Target Pathway Network
Target Pathway Network - Related Videos
14:06
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Cited by 9 •
2012
Systematic, large-scale synthetic genetic (gene-gene or epistasis) interaction screens can be used to explore genetic redundancy and pathway cross-talk. Here, we describe a high-throughput quantitative synthetic genetic array screening technology, termed eSGA that we developed for elucidating epistatic relationships and exploring genetic interaction networks in Escherichia coli.
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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2025
The current work describes a protocol for running the Pathway2Targets algorithm, an R script that predicts and prioritizes therapeutic targets based on the profile of intracellular signaling pathways generated by comparing case versus control samples from a bulk RNA-sequencing experiment.
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,...
01:27
C4 Pathway and CAM
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2019
Most plants use the C3 pathway for carbon fixation. However, some plants, such as sugar cane, corn, and cacti that grow in hot conditions, use alternative pathways to fix carbon and conserve energy loss due to photorespiration. Photorespiration is the process that occurs when the oxygen concentration is high. Under such conditions, the rubisco enzyme in the Calvin cycle binds O2 instead of CO2, which halts photosynthesis and consumes energy. C4 Pathway The C4 pathway is used by plants such as...
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...
FAQs
Central regulators matter because their position can connect multiple disease-related processes or therapeutic targets. Examining these highly connected points helps researchers identify molecules or genes that may influence several downstream relationships rather than one isolated interaction. Tracing the network from such a regulator also supports predictions about what may happen when that target is perturbed, guiding interpretation of broad biological effects.
Pathway crosstalk shows how activity in one biological route can influence another. This interconnectedness can help explain synergistic benefits when therapeutic effects reinforce one another, but it may also clarify why a treatment produces only a limited response. Mapping these relationships gives researchers a way to examine disease biology and treatment effects beyond a single molecular pathway.
An isolated target analysis focuses on the direct role of one molecule or gene, whereas a network view places that target within related pathways and disease processes. This broader context can expose connected regulators, pathway interactions, and possible downstream consequences of perturbation. As a result, researchers can evaluate therapeutic targets according to their wider biological influence rather than their individual activity alone.
Researchers examine the target's connections to relevant molecules, genes, pathways, and disease-related processes, then trace how those relationships may transmit its effects. They can use this structure to identify central regulators, assess pathway crosstalk, and predict consequences of perturbing the target. The resulting analysis helps place a candidate intervention within the broader biology of the disease.
In medicine, network relationships can highlight biological components that are closely connected to therapeutic targets or disease processes. Those components may become candidates for biomarker investigation, while highly influential targets can receive priority for further study. The approach therefore supports both the search for measurable disease-related signals and the comparison of candidate targets in complex disease settings.
A drug may influence relationships beyond its intended therapeutic route, and a network view helps researchers trace those connected effects. By following links among targets, pathways, and disease-related processes, investigators can examine how off-target activity might arise or why responses differ among treatments. This context supports more informed interpretation of limited benefits, synergistic effects, and unintended outcomes.