Curated Optogenetic Publication Database

Abstract: Optogenetics integrates living cells and electronics into powerful cell-silicon systems, but prototyping their dynamics remains challenging. Current tools either require robotic liquid transfers into flow cytometers or rely on custom sensors with a narrow dynamic range that limits controller performance. Additionally, current successful optogenetic feedback controllers only operate in chemostats or microfluidic devices that enforce constant growth, because models for growth-aware controller design in batch culture are lacking. Here, we present LEMOS, a low-cost LED-embedded microplate that runs inside a commercial microplate reader. Coupled with a growth-aware multiscale model of gene expression for controller tuning, this platform enables rapid design-build-test-learn cycles for cell-silicon systems. We demonstrate closed-loop set point tracking of gene expression in batch cultures within a standard microplate reader and show how growth dynamics complicate controller selection and tuning. Together, this platform reduces setup overhead and speeds up iteration, enabling accurate real-time optogenetic feedback control.

Abstract: The dynamics of recombinant gene expression in bacterial systems are often modeled under the assumption of constant growth. However, most synthetic gene circuit assays are performed in enclosed batch cultures, where nutrient depletion and physiological transitions from exponential to stationary phase substantially influence transcription, translation, mRNA degradation, and protein dilution. This chapter introduces the Gene Expression Across Growth Phases (GEAGS) framework-a dual-scale modeling approach that couples molecular reaction networks with logistic population growth through growth-dependent rate modifier functions (RMFs). The framework enables accurate simulation of gene expression dynamics under time-varying growth rate and resource competition. We provide step-by-step methods to construct GEAGS models, starting from a minimal constitutive reporter system and extending to complex applications involving optogenetic regulation and layered feedback control. This protocol establishes a generalizable computational workflow for synthetic biologists aiming to model the effects of growth, molecular crowding, and resource limitation on gene expression dynamics in batch culture.