1 Alzheimer’s disease: PDE Model

  • Figure 1a shows the network within a neuron which leads from ROS to NFTs and the destruction of microtubules (sourced from Hao and Friedman (2016)).

  • Figure 1b shows the network of activated cells, microglia, astrocyte and monocyte-derived macrophages and their effect on neurons and their microenvironment (sourced from Hao and Friedman (2016)).

  • The notaion of variables used are same as the paper of Hao and Friedman (2016).

  • All concentration and densities are in units of \(g/c m^3\) for cells and \(g/m l\) for cytokines

    • \(P\) = MCP-1

    • \(A\) = Astrocytes

    • \(τ\) = hyperphosphorylated tau protein

    • \(H\) = High mobility group box 1 (HMGB1)

    • \(M_1\) = Proinflammatory microglias

    • \(M_2\) = Anti-inflammatory microglias

    • \(N_d\) = Dead neurons

    • \(N\) = Live neurons

    • \(A^i_β\) = Amyloid β inside neurons

    • \(A^o_β\) = Amyloid β outside neurons

    • \(\hat{M}_1\) = Peripheral proinflammatory macrophages

    • \(\hat{M}_2\) = Peripheral anti-inflammatory macrophages

    • \(ROS(R)\) = Reactive oxygen species

    • \(GSK-3(G)\) = Glycogen synthase kinase-type 3

    • \(NFT(F_i)\) = Neuronfibrillary tangle inside neurons

    • \(NFT(F_o)\) = Neuronfibrillary tangle outside neurons

    • \(APP(A_P)\) = Amyloid precursor protein

    • \(AβO(A_O)\) = Amyloid β oligomer (soluble)

    • \(vTNF-α(T_α)\) = Tumor necrosis factor alpha

    • \(vTNF-β(T_β)\) = Transforming growth factor beta

    • \(MG(M_G)\) = Microglias

    • \(IL-10(I_{10})\) = Interleukin 10

1.1 Equation: \(A^i_β\)

1.2 Equation: \(A^o_β\)

1.3 Equation: \(\tau\)

1.4 Equation: \(NFT\)

1.5 Equation: Neurons

1.6 Equation: Astrocytes

1.7 Equation: Dead Neurons

1.8 Equation: \(A^o_β\)

1.9 Equation: \(HMGB-1\)

1.10 Equations: Activated Microglias

1.11 Equations: Macrophages \(\hat{M}\)

1.12 Other Equations

2 Simulation PDE Model

  • rectangular domain simulation \(\Omega ={(x,y),0≤x≤1,0≤y≤1}\)

  • assumption of periodic boundary conditions: \(A_{O}, H, T_{\beta}, I_{10}, T_{\alpha}\)

3 Initial Setting:

  • \(N = 0.14~g/ml\)
  • \(A = 0.14~g/ml\)
  • \(I_{10}= 10^{-5}~g/ml\)
  • \(M_{1}=M_{2}=0.02~g/ml\)
  • \(T_{\beta} = 10^{-6}~g/ml\)
  • \(P = 5 \times 10^{-9}~g/ml\)
  • \(H=1.3\times 10^{-11}~g/ml\)
  • \(A_{\beta}^{i}\,=\,10^{-6}~g/ml\)
  • \(A_{\beta}^{o}\,=\,10^{-8}~g/ml\)
  • \(\tau = 1.37 \times 10^{-10}~g/ml\)
  • \(F^{i} =3.36 \times 10^{-10}~g/ml\)
  • \(F^{o} = 3.36 \times 10^{-11}~g/ml\)
  • \(T_{\alpha} = 2 \times 10^{-5}~g/ml\)
  • \(\hat{M}_{1} = \hat{M}_{2} = N_{d} = 0~g/ml\)
\[\begin{array}{@{}rcl@{}} R=R(t)=\left\{ \begin{array}{rl} R_{0}\frac{t}{100}&0\leq t\leq 100\\ R_{0}&t>100 \end{array}\right. \end{array}\]

4 Case I: TNF-\(\alpha\) Inhibitor

5 Case II: Anti-A \(\beta\) drugs

References

Hao, Wenrui, and Avner Friedman. 2016. “Mathematical Model on Alzheimer’s Disease.” BMC Systems Biology 10 (1): 108. https://doi.org/10.1186/s12918-016-0348-2.