Progettazione e implementazione di una incarnazione biochimica per il simulatore Alchemist

Scopo di questo elaborato di tesi è la modellazione e l’implementazione di una estensione del simulatore Alchemist, denominata Biochemistry, che permetta di simulare un ambiente multi-cellulare. Al fine di simulare il maggior numero possibile di processi biologici, il simulatore dovrà consentire di modellare l’eterogeneità cellulare attraverso la modellazione di diversi aspetti dei sistemi cellulari, quali: reazioni intracellulari, segnalazione tra cellule adiacenti, giunzioni cellulari e movimento. Dovrà, inoltre, essere ammissibile anche l’esecuzione di azioni impossibili nel mondo reale, come la distruzione o la creazione dal nulla di molecole chimiche. In maniera più specifica si sono modellati ed implementati i seguenti processi biochimici: creazione e distruzione di molecole chimiche, reazioni biochimiche intracellulari, scambio di molecole tra cellule adiacenti, creazione e distruzione di giunzioni cellulari. È stata dunque posta particolare enfasi nella modellazione delle reazioni tra cellule vicine, il cui meccanismo è simile a quello usato nella segnalazione cellulare. Ogni parte del sistema è stata modellata seguendo fenomeni realmente presenti nei sistemi multi-cellulari, e documentati in letteratura. Per la specifica delle reazioni chimiche, date in ingresso alla simulazione, è stata necessaria l’implementazione di un Domain Specific Language (DSL) che consente la scrittura di reazioni in modo simile al linguaggio naturale, consentendo l’uso del simulatore anche a persone senza particolari conoscenze di biologia. La correttezza del progetto è stata validata tramite test compiuti con dati presenti in letteratura e inerenti a processi biologici noti e ampiamente studiati.

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments.

Phage therapy: A software system for phage quantification and kinetic model inference.

One of the most serious problems of the modern medicine is the growing emergence of antibiotic resistance among pathogenic bacteria. In this circumstance, different and innovative approaches for treating infections caused by multidrug-resistant bacteria are imperatively required. Bacteriophage Therapy is one among the fascinating approaches to be taken into account. This consists of the use of bacteriophages, viruses that infect bacteria, in order to defeat specific bacterial pathogens. Phage therapy is not an innovative idea, indeed, it was widely used around the world in the 1930s and 1940s, in order to treat various infection diseases, and it is still used in Eastern Europe and the former Soviet Union. Nevertheless, Western scientists mostly lost interest in further use and study of phage therapy and abandoned it after the discovery and the spread of antibiotics. The advancement of scientific knowledge of the last years, together with the encouraging results from recent animal studies using phages to treat bacterial infections, and above all the urgent need for novel and effective antimicrobials, have given a prompt for additional rigorous researches in this field. In particular, in the laboratory of synthetic biology of the department of Life Sciences at the University of Warwick, a novel approach was adopted, starting from the original concept of phage therapy, in order to study a concrete alternative to antibiotics. The innovative idea of the project consists in the development of experimental methodologies, which allow to engineer a programmable synthetic phage system using a combination of directed evolution, automation and microfluidics. The main aim is to make “the therapeutics of tomorrow individualized, specific, and self-regulated” (Jaramillo, 2015). In this context, one of the most important key points is the Bacteriophage Quantification. Therefore, in this research work, a mathematical model describing complex dynamics occurring in biological systems involving continuous growth of bacteriophages, modulated by the performance of the host organisms, was implemented as algorithms into a working software using MATLAB. The developed program is able to predict different unknown concentrations of phages much faster than the classical overnight Plaque Assay. What is more, it gives a meaning and an explanation to the obtained data, making inference about the parameter set of the model, that are representative of the bacteriophage-host interaction.