Regulatory Network
Regulatory Network - Related Videos
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Cited by 3 •
2021
The Inherent Dynamics Visualizer is an interactive visualization package that connects to a gene regulatory network inference tool for enhanced, streamlined generation of functional network models. The visualizer can be used to make more informed decisions for parameterizing the inference tool, thus increasing confidence in the resulting models.
02:02
Cis-regulatory Sequences
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2020
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
16:26
Regulatory T cells: Therapeutic Potential for Treating Transplant Rejection and Type I Diabetes
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Cited by 2 •
2007
10:29
Generation of Human Chimeric Antigen Receptor Regulatory T Cells
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Cited by 5 •
2025
This protocol provides a streamlined workflow to generate and test human chimeric antigen receptor regulatory T cells (CAR Tregs).
02:26
Protein Networks
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2020
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions. These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
FAQs
Their position and activity help determine whether transcription changes at a particular gene. Promoters provide regulatory DNA associated with transcriptional control, whereas enhancers can influence transcription at particular genes. Because transcription factors interact with these elements, different combinations can produce distinct gene-expression patterns, helping cells establish and preserve specialized identities.
Feedback interactions allow outputs within a network to influence other regulatory activities. Signaling interactions add information about changing environmental conditions, while coordinated effects across multiple genes produce a response rather than an isolated transcriptional change. Together, these features help a cell adjust gene activity and maintain stable functions even as conditions change.
A regulatory network can connect a genetic change to broader biological consequences because mutations may affect regulatory DNA or other network components that influence multiple genes. Examining these connections helps distinguish a local transcriptional effect from a change associated with development, disease, or a complex trait. Network models can then highlight control points for experimental investigation.
Mapping a regulatory network organizes relationships among transcription factors, regulatory DNA, signaling interactions, and gene-transcription outcomes. The resulting map can be examined for influential control points. In genetics, this approach supports interpretation of how regulatory relationships contribute to cell identity, environmental responses, developmental processes, disease-associated mutations, and complex traits.
Researchers apply network-based analysis when coordinated regulation across multiple genes is central to the question. The approach is especially relevant for studying how cells establish identity, respond to environmental cues, or preserve stable functions. It also provides a framework for connecting regulatory relationships with developmental outcomes and traits influenced by multiple genetic factors.
In therapeutic research, network models can prioritize regulatory control points for experimental investigation rather than treating every network element as equally informative. Their value lies in linking transcriptional regulation with signaling and feedback relationships, which may indicate where coordinated gene activity is especially regulated. This systems-level view supports research into disease-associated changes and potential intervention points.