(689g) Dual-Scale Dynamical Models Render Synthetic Circuit Dynamics across Growth Stages

Synthetic biology promises to revolutionize technology through precise reprogramming of biological systems, but its potential remains unrealized due to the unpredictable behavior of engineered organisms in noisy, fluctuating environments. To address this challenge, feedback control has emerged as a critical strategy for ensuring the robust and reliable performance of synthetic biological systems. The design of effective feedback control relies on accurate dynamical models that can predict system behavior and inform control strategies. Most current models are mono-scale, focusing exclusively on gene expression dynamics and assuming constant log-phase growth. Although these models have been successful in guiding feedback control in highly controlled environments, such as chemostats, they do not account for growth dynamics. As a result, when growth conditions are not kept constant, these models lose their accuracy.

In this talk, I will introduce the generalizable dual scale Gene Expression Across Growth Stages (GEAGS) model framework, which captures gene expression dynamics in batch cultures, where bacterial cells transition through the lag, log, and stationary phases. Using this model, we explained previously unresolved oscillatory gene expression dynamics observed in batch cultures. In parallel, we explored whether integrating electronics and biology into a closed-loop system could improve the robustness of biological systems by developing an LED-embedded microplate for optogenetic studies (LEMOS). This platform enables seamless communication between electronics and living cells, providing real-time optical feedback to regulate gene expression. By combining the GEAGs model with LEMOS, we identified key limitations in feedback control due to system dead time, which introduces offsets and complicates precise regulation. Together, these tools offer valuable insights into the challenges and opportunities of applying feedback control in biological systems, paving the way for reliable applications of synthetic biology in the real world.