Skip to content

Instantly share code, notes, and snippets.

@Midnighter

Midnighter/summaries.ipynb

Last active August 19, 2020 20:42
Show Gist options
  • Save Midnighter/84590cbc5d46209897abcf6ec2ba812d to your computer and use it in GitHub Desktop.
Save Midnighter/84590cbc5d46209897abcf6ec2ba812d to your computer and use it in GitHub Desktop.
Download ZIP
COBRApy Summaries
Display the source blob
Display the rendered blob
Raw
summaries.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summaries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import logging"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"logging.basicConfig(level=\"INFO\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import cobra"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from cobra.flux_analysis import pfba, flux_variability_analysis\n",
"from cobra.test import create_test_model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from cobra.util.solver import linear_reaction_coefficients"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"config = cobra.Configuration()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"config.solver = \"cplex\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"model = create_test_model(\"textbook\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Metabolite Summary"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.metabolite_summary:Generating new parsimonious flux distribution.\n"
]
}
],
"source": [
"atp_summary = model.metabolites.atp_c.summary()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ATP\n",
"===\n",
"Formula: C10H12N5O13P3\n",
"\n",
"Producing Reactions\n",
"-------------------\n",
"Percent Flux Reaction Definition\n",
" 66.58% 45.51 ATPS4r ADP + 4.0 H+ + Phosphate <=> ATP + H2O + 3.0 H+\n",
" 23.44% 16.02 PGK 3-Phospho-D-glycerate + ATP <=> 3-Phospho-D-glyceroyl phosphate + ADP\n",
" 2.57% 1.758 PYK ADP + H+ + Phosphoenolpyruvate --> ATP + Pyruvate\n",
" 7.41% 5.064 SUCOAS ATP + Coenzyme A + Succinate <=> ADP + Phosphate + Succinyl-CoA\n",
"\n",
"Consuming Reactions\n",
"-------------------\n",
"Percent Flux Reaction Definition\n",
" 12.27% -8.39 ATPM ATP + H2O --> ADP + H+ + Phosphate\n",
" 76.46% -52.27 Biomass_Ecoli_core 1.496 3-Phospho-D-glycerate + 3.7478 Acetyl-CoA + 59.81 ATP + 0.361 D-Eryth...\n",
" 0.33% -0.2235 GLNS ATP + L-Glutamate + Ammonium --> ADP + L-Glutamine + H+ + Phosphate\n",
" 10.94% -7.477 PFK ATP + D-Fructose 6-phosphate --> ADP + D-Fructose 1,6-bisphosphate + H+\n"
]
}
],
"source": [
"print(atp_summary.to_string(names=True))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>atp_c</h3><p>C10H12N5O13P3</p><h4>Producing Reactions</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Percent</th>\n",
" <th>Flux</th>\n",
" <th>Reaction</th>\n",
" <th>Definition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>66.58%</td>\n",
" <td>45.51</td>\n",
" <td>ATPS4r</td>\n",
" <td>adp_c + 4.0 h_e + pi_c &lt;=&gt; atp_c + h2o_c + 3.0 h_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23.44%</td>\n",
" <td>16.02</td>\n",
" <td>PGK</td>\n",
" <td>3pg_c + atp_c &lt;=&gt; 13dpg_c + adp_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2.57%</td>\n",
" <td>1.758</td>\n",
" <td>PYK</td>\n",
" <td>adp_c + h_c + pep_c --&gt; atp_c + pyr_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7.41%</td>\n",
" <td>5.064</td>\n",
" <td>SUCOAS</td>\n",
" <td>atp_c + coa_c + succ_c &lt;=&gt; adp_c + pi_c + succoa_c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><h4>Consuming Reactions</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Percent</th>\n",
" <th>Flux</th>\n",
" <th>Reaction</th>\n",
" <th>Definition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>12.27%</td>\n",
" <td>-8.39</td>\n",
" <td>ATPM</td>\n",
" <td>atp_c + h2o_c --&gt; adp_c + h_c + pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76.46%</td>\n",
" <td>-52.27</td>\n",
" <td>Biomass_Ecoli_core</td>\n",
" <td>1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c + 0.5191 pep_c + 2.8328 pyr_c + 0.8977 r5p_c --&gt; 59.81 adp_c + 4.1182 akg_c + 3.7478 coa_c + 59.81 h_c + 3.547 nadh_c + 13.0279 nadp_c + 59.81 pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.33%</td>\n",
" <td>-0.2235</td>\n",
" <td>GLNS</td>\n",
" <td>atp_c + glu__L_c + nh4_c --&gt; adp_c + gln__L_c + h_c + pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10.94%</td>\n",
" <td>-7.477</td>\n",
" <td>PFK</td>\n",
" <td>atp_c + f6p_c --&gt; adp_c + fdp_c + h_c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<cobra.summary.metabolite_summary.MetaboliteSummary at 0x7fd4729b8710>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"atp_summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Metabolite Summary FVA"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.metabolite_summary:Generating new parsimonious flux distribution.\n",
"INFO:cobra.summary.metabolite_summary:Performing flux variability analysis.\n"
]
}
],
"source": [
"atp_summary_fva = model.metabolites.atp_c.summary(fva=0.99)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"atp_c\n",
"=====\n",
"Formula: C10H12N5O13P3\n",
"\n",
"Producing Reactions\n",
"-------------------\n",
"Percent Flux Range Reaction Definition\n",
" 66.58% 45.51 [41.92; 47.41] ATPS4r adp_c + 4.0 h_e + pi_c <=> atp_c + h2o_c + 3.0 h_c\n",
" 23.44% 16.02 [14.73; 17.48] PGK 3pg_c + atp_c <=> 13dpg_c + adp_c\n",
" 2.57% 1.758 [0.05009; 4.96] PYK adp_c + h_c + pep_c --> atp_c + pyr_c\n",
" 7.41% 5.064 [1.182; 6.627] SUCOAS atp_c + coa_c + succ_c <=> adp_c + pi_c + succoa_c\n",
"\n",
"Consuming Reactions\n",
"-------------------\n",
"Percent Flux Range Reaction Definition\n",
" 0.00% 0 [0; 0.3814] ACKr ac_c + atp_c <=> actp_c + adp_c\n",
" 0.00% 0 [-1.716; 0] ADK1 amp_c + atp_c <=> 2.0 adp_c\n",
" 12.27% -8.39 [-10.11; -8.39] ATPM atp_c + h2o_c --> adp_c + h_c + pi_c\n",
" 76.46% -52.27 [-52.27; -51.75] Biomass_Ecoli_core 1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0...\n",
" 0.33% -0.2235 [-1.937; -0.2212] GLNS atp_c + glu__L_c + nh4_c --> adp_c + gln__L_c + h_c + pi_c\n",
" 10.94% -7.477 [-9.259; -6.17] PFK atp_c + f6p_c --> adp_c + fdp_c + h_c\n",
" 0.00% 0 [-1.716; 0] PPCK atp_c + oaa_c --> adp_c + co2_c + pep_c\n",
" 0.00% 0 [-1.716; 0] PPS atp_c + h2o_c + pyr_c --> amp_c + 2.0 h_c + pep_c + pi_c\n"
]
}
],
"source": [
"print(atp_summary_fva)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>atp_c</h3><p>C10H12N5O13P3</p><h4>Producing Reactions</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Percent</th>\n",
" <th>Flux</th>\n",
" <th>Range</th>\n",
" <th>Reaction</th>\n",
" <th>Definition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>66.58%</td>\n",
" <td>45.51</td>\n",
" <td>[41.92; 47.41]</td>\n",
" <td>ATPS4r</td>\n",
" <td>adp_c + 4.0 h_e + pi_c &lt;=&gt; atp_c + h2o_c + 3.0 h_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23.44%</td>\n",
" <td>16.02</td>\n",
" <td>[14.73; 17.48]</td>\n",
" <td>PGK</td>\n",
" <td>3pg_c + atp_c &lt;=&gt; 13dpg_c + adp_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2.57%</td>\n",
" <td>1.758</td>\n",
" <td>[0.05009; 4.96]</td>\n",
" <td>PYK</td>\n",
" <td>adp_c + h_c + pep_c --&gt; atp_c + pyr_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7.41%</td>\n",
" <td>5.064</td>\n",
" <td>[1.182; 6.627]</td>\n",
" <td>SUCOAS</td>\n",
" <td>atp_c + coa_c + succ_c &lt;=&gt; adp_c + pi_c + succoa_c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><h4>Consuming Reactions</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Percent</th>\n",
" <th>Flux</th>\n",
" <th>Range</th>\n",
" <th>Reaction</th>\n",
" <th>Definition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0.00%</td>\n",
" <td>0</td>\n",
" <td>[0; 0.3814]</td>\n",
" <td>ACKr</td>\n",
" <td>ac_c + atp_c &lt;=&gt; actp_c + adp_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.00%</td>\n",
" <td>0</td>\n",
" <td>[-1.716; 0]</td>\n",
" <td>ADK1</td>\n",
" <td>amp_c + atp_c &lt;=&gt; 2.0 adp_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12.27%</td>\n",
" <td>-8.39</td>\n",
" <td>[-10.11; -8.39]</td>\n",
" <td>ATPM</td>\n",
" <td>atp_c + h2o_c --&gt; adp_c + h_c + pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76.46%</td>\n",
" <td>-52.27</td>\n",
" <td>[-52.27; -51.75]</td>\n",
" <td>Biomass_Ecoli_core</td>\n",
" <td>1.496 3pg_c + 3.7478 accoa_c + 59.81 atp_c + 0.361 e4p_c + 0.0709 f6p_c + 0.129 g3p_c + 0.205 g6p_c + 0.2557 gln__L_c + 4.9414 glu__L_c + 59.81 h2o_c + 3.547 nad_c + 13.0279 nadph_c + 1.7867 oaa_c + 0.5191 pep_c + 2.8328 pyr_c + 0.8977 r5p_c --&gt; 59.81 adp_c + 4.1182 akg_c + 3.7478 coa_c + 59.81 h_c + 3.547 nadh_c + 13.0279 nadp_c + 59.81 pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.33%</td>\n",
" <td>-0.2235</td>\n",
" <td>[-1.937; -0.2212]</td>\n",
" <td>GLNS</td>\n",
" <td>atp_c + glu__L_c + nh4_c --&gt; adp_c + gln__L_c + h_c + pi_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10.94%</td>\n",
" <td>-7.477</td>\n",
" <td>[-9.259; -6.17]</td>\n",
" <td>PFK</td>\n",
" <td>atp_c + f6p_c --&gt; adp_c + fdp_c + h_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.00%</td>\n",
" <td>0</td>\n",
" <td>[-1.716; 0]</td>\n",
" <td>PPCK</td>\n",
" <td>atp_c + oaa_c --&gt; adp_c + co2_c + pep_c</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.00%</td>\n",
" <td>0</td>\n",
" <td>[-1.716; 0]</td>\n",
" <td>PPS</td>\n",
" <td>atp_c + h2o_c + pyr_c --&gt; amp_c + 2.0 h_c + pep_c + pi_c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<cobra.summary.metabolite_summary.MetaboliteSummary at 0x7fd4728996d8>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"atp_summary_fva"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Summary"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.model_summary:Generating new parsimonious flux distribution.\n"
]
}
],
"source": [
"m_summary = model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Objective\n",
"=========\n",
"1.0 Biomass Objective Function with GAM = 0.8739215069684304\n",
"\n",
"Uptake\n",
"------\n",
"Metabolite Reaction Flux N-Number N-Flux\n",
" D-Glucose EX_glc__D_e 10 0 0.00%\n",
" Ammonium EX_nh4_e 4.765 1 100.00%\n",
" O2 EX_o2_e 21.8 0 0.00%\n",
" Phosphate EX_pi_e 3.215 0 0.00%\n",
"\n",
"Secretion\n",
"---------\n",
"Metabolite Reaction Flux N-Number N-Flux\n",
" CO2 EX_co2_e -22.81 0 0.00%\n",
" H2O EX_h2o_e -29.18 0 0.00%\n",
" H+ EX_h_e -17.53 0 0.00%\n",
"\n"
]
}
],
"source": [
"print(m_summary.to_string(names=True, element=\"N\"))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display, HTML"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>Objective</h3><p>1.0 Biomass_Ecoli_core = 0.8739215069684304</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Metabolite</th>\n",
" <th>Reaction</th>\n",
" <th>Flux</th>\n",
" <th>H-Number</th>\n",
" <th>H-Flux</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>glc__D_e</td>\n",
" <td>EX_glc__D_e</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>84.34%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>nh4_e</td>\n",
" <td>EX_nh4_e</td>\n",
" <td>4.765</td>\n",
" <td>4</td>\n",
" <td>13.40%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>o2_e</td>\n",
" <td>EX_o2_e</td>\n",
" <td>21.8</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>pi_e</td>\n",
" <td>EX_pi_e</td>\n",
" <td>3.215</td>\n",
" <td>1</td>\n",
" <td>2.26%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Metabolite</th>\n",
" <th>Reaction</th>\n",
" <th>Flux</th>\n",
" <th>H-Number</th>\n",
" <th>H-Flux</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>co2_e</td>\n",
" <td>EX_co2_e</td>\n",
" <td>-22.81</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>h2o_e</td>\n",
" <td>EX_h2o_e</td>\n",
" <td>-29.18</td>\n",
" <td>2</td>\n",
" <td>76.90%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>h_e</td>\n",
" <td>EX_h_e</td>\n",
" <td>-17.53</td>\n",
" <td>1</td>\n",
" <td>23.10%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(m_summary.to_html(element=\"H\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Summary FVA"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.model_summary:Generating new parsimonious flux distribution.\n",
"INFO:cobra.summary.model_summary:Performing flux variability analysis.\n"
]
}
],
"source": [
"m_summary_fva = model.summary(fva=0.95)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Objective\n",
"=========\n",
"1.0 Biomass_Ecoli_core = 0.8739215069684304\n",
"\n",
"Uptake\n",
"------\n",
"Metabolite Reaction Flux Range C-Number C-Flux\n",
" glc__D_e EX_glc__D_e 10 [9.523; 10] 6 100.00%\n",
" nh4_e EX_nh4_e 4.765 [4.527; 5.163] 0 0.00%\n",
" o2_e EX_o2_e 21.8 [19.9; 23.71] 0 0.00%\n",
" pi_e EX_pi_e 3.215 [3.054; 3.215] 0 0.00%\n",
"\n",
"Secretion\n",
"---------\n",
"Metabolite Reaction Flux Range C-Number C-Flux\n",
" ac_e EX_ac_e 0 [-1.907; 0] 2 0.00%\n",
" acald_e EX_acald_e 0 [-1.271; 0] 2 0.00%\n",
" akg_e EX_akg_e 0 [-0.715; 0] 5 0.00%\n",
" co2_e EX_co2_e -22.81 [-24.67; -18.95] 1 100.00%\n",
" etoh_e EX_etoh_e 0 [-1.107; 0] 2 0.00%\n",
" for_e EX_for_e 0 [-5.72; 0] 1 0.00%\n",
" glu__L_e EX_glu__L_e 0 [-0.6356; 0] 5 0.00%\n",
" h2o_e EX_h2o_e -29.18 [-30.72; -25] 0 0.00%\n",
" h_e EX_h_e -17.53 [-22.37; -16.65] 0 0.00%\n",
" lac__D_e EX_lac__D_e 0 [-1.073; 0] 3 0.00%\n",
" pyr_e EX_pyr_e 0 [-1.271; 0] 3 0.00%\n",
" succ_e EX_succ_e 0 [-0.8371; 0] 4 0.00%\n",
"\n"
]
}
],
"source": [
"print(m_summary_fva)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>Objective</h3><p>1.0 Biomass_Ecoli_core = 0.8739215069684304</p><h4>Uptake</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Metabolite</th>\n",
" <th>Reaction</th>\n",
" <th>Flux</th>\n",
" <th>Range</th>\n",
" <th>C-Number</th>\n",
" <th>C-Flux</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>glc__D_e</td>\n",
" <td>EX_glc__D_e</td>\n",
" <td>10</td>\n",
" <td>[9.523; 10]</td>\n",
" <td>6</td>\n",
" <td>100.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>nh4_e</td>\n",
" <td>EX_nh4_e</td>\n",
" <td>4.765</td>\n",
" <td>[4.527; 5.163]</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>o2_e</td>\n",
" <td>EX_o2_e</td>\n",
" <td>21.8</td>\n",
" <td>[19.9; 23.71]</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>pi_e</td>\n",
" <td>EX_pi_e</td>\n",
" <td>3.215</td>\n",
" <td>[3.054; 3.215]</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><h4>Secretion</h4><table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Metabolite</th>\n",
" <th>Reaction</th>\n",
" <th>Flux</th>\n",
" <th>Range</th>\n",
" <th>C-Number</th>\n",
" <th>C-Flux</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>ac_e</td>\n",
" <td>EX_ac_e</td>\n",
" <td>0</td>\n",
" <td>[-1.907; 0]</td>\n",
" <td>2</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>acald_e</td>\n",
" <td>EX_acald_e</td>\n",
" <td>0</td>\n",
" <td>[-1.271; 0]</td>\n",
" <td>2</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>akg_e</td>\n",
" <td>EX_akg_e</td>\n",
" <td>0</td>\n",
" <td>[-0.715; 0]</td>\n",
" <td>5</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>co2_e</td>\n",
" <td>EX_co2_e</td>\n",
" <td>-22.81</td>\n",
" <td>[-24.67; -18.95]</td>\n",
" <td>1</td>\n",
" <td>100.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>etoh_e</td>\n",
" <td>EX_etoh_e</td>\n",
" <td>0</td>\n",
" <td>[-1.107; 0]</td>\n",
" <td>2</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>for_e</td>\n",
" <td>EX_for_e</td>\n",
" <td>0</td>\n",
" <td>[-5.72; 0]</td>\n",
" <td>1</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>glu__L_e</td>\n",
" <td>EX_glu__L_e</td>\n",
" <td>0</td>\n",
" <td>[-0.6356; 0]</td>\n",
" <td>5</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>h2o_e</td>\n",
" <td>EX_h2o_e</td>\n",
" <td>-29.18</td>\n",
" <td>[-30.72; -25]</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>h_e</td>\n",
" <td>EX_h_e</td>\n",
" <td>-17.53</td>\n",
" <td>[-22.37; -16.65]</td>\n",
" <td>0</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>lac__D_e</td>\n",
" <td>EX_lac__D_e</td>\n",
" <td>0</td>\n",
" <td>[-1.073; 0]</td>\n",
" <td>3</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>pyr_e</td>\n",
" <td>EX_pyr_e</td>\n",
" <td>0</td>\n",
" <td>[-1.271; 0]</td>\n",
" <td>3</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <td>succ_e</td>\n",
" <td>EX_succ_e</td>\n",
" <td>0</td>\n",
" <td>[-0.8371; 0]</td>\n",
" <td>4</td>\n",
" <td>0.00%</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<cobra.summary.model_summary.ModelSummary at 0x7fd472764978>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m_summary_fva"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reaction Summary"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.reaction_summary:Generating new parsimonious flux distribution.\n"
]
}
],
"source": [
"fum_summary = model.reactions.FUM.summary()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"fumarase\n",
"========\n",
"Fumarate + H2O <=> L-Malate\n",
"Bounds: -1000.0, 1000.0\n",
"Flux: 5.064\n",
"\n"
]
}
],
"source": [
"print(fum_summary.to_string(names=True))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>FUM</h3><p>fum_c + h2o_c <=> mal__L_c</p><p>Bounds: -1000.0, 1000.0</p><p>Flux: 5.064</p>"
],
"text/plain": [
"<cobra.summary.reaction_summary.ReactionSummary at 0x7fd4727194a8>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fum_summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reaction Summary FVA"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:cobra.summary.reaction_summary:Generating new parsimonious flux distribution.\n",
"INFO:cobra.summary.reaction_summary:Performing flux variability analysis.\n"
]
}
],
"source": [
"fum_summary_fva = model.reactions.FUM.summary(fva=0.95)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"fumarase\n",
"========\n",
"Fumarate + H2O <=> L-Malate\n",
"Bounds: -1000.0, 1000.0\n",
"Flux: 5.064 [0.7921; 7.382]\n",
"\n"
]
}
],
"source": [
"print(fum_summary_fva.to_string(names=True))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>FUM</h3><p>fum_c + h2o_c <=> mal__L_c</p><p>Bounds: -1000.0, 1000.0</p><p>Flux: 5.064 [0.7921; 7.382]</p>"
],
"text/plain": [
"<cobra.summary.reaction_summary.ReactionSummary at 0x7fd472719d68>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fum_summary_fva"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment