AI-EvoYeast · Harnessing Genomic Instability with Al-Driven Adaptive Laboratory Evolution for Accelerated Yeast Bioproduction
„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“
- Период
- 2026-05-01 → 2028-04-30
- Финансиране от ЕС
- 260 348 €
- Участници
- 1
- Схема
- HORIZON-TMA-MSCA-PF-EF
Линиите свързват координатора с партньорите.
Накратко на български
Дрождите се модифицират чрез изкуствен интелект и еволюция, за да произвеждат повече протеини като албумин за медицината и хранителната индустрия. Това помага за намаляване на разходите и времето за разработка на устойчиви алтернативи на животинските продукти и плазмата.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Цел на проекта
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.
Оригинален текст от CORDIS (на английски).
Участници
- IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE · LondonКоординаторОбединеното кралство
Връзки
Данни: CORDIS, © Европейски съюз
