NeuralNet_QSSA_julia.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "intense-sierra",
   "metadata": {},
   "outputs": [],
   "source": [
    "using DifferentialEquations\n",
    "using PyPlot\n",
    "using Flux\n",
    "using Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "convertible-cheese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Vector{String}:\n",
       " \"lungs\"\n",
       " \"heart\"\n",
       " \"kidney\"\n",
       " \"liver\"\n",
       " \"spleen\"\n",
       " \"Gut tissue\"\n",
       " \"Other Tissue\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmax = 1000.0\n",
    "names = [\"lungs\", \"heart\", \"kidney\", \"liver\", \"spleen\",  \"Gut tissue\", \"Other Tissue\"]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "anonymous-composer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3434614183666664e-5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model Parameters\n",
    "A_i=[.0019956689, .0009621096822, .0033612343, .0045212562, .0004750356, .003333333, .003333333].*1000 #mouse dm^2 \n",
    "l_NC_b  = (100*10^(-9)) # m\n",
    "Q_i=[74.83758415, 138.673326, 763.9168803, 284.6716878, 224.48828292, 333, 1000]./(60*10^6) #L/sec MOUSE\n",
    "V_i_BL=[43.65525742 ,20.04395171 ,1018.55584 ,127.7673516 ,2.226207538 ,90 ,700]./10^6\n",
    "V_i_T=[33.26114851 ,16.03516137 ,56.02057122 ,75.3542703 ,7.91726 ,20 ,1000]./10^6 #L MOUSE\n",
    "\n",
    "V_vein = 466.9/10^6 # mouse L\n",
    "Q_hep = sum(Q_i[4:6]) #flow out of the liver\n",
    "V_hep = sum(V_i_BL[4:6]) #total volume of blood passed thru liver\n",
    "Q_vein = Q_i[3]+Q_hep+Q_i[7] #L/sec Mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "comparative-mambo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Vector{Float64}:\n",
       " 1.0\n",
       " 0.02\n",
       " 0.02\n",
       " 0.02\n",
       " 0.014\n",
       " 0.02\n",
       " 0.02"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rate Constants\n",
    "#Kon and Koff\n",
    "slope1 = (22-4)/(500-50)\n",
    "slope2 = 3.9\n",
    "slope = slope1*slope2\n",
    "a = [4, 15, 50, 79, 100]\n",
    "r = a/2\n",
    "K_EC0=[1.23e42, 10.6565662, 41793.3924, 2.28e12, 7.74e21, 3.33e15, 3.33e15]\n",
    "K_EC0 = log.(K_EC0)\n",
    "K_EC = zeros(5, 7)\n",
    "for i =1:7\n",
    "    K_EC[1:4,i] = slope*r[1:4] .- slope*r[end] .+ K_EC0[i]\n",
    "    K_EC[end, i] = K_EC0[i]\n",
    "end\n",
    "K_EC[:, 2] .= K_EC0[2]\n",
    "\n",
    "Kb = 1.36e-23\n",
    "T = 310\n",
    "mu = 0.0037\n",
    "l = 1e-7\n",
    "r = r*1e-9\n",
    "D=Kb*T./(6*pi*mu*r)\n",
    "k_i_on = D./l^2\n",
    "K_i_on = zeros(5, 7)\n",
    "K_i_off = zeros(5, 7)\n",
    "for i = 1:5\n",
    "  K_i_on[i, :] .= k_i_on[i]\n",
    "  K_i_off[i, :] = K_i_on[i]./K_EC[i, :]  \n",
    "end\n",
    "\n",
    "K_i_on = K_i_on[1, :]\n",
    "K_i_off = K_i_off[1, :]\n",
    "K_deg_vein = 1/6000\n",
    "\n",
    "#Other Parameters\n",
    "p = [2.0e-6 1.0 1.0; 2e-6 0.1 0.02; 0.0004 0.0033 0.02; 5.0e-6 0.005 0.02; 2e-5 0.08 0.014; 3.3e-5 0.01 0.02; 3.3e-5 0.01 0.02]\n",
    "#Rate of Degredation of NP in each organ\n",
    "K_i_deg = p[:, 1]\n",
    "#Rate of uptake of NP in each organ\n",
    "K_i_up = p[:, 2]\n",
    "#Rate of non specific uptake of NP in each organ\n",
    "K_NS= p[:, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sorted-trailer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14×14 Matrix{Float64}:\n",
       " -3023.54           0.0622279      0.0   …    0.0        0.0       0.0\n",
       "     0.0285714  -3024.76           0.0        0.0        0.0       0.0\n",
       "     0.0            0.0        -3022.54       0.0        0.0       0.0\n",
       "     0.0            0.0            0.0        0.0        0.0       0.0\n",
       "     0.0            0.0            0.0       70.4339     0.0       0.0\n",
       "     0.0            0.0            0.0   …    0.0      106.977     0.0\n",
       "     0.0            0.0            0.0        0.0        0.0     106.977\n",
       "  3022.51           0.0            0.0        0.0        0.0       0.0\n",
       "     0.0         3022.51           0.0        0.0        0.0       0.0\n",
       "     0.0            0.0         3022.51       0.0        0.0       0.0\n",
       "     0.0            0.0            0.0   …    0.0        0.0       0.0\n",
       "     0.0            0.0            0.0      -70.514      0.0       0.0\n",
       "     0.0            0.0            0.0        0.0     -106.987     0.0\n",
       "     0.0            0.0            0.0        0.0        0.0    -106.987"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ODE matrix for linear system (dy/dt = Ay)\n",
    "A = [-(Q_i[1]/V_i_BL[1]+K_i_on[1]+K_NS[1]+K_i_deg[1]) Q_i[1]/V_i_BL[2] 0 0 0 0 0 K_i_off[1] 0 0 0 0 0 0;\n",
    "Q_i[1]/V_i_BL[1] -((Q_i[1]+Q_vein)/V_i_BL[2] + K_i_on[2]+K_NS[2]+K_i_deg[2]) 0 0 0 0 0 0 K_i_off[2] 0 0 0 0 0;\n",
    "0 0 -(Q_i[3]/V_i_BL[3] + K_i_on[3]+K_NS[3]+K_i_deg[3]) 0 0 0 0 0 0 K_i_off[3] 0 0 0 0;\n",
    "0 0 0 -(Q_hep/V_i_BL[4]+K_i_on[4]+K_NS[4]+K_i_deg[4]) Q_i[5]/V_i_BL[5] Q_i[6]/V_i_BL[6] 0 0 0 0 K_i_off[4] 0 0 0;\n",
    "0 0 0 0 -(Q_i[5]/V_i_BL[5]+K_i_on[5]+K_NS[5]+K_i_deg[5]) 0 0 0 0 0 0 K_i_off[5] 0 0;\n",
    "0 0 0 0 0 -(Q_i[6]/V_i_BL[6] + K_i_on[6] + K_i_deg[6] + K_NS[6]) 0 0 0 0 0 0 K_i_off[6] 0;\n",
    "0 0 0 0 0 0 -(Q_i[7]/V_i_BL[7] + K_i_on[7] + K_NS[7] + K_i_deg[7]) 0 0 0 0 0 0 K_i_off[7];\n",
    "K_i_on[1] 0 0 0 0 0 0 -(K_i_up[1] + K_i_off[1] + K_i_deg[1]) 0 0 0 0 0 0;\n",
    "0 K_i_on[2] 0 0 0 0 0 0 -(K_i_up[2] + K_i_off[2] + K_i_deg[2]) 0 0 0 0 0;\n",
    "0 0 K_i_on[3] 0 0 0 0 0 0 -(K_i_up[3] + K_i_off[3] + K_i_deg[3]) 0 0 0 0;\n",
    "0 0 0 K_i_on[4] 0 0 0 0 0 0 -(K_i_up[4] + K_i_off[4] + K_i_deg[4]) 0 0 0;\n",
    "0 0 0 0 K_i_on[5] 0 0 0 0 0 0 -(K_i_up[5] + K_i_off[5] + K_i_deg[5]) 0 0;\n",
    "0 0 0 0 0 K_i_on[6] 0 0 0 0 0 0 -(K_i_up[6] + K_i_off[6] + K_i_deg[6]) 0;\n",
    "0 0 0 0 0 0 K_i_on[7] 0 0 0 0 0 0 -(K_i_up[7] + K_i_off[7] + K_i_deg[7])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "public-supply",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "func_qss! (generic function with 1 method)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ODE function with quasi-steady-state component\n",
    "function func_qss!(dydt, y, p, t)\n",
    "  b_vec = [0; -Q_vein*y[1]/V_vein; -Q_i[3]*y[2]/V_vein;-Q_i[4]*y[2]/V_vein;\n",
    "          -Q_i[5]*y[2]/V_vein;-Q_i[6]*y[2]/V_vein;\n",
    "          -Q_i[7]*y[2]/V_vein;\n",
    "           0;0;0;0;0;0;0]\n",
    "\n",
    "  #Steady-State component\n",
    "  y_ss = A\\b_vec\n",
    "\n",
    "  #Vein\n",
    "  dydt[1] = (Q_i[3]*y_ss[3]/V_i_BL[3] + Q_hep*y_ss[4]/V_i_BL[4] + Q_i[7]*y_ss[7]/V_i_BL[7] - Q_vein*y[1]/V_vein - y[1]*K_deg_vein) \n",
    "  # Artery \n",
    "  dydt[2] = (Q_vein*y_ss[2]/V_i_BL[2]-(Q_i[3]*y[2] + Q_i[4]*y[2]+ Q_i[5]*y[2] + Q_i[6]*y[2] + Q_i[7]*y[2])/V_vein - y[2]*K_deg_vein) \n",
    "\n",
    "  # ---------------------- organ tissue ------------------------ #\n",
    "  dydt[3:9] = (K_i_up.*y_ss[8:14] + K_NS.*y_ss[1:7]-K_i_deg[1:end].*y[3:9])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "continental-cardiff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "retcode: Success\n",
       "Interpolation: specialized 4th order \"free\" interpolation\n",
       "t: 199-element Vector{Float64}:\n",
       "    0.0\n",
       "    0.007957043199844107\n",
       "    0.06323262177139947\n",
       "    0.16472680303391563\n",
       "    0.2867199768460904\n",
       "    0.4369899309856841\n",
       "    0.609473187114772\n",
       "    0.805287301256651\n",
       "    1.020711641853088\n",
       "    1.2545064838068016\n",
       "    1.5040753394361939\n",
       "    1.767751303561699\n",
       "    2.0436693270602553\n",
       "    ⋮\n",
       "  718.9548604527521\n",
       "  751.0666988877898\n",
       "  776.767801220842\n",
       "  800.5430770016288\n",
       "  824.59310310939\n",
       "  849.8162650181562\n",
       "  876.3177069311869\n",
       "  903.6817716982944\n",
       "  931.3092308431756\n",
       "  958.7609827120292\n",
       "  985.8827440183219\n",
       " 1000.0\n",
       "u: 199-element Vector{Vector{Float64}}:\n",
       " [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
       " [0.9992587676475958, 0.0006448093495493953, 1.85175199332949e-5, 7.638267614709362e-5, 4.773798943503039e-8, 2.0187180391817488e-8, 1.3815520290808723e-8, 2.52701440209601e-8, 8.466408748488934e-8]\n",
       " [0.9941271748237996, 0.005097813980659317, 0.00014677587514200357, 0.0006054338905838048, 3.00433946039873e-6, 1.2704679282333994e-6, 8.694711318896065e-7, 1.59036033343599e-6, 5.3282801352790515e-6]\n",
       " [0.9847842345946868, 0.013155323833773257, 0.0003805657905021825, 0.0015697908593215449, 2.0260692540454013e-5, 8.567908392339717e-6, 5.863623196181514e-6, 1.0725220520317278e-5, 3.593335311716872e-5]\n",
       " [0.9736888861885, 0.02263931340015752, 0.0006586713517252308, 0.0027169448569479442, 6.0918808022024956e-5, 2.5761962874135316e-5, 1.7630714917051762e-5, 3.224852594145986e-5, 0.00010804418127976866]\n",
       " [0.9602207749007783, 0.0340255924232288, 0.000996940181178762, 0.004112265534008398, 0.0001401947508157944, 5.9288163144877936e-5, 4.0575010267932626e-5, 7.421612882514936e-5, 0.00024865077216934355]\n",
       " [0.9450278629431698, 0.04670098171966077, 0.001379456136009465, 0.005690100600700885, 0.0002698123949492805, 0.00011410591621735133, 7.80905398085556e-5, 0.0001428360244852436, 0.0004785521468726867]\n",
       " [0.9281176120287551, 0.06059333229933205, 0.0018063926086097112, 0.007451165281040318, 0.0004653740582761009, 0.00019681576354670013, 0.00013469445185832903, 0.00024637047440037027, 0.0008254298582954458]\n",
       " [0.9099192662748521, 0.07528312829833168, 0.0022672788679635653, 0.00935226899339943, 0.0007378057134447695, 0.00031204152488002377, 0.00021355106335133254, 0.0003906072265628203, 0.0013086749475790327]\n",
       " [0.8906376055332172, 0.09054376313354766, 0.0027572646724120923, 0.011373405039300018, 0.001098605228058496, 0.0004646499360908957, 0.00031799090798220834, 0.0005816380137957606, 0.0019486969145762134]\n",
       " [0.870578621238595, 0.10607757831826711, 0.0032688781790073842, 0.013483752911343249, 0.0015552223478081722, 0.000657796553177476, 0.0004501734202368683, 0.0008234124689159386, 0.002758728452304207]\n",
       " [0.849956189977297, 0.1216718554390464, 0.0037969224155559914, 0.01566187568679716, 0.0021139678472072267, 0.0008941560730960081, 0.0006119289282642761, 0.0011192782198668678, 0.0037499853205482125]\n",
       " [0.8289846971359356, 0.13712545925631345, 0.0043361217968151235, 0.017886012172989196, 0.002778292482631785, 0.0011751941869238725, 0.0008042603406681934, 0.001471069433510616, 0.004928612639159675]\n",
       " ⋮\n",
       " [6.972374483061342e-11, -1.0955089216683157e-10, 0.02970798912360624, 0.12254209637057087, 0.15677142745442296, 0.08717952479630281, 0.05904580510191592, 0.10703228359625105, 0.3585967145491634]\n",
       " [1.916738246367156e-10, -4.0912818111211e-10, 0.02970608122659277, 0.12253422651109579, 0.1547706174476357, 0.08716552846532927, 0.0590078959028048, 0.106918922582896, 0.35821691430963537]\n",
       " [1.4425740248955052e-10, -3.117318661407696e-10, 0.02970455430864623, 0.12252792814719858, 0.15318765794629333, 0.08715432792853417, 0.05897757233368772, 0.10682827919263353, 0.35791322629279054]\n",
       " [6.853441736640378e-11, -1.4847241064265253e-10, 0.029703141875644108, 0.1225221020207535, 0.15173773180446865, 0.08714396794362791, 0.05894953483257143, 0.10674449628269153, 0.3576325233568997]\n",
       " [3.4806278216465583e-11, -7.552101020769377e-11, 0.029701713187932723, 0.12251620884547046, 0.15028501202281863, 0.08713348949575427, 0.058921186890772005, 0.10665981202508071, 0.35734880058155627]\n",
       " [2.3480039746631567e-11, -5.099941496629073e-11, 0.02970021488368613, 0.12251002850985049, 0.1487763700845457, 0.08712250127662281, 0.05889147081299269, 0.1065710691372104, 0.35705147993248987]\n",
       " [2.151557656833256e-11, -4.675367599826586e-11, 0.029698640728399946, 0.12250353529726787, 0.14720758445894405, 0.08711095768165537, 0.05886026490570671, 0.10647790840863156, 0.35673935792527006]\n",
       " [2.4145683769249207e-11, -5.2475345277288284e-11, 0.029697015421771663, 0.12249683109118223, 0.14560509145327016, 0.08709903994787399, 0.058828060597133955, 0.10638180075227939, 0.35641736264821033]\n",
       " [2.88048364584837e-11, -6.260251938439967e-11, 0.029695374560861187, 0.12249006272544109, 0.14400487026320466, 0.08708700915361625, 0.058795564179500055, 0.10628485601013839, 0.35609256283215]\n",
       " [3.299318798951976e-11, -7.17055202283693e-11, 0.02969374422543422, 0.12248333777612944, 0.1424322459382829, 0.08707505651965476, 0.05876329221513053, 0.1061886153286824, 0.3557701218730626]\n",
       " [3.49942661796634e-11, -7.605461255585396e-11, 0.029692133575796198, 0.1224766940284715, 0.1408953920981859, 0.08706324917603224, 0.058731425579185315, 0.10609361711273282, 0.35545184362109095]\n",
       " [6.008565957481075e-12, -1.3058681906180467e-11, 0.029691295245247572, 0.12247323600973947, 0.14010201174141843, 0.08705710391824678, 0.05871484538598364, 0.10604420284439578, 0.3552862880178455]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solving ODE with non-stiff solver\n",
    "y0=[1.0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "tspan = (0, 1e3)\n",
    "prob = ODEProblem(func_qss!, y0, tspan)\n",
    "sol_qss = solve(prob, Tsit5(), reltol=1e-10, abstol=1e-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "refined-intake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ODEfunc! (generic function with 1 method)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solving ODEs with QSSA with a Neural Network\n",
    "function ODEfunc!(y)\n",
    "\n",
    "    b_vec = [zeros(1, bat_sz); -Q_vein*y[1, :]'/V_vein; -Q_i[3]*y[2, :]'/V_vein;\n",
    "            -Q_i[4]*y[2, :]'/V_vein;-Q_i[5]*y[2, :]'/V_vein;\n",
    "            -Q_i[6]*y[2, :]'/V_vein;-Q_i[7]*y[2, :]'/V_vein;\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz);\n",
    "            zeros(1, bat_sz)]\n",
    "    \n",
    "    #Steady State Component\n",
    "    y_ss = A\\b_vec\n",
    "    a = (Q_i[3]*y_ss[3, :]/V_i_BL[3] .+ Q_hep*y_ss[4, :]/V_i_BL[4] .+ Q_i[7]*y_ss[7, :]/V_i_BL[7] \n",
    "        .- Q_vein*y[1, :]/V_vein .- y[1, :]*K_deg_vein)\n",
    "    # Artery \n",
    "    b = (Q_vein*y_ss[2, :]/V_i_BL[2] .- (Q_i[3]*y[2, :] .+ Q_i[4]*y[2, :] .+ Q_i[5]*y[2, :] \n",
    "            .+ Q_i[6]*y[2, :] .+ Q_i[7]*y[2, :])/V_vein .- y[2, :]*K_deg_vein) \n",
    "\n",
    "    # ---------------------- organ tissue ------------------------ #\n",
    "    c = (K_i_up.*y_ss[8:14, :] .+ K_NS.*y_ss[1:7, :] .- K_i_deg[1:end].*y[3:9, :])\n",
    "    return [a'; b'; c]\n",
    "              \n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "entire-folder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NeuralNetwork (generic function with 1 method)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function NeuralNetwork()\n",
    "    return Chain(Dense(1, 32, tanh),\n",
    "    Dense(32, 32, tanh),\n",
    "    Dense(32, 32, tanh),\n",
    "    Dense(32, 9, sigmoid) )\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "solid-integer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = collect(0.0 : 0.0005 : 1.0)\n",
    "bat_sz = length(t)\n",
    "t = reshape(t, 1, :)\n",
    "data = Flux.DataLoader(t, batchsize=size(t)[2],shuffle=true)\n",
    "\n",
    "m = NeuralNetwork()\n",
    "# g(t) = t.*m(t) .+ y0\n",
    "opt = Flux.ADAM(0.001)\n",
    "\n",
    "ϵ = sqrt(eps(Float32))\n",
    "y0 = [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
    "\n",
    "#Derivative of output from neural network\n",
    "derivative_fun(t) = (-m(t.+2ϵ)+8m(t.+ϵ)-8m(t.-ϵ)+m(t.-2ϵ))/(12ϵ)/tmax\n",
    "\n",
    "#Loss function\n",
    "loss(t) = Flux.Losses.mse(derivative_fun(t)  , ODEfunc!(m(t)); agg = mean) + Flux.Losses.mse(m([0.0]), y0; agg = mean)\n",
    "\n",
    "#Neural Network Model Parameters\n",
    "ps = Flux.params(m)\n",
    "epochs = 30000\n",
    "iter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "architectural-pottery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0002985826277880157\n",
      "9.750818541702844e-5\n",
      "4.64038922632252e-5\n",
      "2.6600967770971958e-5\n",
      "1.7754999648428752e-5\n",
      "1.3031352553128036e-5\n",
      "9.872167886458902e-6\n",
      "7.85869507473704e-6\n",
      "6.636338287630729e-6\n",
      "5.800704579844952e-6\n",
      "5.209439632835853e-6\n",
      "4.817113590772258e-6\n",
      "4.537508044927872e-6\n",
      "4.334457865330153e-6\n",
      "4.201730878636457e-6\n",
      "4.105326315247763e-6\n",
      "4.015371751066875e-6\n",
      "3.9107813019291025e-6\n",
      "3.7921396489876014e-6\n",
      "9.104417612297425e-7\n",
      "3.2196842820426145e-7\n",
      "1.8727324405196195e-7\n",
      "7.930070794698281e-8\n",
      "4.071119021255062e-8\n",
      "2.2784565022598778e-8\n",
      "1.4530224812129191e-8\n",
      "9.22792171108761e-9\n",
      "5.416300589889527e-9\n",
      "4.039420173872401e-9\n",
      "3.359084971924453e-9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9×2001 Matrix{Float64}:\n",
       " 0.999973    0.942474     0.918188     …  0.000418213  0.000418213\n",
       " 4.74793e-5  0.0506937    0.0689952       0.000625661  0.000625658\n",
       " 3.49263e-5  0.000117775  0.000231679     0.0286466    0.0286465\n",
       " 5.58819e-5  0.00625368   0.00931478      0.121174     0.121174\n",
       " 1.56608e-5  0.000129966  0.000393915     0.179642     0.179641\n",
       " 1.18012e-5  6.46537e-5   0.000172507  …  0.0900328    0.0900326\n",
       " 1.75586e-5  6.12242e-5   0.000130899     0.0583052    0.0583051\n",
       " 1.88299e-5  7.76218e-5   0.0002186       0.102667     0.102667\n",
       " 1.18619e-5  0.000177833  0.000681546     0.362319     0.362318"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb1 = function () #callback function to observe training\n",
    "    global iter += 1\n",
    "    if iter % 1000 == 0\n",
    "        print(loss(t), \"\\n\")\n",
    "        flush(stdout)\n",
    "    end\n",
    "end\n",
    "\n",
    "#Training Neural Network\n",
    "for i in 1:epochs\n",
    "    Flux.train!(loss, ps, data, opt, cb = cb1)\n",
    "end\n",
    "#Forward Pass through neural network\n",
    "sol = m(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "loved-flashing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7-element Vector{String}:\n",
       " \"NP (Lungs)\"\n",
       " \"NP (Heart)\"\n",
       " \"NP (Kidney)\"\n",
       " \"NP (Liver)\"\n",
       " \"NP (Spleen)\"\n",
       " \"NP (Gut)\"\n",
       " \"NP (Others)\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = collect(0:0.5:1000)\n",
    "y_ub = sol_qss(t)\n",
    "y_val_ub = zeros(9 , size(y_ub.u)[1])\n",
    "for i=1:size(y_ub.u)[1]\n",
    "    for j=1:9\n",
    "        y_val_ub[j, i] = y_ub.u[i][j]\n",
    "    end\n",
    "end\n",
    "organs = [\"NP (Lungs)\", \"NP (Heart)\", \"NP (Kidney)\", \"NP (Liver)\", \"NP (Spleen)\", \"NP (Gut)\", \"NP (Others)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "therapeutic-excuse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1100x1100 with 8 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.5, 1.0, 'Molar Profiles')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = figure(figsize=(11,11))\n",
    "fig.text(0.5, 0.04, \"Time (Hours)\", ha=\"center\",fontsize=12)\n",
    "# fig.text(0.04, 0.5, \"NP Concentration\", va=\"center\", rotation=\"vertical\",fontsize=12)\n",
    "subplots_adjust(hspace=0.5) # Set the vertical spacing between axes\n",
    "\n",
    "subplot(421) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[1:2, :]', 0.0001.+t/3600, y_val_ub[1:2, :]')\n",
    "ax1.set_title(\"Vein/Artery\")\n",
    "ax1.legend([\"Neural Net(Vein)\", \"Neural Net(Artery)\", \"ODE Solver(Vein)\", \"ODE Solver(Artery)\"])\n",
    "grid()\n",
    "\n",
    "subplot(422) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[3, :], 0.0001.+t/3600, y_val_ub[3, :])\n",
    "ax1.set_title(\"Lungs\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(423) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[4, :], 0.0001.+t/3600, y_val_ub[4, :])\n",
    "ax1.set_title(\"Heart\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(424) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[5, :], 0.0001.+t/3600, y_val_ub[5, :])\n",
    "ax1.set_title(\"Kidney\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(425) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[6, :], 0.0001.+t/3600, y_val_ub[6, :])\n",
    "ax1.set_title(\"Liver\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(426) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[7, :], 0.0001.+t/3600, y_val_ub[7, :])\n",
    "ax1.set_title(\"Spleen\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(427) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[8, :], 0.0001.+t/3600, y_val_ub[8, :])\n",
    "ax1.set_title(\"Gut\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "\n",
    "\n",
    "subplot(428) # Create the 1st axis of a 3x1 array of axes\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "ax1.plot(0.0001.+t/3600, sol[9, :], 0.0001.+t/3600, y_val_ub[9, :])\n",
    "ax1.set_title(\"Other\")\n",
    "ax1.legend([\"Neural Net\", \"ODE solver\"])\n",
    "grid()\n",
    "title(\"Molar Profiles\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.6.1",
   "language": "julia",
   "name": "julia-1.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.6.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}