Modeling Microbial Growth Curves with GCAT

Bukhman, Yury V.; DiPiazza, Nathan W.; Piotrowski, Jeff S.; Shao, Jason; Halstead, Adam G. W.; Bui, Minh Duc; Xie, Enhai; Sato, Trey K.

doi:10.1007/s12155-015-9584-3

Cited by 34 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Background absorbance was subtracted from the average of three negative controls (media without cells). Kinetic parameters for each condition were calculated in GCAT [91]. Average and standard deviations from two independent biological replicates were calculated in R [92].…”

Section: Methodsmentioning

confidence: 99%

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

Peris

Moriarty

Alexander

et al. 2017

Biotechnol Biofuels

Self Cite

View full text Add to dashboard Cite

BackgroundLignocellulosic biomass is a common resource across the globe, and its fermentation offers a promising option for generating renewable liquid transportation fuels. The deconstruction of lignocellulosic biomass releases sugars that can be fermented by microbes, but these processes also produce fermentation inhibitors, such as aromatic acids and aldehydes. Several research projects have investigated lignocellulosic biomass fermentation by the baker’s yeast Saccharomyces cerevisiae. Most projects have taken synthetic biological approaches or have explored naturally occurring diversity in S. cerevisiae to enhance stress tolerance, xylose consumption, or ethanol production. Despite these efforts, improved strains with new properties are needed. In other industrial processes, such as wine and beer fermentation, interspecies hybrids have combined important traits from multiple species, suggesting that interspecies hybridization may also offer potential for biofuel research.ResultsTo investigate the efficacy of this approach for traits relevant to lignocellulosic biofuel production, we generated synthetic hybrids by crossing engineered xylose-fermenting strains of S. cerevisiae with wild strains from various Saccharomyces species. These interspecies hybrids retained important parental traits, such as xylose consumption and stress tolerance, while displaying intermediate kinetic parameters and, in some cases, heterosis (hybrid vigor). Next, we exposed them to adaptive evolution in ammonia fiber expansion-pretreated corn stover hydrolysate and recovered strains with improved fermentative traits. Genome sequencing showed that the genomes of these evolved synthetic hybrids underwent rearrangements, duplications, and deletions. To determine whether the genus Saccharomyces contains additional untapped potential, we screened a genetically diverse collection of more than 500 wild, non-engineered Saccharomyces isolates and uncovered a wide range of capabilities for traits relevant to cellulosic biofuel production. Notably, Saccharomyces mikatae strains have high innate tolerance to hydrolysate toxins, while some Saccharomyces species have a robust native capacity to consume xylose.ConclusionsThis research demonstrates that hybridization is a viable method to combine industrially relevant traits from diverse yeast species and that members of the genus Saccharomyces beyond S. cerevisiae may offer advantageous genes and traits of interest to the lignocellulosic biofuel industry.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0763-7) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

Peris

Moriarty

Alexander

et al. 2017

Biotechnol Biofuels

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such measures can be made in parallel in a plate reader, and may result in hundreds or thousands of absorbance measurements over the course of 24 h. The resulting growth curves are commonly used in a variety of microbiological experiments [ 1 – 3 ], including experimental evolution [ 4 – 7 ]. A variety of methods have been used to obtain metrics from such growth curves [ 1 , 3 , 4 , 8 – 13 ]. Older methods relied on manually plotting the cell count or absorbance readings over time on semi-log graph paper to obtain metrics like the maximum growth rate [ 8 ], an approach which has been computationally mirrored recently by GrowthRates [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves

2016

View full text Add to dashboard Cite

BackgroundPlate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis.ResultsGrowthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.ConclusionsGrowthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

show abstract

“…For fitting growth curves, several alternatives exist3181920, which, although mostly focusing on parametric approaches, do allow spline fitting3 and polynomial regression1820. Both approaches have been criticized, being sensitive to outliers and potentially having systematic biases5, and at least in the case of splines appear less robust (Fig.…”

Section: Discussionmentioning

confidence: 99%

Inferring time derivatives including cell growth rates using Gaussian processes

et al. 2016

View full text Add to dashboard Cite

Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.

show abstract

Modeling Microbial Growth Curves with GCAT

Cited by 34 publications

References 21 publications

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves

Inferring time derivatives including cell growth rates using Gaussian processes

Contact Info

Product

Resources

About