Mapping the Thermodynamic Rules of Toehold Switch Function in Spinach Chloroplast Cell-Free Expression: an LDBT Approach Framework: Learn → Design → Build → Test (Clark-ElSayed et al., 2025)
Abstract Chloroplast cell-free expression (CFE) systems have recently been established as powerful rapid-prototyping platforms for plastid genetic parts, yet whether these systems can support synthetic RNA logic remains entirely untested. Toehold switches — de novo-designed riboregulators that activate translation in response to specific trigger RNAs — represent the most sophisticated programmable RNA gates in synthetic biology. Machine learning models trained on E. coli CFE data have begun to extract sequence-structure features predictive of switch performance using frameworks like SANDSTORM (Riley et al., 2025), but whether those learned relationships hold in a chloroplast ribosome context is unknown. This project addresses that gap directly by applying the Learn-Design-Build-Test (LDBT) framework to map the thermodynamic rules governing toehold switch function in spinach chloroplast CFE.
