Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Understanding gradients in industrial bioreactors.

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

Dietary lipid unsaturation influences survival and oxidative modifications of an amyotrophic lateral sclerosis model in a gender-specific manner.

The implication of lipid peroxidation in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) derive from high abundance of peroxidation-prone polyunsaturated fatty acids in central nervous system and its relatively low antioxidant content. In the present work, we evaluated the effect of dietary changes aimed to modify fatty acid tissular composition in survival, disease onset, protein, and DNA oxidative modifications in the hSODG93A transgenic mice, a model of this motor neuron disease. Both survival and clinical evolution is dependent on dietary fatty acid unsaturation and gender, with high unsaturated diet, leading to loss of the disease-sparing effect of feminine gender. This was associated with significant increases in protein carbonyl and glycoxidative modifications as well as non-nuclear 8-oxo-dG, a marker of mitochondrial DNA oxidation. Comparison of these data with γH2AX immunostaining, a marker of DNA damage response, suggests that the highly unsaturated diet-blunted mitochondrial-nuclear free radical dependent crosstalk, since increased 8-oxo-dG was not correlated with increased DNA damage response. Paradoxically, the highly unsaturated diet led to lower peroxidizability but higher anti-inflammatory indexes. To sum up, our results demonstrate that high polyunsaturated fatty acid content in diets may accelerate the disease in this model. Further, these results reinforce the need for adequately defining gender as a relevant factor in ALS models, as well as to use structurally characterized markers for oxidative damage assessment in neurodegeneration.