relation: https://khub.utp.edu.my/scholars/3537/
title: Prediction of weak acid toxicity in saccharomyces cerevisiae using genome-scale metabolic models
creator: Hyland, P.B.
creator: Mun, S.L.-S.
creator: Mahadevan, R.
description: The use of lignocellulosic biomass is critical for the economic production of transportation fuels and chemicals in renewable bioprocesses. While biomass is an abundant resource, necessary pretreatment to yield fermentable monosaccharides produces toxic compounds that dramatically affect fermentation performance. Weak acids such as acetic acid play an important role in the toxicity of lignocellulosic hydrolysate to Saccharomyces cerevisiae, a commonly used industrial organism. In order to explore the ramifications of weak acid inhibition on cellular metabolism, we adapted a genome-scale metabolic model of S. cerevisiae to describe toxicity of acetic acid by a decoupling mechanism. We evaluated the performance of the model in predicting growth rates and ethanol production characteristics under aerobic and anaerobic cultivations. We found that the model was able to capture the decreased growth during aerobic cultivations in the presence of acetic acid, but was unable to capture the increase in ethanol yield observed. The model was able to predict anaerobic growth rates and ethanol yields; however, at conditions of higher toxicity levels, discrepancies arose. We expect that a model such as this may find application in the optimization of lignocellulose-based bioprocesses in which there exists a critical economic trade-off between neutralization costs and product yields. © Copyright 2013, Mary Ann Liebert, Inc.
date: 2013
type: Article
type: PeerReviewed
identifier:   Hyland, P.B. and Mun, S.L.-S. and Mahadevan, R.  (2013) Prediction of weak acid toxicity in saccharomyces cerevisiae using genome-scale metabolic models.  Industrial Biotechnology, 9 (4).  pp. 229-235.  ISSN 15509087     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881494538&doi=10.1089%2find.2013.0004&partnerID=40&md5=dbccdb1b7ac701063369147765265ba9
relation: 10.1089/ind.2013.0004
identifier: 10.1089/ind.2013.0004