Engineering microbes to sense and eradicate a human pathogen - Saeidi, Nazanin (2013)
Synthetic biology has allowed us to design and construct new biological systems that have the potential to resolve important issues related to healthcare. Considering the stalled development of advanced antibiotics and the emergence of antibiotic-resistant pathogens, we must now strive to exploit synthetic biology approaches for designing a new treatment regimen for infectious diseases. In this study we engineer microbes to detect and kill a pathogen using synthetic biology principles. We synthesize a genetic system comprised of quorum sensing, killing, and lysing devices. This system enables Escherichia coli (E. coli) to sense and kill a pathogenic strain of Pseudomonas aeruginosa (P. aeruginosa) through the production and release of pyocin S5.To reach our objective, we first design and characterize individual devices to understand their functionalities, which help us to construct the final system and verify its behavior. In the following steps, we show that our engineered E. coli detects and kills planktonic state P. Aeruginosa, evidenced by a >99% reduction in viable cells. Moreover, we confirm that our engineered E. coli inhibits the formation of P. aeruginosa biofilm by 90%, leading to much sparser and thinner biofilm matrices. Finally, to further optimize our system, we develop mathematical models using Computer Aided Design (CAD) tools that simulate both the dynamic and static performance of standard parts. We model the pathogen-sensing device of our sensing-killing system that produces Green Fluorescent Protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model are based on experimental results. Since it is important that these models are searchable and readable by machines, standard SBML (System Biology Markup Language) format is used to store the model. All parts and reactions are fully annotated to enable easy searching, and the model follows the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE). The model accurately simulates experimental results.