A brief review on recent development of multidisciplinary engineering in fermentation of Saccharomyces cerevisiae

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

Multivariable advanced nonlinear controller for bioethanol production in a non-isothermal fermentation bioreactor

Design for fermentation bioreactor controllers is challenged by the nonlinear process kinetics and the lack of online measurements for key variables. This work developed a multi-input, multi-output advanced nonlinear control structure for a continuous, non-isothermal, constant volume fermentation bioreactor. Utilizing feedback linearization control for the bioreactor feed to regulate glucose concentration, and backstepping control for the cooling jacket feed to regulate reactor temperature. A developed novel estimator for biomass concentration was incorporated to provide online estimates for the unmeasurable state variable. Simulation results showed the control structure ability in efficiently establishing a combination of dynamic and fixed set points, despite disturbances in the bioreactor feed temperature and glucose concentration. Expanded bioreactor control authority increased operational flexibility and enhanced the potential for performance improvements. This work illustrated the effectiveness of feedback linearization and backstepping control in designing controllers for biological systems with nonlinear dynamics, complex interactions, and input disturbances.

AI-based forecasting of ethanol fermentation using yeast morphological data

Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a non-staining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of > 0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production.