Toward sustainable culture media: Using artificial intelligence to optimize reduced-serum formulations for cultivated meat.

Journal: The Science of the total environment
PMID: 37343855

Abstract

When considering options for future foods, cell culture approaches are at the fore, however, culture media to support the process has been identified as a significant contributor to the overall global warming potential (GWP) and cost of cultivated meat production. To address this issue, an artificial intelligence-based approach was applied to simultaneously optimize the GWP, cost, and cell growth rate of a reduced-serum culture media formulation for a zebrafish (ZEM2S cell line) cultivated meat production system. Response surface methodology (RSM) was used to design the experiments, with seven components - IGF, FGF, TGF, PDGF, selenium, ascorbic acid, and serum - selected as independent variables, given their influence on culture media performance. Radial basis function (RBF) neural networks and genetic algorithm (GA) were applied for prediction of dependent variables, and optimization of the culture media formulation, respectively. The results indicated that the developed RBF could accurately predict the GWP, cost and growth rate, with a model efficiency of 0.98. Subsequently, the three developed RBF neural networks predictive models were used as the inputs for a multi-objective genetic algorithm, and the optimal quantities of the independent variables were determined using a multi-objective optimization algorithm. The suggested RSM + RBF + GA framework in this study could be applied to sustainably optimize serum-free media development, identifying the combination of media ingredients that balances yield, environmental impact, and cost for various cultivated meat cell lines.

Authors

  • Amin Nikkhah
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA. Electronic address: Amin.nikkhah@tufts.edu.
  • Abbas Rohani
    Ferdowsi University of Mashhad, Mashhad, Mashhad, P.O. Box: 9177948974, Iran.
  • Mohammad Zarei
    Department of Chemistry, University of Ulsan, Ulsan, 44610, Korea.
  • Ajay Kulkarni
    Commonwealth Cyber Initiative, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
  • Feras A Batarseh
    The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University (Virginia Tech), Arlington, VA 22203, USA.
  • Nicole Tichenor Blackstone
    Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA. Electronic address: nicole.blackstone@tufts.edu.
  • Reza Ovissipour
    Department of Food Science and Technology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA; Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA. Electronic address: ovissi@vt.edu.

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