from IPython.display import display
import re
Predicting heterologous pathways is an important strategy to generate new viable strains. Because portfolio of available reactions is very large, computer assisted pathway design becomes essential. Cameo implements a pathway search algorithm using an universal biochemical reaction database that enumerates the shortest pathways.
## Making sure you have cplex installed
import cplex
import re
from cobra.io import read_sbml_model
from cameo.strain_design import pathway_prediction
model = read_sbml_model('data/iMM904.xml.gz')
predictor = pathway_prediction.PathwayPredictor(model)
pathways = predictor.run(product="vanillin", max_predictions=4)
equation | lower_bound | upper_bound | |
---|---|---|---|
MNXR5340 | H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih... | -1000 | 1000 |
MNXR5336 | 2.0 H(+) + NADH + vanillate <=> H2O + vanillin... | -1000 | 1000 |
MNXR230 | H(+) + 4-hydroxybenzoate + O2 + NADPH <=> H2O ... | -1000 | 1000 |
Max flux: 1.90533
equation | lower_bound | upper_bound | |
---|---|---|---|
MNXR5340 | H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih... | -1000 | 1000 |
MNXR5336 | 2.0 H(+) + NADH + vanillate <=> H2O + vanillin... | -1000 | 1000 |
MNXR68718 | H2O + 3,4-dihydroxybenzoate <=> 3-dehydroshiki... | -1000 | 1000 |
Max flux: 3.36842
equation | lower_bound | upper_bound | |
---|---|---|---|
MNXR4008 | H(+) + 3-oxoadipate <=> H2O + 5-oxo-4,5-dihydr... | -1000 | 1000 |
MNXR184 | 3-oxoadipyl-CoA + succinate <=> 3-oxoadipate +... | -1000 | 1000 |
MNXR5340 | H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih... | -1000 | 1000 |
MNXR5336 | 2.0 H(+) + NADH + vanillate <=> H2O + vanillin... | -1000 | 1000 |
MNXR228 | CO2 + 5-oxo-4,5-dihydro-2-furylacetate <=> H(+... | -1000 | 1000 |
MNXR4119 | 2.0 H(+) + 3-carboxy-cis,cis-muconate <=> 3,4-... | -1000 | 1000 |
MNXR209 | CoA + 3-oxoadipyl-CoA <=> acetyl-CoA + succiny... | -1000 | 1000 |
MNXR3655 | 2-(carboxymethyl)-5-oxo-2,5-dihydro-2-furoate ... | -1000 | 1000 |
Max flux: 5.59223
equation | lower_bound | upper_bound | |
---|---|---|---|
MNXR5338 | 2.0 H(+) + NADH + 3,4-dihydroxybenzoate <=> H2... | -1000 | 1000 |
MNXR1041 | diphosphate + AMP + caffeoyl-CoA <=> CoA + ATP... | -1000 | 1000 |
MNXR4974 | O2 + 2.0 trans-4-coumarate <=> 2.0 trans-caffeate | -1000 | 1000 |
MNXR227 | diphosphate + AMP + 4-coumaroyl-CoA <=> CoA + ... | -1000 | 1000 |
MNXR5340 | H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih... | -1000 | 1000 |
MNXR5336 | 2.0 H(+) + NADH + vanillate <=> H2O + vanillin... | -1000 | 1000 |
MNXR18369 | CoA + H2O + 4-coumaroyl-CoA + NAD(+) <=> H(+) ... | -1000 | 1000 |
MNXR232 | H(+) + CoA + 4-hydroxybenzoate <=> H2O + 4-hyd... | -1000 | 1000 |
MNXR1039 | acetyl-CoA + 3,4-dihydroxybenzaldehyde <=> H2O... | -1000 | 1000 |
Max flux: 2.24390
pathways.plot_production_envelopes(model, objective=model.reactions.BIOMASS_SC5_notrace)
This is the format of your plot grid: [ (1,1) x1,y1 ] [ (1,2) x2,y2 ] [ (2,1) x3,y3 ] [ (2,2) x4,y4 ]
cameo.models.universal
)?from cameo.models import universal
universal.metanetx_universal_model_bigg_rhea_kegg
Name | metanetx_universal_model_bigg_rhea_kegg |
Memory address | 0x0125436400 |
Number of metabolites | 9478 |
Number of reactions | 21270 |
Objective expression | 0 |
Compartments |
predictor2 = pathway_prediction.PathwayPredictor(model=model,
universal_model=universal.metanetx_universal_model_bigg_rhea_kegg)