This article presents an hybrid and hierarchical model in which two modeling and simulation approaches, discrete event system specification simulation (DEVS) and semantic technologies, were used together in order to help in the analysis of a major healthcare problem, the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). Indeed, the complexity of the SARS-CoV-2 replication process, and the range of hierarchical scales over which it interacts with cellular components (extending from genomic and transcriptomic to proteomic and metabolomic scales), and the intricate way in which they are interwoven, make its understanding very challenging. It is therefore crucial to model the different scales of the replication process, by taking into account all interactions with the infected cell. By combining the advantages of both DEVS simulation and ontological modeling, we propose a hierarchical ontology-based DEVS simulation model of the SARS-CoV-2 viral replication at both the micro-molecular (proteomic and metabolomic) and macro-molecular (genomic and transcriptomic) scales. First, we demonstrate the usefulness of combining DEVS simulation and semantic technologies in a common modeling framework to face the complexity of the SARS-CoV-2 viral replication at different scales. Second, the modeling and simulation of the SARS-CoV-2 replication process on different levels provide valuable information on the different stages of the virus’s life cycle and lays the foundation for a system to anticipate future mutations selected by the virus.

Systems biology models aim to describe and understand the behaviour of a cell. This living organism is represented by a complex biomolecular network. In the literature, most researches focus only on modeling isolated parts of this network, such as the metabolic network or the gene regulatory network. However, to fully understand the behaviour of a cell we should model and analyze the biomolecular network as a whole.

With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the ”transittability” means that complex biomolecular networks can be steered from an unexpected state to a desired state. This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objective optimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this approach.

The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent the different cellular components and the edges represent the interactions occurring among them. Through this network, it is easy to study the transition states and the dynamic behaviour of cells. Indeed, perturbing some nodes of the biomolecular network induce the transition of all the network. This process, known as the ”transittability”, expresses the idea of steering the complex biomolecular network from an unexpected state to a desired state. In this context, we are thus interested in how to use the transittability of biomolecular networks to increase the efficiency of translational medicine for improving human health and disease, including genetic and environmental factors of of patient’s well-being. This is a great opportunity to understand diseases, and find new diagnoses and treatments. Due to its complexity, the transittability of complex biomolecular networks can be considered as an optimization problem. Up to a recent date only few studies have been carried out in this problem. Most of them focused only on the minimization of the required nodes to steer the entire network, and others considered the minimization of the number of stimuli to be applied on the network. However, this assumption is not always realistic, because steering complex biomolecular networks is in general a multi-objective optimization problem. It requires finding appropriate trade-offs among various objectives, for example between the appropriate nodes to be stimulated and the number of external stimuli to be used and their cost, and the impact on patient’s well-being. In this paper, the optimization of the transittability of complex biomolecular networks is investigated from the multi-objective perspective. In the mathematical model four criteria are considered simultaneously: the minimization of the number of external stimuli, the minimization of their total cost, the minimization of the number of target nodes, and the minimization of the patient discomfort. All these objectives are described theoretically and mathematically in detail.

Knowledge-driven reasoning systems require structured representations capable of evolving over time while handling uncertainty, heterogeneous knowledge, and complex relational constraints. Although ontologies provide explicit semantic modeling and constraint-based methods enable rigorous reasoning, existing approaches generally rely on static knowledge representations and crisp constraint satisfaction, limiting their adaptability in dynamic and uncertain environments. To address these limitations, this paper proposes EVRSO-FCSP, a hybrid knowledge-driven reasoning framework that combines evolving ontologies with fuzzy constraint satisfaction for complex object recognition under uncertainty. The approach models objects through an evolving ontology that explicitly represents semantic concepts, spatial relationships, and inference rules, and automatically translates this knowledge into a fuzzy constraint network. A hybrid reasoning strategy integrating fuzzy arc-consistency, Particle Swarm Optimization, and adaptive backjumping is introduced to efficiently explore large solution spaces while preserving semantic consistency. The proposed framework is validated on the semantic interpretation of high-resolution remote sensing scenes involving complex geographic objects. Experimental results demonstrate improvements in recognition accuracy, spatial consistency, adaptability to previously unseen configurations, and reasoning robustness compared with representative knowledge-driven and learning-based baselines. These results demonstrate that combining ontology evolution with fuzzy constraint reasoning provides an effective and transferable paradigm for knowledge-based spatial reasoning under uncertainty.