Volta

Algorithmic Nutrient Engineering via Dynamic Microbial Bioprocesses

This chapter describes the mechanistic approach used to translate nutrigenomic data into clinically meaningful nutritional formulations through artificial intelligence (AI) and precision fermentation (PF). This highlights the central challenge of personalized nutrition: converting complex genomic and metabolic signals into consistent, actionable nutrient specifications. This chapter introduces an AI-driven inverse-design system that generates digital nutrient specifications (DNSs) that define the molecular composition and production parameters. PF is a biomanufacturing platform capable of executing these specifications through engineered microbial systems and real-time process control to produce genotype-aligned peptides, vitamins, and metabolic cofactors. The chapter also outlines the analytical pipeline linking multiomic interpretation to fermentation optimization and provides examples relevant to metabolic and neurodevelopmental applications, concluding with key regulatory and ethical considerations for …

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