Enhancement of cellular memory by reducing stochastic transitions

Açar, Murat; Becskei, Attila; Oudenaarden, Alexander van

doi:10.1038/nature03524

Cited by 478 publications

(598 citation statements)

References 29 publications

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“…4a). A similar study performed on GAL system of S. cerevisiae shows a bi-stable behavior for intermediate galactose concentrations (Acar et al 2005). It has been shown in this case that the positive feedback loop provided by Gal3p is responsible for the observed bistability.…”

Section: Resultssupporting

confidence: 62%

“…Thus, the presence of the positive feedback brought about by Lac12p is critical for the observed wild-type response at various galactose concentrations. It has been experimentally observed that the GAL system of S. cerevisiae shows a bistable behavior when exposed to different preculturing conditions (Acar et al 2005). To verify if such a phenotype exists for the K. lactis KlGAL system, we simulated the dynamic model for two different preculturing conditions, that is, glycerol and galactose and subsequently grown on galactose.…”

Section: Resultsmentioning

confidence: 99%

“…The dynamic modeling strategy discussed above was used to compare the performance of GAL systems in both, mutant (lacking gene GAL80) and wild type strains of S. cerevisiae and K. lactis. Experimental studies have reported that the wild-type GAL system in S. cerevisiae shows bistable behavior at intermediate galactose concentrations (Acar et al 2005). Further, it is known that such short term memory of GAL system in S. cerevisiae was due to the presence of positive feedback loop of Gal3p.…”

Section: Resultsmentioning

confidence: 99%

“…It has been experimentally demonstrated that the GAL system of S. cerevisiae shows different steady state expression levels when grown under identical galactose concentrations depending on the preculturing conditions (Acar et al 2005). In particular, when precultured on galactose, the yeast S. cerevisiae shows higher expression levels than when precultured in an NINR medium such as glycerol or raffinose for a given galactose substrate.…”

Section: Sensitivity Of Bistable Behavior To Model Parametersmentioning

confidence: 99%

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Dynamic analysis of the KlGAL regulatory system in Kluyveromyces lactis: a comparative study with Saccharomyces cerevisiae

et al. 2011

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The GAL regulatory system is highly conserved in yeast species of Saccharomyces cerevisiae and Kluyveromyces lactis. While the GAL system is a well studied system in S. cerevisiae, the dynamic behavior of the KlGAL system in K. lactis has not been characterized. Here, we have characterized the GAL system in yeast K. lactis by developing a dynamic model and comparing its performance to its not-sodistant cousin S. cerevisiae. The present analysis demonstrates the significance of the autoregulatory feedbacks due to KlGal4p, KlGal80p, KlGal1p and Lac12p on the dynamic performance of the KlGAL switch. The model predicts the experimentally observed absence of bistability in the wild type strain of K. lactis, unlike the short term memory of preculturing conditions observed in S. cerevisiae. The performance of the GAL switch is distinct for the two yeast species although they share similarities in the molecular components. The analysis suggests that the whole genome duplication of S. cerevisiae, which resulted in a dedicated inducer protein, Gal3p, may be responsible for the high sensitivity of the system to galactose concentrations. On the other hand, K. lactis uses a bifunctional protein as an inducer in addition to its galactokinase activity, which restricts its regulatory role and hence higher galactose levels in the medium are needed to trigger the GAL system.

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Section: Resultssupporting

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Sensitivity Of Bistable Behavior To Model Parametersmentioning

confidence: 99%

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Dynamic analysis of the KlGAL regulatory system in Kluyveromyces lactis: a comparative study with Saccharomyces cerevisiae

et al. 2011

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“…Another example is the galacatose ultilization network in yeast displaying bimodal patterns in the expression of the GAL family genes responsible for galactose metabolism [24]. A recent report about the presence of several global feedback loops overarching metabolic and transcriptional regulation in E. coli [25] indicates that in fact many more metabolically different stable phenotypes could be uncovered -if we were only able to experimentally probe metabolism of individual cells.…”

Section: Why Single Cell Metabolomics?mentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity. IntroductionQuantitative metabolomics, the technology for large-scale quantification of intracellular metabolite concentrations, is a powerful tool in systems biology research that has recently led to a series of interesting findings (e.g. [1][2][3][4]). Because of the metabolome's chemical diversity, mass spectrometry (MS) is the analytical method of choice [5]. In addition to analytical challenges, quantitative metabolomics as required for addressing (systems) biology questions poses significant challenges in sample processing. One important challenge is the need to preserve the original metabolome during sample processing, which is often difficult because of the presence of enzymes in the sample and the fast metabolic turnover rates.For sensitivity reasons, current metabolomics methods require samples that contain a large number of cells. However, cell populations are not necessarily homogeneous. Besides genetic differences, several other sources for population heterogeneity exist, of which several are also known to cause metabolic differences. Today, methods capable of resolving differences in metabolite levels on the single cell level are provided, within limits, by molecular sensors such as FRET sensors [6,7] or aptamer-based technology [8 ,9]. Both types of molecular sensors, however, are difficult to develop, are limited to specific analytes, and quantitative analyses (e.g. in terms of mol/L) are hardly possible with them. Laserinduced fluorescence, as introduced by Dovichi for single cell proteomics, is limited to fluorescent compounds or labelled species [10,11]. In addition, all these existing methods share the limitation that they can never be extended to the ''-omics'' level, that is to measuring a large number of metabolites at the same time. They will thus not be applicable to discovery type research and research that requires a large number of metabolites to be measured in the same cell.Because of the success of mass spectrometry in population-level metabolome analyses, the analytical community has recently made great strides towards single cell level metabolite analyses (for a review, see [12]). So far, however, hardly any new biological insight has been generated from these endeavours. In this Current Opinion paper, we will thus not only review the current status of MS-based single cell metabolomics but also discuss which of the differ...

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Living in a noisy world—origins of gene expression noise and its impact on cellular decision‐making

Pal,

Dhar

2024

FEBS Letters

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The expression level of a gene can vary between genetically identical cells under the same environmental condition—a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision‐making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision‐making. As advances in single‐cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

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Enhancement of cellular memory by reducing stochastic transitions

Cited by 478 publications

References 29 publications

Dynamic analysis of the KlGAL regulatory system in Kluyveromyces lactis: a comparative study with Saccharomyces cerevisiae

Dynamic analysis of the KlGAL regulatory system in Kluyveromyces lactis: a comparative study with Saccharomyces cerevisiae

Single cell metabolomics

Living in a noisy world—origins of gene expression noise and its impact on cellular decision‐making

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