Dynamic Systems Modeling
Dynamic Systems Modeling - Related Videos
06:20
Transforming Static Barrier Tissue Models into Dynamic Microphysiological Systems
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Cited by 3 •
2024
This protocol describes a reconfigurable membrane-based cell culture platform that integrates the open-well format with fluid flow capabilities. This platform is compatible with standard protocols and allows for reversible transitions between open-well and microfluidic culture modes, accommodating the needs of both engineering and bioscience laboratories.
16:06
Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography
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Cited by 46 •
2011
Simple techniques are described for the rapid production of microfabricated neural culture systems using a digital micromirror device for dynamic mask projection lithography on regular cell culture substrates. These culture systems may be more representative of natural biological architecture, and the techniques described could be adapted for numerous applications.
12:03
Dynamics of Structures
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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...
01:14
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model
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2026
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
09:52
A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites
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Cited by 15 •
2014
Animal models have proven to be invaluable tools in defining host and pathogen specific mechanisms that contribute to the development of chronic inflammation. Here we describe a mouse model of oral infection with the human pathogen Porphyromonas gingivalis and detail methodologies to assess the progression of inflammation at local and systemic sites.
FAQs
State variables describe the system’s current condition, while parameters specify fixed or adjustable properties of the model. Inputs represent influences applied to the system, and initial conditions give the starting values of the state variables. Separating these roles helps researchers identify which quantities evolve, which assumptions remain constant, and which external factors drive later behavior.
Feedback links a system’s current state to changes that affect future states. Depending on the model, this interaction can reinforce changes or counteract them. Stability concerns whether behavior remains bounded or moves away from a condition after changes in starting values or inputs. Examining feedback and stability helps explain long-term behavior rather than only immediate changes.
Differential equations describe change using continuous variation, making them appropriate when a model treats time as continuously evolving. Difference equations update states across discrete time steps. The choice depends on how the system is represented and how observations or calculations are organized. Both approaches can express rules that connect present conditions with subsequent system states.
Predictions may change when parameters, inputs, or initial conditions are altered. Sensitivity analysis tests how strongly these changes affect calculated states and longer-term behavior. A model can therefore reveal which assumptions matter most for its predictions. Comparing simulated results with observed data provides an additional way to evaluate whether the selected structure and values are informative.
A typical workflow identifies relevant state variables, parameters, inputs, and initial conditions, then selects differential equations, difference equations, or related computational rules. The resulting model is simulated to calculate future states. Researchers can vary parameters or starting conditions, perform sensitivity tests, and compare model predictions with observed data to assess behavior and usefulness.
The approach can represent population growth, ecological interactions, mechanical motion, electrical circuits, and economic processes. In each case, the model connects changing states with the conditions that influence them. This range allows mathematics to support analysis of feedback, stability, control, and long-term behavior across systems that differ substantially in their components and real-world setting.