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Model-Guided Optimization of a Low-Serum Medium for Madin-Darby Canine Kidney Cells Using a Kolmogorov-Arnold Network and Bayesian Optimization
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AI
M
J
L
J
刘
孟
DOI：10.1007/s12010-026-05846-z.png)
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Abstract
En 中文
Madin–Darby canine kidney (MDCK) cells are important hosts for cell culture-based viral vaccine production, but fetal bovine serum (FBS) supplementation increases cost, batch variability, adventitious-agent risk, and regulatory burden. This study developed a 2% FBS low-serum medium optimization workflow for MDCK cells by integrating design of experiments (DoE) with interpretable machine learning. A Plackett–Burman (PB) design screened 27 nutritional components and identified seven factors that significantly affected proliferation. L-glutamine, L-asparagine, and L-tyrosine showed positive effects and were further optimized using a three-factor Box–Behnken design (BBD). A Kolmogorov–Arnold network (KAN) modeled the nonlinear proliferation response, and its response function served as the objective function for Bayesian optimization (BO). After two KAN–BO validation cycles, the optimized formulation contained 292.30 mg/L L-glutamine, 54.36 mg/L L-tyrosine, and 11.34 mg/L L-asparagine. The predicted relative proliferation rate was 159.38%, in close agreement with the experimental value of 158.63 ± 1.70%. Compared with Baseline medium, Optimized medium enhanced MDCK cell proliferation while maintaining typical adherent epithelial-like morphology and high viability. In H1N1 BVR-26 production assays, Optimized medium achieved a peak titer of 3.50 ± 0.05 log10 50% tissue culture infectious dose (TCID50)/mL at 48 h post-infection, outperforming both Baseline medium and Commercial medium. Metabolic analysis showed higher apparent glucose consumption and lower lactate yield, indicating improved glucose utilization and reduced metabolic burden. These results demonstrate that the PB–BBD–KAN–BO workflow is an efficient, interpretable, and experiment-saving strategy for low-serum medium development.
Keywords:
MDCK cells
Low-serum medium
Bayesian optimization
Design of experiments
Kolmogorov–Arnold network
Virus production
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