Network Complexity
Network Complexity - Related Videos
07:34
The Power of Simplicity: Sea Urchin Embryos as in Vivo Developmental Models for Studying Complex Cell-to-cell Signaling Network Interactions
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Cited by 5 •
2017
This video article details a straightforward in vivo methodology that can be used to systematically and efficiently characterize components of complex signaling pathways and regulatory networks in many invertebrate embryos.
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,...
03:31
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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2023
The present protocol describes a novel end-to-end salient object detection algorithm. It leverages deep neural networks to enhance the precision of salient object detection within intricate environmental contexts.
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...
12:27
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
Connectivity helps researchers locate elements that occupy influential positions within a biological or clinical network. A highly connected node may link multiple pathways, cell types, or clinical relationships, making its position relevant to how a disease pattern emerges. Examining these relationships can help prioritize influential molecular targets rather than considering each component in isolation.
Feedback can show how activity within a system influences related elements, while modularity highlights groups of components that are more closely connected to one another. Together, these features help researchers examine organized disease mechanisms and distinguish patterns within complex systems. Modularity may also contribute to identifying disease subtypes that are not apparent from individual components.
An isolated-defect model focuses attention on a single abnormal component, whereas network complexity examines how multiple genes, proteins, cells, organs, or patients relate to one another. This broader view can reveal interactions, shared pathways, and comorbid relationships that a component-by-component approach may overlook. It therefore supports a more integrated interpretation of disease mechanisms.
Researchers first represent relevant entities, such as genes, proteins, cells, organs, or patients, as nodes. They then define biological or clinical relationships as connections between those nodes. Analysis can examine connectivity, feedback, modularity, and changes over time. The resulting network representation provides a structured basis for studying disease mechanisms and clinically relevant patterns.
Network-based analysis can identify influential molecular targets, reveal disease subtypes, and clarify relationships among comorbidities. It can also support integrated approaches to diagnosis, prognosis, and therapeutic development by connecting molecular, cellular, organ-level, and patient-level information. These outcomes help researchers interpret disease as an interacting system rather than as a collection of unrelated findings.
Researchers can represent patients or clinical conditions as nodes and connect them through relevant clinical relationships. Examining the resulting structure may clarify how different conditions are associated within a broader disease pattern. This network perspective extends analysis beyond one diagnosis, helping support more integrated approaches to understanding patients, prognosis, and therapeutic development.