A kinetic model of catabolic adaptation and protein reprofiling in Saccharomyces cerevisiae during temperature shifts

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

Development of a model for the alcoholic fermentation of cocoa beans by a Saccharomyces cerevisiae strain

The aromatic quality of chocolate requires the use of cocoa with high aromatic potential, this being acquired during the fermentation of cocoa beans. Traditional fermentation is still often carried out on a small scale with wild strains of yeasts and acetic bacteria and under poorly controlled conditions leading to cocoa quality ranging from best to worst. This study is the first part of a project aiming to control quality of cocoa to produce high aromatic quality chocolate by using a mixed starter of selected strains of yeast and acetic bacteria and by controlling the conditions of fermentation. To achieve this objective, a mathematical model of the alcoholic fermentation of cocoa beans has been developed. The growth, glucose consumption and ethanol production of Saccharomyces cerevisiae LM strain in synthetic broth were modeled for the most important intrinsic (pH, glucose, ethanol, free nitrogen and oxygen levels) and extrinsic (temperature, oxygen level) fermentation parameters. The model was developed by combining the effects of individual conditions in a multiplicative way using the gamma concept. The model was validated in liquid synthetic medium at two different inoculation levels 10⁴ and 10⁶ CFU/mL with an increase in temperature that recorded during spontaneous fermentations. The model clearly shows that the level of inoculation and the speed of the increase in temperature clearly drive yeast growth, while other factors including pH and ethanol, free nitrogen and oxygen levels have no significant impact on yeast growth.

Molecular and physiological basis of Saccharomyces cerevisiae tolerance to adverse lignocellulose-based process conditions

Lignocellulose-based biorefineries have been gaining increasing attention to substitute current petroleum-based refineries. Biomass processing requires a pretreatment step to break lignocellulosic biomass recalcitrant structure, which results in the release of a broad range of microbial inhibitors, mainly weak acids, furans, and phenolic compounds. Saccharomyces cerevisiae is the most commonly used organism for ethanol production; however, it can be severely distressed by these lignocellulose-derived inhibitors, in addition to other challenging conditions, such as pentose sugar utilization and the high temperatures required for an efficient simultaneous saccharification and fermentation step. Therefore, a better understanding of the yeast response and adaptation towards the presence of these multiple stresses is of crucial importance to design strategies to improve yeast robustness and bioconversion capacity from lignocellulosic biomass. This review includes an overview of the main inhibitors derived from diverse raw material resultants from different biomass pretreatments, and describes the main mechanisms of yeast response to their presence, as well as to the presence of stresses imposed by xylose utilization and high-temperature conditions, with a special emphasis on the synergistic effect of multiple inhibitors/stressors. Furthermore, successful cases of tolerance improvement of S. cerevisiae are highlighted, in particular those associated with other process-related physiologically relevant conditions. Decoding the overall yeast response mechanisms will pave the way for the integrated development of sustainable yeast cell-based biorefineries.

The potential of the newly isolated thermotolerant Kluyveromyces marxianus for high-temperature ethanol production using sweet sorghum juice

In this work, the newly isolated thermotolerant Kluyveromyces marxianus DBKKUY-103 exhibited a high ethanol fermentation efficiency at high temperatures using sweet sorghum juice (SSJ). The highest ethanol concentrations and productivities achieved under the optimum conditions using thermotolerant K. marxianus DBKKUY-103 were 85.16 g/l and 1.42 g/l.h at 37 °C and 83.46 g/l and 1.39 g/l.h at 40 °C, respectively. The expression levels of genes during ethanol fermentation at 40 °C were evaluated and the results found that the transcriptional levels of the RAD10, RAD14, RAD33, RAD50, ATPH, ATP4, ATP16, and ATP20 genes were up-regulated compared with those at 30 °C, suggesting that the high growth and high ethanol production efficiencies of K. marxianus DBKKUY-103 during high-temperature ethanol production associated with the genes involved in DNA repair and ATP production.

Physiological response of Saccharomyces cerevisiae to weak acids present in lignocellulosic hydrolysate

Weak acids are present in lignocellulosic hydrolysate as potential inhibitors that can hamper the use of this renewable resource for fuel and chemical production. To study the effects of weak acids on yeast growth, physiological investigations were carried out in batch cultures using glucose as carbon source in the presence of acetic, formic, levulinic, and vanillic acid at three different concentrations at pH 5.0. The results showed that acids at moderate concentrations can stimulate the glycolytic flux, while higher levels of acid slow down the glycolytic flux for both aerobically and anaerobically grown yeast cells. In particular, the flux distribution between respiratory and fermentative growth was adjusted to achieve an optimal ATP generation to allow a maintained energy level as high as it is in nonstressed cells grown exponentially on glucose under aerobic conditions. In addition, yeast cells exposed to acids suffered from severe reactive oxygen species stress and depletion of reduced glutathione commensurate with exhaustion of the total glutathione pool. Furthermore, a higher cellular trehalose content was observed as compared to control cultivations, and this trehalose probably acts to enhance a number of stress tolerances of the yeast.