Microbial heterogeneity affects bioprocess robustness: dynamic single-cell analysis contributes to understanding of microbial populations.
about
Biochemical Engineering Approaches for Increasing Viability and Functionality of Probiotic BacteriaFluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and ApplicationsAdaptation to low pH and lignocellulosic inhibitors resulting in ethanolic fermentation and growth of Saccharomyces cerevisiaeStochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences.The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stressThe private life of environmental bacteria: pollutant biodegradation at the single cell level.Microfluidic single-cell analysis links boundary environments and individual microbial phenotypes.Transcription factor-based biosensors in biotechnology: current state and future prospects.Microbial lipopeptide production and purification bioprocesses, current progress and future challenges.Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses.Maximizing the stability of metabolic engineering-derived whole-cell biocatalysts.Exacerbation of substrate toxicity by IPTG in Escherichia coli BL21(DE3) carrying a synthetic metabolic pathway.Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community.Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains.Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Pseudomonas putida KT2440.Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling.The glycerol-dependent metabolic persistence of Pseudomonas putida KT2440 reflects the regulatory logic of the GlpR repressor.Robustness of a model microbial community emerges from population structure among single cells of a clonal population.Impact of plasmid architecture on stability and yEGFP3 reporter gene expression in a set of isomeric multicopy vectors in yeast.Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells.Beyond the bulk: disclosing the life of single microbial cells.Engineering Microbial Metabolite Dynamics and Heterogeneity.Dynamic behavior of Yarrowia lipolytica in response to pH perturbations: dependence of the stress response on the culture mode.Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law.A versatile, non genetically modified organism (GMO)-based strategy for controlling low-producer mutants in Bordetella pertussis cultures using antigenic modulation.A novel approach to monitor stress-induced physiological responses in immobilized microorganisms.Editorial: Latest methods and advances in biotechnology.Constraint-based modeling in microbial food biotechnology.Making variability less variable: matching expression system and host for oxygenase-based biotransformations.Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy.
P2860
Q26748468-86C332C3-C30B-4670-ADD1-4200D4C54F78Q26781055-EFDF2CC1-8253-44C4-ADFC-FEF716F4A8CBQ28596941-513581F3-C945-40C4-9C77-DF52B01E93B6Q35571789-1D0EA7D8-9FFA-4C9D-AEC8-69684DCC58A6Q35584400-643BD70C-16AC-47B0-B178-D5777BDA18EEQ38171535-EA8B8C0C-9F51-4B86-8A4D-45CA4549FCCDQ38261448-D3EA8D2F-0C3E-417D-B3EA-26E750CACA86Q38621423-9EE41A41-E96A-4E8B-8DE6-BECA46168FA5Q38668574-7C5703DB-9795-4D3C-A156-B8897F7525E4Q39330220-85FE4B32-DC7F-483E-B954-37FF2C0C2A3DQ39441761-41CDF8C2-BBF8-4835-BFD2-F914C5746CA6Q40184457-A7924A87-FC94-4FDF-95A7-C3ACDC8A14F9Q41143180-DE875FAF-F918-4C17-8AAB-B4CDDB2FF8DCQ41548175-F4EDF4BB-56E2-4C07-97A3-7CFD6494B1BEQ41771529-1EF5778F-8510-45BF-BCDB-8E5520DA0CD1Q42362408-22F871D5-0D36-4281-9417-DC2A31D0B4EEQ42747734-B0B9F385-9037-42FD-97A0-03200901BDB3Q46381406-842AFF0D-7801-48FD-A3E5-8FF2CAC90509Q47095086-A196D877-B50F-4C26-A926-53A178F850B6Q47096773-111426F4-9CFD-4B1B-BA26-A007040FA2E9Q47446895-94548F36-34FB-42AF-A923-E6B43B4E6A75Q47681036-B747DB27-5CA5-45B1-B952-B4AC578D0B5CQ48307307-47E53E26-8900-4F68-A235-3E4B27300E5DQ50259770-5F0D2A8B-FFAC-4791-AE35-F805455AD901Q50972184-B2425653-F9BC-47D5-B4E2-30D47377937EQ50998580-5E3F0C65-9D44-4DD3-8CC6-25BA16DA9FA8Q51750541-EECFE294-DC4E-46DF-A64A-34FAAF95F2E5Q52626796-B8F42528-DA2B-4AED-A189-E8698A4B2D3DQ53262090-C44D499C-87B1-4B06-885F-4E70581ED124Q54978969-6EDB4985-7DAC-4C8D-846F-3DC47C15A334
P2860
Microbial heterogeneity affects bioprocess robustness: dynamic single-cell analysis contributes to understanding of microbial populations.
description
article científic
@ca
article scientifique
@fr
articol științific
@ro
articolo scientifico
@it
artigo científico
@gl
artigo científico
@pt
artigo científico
@pt-br
artikel ilmiah
@id
artikull shkencor
@sq
artículo científico
@es
name
Microbial heterogeneity affect ...... ding of microbial populations.
@en
type
label
Microbial heterogeneity affect ...... ding of microbial populations.
@en
prefLabel
Microbial heterogeneity affect ...... ding of microbial populations.
@en
P2860
P356
P1476
Microbial heterogeneity affect ...... nding of microbial populations
@en
P2093
Philippe Goffin
P2860
P356
10.1002/BIOT.201300119
P577
2013-10-23T00:00:00Z