European dairy industry is an important agri-food sector; it represents more than 300,000 jobs and 10 billion € positive trade balance. Five out of ten top global dairy companies are European and more than 80% of them are SMEs. More than 300 European cheeses and dairy products are sold all over the world and are protected as geographical indications or traditional specialties. Mastering a cheese-ripening process to avoid sanitary risk and waste, and produce typical cheeses with organoleptic properties valued by the consumers is of economic and social significance.
E-MUSE aims to develop innovative modelling methodologies to improve knowledge about complex biological systems and to control and/or predict their evolution by combining artificial intelligence and systems biology. This multidisciplinary strategy integrating genome-scale metabolic models, dynamic modelling methodologies, together with the design of efficient statistical and machine learning tools, will allow analysis of multi-omics data and application of the results to macro-scale properties related to cheese ripening and consumer preference.
Moreover, in the context of sustainable development, more and more consumers are diversifying their diet and consume plant-based food. Introduction of plant-based proteins in the cheese process brings issues such as acidity or safety. Modelling strategies of E-MUSE will help to target and solve these issues. E-MUSE will train researchers with multidisciplinary skills in mathematics, bioinformatics and biology to design and use innovative multiscale modelling methodologies, giving researchers a harmonised language to address future research questions about complex biological systems.
Finally, the ultimate outcome of E-MUSE is to develop, for the industry, a dynamic modelling software to control the food process.