Development of synthetic, methylation based, epigenetic gene circuits

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In our recent publication, we described synthetic epigenetic circuits in E. coli that can sense transient trigger signals and store this information in form of DNA methylation patterns over several cell generations. Potential applications of such synthetic epigenetic systems are manifold and include the development of bacteria that could be used as live sensors to monitor cooling chains, detect environmental pollution next to production sites or atomic power plants, or passage through the human body and collect input about disease markers and metabolic states. Biotechnological applications include memory switches for induction of industrial protein expression or in biocontainment systems. This project aims to further develop fundamental properties of these novel systems and better understand their signaling characteristics. We will improve the existing heat detection and adopt new input signals allowing the detection of antibiotics, metabolites, toxins, bacterial cell density and radioactivity. To improve signal processing, we plan to develop additional systems working in an inverted manner than the existing ones, which will allow to develop systems that oscillate with the bacterial cell cycle to identify metabolically active bacteria and distinguish them from dead or dormant cells. One particular strength of the DNA methylation based memory systems developed here is their amenability to multiplexing allowing to collect many different input signals and deposit the information in different DNA methylation codes in one cell. Heading towards this goal, we plan to integrate additional compatible DNA methyltransferases and novel methylation sites. The different input devices will be combined to generate logic gates exhibiting AND or OR behavior for initial data processing. We aim to use quantitative dynamic modeling as a major tool to obtain a mechanistic understanding of the regulation principles, information processing and storage by epigenetic circuits. These models and their analysis will also be highly supportive for the entire design and implementation cycle. In summary, our work will pave the way towards the application of designer bacteria containing synthetic, epigenetic circuits in the detection of biological signals as live biosensors, storing the information in DNA methylation patters as bacterial storage devices and performing initial data processing as bacterial microprocessors.