A computational platform of porcine cell metabolism for process optimization of cultivated meat production
Year: 2024
Type: article
Abstract: Cultivated meat (CM) has emerged as a sustainable alternative to current industrial livestock farming, addressing ethical and environmental concerns. While significant progress has been made during the last years to scale CM production, challenges persist, including reaching higher cell densities and proliferative capacities, adapting cells to suspension growth, and decreasing media costs. To tackle these obstacles, understanding cell metabolism is crucial. Modern sequencing technologies and high-throughput techniques provide a wealth of biological data, such as -omics data. By leveraging this data along with experimental studies, researchers can develop biologically constrained genome-scale metabolic models. These models, based on stoichiometric mass balances and linear equations, integrate information about genes, metabolic pathways, reactions, and gene-protein-reaction rules within an organism. They elucidate the interactions between metabolites and enzymes, enabling simulations of metabolite flux, biomass production, and metabolic responses to environmental changes. This work, funded by the Good Food Institute, aims to generate a genome-wide metabolic model constrained by proteome data of porcine-specific cell lines. The main goal is to optimize the feeding of animal cells for maximum metabolic efficiency, focusing on enhancing yield, cell density, and proliferative capacity. The resulting model will guide the optimization of culture media composition, determine nutrient consumption rates, identify metabolic bottlenecks, and enhance biomass production efficiency, addressing the challenges faced in cultivated meat production.
Authors: Krauss, Camila, Nuñez, Diego, Cataldo, Vicente, Ureta, Gonzalo, Rigual, Yandi, Mendoza, Sebastián N., Maass, Alejandro, Agosin, Eduardo
Institutions: Center for Mathematical Modeling, University of Chile
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6949186185
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