Ensemble-Based Modelling of Optogenetic Bioprocesses under Phototoxicity

Abstract

Optogenetics enables methanol-free protein production in the yeast $\textit{Komagataella phaffii}$ using light as a control input, facilitated by the blue-light-responsive transcription factor EL222. This opens the door to more programmable protein production in the food and biopharmaceutical industries. However, excessive illumination can inhibit growth and protein accumulation in some of these engineered cell factories. Previous hybrid modelling used black-box regression across sparsely sampled conditions to infer light-dependent kinetic parameters, without explicitly representing intracellular regulation. Here, we develop a multiscale mechanistic model linking EL222 activation, transcription, and translation to protein, biomass, and substrate dynamics. A latent light-associated stress state represents photoinhibitory effects on growth, transcription, and translation. An ensemble-based parameter-estimation strategy combines repeated global-local optimisation with screening for parameter sets supporting locally stable intracellular steady states under specified light conditions. Using available batch data for yeast-enhanced green fluorescent protein production in a $\textit{K. phaffii}$ strain carrying eight EL222 gene copies, the ensemble-mean predictions captured the main experimental trends in protein accumulation and growth across six constant light intensities. Parameter variability was propagated to time-course predictions, providing an empirical assessment of parameter-related uncertainty. Wider prediction bands coincided with less complete experimental coverage of the growth phases. Overall, the framework provides a mechanistic basis for future model-based optimisation and control of optogenetic bioprocesses under phototoxicity.