Toward in Silico Strain Evaluation: A Multimodal Surrogate for Fermentation Dynamics with Metabolic Graph Pretraining

Strain engineering for industrial fermentation faces a structural design-test scale gap: combinatorial pathway perturbations of $2$–$3$ enzymes already span $10^4$–$10^6$ candidate strains, while bench-scale fermentation throughput remains on the order of $10^2$ runs per laboratory per year. One way to narrow this gap is in silico evaluation by perturbing a learned gene-expression $\to$ phenotype mapping (conditioned on process state); the precondition is that such a mapping can be learned from bench-scale-sized datasets. As a proof-of-concept, we ask whether a genome-scale metabolic model (GEM)-graph mechanistic prior supports learning such a mapping from $\sim 44$ bioreactor runs of an engineered Yarrowia lipolytica astaxanthin-producing strain. We propose a spatio-temporal GNN encoder structured as a Mass Flow Graph over the GEM, paired with a multimodal input head over gene-expression snapshots, sensor streams, time-series assays, and run-level metadata. The encoder is pretrained by masked-flux prediction on Flux Balance Analysis (FBA) samples, distilling the GEM's stoichiometric and mass-balance constraints into it before fermentation data are seen. Under $5$-fold cross-validation across $5$ seeds, the surrogate attains the lowest mean composite test RMSE among the baselines we compare against. Structural ablations show that the GEM-graph prior is necessary—an order-of-magnitude RMSE divergence when removed—while pretraining together with the gene-expression modality make a significant joint contribution, supporting the proof-of-concept claim.