Disease Mechanism Modeling
Disease Mechanism Modeling - Related Videos
08:09
Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Cited by 10 •
2014
Lipoxygenase (LOX) isozymes can generate products that may increase or decrease neuroinflammation and neurodegeneration. A gene-environment interaction study could identify LOX isozyme-specific effects. Using the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of nigrostriatal damage in two LOX isozyme-deficient transgenic lines allows for comparison of the contribution of LOX isozymes on dopaminergic integrity and inflammation.
15:48
ALS - Motor Neuron Disease: Mechanism and Development of New Therapies
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Cited by 1 •
2007
Jeffrey D. Rothstein speaks about the pathology and mechanisms underlying amyotrophic lateral sclerosis or ALS, advances in ALS research, and current strategies towards the development of therapies.
02:45
The Quantum-Mechanical Model of an Atom
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2020
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra. Schrödinger...
05:08
A TNBS-Induced Rodent Model to Study the Pathogenic Role of Mechanical Stress in Crohn's Disease
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Cited by 5 •
2022
The present protocol describes the development of a Crohn's-like colitis model in rodents. Transmural inflammation leads to stenosis at the TNBS instillation site, and mechanical enlargement is observed in the segment proximal to the stenosis. These changes allow studying mechanical stress in colitis.
09:23
Parasite Induced Genetically Driven Autoimmune Chagas Heart Disease in the Chicken Model
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Cited by 12 •
2012
The inoculation of Trypanosoma cruzi in fertile eggs prior to incubation renders the parasite kDNA minicircle integration in embryo cells genome. Crossbreeding reveals the vertical transfer of the mutations to progeny. The kDNA integrates into coding regions at several chromosomes and the chickens die with an inflammatory autoimmune heart disease.
FAQs
Causal relationships specify how an initiating change can influence downstream biological processes and measurable outcomes. Representing these links helps researchers trace propagation through signaling pathways, cell behavior, organ function, or environmental conditions rather than simply describing associations. The resulting model can therefore support hypothesis generation about disease progression and identify points where altering one process may affect others.
Multiscale integration connects observations from genes and signaling pathways with cellular behavior and organ-level function. This connection is important because disease-related changes may begin at one biological scale but become measurable at another. A model that links these levels can help explain how molecular or cellular alterations contribute to pathology and clarify which measurements best represent the underlying disease process.
Conceptual models organize biological relationships and proposed mechanisms, while mathematical models express those relationships through equations. Computational models use simulations to examine how the represented system behaves when conditions or components change. These forms can work together: a conceptual structure guides formalization, equations make relationships explicit, and simulations allow researchers to explore consequences that may be difficult to observe directly.
Predictions depend on the biological components included, the causal relationships assigned among them, and the conditions represented in the model. Changes in genes, signaling pathways, cell behavior, organ function, or environmental conditions can propagate differently through the system. Including or modifying these factors may alter predicted disease progression, measurable outcomes, or treatment responses.
A typical workflow begins by identifying relevant biological processes and measurable outcomes. Researchers then represent relationships among genes, pathways, cells, organs, or environmental conditions using a conceptual structure, equations, or computational rules. Experimental observations can be integrated to examine the model, after which simulations or other analyses explore how changes propagate and generate testable hypotheses.
By linking biological mechanisms to measurable outcomes, a model can highlight variables that may serve as disease biomarkers. Researchers can also examine how changes in a therapeutic target influence connected processes and predicted outcomes. Modeling treatment responses in this way supports comparison of possible interventions and helps identify mechanisms that may explain why responses differ across modeled disease conditions.
Disease progression often reflects interacting changes across molecular, cellular, and organ levels. Modeling these connections allows researchers to analyze how an alteration propagates through the system over the course of disease. In medicine, such analysis can connect experimental observations with progression patterns, support more precise diagnostic strategies, and guide therapeutic development without reducing pathology to a single biological factor.