Virtual Morphogenesis
Virtual Morphogenesis - Related Videos
02:50
Morphogenesis
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2026
Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant. Plant growth and cell differentiation are under complex hormonal control. Plant hormones regulate gene expression, often in response to environmental stimuli. For example, many plants...
09:16
Analyzing Craniofacial Morphogenesis in Zebrafish Using 4D Confocal Microscopy
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Cited by 18 •
2014
Time-lapse confocal imaging is a powerful technique useful for characterizing embryonic development. Here, we describe the methodology and characterize craniofacial morphogenesis in wild-type, as well as pdgfra, smad5, and smo mutant embryos.
01:20
Virtual Work
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2023
The principle of virtual work states that if a body is in static and dynamic equilibrium, then the sum of all the virtual work done by all external forces and couple moments for any given virtual displacement must be zero. In static equilibrium, a body can experience an imaginary or virtual movement, such as displacement or rotation. The virtual work done by a force is equal to the dot product of force and virtual displacement in the direction of the force. When it comes to virtually rotating a...
06:52
The use of Biofeedback in Clinical Virtual Reality: The INTREPID Project
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Cited by 48 •
2009
The Project on Virtual Reality Intelligent Multi-sensor System for the Treatment of Anxiety-related Disorders (INTREPID) is aimed at developing a multi-sensor context-aware virtual reality system for the treatment of anxiety-related disorders.
09:17
Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior
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Cited by 10 •
2017
This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.
FAQs
Pattern formation emerges from the combined effects of local cellular behaviors rather than from a single controlling instruction. Proliferation changes cell numbers, migration changes positions, differentiation creates distinct cell states, adhesion influences contacts, and signaling coordinates responses. Varying spatial or temporal conditions allows the model to test how these interacting rules produce different tissue arrangements.
Changes in proliferation, migration, differentiation, adhesion, signaling, or their spatial and temporal conditions can alter the simulated pattern. Examining these parameters helps identify which developmental rules are most closely associated with a particular outcome. In neuroscience, this approach can test whether altered rules plausibly contribute to atypical neural tissue or brain organization.
A simulation becomes more informative when its resulting patterns can be compared with experimental observations. Agreement can support a proposed set of developmental rules, whereas discrepancies can reveal missing interactions or unsuitable parameter choices. This comparison helps convert complex developmental behavior into testable hypotheses about tissue patterning and neural organization.
A typical workflow represents cells or cellular agents, specifies rules for proliferation, migration, differentiation, adhesion, and signaling, then applies those rules across changing spatial and temporal conditions. The resulting tissue pattern is examined and compared with experimental observations. Researchers can then adjust relevant parameters and evaluate whether the revised simulation better reflects the observed developmental outcome.
In neuroscience, these models connect cellular developmental rules with the emergence of neural tissue, brain architecture, and circuit organization. They provide a way to examine how local cellular interactions may contribute to larger structural patterns without observing every process directly. The resulting simulations can guide hypotheses about neurodevelopment and help interpret complex developmental data.
Researchers can modify selected developmental parameters and observe how the simulated tissue pattern changes. For example, altering rules associated with proliferation, migration, differentiation, adhesion, or signaling can provide a structured way to examine possible routes to atypical outcomes. Comparing those outcomes with experimental findings helps prioritize hypotheses for further study of neurodevelopmental disease.