Multiple sclerosis disease: A computational approach for investigating its drug interactions

Multiple Sclerosis (MS) is a chronic and potentially highly disabling disease that can cause permanent damage and deterioration of the central nervous system. In Europe it is the leading cause of non-traumatic disabilities in young adults, since more than 700,000 EU people suffer from MS. Although recent studies on MS pathophysiology have been performed, providing interesting results, MS remains a challenging disease. In this context, thanks to recent advances in software and hardware technologies, computational models and computer simulations are becoming appealing research tools to support scientists in the study of such disease. Motivated by this consideration, we propose in this paper a new model to study the evolution of MS in silico, and the effects of the administration of the daclizumab drug, taking into account also spatiality and temporality of the involved phenomena. Moreover, we show how the intrinsic symmetries of the model we have developed can be exploited to drastically reduce the complexity of its analysis

Multiple Sclerosis disease: a computational approach for investigating its drug interactions

Multiple Sclerosis (MS) is a chronic and potentially highly disabling disease that can cause permanent damage and deterioration of the central nervous system. In Europe it is the leading cause of non-traumatic disabilities in young adults, since more than 700,000 EU people suffer from MS. Although recent studies on MS pathophysiology have been provided, MS remains a challenging disease. In this context, thanks to recent advances in software and hardware technologies, computational models and computer simulations are becoming appealing research tools to support scientists in the study of such disease. Thus, motivated by this consideration we propose in this paper a new model to study the evolution of MS in silico, and the effects of the administration of Daclizumab drug, taking into account also spatiality and temporality of the involved phenomena. Moreover, we show how the intrinsic symmetries of the system can be exploited to drastically reduce the complexity of its analysis.Comment: Submitted to CIBB 2019 post proceedings - LNC

U-check: Model Checking and Parameter Synthesis under Uncertainty

Novel applications of formal modelling such as systems biology have highlighted the need to extend formal analysis techniques to domains with pervasive parametric uncertainty. Consequently, machine learning methods for parameter synthesis and uncertainty quantification are playing an increasingly significant role in quantitative formal modelling. In this paper, we introduce a toolbox for parameter synthesis and model checking in uncertain systems based on Gaussian Process emulation and optimisation. The toolbox implements in a user friendly way the techniques described in a series of recent papers at QEST and other primary venues, and it interfaces easily with widely used modelling languages such as PRISM and Bio-PEPA. We describe in detail the architecture and use of the software, demonstrating its application on a case study

Combining Machine Learning and Formal Methods for Complex Systems Design

During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study.During the last 20 years, model-based design has become a standard practice in many fields such as automotive, aerospace engineering, systems and synthetic biology. This approach allows a considerable improvement of the final product quality and reduces the overall prototyping costs. In these contexts, formal methods, such as temporal logics, and model checking approaches have been successfully applied. They allow a precise description and automatic verification of the prototype's requirements. In the recent past, the increasing market requests for performing and safer devices shows an unstoppable growth which inevitably brings to the creation of more and more complicated devices. The rise of cyber-physical systems, which are on their way to become massively pervasive, brings the complexity level to the next step and open many new challenges. First, the descriptive power of standard temporal logics is no more sufficient to handle all kind of requirements the designers need (consider, for example, non-functional requirements). Second, the standard model checking techniques are unable to manage such level of complexity (consider the well-known curse of state space explosion). In this thesis, we leverage machine learning techniques, active learning, and optimization approaches to face the challenges mentioned above. In particular, we define signal measure logic, a novel temporal logic suited to describe non-functional requirements. We also use evolutionary algorithms and signal temporal logic to tackle a supervised classification problem and a system design problem which involves multiple conflicting requirements (i.e., multi-objective optimization problems). Finally, we use an active learning approach, based on Gaussian processes, to deal with falsification problems in the automotive field and to solve a so-called threshold synthesis problem, discussing an epidemics case study