Cella: Predicting Microbial Cultivation Requirements on a Multiscale Metabolic Graph

Microbial growth is determined by interacting processes across a wide range of temporal and spatial scales, from molecular structure to phylogeny. Multiple modalities, including genomic data, genome-scale metabolic models, chemical structures, and media properties, often inform expert predictions. However, previous foundation models have been restricted to data available in large quantities and readily consumable by ML algorithms (PDB, sequences, genomeLMs). In this work, we present Cella, a heterograph model which enables predictions based on different data sources at scale while encoding real mechanistic relationships and handling coverage gaps gracefully. We fit the model on a newly constructed collection of 4,220 prokaryotic organisms and their associated metabolic models sampled exhaustively at the genus level. We evaluate Cella on various industrially relevant tasks, including media and component growth prediction, carbon-source utilization, environmental preference, and component substitution, and strain ranking by shared cultivation requirements. Tasks are scored under held-out organism and component splits, against popularity, phylum-only, and label-blind baselines that the adjacent literature omits. We predict growth media on the nearly half of species in our collection that do not record one, while providing a traceable path to the evidence supporting the predictions. Cella enables multimodal data integration while retaining interpretable interactions and points towards underlying biological patterns learnable with foundation-model-style approaches that generalize across the microbial landscape.