Yeast Growth Assays
Yeast Growth Assays - Related Videos
09:06
Growth Assays to Assess Polyglutamine Toxicity in Yeast
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Cited by 3 •
2012
This manuscript describes three complementary protocols for assessing the toxicity of polyglutamine (polyQ)-expansion proteins in the yeast Saccharomyces cerevisiae. These protocols can easily be modified to monitor the toxicity of other misfolded proteins in yeast.
05:20
Yeast Two-Hybrid Assay to Determine Protein Self-Association in Yeast Cells
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2025
This video demonstrates the yeast two-hybrid assay to detect specific protein self-interactions. In this assay, the protein of interest is tagged to two domains of a transcription factor that encodes the β-galactosidase enzyme. When the two proteins interact, this causes transcription and translation to form the enzyme, which is then detected using a biochemical assay.
11:19
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
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Cited by 4 •
2017
We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.
07:56
Modified Yeast-Two-Hybrid System to Identify Proteins Interacting with the Growth Factor Progranulin
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Cited by 14 •
2012
We have modified the conventional yeast two-hybrid screening, an effective genetic tool in identifying protein interaction. This modification markedly shortens the process, reduces the workload, and most importantly, reduces the number of false positives. In addition, this approach is reproducible and reliable.
09:34
Identification of Growth Inhibition Phenotypes Induced by Expression of Bacterial Type III Effectors in Yeast
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Cited by 33 •
2010
In this video, we describe a procedure for the expression of bacterial type III effectors in yeast and the identification of effector-induced growth inhibition phenotypes. Such phenotypes can be subsequently exploited to elucidate effector functions and targets.
FAQs
Changes across lag, exponential, and stationary phases provide a time-resolved view of population behavior. The lag phase reflects the period before measurable population increase, exponential growth shows sustained increase, and stationary growth indicates that the measured population is no longer increasing in the same way. Comparing phase timing across conditions helps identify altered biological responses.
Optical density tracks changes in cell density; colony formation records the ability of cells to produce colonies; viability measures whether cells remain viable. These readouts do not represent exactly the same aspect of population behavior. Selecting among them, or comparing them, can clarify whether a treatment changes apparent abundance, reproductive capacity, or survival.
Defined nutrients and environmental conditions allow investigators to attribute changes in growth to selected variables. Assays can examine nutrient requirements, stress responses, or the effects of antimicrobial compounds and other treatments. Because measurements are collected over time, the resulting curves show whether these factors are associated with differences in population increase under the tested conditions.
Time-course measurement distinguishes an early delay from a later change in population increase. A condition may alter the length of lag, the pattern of exponential growth, or the transition to stationary growth. Recording density, colony formation, or viability across time therefore provides more interpretable growth curves than relying on a single observation.
A basic workflow begins by exposing yeast populations to defined biological or environmental conditions. Measurements are then collected over time using optical density, colony formation, or viability. The observations are organized into growth curves, allowing investigators to compare lag, exponential, and stationary behavior between conditions and assess how a selected factor affects growth.
These assays support questions about metabolism, nutrient requirements, stress responses, gene function, and responses to antimicrobial compounds or other treatments. By comparing changes in population measurements under selected conditions, investigators can evaluate whether a factor is associated with altered growth. The resulting data connect a biological question to a measurable outcome in yeast populations.
Yeast provides a practical model for studying cellular physiology because its cells share fundamental processes with other eukaryotes. Growth assays extend that model by linking those processes to measurable changes in population behavior. Consequently, researchers can investigate how biological or environmental factors influence growth while using an experimental system that informs broader eukaryotic biology.