Kinetic models for growth and product formation on multiple substrates

Modeling interactions of Clostridium cadaveris and Clostridium sporogenes in anaerobic acidogenesis of glucose and peptone

This study focuses on developing a mathematical model to assess interaction among acidogenic bacteria during the anaerobic degradation of two substrates. Clostridium cadaveris and Clostridium sporogenes were cultured in various combinations with glucose and peptone. Parameter estimates are given for both conventional Monod parameters from single substrate-single species cultures and sum kinetics with interaction parameters obtained from dual substrate-single species cultures. The presence of multiple substrates led to both inhibitory and enhancing effects on biodegradation rates for dual substrates compared to single substrate cultures. A new model of interspecies interaction was developed within the framework of Lotka-Volterra incorporating substrate interaction parameters, with a focus on accuracy, realism, simplicity, and biological significance. The model demonstrated competitive interaction for resource sharing and the additional non-linearity parameter eliminated the constraint of the linear relationship between growth rate and population density.

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

Batch and Continuous Lactic Acid Fermentation Based on A Multi-Substrate Approach

The utilisation of waste materials and industrial residues became a priority within the bioeconomy concept and the production of biobased chemicals. The aim of this study was to evaluate the feasibility to continuously produce L-lactic acid from different renewable substrates, in a multi-substrate strategy mode. Based on batch experiments observations, Bacillus coagulans A534 strain was able to continuously metabolise acid whey, sugar beet molasses, sugar bread, alfalfa press green juice and tapioca starch. Additionally, reference experiments showed its behaviour in standard medium. Continuous fermentations indicated that the highest productivity was achieved when molasses was employed with a value of 10.34 g·L⁻¹·h⁻¹, while the lactic acid to sugar conversion yield was 0.86 g·g⁻¹. This study demonstrated that LA can be efficiently produced in continuous mode regardless the substrate, which is a huge advantage in comparison to other platform chemicals.

Targeted poly(3-hydroxybutyrate-co-3-hydroxyvalerate) bioplastic production from carbon dioxide

A microbial production process was developed to convert CO₂ and valeric acid into tailored poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) bioplastics. The aim was to understand microbial PHBV production in mixotrophic conditions and to control the monomer distribution in the polymer. Continuous sparging of CO₂ with pulse and pH-stat feeding of valeric acid were evaluated to produce PHBV copolyesters with predefined properties. The desired random monomer distribution was obtained by limiting the valeric acid concentration (below 1 gL^-1). ¹H-NMR, ¹³C-NMR and chromatographic analysis of the PHBV copolymer confirmed both the monomer distribution and the 3-hydroxyvalerate (3HV) fraction in the produced PHBV. A physical-based model was developed for mixotrophic PHBV production, which was calibrated and validated with independent experimental datasets. To produce PHBV with a predefined 3HV fraction, an operating diagram was constructed. This tool was able to predict the 3HV fraction with a very good accuracy (2% deviation).

Enzyme inactivation by ethanol and development of a kinetic model for thermophilic simultaneous saccharification and fermentation at 50 °C with Thermoanaerobacterium saccharolyticum ALK2

Studies were undertaken to understand phenomena operative during simultaneous saccharification and fermentation (SSF) of a model cellulosic substrate (Avicel) at 50°C with enzymatic hydrolysis mediated by a commercial cellulase preparation (Spezyme CP) and fermentation by a thermophilic bacterium engineered to produce ethanol at high yield, Thermoanaerobacterium saccharolyticum ALK2. Thermal inactivation at 50 °C, as shown by the loss of 50% of enzyme activity over 4 days in the absence of ethanol, was more severe than at 37 °C, where only 25% of enzyme activity was lost. In addition, at 50 °C ethanol more strongly influenced enzyme stability. Enzyme activity was moderately stabilized between ethanol concentrations of 0 and 40 g/L, but ethanol concentrations above 40 g/L accelerated enzyme inactivation, leading to 75% loss of enzymatic activity in 80 g/L ethanol after 4 days. At 37 °C, ethanol did not show a strong effect on the rate of enzyme inactivation. Inhibition of cellulase activity by ethanol, measured at both temperatures, was relatively similar, with the relative rate of hydrolysis inhibited 50% at ethanol concentrations of 56.4 and 58.7 g/L at 50 and 37 °C, respectively. A mathematical model was developed to test whether the measured phenomena were sufficient to quantitatively describe system behavior and was found to have good predictive capability at initial Avicel concentrations of 20 and 50 g/L.