A Control Perspective on Antimicrobial Resistance Inhibition: From Systems to Synthetic Biology

Over billions of years, bacteria have gained the ability to adapt and evolve in response to environmental pressures. This natural evolutionary process is driven by genetic mutations, horizontal gene transfer and complex cell-to-cell communication systems, enabling bacteria to acquire tolerance to antimicrobial agents. Together, these adaptive mechanisms have contributed to the rapid rise of antimicrobial resistance (AMR), which now represents one of the most critical global health threats. The progression of AMR has been further accelerated by the overuse and misuse of antimicrobials, not only in human medicine but also in agriculture and animal husbandry. In this context, Synthetic biology is emerging as a powerful tool, opening a new frontier in the fight against resistant pathogens. This thesis investigates two synergistic strategies to address AMR. The first strategy focuses on engineering bacteria to disrupt pathogen communication systems, specifically Quorum Sensing (QS). QS is a cell-to-cell communication mechanism where bacteria release and detect small signaling molecules, known as autoinducers, to coordinate behaviors such as biofilm formation, a key driver of AMR. By targeting and disrupting QS, engineered bacteria can reduce community-level resistance mechanisms, thereby weakening bacterial defenses. The second strategy involves using bacteriophages to deliver a CRISPR interference (CRISPRi) system directly into the target pathogen. CRISPRi enables sequence-specific regulation of gene expression, effectively silencing AMR-associated genes and restoring bacterial sensitivity to antibiotics. This thesis contributes to advancing both strategies. For the first approach, two pivotal QS architectures are mathematically modeled to capture their structural properties and dynamic behaviors. The equilibrium analysis proved the bistable and hysteretic behavior of both systems. The local sensitivity analysis allowed to identify key model parameters that play a key role in governing QS mechanisms. Moreover, four QS inhibition strategies are mathematically modeled and their effect on QS communication systems have been simulated. This analysis provided a comprehensive, model-driven evaluation of QS inhibition approaches. This represents one of the main contribution of this thesis, filling a gap previously unaddressed in the literature. These results offer critical insights for synthetic biology, guiding the selection and optimization of appropriate strategies, and provides a specific assessment of their effectiveness. To experimentally validate model predictions and, in particular, the hysteretic behavior of QS, the two QS architectures were cloned into a host strain of E. coli. Exploiting the fully automated and high-throughput platform Evotron, this work achieves the first experimental validation and reconstruction of QS hysteresis as a function of cell density. This represents a significant advancement over previous studies, which relied on manually adding signal molecules to validate hysteresis as a function of autoinducer concentration. For the second strategy, a mathematical model of the phage-mediated delivery of CRISPRi is developed, incorporating the dynamics of mutation emergence - a critical factor affecting long-term therapeutic success. By explicitly modeling mutation dynamics, this work provides a robust framework to evaluate system performance and identify key risks associated with resistance evolution.