Active Materials & Manufacturing Food & Agriculture

Harnessing Genomic Instability with Al-Driven Adaptive Laboratory Evolution for Accelerated Yeast Bioproduction

Summary

Original abstract (not yet simplified)

The production of high-volume albumins, essential proteins for therapeutics and sustainable food, faces critical bottlenecks. In medicine, the supply of human serum albumin (HSA) is constrained by a reliance on plasma, which carries pathogen risks and supply vulnerabilities. In the food sector, ovalbumin production via precision fermentation offers a sustainable alternative to animal agriculture. Still, they both require overcoming major...

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The production of high-volume albumins, essential proteins for therapeutics and sustainable food, faces critical bottlenecks. In medicine, the supply of human serum albumin (HSA) is constrained by a reliance on plasma, which carries pathogen risks and supply vulnerabilities. In the food sector, ovalbumin production via precision fermentation offers a sustainable alternative to animal agriculture. Still, they both require overcoming major yield and cost-effectiveness barriers to be viable at an industrial scale. To address these challenges, AI-EvoYeast will exploit the inherent instability of polyploid yeast as an engine for accelerated evolution, integrating it with product-coupled selection to drive the evolution of albumin-hyperproducing strains. By employing an unbiased evolutionary approach, the platform enables the cell itself to explore a vast solution space of mutations, gene expression changes, and network-level adaptations, overcoming the stress of protein hyperproduction and the limits of rational design. The AI-EvoYeast project will develop a next-generation yeast (S. cerevisiae) platform, transforming a biological challenge, genomic instability, into a powerful engineering asset. Unlike traditional Adaptive Laboratory Evolution (ALE), AI-EvoYeast integrates Artificial Intelligence (AI) and Machine Learning (ML), fuelled by multi-omics data, to decipher adaptive mechanisms and build a predictive model for optimal genomic configurations. Insights from explainable AI (XAI) will then guide precise CRISPR interventions to reconstruct superior phenotypes in a stable industrial chassis. This project pioneers a highly generalisable AI-augmented evolutionary strategy. By creating a predictive platform technology estimated to slash R&D timelines by up to 50%, it will secure a sustainable, cost-effective European supply of vital proteins for the pharmaceutical and food industries. This directly supports EU strategic autonomy and leadership in the global bioeconomy.

Related Research

Grants with similar aims, by meaning.

Non-conventional yeast strain optimisation for industrial protein production using deep learning
21EBTA: Engineering Biology with Synthetic Genomes (EBSynerGy)
Automation and Digitalisation Technology for Evolutionary Development of Microbial Chassis
Next-Generation Adaptive Evolution Toolkit to Increase Protein Production in Precision Fermentation
SMARTY: Sustainable Methacrylate Advanced Research Technology for Yeast

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HORIZON

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