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NetLogo User Community Models
## WHAT IS IT?
**This model explores the interactions between APOE and the buildup of neurofibrillary tangles and amyloid-beta plaques in the brain.**
Classic symptoms of Alzheimer’s disease include a buildup of beta-amyloid plaques and neurofibrillary tangles in the neural tissue. This model presents a very simplified possible version of one of APOE’s mechanisms in the body - breaking down waste.
APOE is a lipoprotein that metabolizes fats – as well as peptides like beta-amyloid - that has three variants that differ at a single-base level: APOE2, APOE3, and APOE4.
APOE2 has a protective effect on against the onset of Alzheimer’s but we are not sure why
## HOW IT WORKS
APOE breaks down the neurofibrillary tangles (NFT) and amyloid-beta plaques (AB) in the neural tissue. APOE loses ‘energy’ as it moves – this is a simplification of protein inactivation, competition, and degradation as time passes. To replenish energy, APOE must bind and break down NFT and/or AB. It gains a small amount of energy from the cells it passes over, this helps stabilize the model. If energy is not replenished, APOE dies.
AB also loses ‘energy’ as it moves about the tissue, this is a simplification of the cellular mechanisms that each AB has within the brain. Both AB and NFT are necessary for a healthy system – but they can quickly build up and cause the system to fail. NFT and AB perform tasks at each cell but build up rapidly and make the cell unhealthy, turning it brown. The NFT or AB must move on and the cell must recover to regain its green and become a healthy cell again.
**APOE only acts on NFT if there is AB present.** This represents one hypothesis about the mechanism of Alzheimer’s Disease - the AB cascade hypothesis (Jack, 2011).
## HOW TO USE IT
1. Adjust the slider parameters, choose a live threshold and APOE variant (see below).
2. Press the Set-up button.
3. Press the color-patches button
4. Press the Go button to begin the simulation.
5. Look at the monitors to see the current population sizes
6. Look at the Populations plot to watch the populations fluctuate over time
**APOE Variant:** The user chooses one of three genetic variants of APOE Disease
**Progression:** The initial percent unhealthy cells, as well as how long it takes for cell to recover from a AB or NFT task AB-Buildup: The initial volume of AB buildup
**NFT-Buildup:** The initial volume of NFT buildup Initial-APOE: The initial volume of APOE
**AB-Transcription-Level:** The probability of a NFT being transcribed at each time step
**NFT-Transcription-Level:** The probability of a AB being transcribed at each time step
**APOE-Transcription-Level:** The probability of a APOE being transcribed at each time step
**%LiveRequired:** User chooses the threshold at which the model dies.
## THINGS TO NOTICE
Watch as the NFT/AB and APOE populations fluctuate. Notice that increases and decreases in the sizes of each population are related. In what way are they related? What eventually happens?
Notice the Tissue Health plot representing fluctuations in the overall number of live cells. How do the sizes of the NFT, AB, APOE and live cell populations appear to relate? What is the explanation for this?
Why do you suppose that some variations of the model might be stable while others are not?
## THINGS TO TRY
Try adjusting the transcription parameters under various settings. How sensitive is the stability of the model? Repeat with the initial buildup, although this will not affect the model beyond starting-up
Which slider is the model most sensitive to? Which drop-down menu?
Can you find any other parameter settings that generate a stable ecosystem?
What happens if all three turtles are the same size?
What happens if NFT is also allowed to move?
Try changing the transcription rules – for example, what would happen if transcription depended on energy rather than being determined by a fixed probability?
Try changing the energy values in the code - how much energy does APOE need from each AB and NFT to survive?
## NETLOGO FEATURES
Some breeds are stuck in place, while others move.
Note the use of patches to model CellState.
Note use of the ONE-OF agentset reporter to select a random AB or NFT to be broken down by an APOE.
## RELATED MODELS
Look at Child of Wolf Sheep Predation and Rabbits Grass Weeds for other models of interacting populations with different rules.
## CREDITS AND REFERENCES
Bizzle, J. (2014). Child of Wolf Sheep Predation, Model ID 3513 – NetLogo Modeling Commons. modelingcommons.org/browse/one_model/3513#model_tabs_browse_info.
Wilensky, U. & Reisman, K. (1999). Connected Science: Learning Biology through Constructing and Testing Computational Theories – an Embodied Modeling Approach. International Journal of Complex Systems, M. 234, pp. 1 - 12.
Wilensky, U. & Reisman, K. (2006). Thinking like a Wolf, a Sheep or a Firefly: Learning Biology through Constructing and Testing Computational Theories – an Embodied Modeling Approach. Cognition & Instruction, 24(2), pp. 171-209.
Jiang, Q. Y., Lee, C. E., Mandrekar, S. M., Wilkinson, B. L., Cramer, P. C., Landreth, G. D., Holtzman, D. (2008). ApoE Promotes the Proteolytic Degradation of Aβ. Neuron, 58(5), 681-693.
Cho, Yunhee, et. al. (2018) IPSC & CRISPR/Cas9 Technologies Enable Precise & Controlled Physiologically Relevant Disease Modeling for Basic & Applied Research. Applied StemCell Inc. www.appliedstemcell.com.
Genaro Gabriel Ortiz, et. al. (July 1st 2015). Genetic, Biochemical and Histopathological Aspects of Familiar Alzheimer’s Disease, Alzheimer’s Disease, Inga Zerr, IntechOpen, DOI: 10.5772/59809.
Farfel, Yu, De Jager, Schneider, & Bennett. (2016). Association of APOE with tau-tangle pathology with and without β-amyloid. Neurobiology of Aging, 37, 19-25.
Jack CR Jr, et. al.Alzheimer’s Disease Neuroimaging, I. Evidence for ordering of Alzheimer disease biomarkers. Archives of neurology. 2011; 68(12):1526–1535.
Olsson F, Schmidt S, Althoff V, Munter LM, Jin S, Rosqvist S, Lendahl U, Multhaup G, Lundkvist J (January 2014). “Characterization of intermediate steps in amyloid beta (Aβ) production under near-native conditions”. The Journal of Biological Chemistry. 289 (3): 1540–50. doi:10.1074/jbc.M113.498246
Sontheimer, H. (2015). Chapter 4 - Aging, Dementia, and Alzheimer Disease. In Diseases of the Nervous System (pp. 99-131).
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