DNAplotlib_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import dnaplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize Parts\n",
    "# tetr  is orange [1.00, 0.75, 0.17]\n",
    "# lacI  is green  [0.38, 0.82, 0.32]\n",
    "# gamma is blue   [0.38, 0.65, 0.87]\n",
    "plac = {'name':'P_lac', 'start':1, 'end':10, 'type':'Promoter', 'opts': {'color':[0.38, 0.82, 0.32]}}\n",
    "rbs1 = {'name':'RBS', 'start':11, 'end':20, 'type':'RBS', 'opts':{'linewidth': 0, 'color':[0.0, 0.0, 0.0]}}\n",
    "tetr = {'name':'tetR', 'start':21, 'end':40, 'type':'CDS', 'opts':{'label': 'tetR', 'fontsize': 8,  'label_y_offset': 0, 'label_x_offset': -2, 'label_style':'italic', 'color':[1.00, 0.75, 0.17]}}\n",
    "term1 = {'name':'Term', 'start':41, 'end':55, 'type':'Terminator'}\n",
    "pgamma = {'name':'P_gamma', 'start':56, 'end':65, 'type':'Promoter', 'opts': {'color':[0.38, 0.65, 0.87]}}\n",
    "rbs2 = {'name':'RBS', 'start':66, 'end':75, 'type':'RBS', 'opts':{'linewidth': 0, 'color':[0.0, 0.0, 0.0]}}\n",
    "laci = {'name':'lacI', 'start':76, 'end':95, 'type':'CDS', 'opts':{'label': 'lacI', 'fontsize': 8,  'label_y_offset': 0, 'label_x_offset': -2, 'label_style':'italic', 'color':[0.38, 0.82, 0.32]}}\n",
    "term2 = {'name':'Term', 'start':96, 'end':110, 'type':'Terminator'}\n",
    "ptet = {'name':'P_tet', 'start':111, 'end':120, 'type':'Promoter', 'opts': {'color':[1.00, 0.75, 0.17]}}\n",
    "rbs3 = {'name':'RBS', 'start':121, 'end':130, 'type':'RBS', 'opts':{'linewidth': 0, 'color':[0.0, 0.0, 0.0]}}\n",
    "gamma = {'name':'gamma', 'start':131, 'end':150, 'type':'CDS', 'opts':{'label': 'gamma', 'fontsize': 8, 'label_y_offset': 0, 'label_x_offset': -1, 'label_style':'italic', 'color':[0.38, 0.65, 0.87]}}\n",
    "term3 = {'name':'Term', 'start':151, 'end':165, 'type':'Terminator'}\n",
    "\n",
    "lac_repress = {'from_part':laci, 'to_part':plac, 'type':'Repression', 'opts':{'linewidth':1, 'color':[0.38, 0.82, 0.32]}}\n",
    "gamma_repress = {'from_part':gamma, 'to_part':pgamma, 'type':'Repression', 'opts':{'linewidth':1, 'color':[0.38, 0.65, 0.87]}}\n",
    "tet_repress = {'from_part':tetr, 'to_part':ptet, 'type':'Repression', 'opts':{'linewidth':1, 'color':[1.00, 0.75, 0.17]}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216.0, [-2.16, 218.16], [-35, 35])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AoE7GGI2/4VH1HDnBd1eSpIXGmDYOykITIpQDqJUxRk/f2k2XndnWd1dPeYJ5ewdlAUCdTITRmQ+PVrcxfvl7kKT/GmPiHZQFAHWKiIrWWXc8q8R+w313jZb0mjEm2kFZaCIsiYaQYIyJlXST6zpaGL/XRzWJjDR6+Te9NOXnO7V8XX71XcPk6co+1VpbWMPHgWbNGNNOEjN7B9fg+h4YGROps58cq0UzPtWRbdnVd42T9IoxZpq1trTRKwTCgDGmmzzLniJ4Rtf3wOi4Npp8zxwt/O005abtqb7rPEkvGGNmWmtZHaoZIJQjVMRKetp1EahZXGyE3n64t864PVWbdhVV3zVRnsnfpltryxyVB7iUKNqvkBabEKNzZ5+ud6/7RPkHj1bfdYmkZ4wxt3Bjixaqj2i/Qlp8+846996/a+GDl6sw93D1XT+UtFvSA04KQ6MilLdgxhgjaUD5f3GSIiUVSsqU9K21NsdheQhBie2i9N5jfTXullQdyCypvusKSXslsY45gsJ4FnIdLM/4ujh5hmMVSkqTlGytzXNYHkJQ667xOnf2eC364ScqyimuvutmSXskPeqmMrQ0xphISUPlCcRx5ZsLJR2Up/0qcFUbQlO7Hv01+ed/0/uPXKPSYq+OifcbY/Zaa593VRsaB6G8hTHGnCbp+5LGSBolz4RdgVhjzFZJKyV9JukVa21WcKpEKOvXLUbvPdZHE+7YoZx8rxfjPzPG7LHWPuGqNjRvxpiJki6Vp+vfKfLMpB1ImTFmkzzt1zJJr1pr82s4Fi1IhwEJOuepsfrgJ8tVVuzVfj1SfmP7D1e1ofkqfwkyRdJF8rRfJ6sqjPsqMcZskKf9+kjS6wwPgyR1GTRKE2+bpaVP3iyfjomzjTH7rLXvuKoNx49Q3gIYY+IkTZfnbcCY+n5MnolwBpV/9nFjzMuSZltrv22SQn2cPixOrWKYizBYIiPrf+zJA+K04He9dcEvd6rEeyTmX8pvbOc3cnlooYwxbSVdL0/7NayeH4uQ5y3UUEnXSXrCGPMPedqvzU1SqI9uYzrJRJhgXAqSYtvH1PvYbmM6a8IjY7Tsnq99d71ojNlvrf2wUYtDi2WM6SBppqSfyNMrsT6iJI0s/2+GpKeMMXMlPWet3dEUdXoxRt2Hjm3yy6BKm871X8im75gpGjvjd1ox9/7qmyMkzTfGnG2t/bKx60NwEMqbOWPMdEmz5Fnb8Hi0lnSjpBuNMW9Lutlau/9466vNqw/0Vu8uTCwZqqaMaaO59/TU9X/Y67vrn8aYg9baZQ7KQjNijLlJ0mOquUdPfbWTdLuk28sfLt5urc083vpqM+Wv4xXVqgFPuhBUSVN7KT+tQF8/nlx9c5SkBcaYidbaVY5KQzNQPrzmbkkPSjreGf47SbpH0s+NMS9I+oW1Nvc4z1mjqJhWOu83LzfV6dEITpx8jfIyDyj5rb9W3xwn6R1jzHhr7RZHpeE48BqymTLGdDXGvCHpFR1/IPd1iaT1xpjrGvm8CDPXndtef/hxF9/NMfLMyD7CQUloBowxfY0xSyT9n44/kPu6Rp7265JGPi/CzLBrB2jYdX4vL9tIWmSMSXJQEpoBY8xgScsl/UnHH8iri5Dnjfs6Y8w5jXhehKFRl9+lAROm+W7uJOl9Y0xXByXhOBHKmyFjzLmS1ku6rAkv00GeN6JvsM5ry3bv9E665dKOvpvbybOGef37ZAGSjDGXS1orqSlvOrvJ8+DoJdZ5bdlOvWuEkqb6rQ7ZVZ4b28Z+oI1mzhhzg6Q1kpqy/3dfSYuNMU+Wv5FHC2SM0fgbHlXPkRN8dyVJWmiMqWnOFYQofpibGWPMNEnvyLNETzBcJukDY0xCkK6HEGOM0dO3dtNlZ7b13dVTnmDe3kFZCEPlN7SvSvL7ZmoiP5QnnNc04RKaORNhdObDo9VtjF/+HiTpvzx0Rn0ZY+6R9DdJrYJxOUl3Svq3MYahqC1URFS0zrrjWSX2G+67a7Sk13joHF4I5c2IMeZieW5o6z/jTeM4Q56nctzYtlCRkUYv/6aXTh/m9y0wTJ7QE+ugLIQRY8wPJL2g4P9eukDcvLRokTGROvvJsWo/wO/Z8jhJr5QvXwXUyBjzM3nmvwi26ZL+Xj67O1qg6Lg2mnzPHLXp7Nfj5zxJL/C9ET4I5c2EMWa0pPlyN3nfGfKMX0cLFRcbof8+0kcn9vF7JjRRnqEOtDcIyBhztqQ58rz9ceFCSbMdXbvJLJrxiV466Q29dsH7rksJebEJMTp39umK7+r3YPESSc9wY4uaGGO+L+kvDku4RtKjDq8Px+Lbd9a59/5dsW06+O76oaTfBb8iHAtukpsBY0yMpL8rOF2manOpMeZ6xzU0S7MWZMictT7gfzFTNqj7tM06/96d+sf7R1Raap3VmdguSu891lfdOvo9G7pC0p8dlIQQV77k2VxJrt9G3mCMudBxDY3GWqvMzVmSpMQhoTmCJHNLll466Q29dNIb+vCW5a7LUeuu8Tp39njFtPXrNHGzpF85KAkhzhjTRdJzcvdAscI9xphxjmuAQ+169Nfkn/9NkdF+HRPvL1/JBCGOUN48PCDJb0CJI08bY3q4LuJYjL81Reas9Rp47dagXO9v72ZWBuvla/NrPXbV1oIa9xWXWB3ILNH7X+fqh4/t1aSf7VBOfmmNxze1ft1i9N5jfdQ23q95+Zkx5i4XNSGkPS7PxEWh4IXmMgdC9s5cFeeVSJI6DfV7exISMjYeqfxzqDw46DAgQec8NVYR0X7t1yPlQyyA6mar8Ve4ORYRkl4yxrh+OQOHugwapYm3zVKAjomzjTEXuagJ9UcoD3PGmFMk3eu6jmraS3redRENVVRcVhl8xw4NztD4FeuPSpKio4xGD6799+iqrZ5jIyKklc/319q5J2jt3BOUPOcELfpjH93+vY6KKn/P+PnafN357IEmrb0uJw+I04Lf9a6sqZq/GGOudFASQlB5t/VQeoLfQ9KTrotoDF6B98TQCLy+qtfYKURCuSR1G9NZEx4ZE2jXi+WrmwAVK0X4rUnl0GBJv3ddBNzqO2aKxs7w67EeIWm+MaYpVwXAcSKUh7/75G4ceU2+a4wZ5bqIhli9tUAFRZ5u32OHBCmUb/C8HT/phFi1iqn5R7GgqEwbdxZKkgb2jNGoQXEantRKw5NaaUT/Vjr/O2319G3d9cLdVR0U/r0kS7lH3b0tl6QpY9po7j09A+36pzFmUpDLQWj6resCArjeGNPPdRHHK2NT6L2F9pWx8XDlnxOHhNbb/KSpvXTaL0b6bo6StCDcfr+hyYRi+3WrMcZvjVK0LCdOvkYjL73Fd3OcpHeMMYMclIR6IJSHMWNMT0kXu66jBj91XUBDrNhwtPLPY4c2/Qo4R3JLtXl3Ub2u9+22ApWU5+uTTqj5jfqM8zuoXWvPj3RRsdWW8vO7dN257fWHH3fx3Rwjz4zsIxyUhBBhjBkmyW+B1RAQIeknros4XhVvoVt3i1OrjqG3+IEtqxrzHts+Rm16hN7KY8OuHaBh1w3w3dxG0iJjTJKDkhAijDET5VldJNS0kjTTdRFwb9Tld2nABL+OHJ0kvW+M6eqgJNQh1N6womFuVOj+P5xujPm5tfZI3Ye6c9KPtik5pdBr25ifpPgd99msfjpjRGuvbe9/naP/LM3W8vX52p9RoqJiq56dozTppNa68/uJGtHfP0A/+Vq67pp90Gvbs29m6tk3M722/eiC9nrxF563zCu3VI0nP3lA7d3c+3WL0bfbPccXl7ib8K26e6d30p60Ev31La+vsZ08a5iPs9budlQa3ArlB3czjTG/tdYW1n1oaKoI5TW9JU/feFh7PjuotOQMZaXm6mh6gUpLyhTTNlodTkhQvyk9NWhakiL9x1b7KSsp047Fe7X70wNKW5epgoxC2VKr+K5xate3jXpP6q6+Z/XwejiQlZqjkvLePKHavV6STr1rhPIPFSj1gz3VN3eV58Z2vLU23VFpcCuU26+fGGP+Yq1tspuA9JS12vzRK9q37gsdPXJIMa0T1HngKRpx4Y3qMmiUUr9cpGWzbpUkTf7FHPU+5Syvzx/NStPuVR/rwKZvdHjXRuWm7VVxQZ4iY1qpTeee6jHsdA09b4baduldYw0rXnpAmxb/WzJG1774rUxktLYum6/UFe/qyL4UlRYXKqFrXw2efLUGnXWlIiKqxtPtW/u5Ni+dr/Tt3yr/8CG1attBvU6epFFX3K24doGnCEjb/q3evf8ySdJ3fvCghk69XvvXr9CWpfN1cMv/VJCVobj2XdRz5Bk6+Xu3K75DVe7NTd+rzR+/qt0rlygnbY+MMUpMGqGTLrtFPYadXvu/deo67VmzTGlbVylrX4qOZqWptKRYMfEJ6tB7kPqddp4GnX2VIqO8V74xxmj8DY/q6JE07U3+tPquJHmWMZ5krc2t9eIIqlANdKhD+fIsN7iuoxbx8izT8VfXhdSksKhMG3bW7557RFJVGN66p1DXPLJX32w66ndcyr5ipew7on8tztKzt3fTjRd59yKrbcK26kZWC/QV48ml2t+US9KhI56JnYyRTugZ7OXqAzPG6Olbu2lfRrHe/Cyn+q6e8gTzM0L94Q0aV/lkRNe5rqMWnSVdKs8yk2EnZ0+eirKLJQUOvJv+k6IVj6wJ+NnCw0U68L90Hfhfura+vVNTnz9DsQk1tyX7vjqk5Q+uUu4+/8kqs3fmKntnrnZ/ekC5e/M0+vaq+Ui9xrwPDd1QbiKMznx4tI5mFOjA/7zy9yBJ/zXGTLbW1j5TJ5oVY0wnSZe5rqMWJ0g6W9JHjX3istISffXPh7RpyctStcx/9Eiadn3zoXav+ljjb3hUeRn7Kvcl9vPuUFBckKf5t5wuW+Y/xK6kIE9Hdm/Rkd1btGXpfJ1953PqeVLgDlUZqeslSW0791b2wZ1aNus2ZR/Y4XVM5s4NWjHnPh3etUnjZjykwtwsfTHnN9rx1SKv4/IPH9SWpfN1YONXuujhtxUT39b/ejvWV/65XY8kfTr7Lm3//C2vY3LTdmvzR/O0N/kzffehNxTXrpM2fzRPX//7YZUUet8zHtiwQgc3fqVz7n5evUedE/Br3LTkZa2Ye3/AfYU5mTqw4Usd2PCltn7yuqb++t+KbZ3gdUxEVLTOuuNZvff7q5WxY131XaMlvWaMudhaWxzwAgg6Qnn4OkGeSYlC2QSFcCiPiDBa/bcTVFoqnXpziopLrC49o61+P9O7u3VkhFG7Np4nrMnbCzThjlRl5ZWpVYzRD6a219mntFa/btE6nFOmz9fladYbmcrOK9PNT+3X0H6xXm/YH5rRRfdO76QHXjqkNz/LUVSk9M3/9VdUpPdqKn26VC3JU9835cvXet7YS9LEk+LVqV3o/HhHRhq9/Jtemnz3Dn2x3usX0zB5urJPDee3kmiwkyX53/WElgkK01DuNZ48QOBNX39YrbvFqef4ruo8oqMS+rZRdHyUCrOKdHhbtra8nqojKTnK2HBEq55Zr3G/OSXgdba8kaovfr9atkyKiI5Q//N7qdcZ3dSmZ7xUJmXvytXuzw5o50f71Gm49wPK9BCceb0mkTGROvvJsVo041Md2ZZdfdc4Sa8YY6ZZa91O4oFgGivJb928EDNBjRzKrbVa9szt2vn1+5KkDn1O1ImTr1Fiv2EqKyvV/nXLtfad57XipQcqg3hc+86K7+B9T5WxY70ioqLVfdgEdRsyVh16D1Krth1VVlKsvMz92vHlIu345n2VFB7Vp8/dre8/tUzRrbx7KpaVlerw7k2SpIjIKH3w6HUyEZEadcXd6nbiaZIx2vPtJ1r7zvOypSXatPjfGnTWVfrixV8pY+dGDZx0ufqeep7i2iXq8K7NWvX6U8rP3K/sAzu0+aNXNOIi//lHKx4CSNLq159W+rY16j/+EiWN/a7iO3RW1r4UrXrtSeWm7VZu2h5t+OAfio6N08r5f1Zi0nANmXKd2vcepKLcLG388J/avfpjWVumb+Y9VmMoT09JVuvE7up50iR1HnCSErr1U3SrNirMO6LDuzZry8ev6sjercpIXadV8x/XuJn+8/xFx7XR5HvmaOFvpyk3zavHz3mSnjfG/Kgpe1UgfuuyAAAgAElEQVSg/kLnrh0NNdp1AfUQ0jVGRxkNT2ql9akFlV29J4yM1/CkwMF3T1qxzv/lTmXllWnUwFZ659E+6tHJ+/fy1NPa6Ppz22v0TSnKyS/TH19J17t/qPplktTd88Zp9yHPg8mhfWN18oCaJ5YrLCrT+h2erNq5faTf9SqkHSnRDX/eK0mKipT+dFO3+vwTBFVcbIT++0gfnXF7qjbt8hrvPlGeyd+mW2vLHJWH4ArptqFcONQYUF1LjY2YMUjjHxwlT4crbz2+00UnXtFfi374idLXHVbK+3sChvKdS/fpi4fXyJZJ7ZLa6pynx6ld3zZex3Qe2VEnfLePsnfn+r1t95p5PUSXbKsuNiFG584+Xe9e94nyD3o9WLxE0jPGmFu4sW0xwqFtaPQa1/73ucpAPvic6Ro74yGvLuFdB41Wh96D9fGTN+vQlpWS/N+SS1JC1766avaXiolP8NsnSUljL9T69+bq6389rILsDO1b94X6jpnidUzWvu2Vb56z9qeoy6DROueu59UqoerhX9fBY2TLSrX2v/8nSXr/4aslWZ33m397gnu5Tv1HqkPfIXrnN54pmvZv+DJgKM+s9qb58K5NmvLLl9RzxJle50lMGq4375kqWatNH/5LRfnZGv7dGzXmqntkIqqGAnUfMV7v/OYSZe7coKy923Q0Kz1gt/kR371J43/8x8Bt9bDTdeKUa7Tod1cqffu3SlnxbsBQLknx7Tvr3Hv/roUPXq7C3MPVd82QtEeepZXhGBO9ha9w+KVwQjis+Vv9TfSogTUH5Ntm7de+9BIN7h2jpU/2qzEgD+wVq2kTPL9sPguw/nhxidXa8nHsowbVPtP72tTCygcGvl3XS0utdh0s0v/9N1On/Hi7Nu0qUnwro1fv761TTwzODPINldguSu891lfdOvo9D7xC0p8dlAQ3wqH9OskYE5YPrisCb1znVorv7N8WtOvXNuBNXoXI6Aj1Oau7JKkou1jFed69GwsOF+rzB1Z6xo13aaWpz5/hF8irS+jdRrHtqkK5tVaZ5W/zY9pGq22v1jV9NKS07hqvc2ePV0xbv7b/Zkm/clAS3AiH9qtRa8w5tFurX39aktRt6Fi/QF6h76lT1aHPiZV/T+w33O+Y+A5dawzkFZLGXlj55+pd4Stk7thQ+efWnXpo8i/meAXyCr1Omlj556L8bE26/VmvQF6hU9Jwxbb1fL4oL9tvf1lpiQ7v3lL59wk//YtXIK/QvucAtenUs/J6/U+/WKde/UuvQC5JERGR6jF8fFVtAa4pSe169K+9rY6KUZ/Rk8vPkaXiozUPEW/Xo78m//xvioz2m/jzfmNMKC1N2mKF/A2HMSZCUmJN+0feMDhx2DXes6Ouf3lbonmx5m/iZsK/VQlNE40xX9TjuJrv6JrYyi2ep63GSKcMDPyW/NNv8/TW557x0E/f2l0Jrf1/GVXXpb1nf3ZemUpLrSKrdU9fl1qgwmJP0B49qI71ybdUvZFZsjJP5qz1AY/r1jFKt1zaUXddnqj+PUJjLHlN+nWL0XuP9dGEO3YoJ9/rxfjPjDGHJf1fEMoI1KYk1vbLrymMvOSnicPOn+G1bf17LyWaq2cHtQ4HTnVdQD20kjTOGLOpHseG1BJElZO81WMCtdLiMhVkFqo4v0S2pOrnMSulav6HyBjv9m717A2VY9bPeHC0Wndt2EPA7J25Ks7zDLUJ9a7rvjoMSNA5T43VBz9ZrrJir/brEWNMtoIz5CEk2q9fX9Mp8c7ve5fy1OsZiY+e1ezvv8IhlHcrX+HiUD2OrfOHcM0bs1RWWixjInT6zIcDBvIKHXoN0uFdnmYzManuCeoLsjNVdDRHpcWFlePUs/Ztr9wfFeN/n5SRWvXWevSVv/AbS12l6nuxz6nnqudI/yBdqfzaMQHOdWTvVk99knqMOEN9T51a83nKrxkZHavTrruv5supqmNNTOu6R3OVlhSpIDtTxUfzZMtKKrdX/7cKELi9dBk0ShNvm6WlT94sn46Js40xuZI+rLOQ4+eq/coI9d6YIR/K5fmfV2OjkvziZiW/uNl388YmrQgN8Vbdh7hVMfnagJ4xNYbtOYuquvucd+/Oep+7bXyEVyCXpFX1fDMveb/Fr01WXqkG944J+UBe4eQBcVrwu9664Jc7K5d7K/dQ+X8uBL3dSH57tpLf9gvgtF+h49O6DwkteQfzVZDpuXmsKfBm7czV1jd3aPdnB5S1I0e2lpUa4rvGKaLaDOwlBaXa9s4uSVLX0Z3Uc3zDV9apq3t9qOs2prMmPDJGy+752nfXM+X/uRD0duPRl9P16Mt+k8/TfoWOdXUfUreSwqNKXfGuJKn3qHPUrkf/Wo+Pjqt6x9IxQPf1kqICbf/sTe34apHSU5JVlJ/jd0x1bTr38ttWMeladFwbJX3ngho/m30gtfLPgyZdUeNxhXnZld26E7r5r3ZYfTz5wFrOU1pcWPlmv/cpZ9c4k7sk5Rzw3EtGx7VRXLvOAY/J2p+qrcv+o91rliprX4psaUnA4yQpvmN3RUTVPdVB3zFTNHbG73wnkIuQ9O86P9x0gtFudJGUFoTrHLNwCOVAkykrs1qzzRN8R9Xwlry01Grhl8e2asTAADOgV7yZj4ioezb16jOvf/JUP3VM8Dw0KCq22n2oWC8vydJrn2TraKHVHc8e0Nih8SHbdd3XlDFt9Mzt3XXzk/tdlwI0G7XNam6t1aq/btC6l7aorJ5LJnYY6P3WaO+Kg5VLmSVN7Xn8NYZhKJekpKm9lLUjR6tnk0HRvO1bt7zyLXHvUWfXeXxhXpYkKbZNe7X1CdQHNn6lT2ffpbyM+v/e79B7kNffrbXK3Onpvt5l0Ohag2jFG3VjIgJ2W6+QWW1m9cS+Q/zPU21/96Hfqfk8OzdVzirfbdjYGo+TpIydnnN27DvUb5+1Vqtee0Lr3nlBZaX1mxzd99+pNidOvkZH9m7Xxg/+Xu/PoOkRytGibd5dpNyjnt4sNb213pNWrIxsTyP7y6s76ZrJ7ep9/oR4/2kbKt5+D+4dq9ZxNU/rUH3sedcOUZpwkve4y1GD4nTJGQm689n9enpBpqyV/jw/XfN/W/O6nqGkoKhM8z7Kcl0G0KzUFni/eixZG+d5ujq27d1ag77XT91Gd1LbXq0V0za6spv6kdQcvXnpYklSJ59zZG6u+pntclKNI8tqlb4h/EN5UW6xdn7kP9YVaG6qdxXvdMLIOo8/vNPzoMo3bB7askof/vEHKi0uUmR0rAZM+J56nXy22vcaoLh2nRUV06py7PW7D3xPadvWKL5jd7+3yDmHdlW+Xe/Uv/Z60strT+jR3+sNvt/XWD2U9/cfB18R2lsn+tfjdZ7UtZV/7pRUc20FOYeVl76v/Dj/6331z4e08YN/SJLadu2rQWddqW4nnqa2XXsrJj6hspv6kb3b9eYvppSfZ0SN1/N1NCtde9YsrffxCA5COVq06mO2R9UwvvtAZlV3oWH9Ymucnb0+SkqtklNqfzNfYf2O+o09f/CHXfTiwiPKKyjTW8tzlJVbWrmEW6gqK7O6/tG9+jSZ5X2BxlQRymM7xKhNt3iv7RWBvNeZ3XT2E9/xGyteYe/yg5V/TvSZGf1oWtWQmrhOx9YWVkzyFt06Sgm1TBAXqkqLy7T07q+8HlAAzVX+kaoev7V1x5Y8E8JllXcZT/QJmyteul+lxUWKiU/QBb+drw69Bwc8R0HOYaWneMJtoMBavSu57zWqKystqRzbHug83uf0XC8yOlbtew702metVWblGPnag2/FQwATGeU14Z3/9aoedPh+DRk71lcG8l6nnKWz75xd41jxvcmf1HiemhQX5GvJ4zco52D9h2IiOMIhlGfIMw4goNj2MWbw95O8JtnZ/HpqZuGRIpYmCS9tJKUE+6IV48mlmkPy0WrfShlZx7cU7cadhSooP19doXzl5qraRtcyS3v7NpG6ZHxbvfJRloqKrd5enqPrp4b226e7nzug1z7xm230oKTz5Vmeo6kZ+U/OlSkpqO1GbNsOZvDZ073br4/nZRbmHKb9Ci99JX3jugipKpT7vuHesbjqx2rMncNrDORlpVabX68ah+nbBb60qKoNLCtp+Jw52btzVZTj6Y7Z8cT2tc4sHIqstVr+25Xa96XfVDepki6U5DfIugmERPvVqV2kufG7HbzqeOHdw5npWaW0X+HlFEkf1LSzouu6JBXl59T6pnjjB/+onDCt+nJoWftTlFn+Bn3QWVfWGMglafNHr1R2AQ8UNDOqLU1WW9g+sqdqcra6AmtF0O/Q50RFRHpHo+z9qZWzmtd9Hk9tHXoNVFRMzZOuVf8afM+548tFlX8eM/3eGgN5WVmpNn80r+o8Ad7w+32mtETLZt2q9JRk313rJF0s6djGajaMq/Yro4nPf9xCPpSXz5RX18B879+Of2uyctBEjDGFdR/V+Dbt8ly2e2KUOiYE/nHo2alq+6KvcnTH94+ty2b160mq84179fHkdc3SftmZnlAuSW9+nh3SofyJ/6TrqdczfTfnSDrPWrsmiKXUZ1baYPCp469uqsAxM8bUPXVuEBzNKFD+Ic/DPN9u4bn7q9qTuMSabxaT52xWVqqna2irjrFeb9slKb5zVVuUvvaw3/66eK1PHoZd11fOWq/tC3f7bk6TdK61dlsQSwnJ9usRV1XgmBljjtS2P6591dvxw7s3q133wBO9paeu08YP/1X59+ozr1d01ZakVrW8bc/al6Lkt5+r/Hun/v5vpisCdKuERLVO7F7judKrdSWvLUwXF+Qp+8AOz3EBJqar70OA0uJCHdmztc7rSVVfQ1RsvN+/Z261JeDiEmr+t0p++7nKmddbJSSqTWKPWq9prdWKufdrz5plvrt2SppqrQ3meJxQab9CCuuUo0XLKR9PXlLLg/2BvWJ1Qvms5h/+L08vLjxc47GSlF9QpmfeyFBpgHNWXwKsuI6JlrzWT69jPfPzTmujVjGeN04ffJOr/ILQXPXh1Y+zdPdzB303F0u6LMiBHGh2vMeTe3c7r7629p7P/X4GJUnr/7VVq2dXrf/r+5ZcknqMrZptfc3zG/3WMK8u7+BRFWYVedcYxuPJN8zbrrVzt/huzpd0YZADORA0XQZWrQCX/NZzKgswA/jh3Zu15PEbKicli2rV2msW8+j4queWe7/9xO/zkpS5a6M++OP1KimsGtYWKNxWTPJW37fWxkQEDNuV59uxoXJ5sIAPAaqtiV7bNTN3bar8+uusbUfFJG9D/NYwj6n2b7Wnhn+r9YvmaPXrT9arrgpr3pilLUv9VmzMlOeFCBNkhICQf1MONKUTesTos+R8pR0p1U+e2Kdrp7RT+/Lx2PGxEZVLjD3yoy666vee7p8//vM+LfwyR1ed1U4DymdXP5Jbqo27CrV8Xb7eXZGrXp2jdNv3/N+on1BtybJfvXhQJaVW/brFKLK8J+mgXjGKiY5QabWx553bR6p3l9qXuWgTF6lzRrXWwi9zdbTQ6oNvcnXZmTWt2+nG0tV5uv4PewPtmmGt/SjY9QDNTW2TvPWZ2F2b5ntGCK14dI1y9+er+5hOioiK0OFt2dry5g6lJWeq6ymJOrja08sv0Jvs7qd1VtfRnXRwZboOb83W21d8rCFX9VenYR0UFR+lwqwiHd6Wrb3LD2r/V4d01ccX1lxjgNAfqnYs2auvHvvWd3OppMuttSExdAFoCr1PnqQ2nXspN22PMnas0/sPX62h589U2y69VZCdqd2rP9aWj19VZHSMYuITVJSfrcS+Q7yGpiT2G6b4jt2Vn7lf+9d/oY/+cpMGnX2V4tp3Vn7mQe1etUTbPntT8R26Kr5jN+VnHiif5M37TXFexn4VZJe3T3WM764I5QndkxTdqnWNx6VXG98daAm3ivMEqifQcXXVVpSfrZxDnmUlA4XpPqMma9Niz+pkK156QLnpe9V9yHcUERWtw7s3a8vS/yht22p1HTxGBzf/r87rSdKWpfO1ZsHTvpsLJF1srd1U64cRNIRytGh3TOuo+UuzdLTQ6vl3Duv5d6regl8xKaFyJvMrz26nA5kluueFgyoqtnrr8xy99XnNa2uOGxq4S+eZI+N1+rA4fbH+qNamFOrS+6u6QUZESNnvDlFMtLRhZ6GOFlZM8la/Jc4uPSOhcum2Nz/LDqlQvjalQJfevytQ74B7rbUvu6gJaG4qAm9MQrTa9vK+Ce05vqsGXNxH2/67SyX5JVr91w1aXW2/iTQaMXOQOgxsVxnKfSd5q3D2n7+jxbd9ofR1h5WzJ09f/3ltwOPa9mqt2Hbey0JmlE/yFhUXqXb9QqLXf50OrkrXp7/6JtCIx5ustYsCfARoNiKiojXxlqf04WM/VPHRXB3c/L/KMFghoXuSJtz8hN77/ZWSpM6DRnmfIzJKZ9z4Ry35849VVlKkXSsXa9fKxV7HdB5wiibe9rTeumeqpBomedtR/0neKsaw1/etdURUTMBlxSrezNc9WVz1Sd78l1WrOm591bj7AOfsedIEDZgwTds+XaCSgjytfu0J77Y6IlIjLv6JOvQeXPn/obavcffqj/XFnPt8N1tJV1trl9f6RSGoCOVo0U4eEKcvnk3SH15O1+fr8nXocIlKyucxGjPYOwzf8f1EXTiujV5457CWrsnTtr1FyskvU0y0Uef2URrYM0Zjh8bp/NPa6vThgYN0RITR+3/qqz+9mq43P8tRyv6iyvB9YrUl0lZtqXsCOl8Xn95WN0VIZWXSu1/mqqTUKirS/SRKuw8V6/x7dyo7z69L/bOSHndQEtAsVYTyxBMDv4E+46HR6nJyoja/nqoj27NlIiPUumuceoztokHT+qnjwHZa+Uy1m94a3mS36hirC/8xUSnv7dGOD/coY+MRFRwpUkR0hOI7tVKbnvHqMa6L+kz0Hu+Zuy9fhUc83dk7Dm4nE+G+farLkZRsLbljhUqL/NqvB621c1zUBARbl0GjdNHDb+vbt/6q/euWqyA7U7Ft2qlDnxPV77TzNWDiNB3askqlxZ6f7+5Dx/mdo+fIM/Xdh97Qt28+owMbv1ZxQZ5aJSQqMWmY+o/7rpLGXaTsAztUUuiZ/yLwJG+1L11W4cjeqkne6gzTOyomZxukyCjvh4i5aXtVmHukxnqqq3jj3r7ngDomeav2NdTQrf6Mm/6kLoNGa/NHr+jInq0ykVFq3bGbeow4Q4POulId+5yolfP/XHWeGv4t0rZ/q2WzbqucOK+a26y1b9b6BSHojLVMkgn3jDEJkrzWl9k1f1Cd3bYR2g7nlOrM21O1foffPH5vSLrCWnt809kDIcAY01/S9urbrvvqEkW1Cu2lCVG7/ENH9e71y5RXbZK8ci9KutFyA4VmwBhzmqSvKv4eFRun615aX8snAvt09t3a/vmbapWQqCueXe4XcBFc2Qd2aOGDl1d296/mj9baX7moCbVjojcATaKgqEyX3rcrUCBfLulaAjmAUFWUW6zFt34RKJAvlHQzgRyocnDTN0pZ/rYkafA5VxPIHTuala4PH5sRKJD/W9KvHZSEeiCUA2h0ZWVW1z+6V58m5/vu2iTPxCJ+d7oAEApKi8u09O6vlLk5y3fXN5KutNb6Tz8NNFPZB3aotmdQ+9ev0JK/3Chry9S6Uw+NuOimIFYHX8UF+Vry+A3KObjTd9cSST/igWLoYkw5gEZlrdVdsw/otU+yfXftl2fpDb9FygEgFNgyq88fWKl9X/oto7tN0nettXkOygKcWf7ir1WQk6n+4y5Sp/4j1Sqho0qLi5S1P0U7v/lAu1d9JFmr6Lg2Oueu5xXdKvBEt2h6ZaUlWjbrVqWnJPvuWiNpmrW2KMDHECII5QAa1ROvZejpBX65O0fS+dZav0e3ABAqVs5ar5RFu303p8nzQNEvqQPNXeaODSrKz9aq3X+p8ZgOvQdr4q1PqUPvwUGsDNVZa/XFnPu0Z80y3107JV1grfV7U4LQQigH0Ghe/ThLP3/uoO/mYkmXWWv9FvkFgFCxYd52rX1pi+/mfEkXWmu3B/gI0KzZsjKNv/GP2rNmqdK2f6vC7EwV5mYpIjpGce06qfOAk9XvtPPVZ/RkmQhGxLq05o1Z2rrsP76bM+V5oLjfQUloIEI5gEaxdHWerv/D3kC7ZlhrPwp2PQBQXzuW7NVXj/k9NyyVdLm19hsHJQHOmYgI9TvtPPU77TzXpaAWW5bO15oFT/tuLpBnDp9NDkrCMeCxFoDjtjalQJfev0vFJX7zh9xrrX3ZRU0AUB8HV6Xr0199I/lPf3STtXaRg5IAoF52r/5YX8y5z3ezlXS1tXa5g5JwjAjlAI7LroNFOu/encrOK/Pd9aykxx2UBAD1ciQlW0vuWKHSIr/260Fr7RwXNQFAfaRt+1bLZt0mW+a3wuxt1to3XdSEY0coB3DMDueU6vx7d2lfut8KQW9IupOlNwCEqvxDR/XhT5erKLvYd9eLkh5yUBIA1Ev2gR1a/PiPVFLot8LsH621f3VRE44PY8oRslZtOaq96X43S2giRtJ3htZ/KZOCojJdet8ubdhZ6LtruaRrrbV+j26BliJtbaYiYyNdl9FiREQZdRraod7HF+UWa/GtXyhvv98N7UJJN/NAES2VLSvVoa2rXZfRokTFtlLHPkPqffzRrHR9+NgMFeb4rXTzb0m/bszaEDyG3zsIBcaYBElZrutoyaIipeIlw+p1bFmZ1VUP7Qm0FvkmSeNZixwtiTGmvyRm53YornMrXbXkgnodW1pcpsW3LNf+r9J8d30j6SzWIkdLYow5TdJXrutoyTr0HqxLH3uvXscWF+Tr/YevDrQW+RJ5VopgLfIwRfd1AA1irdVdsw8ECuT75Vl6g0AOICTZMqvPH1gZKJBvk/RdAjmAUFVWWqJls24NFMjXSJpGIA9vhHIADfLEaxl6eoFf7s6RdL61dqeDkgCgXlbOWq+URbt9N6fJ80DxkIOSAKBO1lp9Mec+7VmzzHfXTkkXWGv93pQgvDCmHKEk13UBLUykpLiGfODVj7P08+cO+m4ulnSZtdZvkV+ghbCi/Qq2KEmtGvKBDfO2a+1LW3w358vT5ZPhB2ipykT7FWzRkmIb8oE1b8zS1mX/8d2cKc8Dxf2NVRjcIZQjJJQ/4Wvruo6WxBhzsqR6z+by8apcXf+HvYF2zbDWftRohQFhxlqbKtqvoDLGTJa0uL7H71iyV1895vfcsFTS5dbabxqzNiCcWGv/J9qvoDLGXCVpXn2P37J0vtYseNp3c4Gki621mxqzNrhD93UAdUreXqDLHtit4hK/iSHvtda+7KImAKiPg6vS9emvvvH0Z/B2k7V2kYOSAKBedq/+WF/Muc93s5V0tbV2uYOS0EQI5QBqtetgkc7/5U5l55X57npW0uMOSgKAejmSkq0ld6xQaZFf+/WgtXaOi5oAoD7Stn2rZbNuky3zW2H2Nmvtmy5qQtMhlAOo0eGcUp1/7y7tSy/x3fWGpDtZyxdAqMo/dFQf/nS5irKLfXe9KOkhByUBQL1kH9ihxY//SCWFR313/dFa+1cXNaFpEcoBBFRQVKZL79ulDTsLfXd9Lulaa63fo1sACAVFucVafOsXytvvd0O7UNLNPFAEEKqOZqXrw8dmqDDHb6Wbf0n6tYOSEASEcgB+ysqsrn90rz5NzvfdtVHSJdZavztdAAgFpcVl+viuL5W5Oct319eSrrTW+nX9AYBQUFyQryWP36Ccg34rzC6RdAMPFJsvQjkAL9Za3TX7gF77xG/Jy/3yrEXu9+gWAEKBLbP6/IGV2v9Vmu+ubZIustbmOSgLAOpUVlqiZbNuVXpKsu+uNZKmWWuLHJSFICGUA/DyxGsZenqBX+7OkSeQ+z26BYBQsXLWeqUs2u27OU2etXwPOSgJAOpkrdUXc+7TnjXLfHftlHRB+dLBaMYI5QAqzfsoSz9/7qDv5mJJl1lr/Rb5BYBQsWHedq19aYvv5nxJF1prtzsoCQDqZc2Cp7V12X98N2fK80Bxv4OSEGSGoQlAy2SMOVnS6urboqNMoLXIr2UtcgChxBgzWdLiyr9HGdlS67sWeamki1mLHEAoMcZcJWlexd8jomJUVuLXM71A0mTWIm85olwXACB0BAjk9xLIAYQ66992SdJNBHIAoS5AILeSriaQtyx0XwdQk2ckPe66CAA4Br+11s5xXQQAHIPbrLVvui4CwUUoBxDIG5J+xtIbAMLQ3yT93nURAHAM/mit/avrIhB8hHIAvj6XZxx5qetCAKCBFkr6KQ8UAYShf0n6tesi4AahHEB1GyVdYq096roQAGigryVdaa0tcV0IADTQEkk38ECx5SKUA6iwX561yP0WKQeAELdN0kXW2jzXhQBAA62RNM1a6zfjG1oOQjkAScqRJ5DvdF0IADRQmjxr+R5yXQgANNBOSRdYa7NdFwK3COUAiiVdZq391nUhANBA+ZIutNZud10IADRQpjwPFPe7LgTuEcoBzLDWfuS6CABooFJJl1trv3FdCAA0UIGki621m1wXgtBAKAdatnuttS+7LgIAjsFN1tpFrosAgAYqk3S1tXa560IQOgyT/AEtkzEmRlIxM30CCDfGmBgmRQIQjmi/EAihHAAAAAAAR+i+DgAAAACAI4RyAAAAAAAcIZQDAAAAAOAIoRwAAAAAAEcI5QAAAAAAOEIoBwAAAADAEUI5AAAAAACOEMoBAAAAAHCEUA4AAAAAgCOEcgAAAAAAHCGUAwAAAADgCKEcAAAAAABHCOUAAAAAADhCKAcAAAAAwBFCOQAAAAAAjhDKAQAAAABwhFAOAAAAAIAjhHIAAAAAABwhlAMAAAAA4AihHAAAAAAARwjlAAAAAAA4QigHAAAAAMARQjkAAAAAAI4QygEAAAAAcIRQDgAAAACAI4RyAAAAAAAcIZQDAAAAAOAIoRwAAAAAALTCXe0AAAL4SURBVEcI5QAAAAAAOEIoBwAAAADAEUI5AAAAAACOEMoBAAAAAHCEUA4AAAAAgCOEcgAAAAAAHCGUAwAAAADgCKEcAAAAAABHCOUAAAAAADhCKAcAAAAAwBFCOQAAAAAAjhDKAQAAAABwhFAOAAAAAIAjhHIAAAAAABwhlAMAAAAA4AihHAAAAAAARwjlAAAAAAA4QigHAAAAAMARQjkAAAAAAI4QygEAAAAAcIRQDgAAAACAI4RyAAAAAAAcIZQDAAAAAOAIoRwAAAAAAEcI5QAAAAAAOEIoBwAAAADAEUI5AAAAAACOEMoBAAAAAHCEUA4AAAAAgCOEcgAAAAAAHCGUAwAAAADgCKEcAAAAAABHCOUAAAAAADhCKAcAAAAAwBFCOQAAAAAAjhDKAQAAAABwhFAOAAAAAIAjhHIAAAAAABwhlAMAAAAA4AihHAAAAAAARwjlAAAAAAA4QigHAPx/+3UsAAAAADDI33oYe8oiAAAmUg4AAAATKQcAAICJlAMAAMBEygEAAGAi5QAAADCRcgAAAJhIOQAAAEykHAAAACZSDgAAABMpBwAAgImUAwAAwETKAQAAYCLlAAAAMJFyAAAAmEg5AAAATKQcAAAAJlIOAAAAEykHAACAiZQDAADARMoBAABgIuUAAAAwkXIAAACYSDkAAABMpBwAAAAmUg4AAAATKQcAAICJlAMAAMBEygEAAGAi5QAAADCRcgAAAJhIOQAAAEykHAAAACZSDgAAABMpBwAAgImUAwAAwETKAQAAYCLlAAAAMJFyAAAAmEg5AAAATKQcAAAAJlIOAAAAEykHAACAiZQDAADARMoBAABgIuUAAAAwkXIAAACYSDkAAABMpBwAAAAmUg4AAAATKQcAAICJlAMAAMBEygEAAGAi5QAAADCRcgAAAJhIOQAAAEykHAAAACZSDgAAABMpBwAAgImUAwAAwETKAQAAYCLlAAAAMJFyAAAAmEg5AAAATKQcAAAAJlIOAAAAEykHAACASUjejCs4cfKQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 2),dpi=300)\n",
    "gs = gridspec.GridSpec(1, 1)\n",
    "\n",
    "# Plot of repressilator circuit\n",
    "ax = plt.subplot(gs[0])\n",
    "dnaplotlib.plot_sbol_designs([ax], [[plac, rbs1, tetr, term1, pgamma, rbs2, laci, term2, ptet, rbs3, gamma, term3]],\n",
    "                             [[lac_repress, gamma_repress, tet_repress]])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}