Speaker
Description
Transcriptional networks represent one of the most extensively studied types of reaction networks in synthetic biology. While transcriptional networks typically rely on cooperativity and highly non-linear behavior of transcription factors to regulate production of proteins, they are often modeled with simple linear degradation terms. In contrast, general analog computation requires both non-linear positive as well as negative terms, seemingly necessitating control over not just transcriptional (i.e., production) regulation but also the degradation rates of transcription factors. Surprisingly, we prove that controlling transcription factor production (i.e., transcription rate) without explicitly controlling degradation is mathematically complete for analog computation, achieving equivalent capabilities to systems where both production and degradation are programmable. We demonstrate our approach on several examples including oscillatory and chaotic dynamics, analog sorting, memory, PID controller, and analog extremum seeking. Our results provide a systematic methodology for engineering novel analog dynamics using synthetic transcriptional networks without the added complexity of degradation control and informs our understanding of the capabilities of natural transcriptional networks.
