Protein Expression Dynamics
Protein Expression Dynamics - Related Videos
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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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.
08:46
Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli
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Cited by 34 •
2015
A streamlined approach to screening for the expression of recombinant membrane proteins in Escherichia coli based on fusion to green fluorescent protein is presented.
07:47
Staining of Proteins in Gels with Coomassie G-250 without Organic Solvent and Acetic Acid
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Cited by 94 •
2009
A short protocol for protein staining with Coomassie Brilliant Blue (CBB) G-250 in polyacrylamide gels is described without using organic solvents or acetic acid as in the classical staining procedures with CBB.
09:03
HeLa Based Cell Free Expression Systems for Expression of Plasmodium Rhoptry Proteins
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Cited by 16 •
2015
Expression of malarial proteins in cell based systems remains challenging. We demonstrate two step and one step IVT (in vitro translation) cell free expression systems for expressing malarial recombinant rhoptry proteins from HeLa cells. We use a Ni-resin affinity based purification system to purify the rhoptry proteins.
09:51
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Cited by 2 •
2017
A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.
FAQs
Changes in protein abundance reflect more than transcription alone. They can result from altered gene transcription, mRNA stability, translation, post-translational modification, cellular localization, or degradation. Considering these layers helps explain why a protein level may rise, fall, or remain stable even when upstream gene activity changes. This integrated view is important when interpreting time-dependent cellular responses.
Post-translational modification and cellular localization add functional context to abundance measurements. A protein may be present at a similar amount while its modification state or location changes, potentially altering pathway activity. Protein expression dynamics therefore links quantitative measurements with changes in protein state and distribution, helping investigators distinguish simple abundance shifts from broader regulatory responses.
Transient and sustained changes carry different biological implications. A short-lived alteration may reflect an immediate response to a signal or changing condition, whereas persistence suggests continued regulation or a longer-lasting cellular effect. Time-resolved analysis makes this distinction visible, preventing a single measurement from being mistaken for the full behavior of a disease- or treatment-related pathway.
To study these dynamics, investigators compare protein-related measurements across time while cells or tissues experience relevant signals or changing conditions. The analysis can consider abundance, production, modification, localization, and turnover together rather than relying on one readout. This time-resolved design reveals when a response begins, whether it persists, and which regulatory layer may contribute.
In medicine, these measurements can connect changing protein behavior with disease mechanisms and biomarker discovery. Tracking patterns over time may show whether a molecular change accompanies disease-related states or identifies a clinically informative condition. Because the approach examines change rather than a static level, it provides context for interpreting how cellular pathways behave during disease.
Treatment response assessment is a central application. Comparing protein dynamics before and after therapy can reveal whether treatment alters the intended cellular pathways and whether effects are transient or sustained. These measurements may also support drug development by clarifying therapy-related biological effects, while patient-specific patterns can contribute to personalized medicine.