High throughput parameter estimation and uncertainty analysis applied to the production of mycoprotein from synthetic lignocellulosic hydrolysates.

The current global food system produces substantial waste and carbon emissions while exacerbating the effects of global hunger and protein deficiency. This study aims to address these challenges by exploring the use of lignocellulosic agricultural residues as feedstocks for microbial protein fermentation, focusing on Fusarium venenatum A3/5, a mycelial strain known for its high protein yield and nutritional quality. We propose a high throughput microlitre batch fermentation system paired with analytical chemistry to generate time series data of microbial growth and substrate utilisation. An unstructured biokinetic model was developed using a bootstrap sampling approach to quantify uncertainty in the parameter estimates. The model was validated against an independent data set of a different glucose-xylose composition to assess the predictive performance. Our results indicate a robust model fit with high coefficients of determination and low root mean squared errors for biomass, glucose, and xylose concentrations. Estimated parameter values provided insights into the resource utilisation strategies of Fusarium venenatum A3/5 in mixed substrate cultures, aligning well with previous research findings. Significant correlations between estimated parameters were observed, highlighting challenges in parameter identifiability. The high throughput workflow presents a novel, rapid methodology for biokinetic model development, enabling efficient exploration of microbial growth dynamics and substrate utilisation. This innovative method directly supports the development of a foundational model for optimising microbial protein production from lignocellulosic hydrolysates, contributing to a more sustainable global food system.