Control of specific growth rate in Saccharomyces cerevisiae

Snoep, Jacky L.; Mrwebi, Mandisi; Schuurmans, J. Merijn; Rohwer, Johann M.; Mattos, M. Joost Teixeira de

doi:10.1099/mic.0.023119-0

Cited by 34 publications

(32 citation statements)

References 25 publications

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“…This model requires two parameters: the maximum growth rate ( μ max ) and the glucose concentration at which growth rate is half-maximal ( K s ). Our estimates of μ max (0.43–0.52 h −1 ) and K s (0.1–0.2 mM) are similar to values estimated for S. cerevisiae strains using bulk population growth rates in batch cultures and chemostats (Snoep et al 2009), respectively. As our estimates are generated from a large number of measurements (28,000–42,000 growth rates per strain), these parameters are estimated extremely accurately with standard errors on the order of 10 −4 (i.e., three orders of magnitude smaller than the parameters).…”

Section: Resultssupporting

confidence: 81%

Genetic and Nongenetic Determinants of Cell Growth Variation Assessed by High-Throughput Microscopy

Ziv¹,

Siegal²,

Gresham³

2013

Molecular Biology and Evolution

105

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In microbial populations, growth initiation and proliferation rates are major components of fitness and therefore likely targets of selection. We used a high-throughput microscopy assay, which enables simultaneous analysis of tens of thousands of microcolonies, to determine the sources and extent of growth rate variation in the budding yeast (Saccharomyces cerevisiae) in different glucose environments. We find that cell growth rates are regulated by the extracellular concentration of glucose as proposed by Monod (1949), but that significant heterogeneity in growth rates is observed among genetically identical individuals within an environment. Yeast strains isolated from different geographic locations and habitats differ in their growth rate responses to different glucose concentrations. Inheritance patterns suggest that the genetic determinants of growth rates in different glucose concentrations are distinct. In addition, we identified genotypes that differ in the extent of variation in growth rate within an environment despite nearly identical mean growth rates, providing evidence that alleles controlling phenotypic variability segregate in yeast populations. We find that the time to reinitiation of growth (lag) is negatively correlated with growth rate, yet this relationship is strain-dependent. Between environments, the respirative activity of individual cells negatively correlates with glucose abundance and growth rate, but within an environment respirative activity and growth rate show a positive correlation, which we propose reflects differences in protein expression capacity. Our study quantifies the sources of genetic and nongenetic variation in cell growth rates in different glucose environments with unprecedented precision, facilitating their molecular genetic dissection.

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Section: Resultssupporting

confidence: 81%

Genetic and Nongenetic Determinants of Cell Growth Variation Assessed by High-Throughput Microscopy

Ziv¹,

Siegal²,

Gresham³

2013

Molecular Biology and Evolution

105

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“…Monod half saturation values (K S ) are typically obtained experimentally (Heijnen and Kleerebezem, 2010) and no general agreement on their mechanistic interpretation has been achieved (Liu, 2007;Wang et al, 1998;Snoep et al, 2009). For the purpose of this model, a general theoretical approach was proposed based only on the relative differences between the diffusivities of the different substrates (which implies no differences in the K S values for the species competing for the same substrate) to estimate Ks values of both the existing and postulated microbial metabolic activities (more details on the K S modelling are presented in Supplementary Information S1.4).…”

Section: Model Descriptionmentioning

confidence: 99%

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

González-Cabaleiro

Ofiţeru²,

Lema

et al. 2015

The ISME Journal

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The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomassspecific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate.

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“…Criticisms in particular of the empirical constants K s and Y have been made by Ferenci [59] who remarked on the lack of attention given to determining the mechanistic bases for such ''supposed constants''. A recent attempt to address this issue was made by Snoep et al [171] who explored the relationship between the saturation constant and the affinity of the transporter for the growth-limiting substrate in glucose-limited chemostat cultures of Saccharomyces cerevisiae. These authors have developed a core model and theory based on metabolic control analysis such that in the chemostat the pump rate controls l via its effect on the growth-limiting substrate concentration (G S P ) where the change in steady state concentration of s is given by…”

Section: Theorymentioning

confidence: 99%

The renaissance of continuous culture in the post-genomics age

Bull

2010

J Ind Microbiol Biotechnol

108

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The development of continuous culture techniques 60 years ago and the subsequent formulation of theory and the diversification of experimental systems revolutionised microbiology and heralded a unique period of innovative research. Then, progressively, molecular biology and thence genomics and related high-information-density omics technologies took centre stage and microbial growth physiology in general faded from educational programmes and research funding priorities alike. However, there has been a gathering appreciation over the past decade that if the claims of systems biology are going to be realised, they will have to be based on rigorously controlled and reproducible microbial and cell growth platforms. This revival of continuous culture will be long lasting because its recognition as the growth system of choice is firmly established. The purpose of this review, therefore, is to remind microbiologists, particularly those new to continuous culture approaches, of the legacy of what I call the first age of continuous culture, and to explore a selection of researches that are using these techniques in this post-genomics age. The review looks at the impact of continuous culture across a comprehensive range of microbiological research and development. The ability to establish (quasi-) steady state conditions is a frequently stated advantage of continuous cultures thereby allowing environmental parameters to be manipulated without causing concomitant changes in the specific growth rate. However, the use of continuous cultures also enables the critical study of specified transition states and chemical, physical or biological perturbations. Such dynamic analyses enhance our understanding of microbial ecology and microbial pathology for example, and offer a wider scope for innovative drug discovery; they also can inform the optimization of batch and fed-batch operations that are characterized by sequential transitions states.

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Control of specific growth rate in Saccharomyces cerevisiae

Cited by 34 publications

References 25 publications

Genetic and Nongenetic Determinants of Cell Growth Variation Assessed by High-Throughput Microscopy

Genetic and Nongenetic Determinants of Cell Growth Variation Assessed by High-Throughput Microscopy

Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The renaissance of continuous culture in the post-genomics age

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