Gene Networks
Gene Networks - Related Videos
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,...
10:40
CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
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Cited by 1 •
2022
This protocol describes a high-throughput clustered regularly interspaced short palindromic repeats (CRISPR) gene editing workflow for microRNA cluster network analysis that allows the rapid generation of a panel of genetically modified cell lines carrying unique miRNA cluster member deletion combinations as large as 35 kb within a single experiment.
02:18
Network Covalent Solids
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2020
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds. To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
12:09
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Cited by 35 •
2014
The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).
12:27
Piping Networks and Pressure Losses
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2023
Source: Alexander S Rattner, Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA This experiment introduces the measurement and modeling of pressure losses in piping networks and internal flow systems. In such systems, frictional flow resistance from channel walls, fittings, and obstructions causes mechanical energy in the form of fluid pressure to be converted to heat. Engineering analyses are needed to size flow hardware to ensure...
FAQs
These interactions coordinate when and where genes are expressed rather than treating each gene as an isolated unit. Transcription factors help regulate gene activity, while signaling pathways connect cellular responses to internal or external conditions. The combined pattern of regulation can produce a particular biological state, making network-level analysis more informative than examining individual genes alone.
Feedback loops allow regulatory activity within a network to influence subsequent gene expression, helping link an initial signal with later cellular changes. Because these loops operate alongside transcription factors and signaling pathways, they can contribute to how biological states arise and change. Studying them helps researchers interpret regulation as a coordinated process rather than a one-time event.
Changes in internal or external conditions can alter signaling pathways and regulatory interactions, which then modify when and where genes are expressed. The resulting network activity reflects the cell’s response to those conditions. This perspective is useful for explaining why the same cellular system may display different biological states as regulatory inputs change.
A network-based analysis examines patterns of gene activity together with relationships among genes, transcription factors, signaling pathways, and feedback loops. Researchers then relate those patterns to biological states or disease-associated changes. This approach can reveal disrupted regulation, support biomarker identification, and indicate molecular processes that may warrant further therapeutic investigation.
These disorders can be studied as changes in coordinated regulation rather than as abnormalities in single genes alone. Examining network activity helps researchers connect disrupted interactions and signaling with disease-associated biological states. Across cancer, diabetes, and neurological disorders, this framework may reveal shared or disease-specific regulatory changes that are relevant to biomarkers and therapeutic targets.
Patient-specific gene activity can be related to disease risk, treatment response, and clinical outcomes through network-based analysis. This adds regulatory context to molecular measurements, helping researchers examine why individuals may differ in disease-associated states or responses to therapy. The approach therefore supports precision medicine by connecting a patient’s molecular profile with clinically relevant patterns.