Network Pharmacology
Network Pharmacology - Related Videos
11:06
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
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Cited by 11 •
2023
The present protocol describes an integrated strategy for exploring the key targets and mechanisms of Fructus Phyllanthi against hyperlipidemia based on network pharmacology prediction and metabolomics verification.
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,...
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...
09:58
Pharmacological and Functional Genetic Assays to Manipulate Regeneration of the Planarian Dugesia japonica
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Cited by 17 •
2011
An attractive model for studying stem cell differentiation within a live animal is the planarian flatworm. Regeneration is studied by simple amputation experiments that are easily performed in a basic laboratory and are amenable to pharmacological and genetic (in vivo RNAi) manipulation as detailed by protocols in this article.
13:18
Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
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Cited by 3 •
2023
This study reveals the mechanism of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma based on network pharmacology and experimental verification. The study also demonstrates that the PI3K/AKT signaling pathway plays a vital role in the action of Trichosanthes-Fritillaria thunbergii in treating lung adenocarcinoma.
FAQs
It follows how a target perturbation may influence connected signaling pathways and downstream cellular responses. This shifts analysis from asking whether a drug binds one target to examining how changes propagate through related biological relationships. The resulting network view can help identify coordinated mechanisms and generate hypotheses about why a treatment may produce broader effects.
Disease-associated nodes provide focal points for connecting molecular interactions with disease biology. By locating these nodes within drug-target and pathway relationships, investigators can prioritize targets that may be mechanistically relevant rather than merely experimentally accessible. This supports more focused hypothesis generation and helps direct subsequent experimental validation toward potentially informative molecular relationships.
An isolated pair emphasizes a direct drug-target relationship, whereas the network approach considers that relationship alongside connected targets, pathways, and disease-associated nodes. This broader context is especially relevant when several molecular processes contribute to a complex disease. It also allows investigators to examine how multiple targets or linked pathways may contribute to a cellular response.
The analysis brings together pharmacological data, molecular biology information, and computational network analysis. These inputs are used to map relationships among drugs, targets, pathways, and diseases. Once connected, the resulting structure can reveal candidate nodes and mechanisms for further study, while linking molecular evidence with disease-related information when the research question requires it.
A typical workflow begins by mapping drug-target interactions and disease-associated molecular relationships. Investigators then examine connected pathways and use network analysis to prioritize candidate targets or mechanisms. The resulting predictions are not treated as final evidence; they guide experimental validation. This workflow helps convert large sets of molecular relationships into testable therapeutic hypotheses.
For drug repurposing, the approach can connect an existing drug's target relationships with pathways or disease-associated nodes relevant to another condition. For combination therapy, it can examine how multiple drugs or targets may influence interconnected mechanisms. These applications are useful when treatment effects depend on several molecular processes rather than a single isolated interaction.