System Dynamics Modeling
System Dynamics Modeling - Related Videos
07:10
Dynamic Light-Induced Protein Patterns at Model Membranes
0 Views •
Cited by 2 •
2024
Here, a protocol is described for generating light-regulated and reversible protein patterns with high spatiotemporal precision at artificial lipid membranes. The method consists of the localized photoactivation of the protein iLID (improved light-inducible dimer) immobilized on model membranes that, under blue light, binds to its partner protein Nano (wild-type SspB).
12:03
Dynamics of Structures
0 Views •
2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
06:37
Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
0 Views •
Cited by 13 •
2022
Differential dynamic microscopy (DDM) combines features of dynamic light scattering and microscopy. Here, the process of using DDM to characterize reconstituted cytoskeleton networks by quantifying the subdiffusive and caged dynamics of particles in vimentin networks and the ballistic motion of active myosin-driven actin-microtubule composites is presented.
05:00
New Features in Visual Dynamics 3.0
0 Views •
2024
Visual Dynamics is an open-source tool that accelerates implementations and learning in molecular dynamics simulation using Gromacs. The presented protocol will guide you through the steps to perform a protein-ligand simulation prepared in ACPYPE with ease and general steps to other simulation models.
12:29
Rat Mesentery Exteriorization: A Model for Investigating the Cellular Dynamics Involved in Angiogenesis
0 Views •
Cited by 19 •
2012
This article describes a simple model for stimulating angiogenesis in the rat mesentery. The model produces dramatic increases in capillary sprouting, vascular area and vascular density over a relatively short time course in a tissue that allows en face visualization of entire microvascular networks down to the single cell level.
FAQs
The separation of stocks and flows makes accumulation explicit: a stock records a system’s changing condition, while a flow represents the process that increases or decreases it. This distinction lets a model connect changes in one part of an engineering system to changes elsewhere, rather than treating behavior as a static snapshot. It also provides the structure needed for equations or simulation.
Feedback loops show how a change in one part of a system can influence later changes elsewhere, including effects that return to the original part. Because these interactions unfold over time, they can produce unexpected consequences or nonlinear responses rather than simple proportional results. Representing the loops helps identify relationships that may otherwise remain hidden during engineering analysis.
Time delays separate an action from its visible effect, so the model may show system behavior developing gradually rather than immediately. This timing can change how engineers interpret trends, compare conditions, and recognize unintended consequences. Including delays is especially important when interconnected processes respond at different times and when short-term observations may not reflect the system’s later behavior.
A study begins by translating the real-world engineering situation into interconnected stocks, flows, feedback loops, and time delays. The relationships are then expressed through equations or represented in a computational simulation. Researchers can run the model under different conditions and examine how the system changes over time. This workflow supports structured testing of assumptions and design alternatives.
The approach is useful when engineering outcomes depend on interacting components and develop over time. It can support analysis of dynamic processes, resource use, capacity, reliability, and alternative designs. Rather than focusing only on an individual component, engineers can examine system-level behavior and assess how changes in one area may influence performance elsewhere.
The analysis can reveal how system behavior changes under different conditions, including unintended consequences and nonlinear responses. It can also help identify leverage points, meaning parts of the system where a change may strongly influence overall behavior. These results inform decisions, support comparison of design alternatives, and guide further investigation when system interactions are difficult to assess directly.