Temporal Expression Dynamics
Temporal Expression Dynamics - Related Videos
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
0 Views •
2017
The segmentation clock drives oscillatory gene expression across the pre-somitic mesoderm (PSM). Dynamic Notch activity is key to this process. We use imaging and computational analyses to extract temporal dynamics from spatial expression data to demonstrate that Delta ligand and Notch receptor expression oscillate in the vertebrate PSM.
07:59
Temporal Quantification of MAPK Induced Expression in Single Yeast Cells
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Cited by 6 •
2013
Two complementary methods based on flow cytometry and microscopy are presented which enable the quantification, at the single cell level, of the dynamics of gene expression induced by the activation of a MAPK pathway in yeast.
12:00
Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1
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Cited by 2 •
2009
Protocol for Vaccinia infection of HeLa cells and analysis of host and viral gene expression. Part 1 of 3.
08:11
Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria
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Cited by 22 •
2013
The goal of these experiments is to generate quantitative time-course data on the growth and gene expression dynamics of attenuated S. typhimurium bacterial colonies growing inside tumors. This video covers tumor cell preparation and implantation, bacteria preparation and injection, whole-animal luminescence imaging, tumor excision, and bacterial colony counting.
07:35
Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 3
0 Views •
2009
Protocol for Vaccinia infection of HeLa cells and analysis of host and viral gene expression. Part 3 describes the process of fluorescently labeling the amplified RNA from both host and viral samples by amino allyl coupling of dyes. Part 3 of 3.
FAQs
The observed expression pattern reflects several regulatory layers acting together rather than transcription alone. Transcription initiates RNA production, RNA processing modifies the resulting transcripts, translation determines how RNA levels influence protein production, and degradation removes RNA or molecular products. Their coordination controls signal amplitude, duration, and frequency, allowing the same biological program to produce brief, sustained, or recurring responses.
Degradation helps determine how long an expression signal remains detectable after production changes. Faster removal can shorten a response, while slower removal can prolong it, even if transcription has already changed. Accounting for this layer prevents researchers from treating a persistent signal as continued activation and helps separate ongoing production from delayed molecular clearance.
A transient response shows a limited change that begins, reaches a maximum, and then declines, whereas a stable state change persists after the initial transition. Time-resolved measurements reveal these different trajectories directly. This distinction matters because similar expression levels at one time point can represent very different regulatory conditions when their preceding and subsequent patterns are considered.
Frequency describes how often expression changes or molecular signals recur over time, adding information beyond their amplitude and duration. A program with repeated fluctuations differs from one that produces a single pulse or remains continuously active. Examining frequency can therefore improve models of cellular decision-making by showing whether regulation depends on recurring timing patterns rather than one isolated change.
Sequential sampling collects expression measurements at multiple stages or time points, creating a series for comparing onset, peak, persistence, and decline. Live-cell reporters instead follow expression-related signals in individual cells over time. Together, these approaches connect molecular measurements with temporal behavior and can reveal changes associated with development, cell cycles, environmental responses, or disease progression.
Researchers can track expression trajectories across developmental stages, cell cycles, environmental responses, and disease progression. Each context frames time differently: development follows changing biological stages, the cell cycle follows recurring cellular progression, and environmental or disease studies compare responses as conditions evolve. Such measurements help identify whether regulation changes briefly or establishes a longer-lasting cellular state.
Time-resolved data can identify when a biological program begins, reaches its maximum, persists, or declines. These landmarks support comparisons between normal and dysregulated processes and help build models of tissue development, cellular decision-making, and disease-associated changes. The resulting trajectories are more informative than isolated measurements because they preserve the order and timing of molecular events.