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NetLogo User Community Models
## WHAT IS IT?
This model demonstrates how T-regulatory (Treg) effectiveness correlates with inflammation in Rheumatoid Arthritis (RA).
## HOW IT WORKS
Rheumatoid Arthritis is an autoimmune disease. Autoimmunity occurs when the body is no longer able to differentiate between forgein invaders, such as viruses and pathogenic bacteria, from the body's own cells and tissues. This confusion causes the immune system to mistakenly attack the body's own cells and tissues. Autoantibodies and autoreactive T cells are two known contributing factors to the body attacking itself. In RA, autoantibodies and autoreactive T cells cause inflammation in the tissues that surround the joints (the synovium). The autoantibodies and autoreactive T cells can be suppressed, or deleted, by T-regulatory cells. However, in RA, it is known that the effectiveness of these T-regulatory cells can vary. Low T-regulatory effectiveness leads to a high population of autoantibodies, a high population of autoreactive T cells, and elevated inflammation - all playing a role in the progression of Rheumatoid Arthritis. Whereas, a higher T-regulatory effectiveness will enforce a lower population of autoantibodies and autoreactive T cells, in turn, showing decreased levels of inflammation.
Adjust the Treg-effectiveness slider to compare levels of inflammation, autoantibodies, and autoreactive T cells in different Treg effective enviroments.
## HOW TO USE IT
SETUP: Clears the world and displays autoreactive T cells, autoantibodies, and T-regulatory cells in the presence of a synovium lining a joint within the body.
GO: Runs the simulation.
TREG-EFFECTIVENESS SLIDER: The slider displays the effectiveness of Tregs and how successful they are in deleting autoantibodies and autoreactive T cells. A high value represents an efficient Treg population and a lower value represents a less effective Treg population. The effectiveness of the Treg population has an inverse relationship with the amount of inflammation present within the synovium, as well as, the amount of autoantibodies and autoreactive T cells.
TREG-EFFECTIVENESS PLOT: Plots the number of autoantibodies and autoreactive T cells against time.
INFLAMMATION PLOT: Plots the amount of inflammation against time.
## THINGS TO NOTICE
A large population of autoantibodies and autoreactive T cells, as well as, low Treg-effectiveness will display elevated levels of inflammation. The inflammation decreases as the number of autoantibodies and autoreactive T cells decreases.
## THINGS TO TRY
Increasing the Treg-effectiveness slider will create a more efficient Treg population. Decreasing the Treg slider will create a dysfunctional Treg population, leading to either a slower deletion or no deletion of autoantibodies and autoreactive T cells when the Treg-effectiveness slider is at 0. This, in turn, will cause inflammation and play a role in the progression of Rheumatoid Arthritis.
## EXTENDING THE MODEL
To create a more realistic Rhemuatoid Arthritis enviroment, try incorporating autoreactive effector B and T cells, as well as, adding other anti-inflammatory regulators, such as the cytokines TGFb and IL-10. Autoreactive effector B and T cells will represent proliferating autoreactive B and T cells that promote inflammation. TGFB and IL-10 will play a similar role to T-regulatory cells, where they will assist in depleting inflammation.
## NETLOGO FEATURES
The "Tools-->Turtle Shapes Editor-->Import from Library" function allows you to edit the shapes of the turtles within the model
## RELATED MODELS
New Villi Food Model:
Adaptive Immunity Model:
## CREDITS AND REFERENCES
Gift, S. and J.A. Klemens. (2017). Netlogo Adaptive Immunity Model 1.0. https://github.com/klemensj/Immune.
Wilensky, U. (2006). NetLogo Connected Chemistry 8 Gas Particle Sandbox model. http://ccl.northwestern.edu/netlogo/models/ConnectedChemistry8GasParticleSandbox. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
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