  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 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 dataset 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. This work provides a
foundational model for optimising the production of microbial protein from
lignocellulosic waste, contributing to a more sustainable global food system.
