functioning and communication. Its complex organization whichis important to ensure proper functioning of the brain changes during aging, learning or neurodegenerative diseases. However, its local dimensions and viscosity are still poorly known.To understand these key parameters, in this thesis, we developed a strategy based on the tracking of single luminescent carbon nanotubes. We applied this strategy to measure the structural and viscous properties of the extracellular space of living rodent brains slices at the nanoscale. The organization of the manuscript is as follows. After an introduction of the photoluminescence properties of carbon nanotubes, we present the study that allowed us to select the optimal nanotube encapsulation protocol to achieve our biological applications. We also present a quantitative study describing the temperature increase of the sample when laser irradiations at different wavelengths are used to detect single nanotubes in a brain slice.Thanks to a fine analysis of the singular diffusion properties of carbon nanotubes in complex environments, we then present the strategy set up to reconstruct super-resolved maps (i.e. with resolution below the diffraction limit) of the brain extracellular space morphology.We also show that two local properties of this space can be extracted : a structural complexity parameter (tortuosity) and the fluid’s in situ viscosity seen by the nanotubes. This led us to propose a methodology allowing to model the viscosity in situ that would be seen, not by the nanotubes,but by any molecule of arbitrary sizes to simulate those intrinsically present or administered in the brain for pharmacological treatments. Finally, we present a strategy to make luminescent ultra-short carbon nanotubes that are not intrinsically luminescent and whose use could be a complementary approach to measure the local viscosity of the extracellular space of the brain.