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"We formulate a 3D ODE for photosynthesis that explicitly tracks the concentrations\n",
"of $[R]$, $[ATP]$, and $[H^+]$ (the proton concentration, which modulates $j_{PQ}$.\n",
"We use mass conservation laws to track the concentrations of $[ADP]$, $[P_i]$, and\n",
"$[PGA]$:\n",
"
\n",
"Conservation of phosphate:\n",
"$$\n",
"C_1 = 2*[R] + [PGA] + [P_i] + [ATP] \n",
"$$\n",
"Exchange of ADP/ATP:\n",
"$$\n",
"C_2 = [ATP] + [ADP]\n",
"$$\n",
"Equation for photochemical quenching flux $j_{PQ}$:\n",
"$$\n",
"j_{PQ} = \\frac{k_{PQ}}{k_{PQ} + k_{f} + k_{NPQ}}I\n",
"$$\n",
"where $I$ is the incident light flux and the terms in the numerator and denominator\n",
"are the probabilities of photochemical quenching, flourescence, and NPQ. Thus \n",
"$k_{PQ} = 1 - k_{NPQ} - k_{f}$. $k_{NPQ}$ depends on $[H^+]$:\n",
"$$\n",
"k_{NPQ} = (1-k_f)\\frac{k^*_{NPQ}[H^+]}{k^*_{NPQ}[H^+] + 1}\n",
"$$\n",
"Thus k_{NPQ} will increase with $[H^+]$ and saturate at $1-k_{f}$. \n",
"
\n",
"ATP is synthesized via the ATP Synthase SU, which processes ADP, P$_i$, and protons\n",
"to produce ATP:\n",
"$$\n",
"j_{ATP-syn} = \\frac{[E_{syn}]}{\\frac{1}{k_{syn}} + \\frac{3}{f([ADP])} + \n",
"\\frac{12}{f([H^+])} + \\frac{3}{f([P_i])} - \\frac{1}{f([ADP])/3+f([H^+])/12}\n",
" - \\frac{1}{f([ADP])/3 + f([P_i])/3} - \\frac{1}{f([P_i])/3+f([H^+])/12} \n",
"+ \\frac{1}{f([ADP])/3+f([P_i])/3+f([H^+])/12}}\n",
"$$\n",
"Where $f(A)$ is the diffusion rate of substrate A with binding rate $k^+_A$:\n",
"$f(A) = k^+_A[A].$\n",
" $E_{syn}$ is the concentration of\n",
"ATP Synthase, and $k_{syn}$ is its rate of production. \n",
"
\n",
"RuBP is consumed via Carboxylation and Oxygenation and is regenerated via the RuBP\n",
"regeneration SU, which also consumes ATP and regenerates P$_i$ and $[ADP]$ (we\n",
"assume here that all of the orthophosphate that is consumed to produce ATP is regenerated\n",
"along with RuBP. We do not yet explicitly model starch and sucrose synthesis.)\n",
"$$\n",
"j_{cA} = \\frac{d[C]}{dt} = \\frac{E_t}{1/k_{ERC} + 1/(f(O) + f(C)) + 1/f(R) - 1/(f(C) + f(O) + f(R))}\n",
"$$\n",
"$$\n",
"j_{oA} = \\frac{d[O]}{dt} = \\frac{E_t}{1/k_{ERO} + 1/(f(O) + f(C)) + 1/f(R) - 1/(f(C) + f(O) + f(R))}\n",
"$$\n",
"and $j_{PGA-prod} = 2j_{cA} + 1.5j_{oA}$.\n",
"*(Note that $E_t$ and $k_{ERO}$ likely needs to approximate the reactions of the oxidative\n",
"pathway in addition to Carboxylation)*\n",
"
\n",
"The RuBP regeneration SU acts as a simplified SU of the many enzymatic reactions that\n",
"eventually convert PGA back to RuBP. 2 mol PGA and 3 mol ATP produce 1 mol RuBP.\n",
"$$\n",
"j_{RuBP-regen} = \\frac{E_{CC}}{1/j_{CC} + 3/f(ATP) + 2/f(PGA) - \\frac{1}{(f(ATP)/3 + f(PGA)/2}}\n",
"$$\n",
"Thus we have the following ODE system:\n",
"$$\n",
"\\frac{d[R]}{dt} = j_{RuBP-regen} - \\frac{8}{15}j_{PGA-prod} + j_{leak}\n",
"$$\n",
"where $j_{leak}$ is the RuBP lost to oxygenation. \n",
"$$\n",
"\\frac{d[ATP]}{dt} = 3j_{ATP-syn} - 3j_{RuBP-regen}\n",
"$$\n",
"$$\n",
"\\frac{d[H^+]}{dt} = 3j_{PQ} - 12j_{ATP-syn}\n",
"$$\n"
]
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"execution_count": 2,
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"source": [
"from scipy import integrate as spint\n",
"from scipy.optimize import fsolve\n",
"from sympy import Matrix\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import ipywidgets as widgets\n",
"\n",
"\n",
"from ipywidgets import interact, interact_manual\n",
"inv = np.linalg.inv\n",
"norm = np.linalg.norm\n",
"eye = np.identity"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
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"source": [
"#Parameter values\n",
"\n",
"\n",
"#from Farquhar 1980\n",
"\n",
"#turnover numbers for carboxylation and oxygenation\n",
"kERC = 2.5\n",
"kERO = .21*kERC\n",
"\n",
"#turnover rate for carboxylation (1/s)\n",
"kc = 4.14*((10)**(-3))\n",
"#turnover rate for oxygenation (1/s)\n",
"ko = 7.57*(10**(-6))\n",
"#RuBP binding rate\n",
"kr = 2*(10**(-5))\n",
"#light intensity (umol)\n",
"I = 1000\n",
"#total enzyme concentration (umol/carboxylation site)\n",
"Et = 87.2\n",
"#CO2 concentration (ubar)\n",
"C = 230 \n",
"#O2 concentration (ubar)\n",
"O = 210 * 10**(3)\n",
"\n",
"\n",
"\n",
"\n",
"kprod = kc\n",
"kE = kc\n",
"\n",
"\n",
"\n",
"kappac = 2/3\n",
"kappar = 1/3\n",
"\n",
"ATPo = 200\n",
"Ro = 350\n",
"ADPo = 100\n",
"Po = 100\n",
"PGAo = 200\n",
"Ho = 100\n",
"#total ATP + ADP (umol)\n",
"OP = ATPo + ADPo\n",
"OPt = ATPo + Po\n",
"#total RuBP + intermediates (umol)\n",
"tot = 2*Ro + OPt + PGAo\n",
"\n",
"Ecalvin = 100\n",
"Esyn = 100\n",
"\n",
"kcalvin = 2\n",
"ki = 2\n",
"kAC = 2\n",
"kATP = 2\n",
"jsyn = 2\n",
"kp = 2\n",
"knpq = .02\n",
"kf = .05\n",
"kh = 2\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
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"outputs": [
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"source": [
"#ODE system\n",
"def Hmodel(x, I = I, knpq = knpq, C=C, O=O, kr = kr, ATPo = ATPo, Ro = Ro, PGAo = PGAo, Po=Po, ADPo=ADPo):\n",
" R, ATP, H = x[0], x[1], x[2]\n",
" \n",
" \n",
" Hd = knpq*H\n",
" fnpq = (1-kf)*(Hd/(1+Hd))\n",
" jpq = (1 - (kf + fnpq))*I\n",
" \n",
" #total ATP + ADP (umol)\n",
" OP = ATPo + ADPo\n",
" OPt = ATPo + Po\n",
" #total RuBP + intermediates (umol)\n",
" tot = 2*Ro + OPt + PGAo\n",
" \n",
" P = OPt - ATP\n",
" INte = tot - (ATP + 2*R + P)\n",
" ADP = OP - ATP\n",
"\n",
" fC = kc*C\n",
" fO = ko*O\n",
" fR = kr*R\n",
" #new = \"\"\"\n",
" jcA = Et/((1/kERC) + (1/(fC+fO)) + 1/fR - 1/(fC+fO+fR))\n",
" joA = Et/((1/kERO) + (1/(fC+fO)) + 1/fR - 1/(fC+fO+fR))\n",
" jPGAprod = 2*jcA + 1.5*joA\n",
" #\"\"\"\n",
" #ATP consumption\n",
" jRuBPregen = Ecalvin/(1/kcalvin + 3/(kAC*ATP) + 2/(ki*INte) - 1/(ki*INte/2+kAC*ATP/3))\n",
" \n",
" #Pi consumption/ATP synthesis\n",
" fATP = kATP*ADP/3\n",
" fP = kp*P/3\n",
" fH = kh*H/12\n",
" jPcons = Esyn/(1/jsyn + 1/fATP + 1/fH + 1/fP - (1/(fH+fATP)+1/(fH+fP)+1/(fP+fATP)) + 1/(fH+fP+fATP))\n",
" \n",
" #this is the amount of phosphate lost due to oxygenation. See below\n",
" leak = 232533.333333333/(20000/(20000*R + 2.54190000000000) - 1/R - 15868.1301388725) + 174400.000000000/(20000/(20000*R + 2.54190000000000) - 1/R - 45963.3682341106)\n",
" \n",
" cons = [jPGAprod, jRuBPregen, jPcons, jpq, leak]\n",
" \n",
" S = [[-8/15, 1, 0, 0, -1/2], [0, -3, 3, 0, 0], [0, 0, -12, 3, 0]]\n",
" DEs = np.matmul(S, cons)\n",
" return DEs, jpq"
]
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"cell_type": "code",
"execution_count": 5,
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"source": [
"\n",
"vs = [\"R\", \"ATP\", \"H\"]\n",
"dt = .001\n",
"steps = 10000"
]
},
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"cell_type": "code",
"execution_count": 23,
"metadata": {
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],
"source": [
"def findPGA(R, ATP, Ro=Ro, ADPo=ADPo, PGAo=PGAo, ATPo=ATPo, Po=Po):\n",
" OPt = ATPo + Po\n",
" P = OPt - ATP\n",
" tot = 2*Ro + OPt + PGAo\n",
" PGA = tot - (ATP + 2*R + P)\n",
" return PGA"
]
},
{
"cell_type": "code",
"execution_count": 34,
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"interactive(children=(IntSlider(value=10000, description='steps', max=50000, min=10), FloatSlider(value=0.001,…"
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"source": [
"#numerical simulation\n",
"@interact(dt = (.00001, .01, .00001), I=(0,2000,10), steps=(10, 50000, 1), knpq = (.001, 1, .001), kr = (.01, 20000, .01), Ho = (0,100, 1))\n",
"def simiter3(steps = steps, dt = dt, Ro = Ro, Ho=Ho, ATPo=ATPo, PGAo = PGAo, Po=Po, ADPo=ADPo, I=I, O=O, C=C, knpq=.5, kr = kr):\n",
" x0 = [Ro, ATPo, Ho]\n",
" a = len(x0)\n",
" sol = np.zeros((steps, a))\n",
" sol[0] = x0\n",
" jpqlist = []\n",
" for i in range(steps-1):\n",
" DE, jpq = Hmodel(sol[i], I=I, knpq=knpq, C=C, O=O, kr =kr, ATPo = ATPo, Ro = Ro, PGAo = PGAo, Po=Po, ADPo=ADPo)\n",
" step = DE*dt\n",
" sol[i+1] = np.add(sol[i], step)\n",
" jpqlist.append(jpq)\n",
" print(sol[-1])\n",
" check, jp = Hmodel(sol[-1], I=I, knpq=knpq, C=C, O=O, kr =kr, ATPo = ATPo, Ro = Ro, PGAo = PGAo, Po=Po, ADPo=ADPo)\n",
" print(check, jp)\n",
" \n",
" PGAlist = [findPGA(x[0], x[1], Ro=Ro, ADPo=ADPo, PGAo=PGAo, ATPo=ATPo, Po=Po) for x in sol]\n",
" \n",
" for k in range(a):\n",
" y = [x[k] for x in sol]\n",
" plt.plot(range(steps), y, label=\"{}\".format(vs[k]))\n",
" \n",
" R = sol[-1][0]\n",
" fC = kc*C\n",
" fO = ko*O\n",
" fR = kr*R\n",
" jcA = Et/((1/kERC) + (1/(fC+fO)) + 1/fR - 1/(fC+fO+fR))\n",
" print(jcA)\n",
" plt.plot(range(steps), PGAlist, label=\"PGA\")\n",
" plt.plot(range(steps-1), jpqlist, label = \"jpq\")\n",
" plt.legend()\n",
" plt.show()"
]
},
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",
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