8. Inspecting predicted fluxes

In this tutorial we will load and inspect the fluxes predicted by the Bacillus subtilis ME-model.

8.1. Import libraries

[1]:

from coralme.builder.main import MEBuilder
from coralme.util.flux_analysis import flux_based_reactions
import coralme
import pandas
import tqdm

8.2. Load

Load the ME-model coming out of the Troubleshooter as a JSON

[2]:

me = coralme.io.json.load_json_me_model("./bsubtilis/MEModel-step3-bsubtilis-TS.json")

Adding Metabolites into the ME-model...                                    : 100.0%|██████████|  4630/ 4630 [00:00<00:00]
Adding ProcessData into the ME-model...                                    : 100.0%|██████████|  4752/ 4752 [00:00<00:00]
Adding Reactions into the ME-model...                                      : 100.0%|██████████|  7758/ 7758 [00:18<00:00]
Updating ME-model Reactions...                                             : 100.0%|██████████|  6369/ 6369 [00:25<00:00]

8.3. Solve

[3]:

me.optimize()

The MINOS and quad MINOS solvers are a courtesy of Prof Michael A. Saunders. Please cite Ma, D., Yang, L., Fleming, R. et al. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. Sci Rep 7, 40863 (2017). https://doi.org/10.1038/srep40863

Iteration        Solution to check      Solver Status
---------       ------------------      -------------
        1       1.4050280687025918      Not feasible
        2       0.7025140343512959      Not feasible
        3       0.3512570171756479      Not feasible
        4       0.1756285085878240      Not feasible
        5       0.0878142542939120      Optimal
        6       0.1317213814408680      Not feasible
        7       0.1097678178673900      Not feasible
        8       0.0987910360806510      Optimal
        9       0.1042794269740205      Not feasible
       10       0.1015352315273357      Not feasible
       11       0.1001631338039934      Optimal
       12       0.1008491826656645      Optimal
       13       0.1011922070965001      Optimal
       14       0.1013637193119179      Not feasible
       15       0.1012779632042090      Optimal
       16       0.1013208412580635      Not feasible
       17       0.1012994022311363      Not feasible
       18       0.1012886827176726      Not feasible
       19       0.1012833229609408      Not feasible
       20       0.1012806430825749      Not feasible
       21       0.1012793031433920      Optimal
       22       0.1012799731129835      Not feasible

[3]:

True

8.4. Inspecting fluxes

8.4.1. Predicted fluxes

[4]:

fluxes = me.solution.to_frame()
fluxes

[4]:

	fluxes	reduced_costs
biomass_dilution	1.012793e-01	-5.648980e-01
BSU15140-MONOMER_to_generic_16Sm4Cm1402	1.506864e-12	-1.792344e-33
RNA_BSU_rRNA_1_to_generic_16s_rRNAs	1.465206e-06	-1.448997e-32
RNA_BSU_rRNA_16_to_generic_16s_rRNAs	0.000000e+00	-6.326530e-02
RNA_BSU_rRNA_19_to_generic_16s_rRNAs	5.674343e-07	-1.849227e-32
...	...	...
translocation_BSU25290_Plasma_Membrane	3.013728e-12	-4.779545e-34
translocation_BSU27650_Plasma_Membrane	1.354157e-06	0.000000e+00
translocation_BSU33630_Plasma_Membrane	1.370728e-06	0.000000e+00
translocation_BSU35300_Plasma_Membrane	1.354988e-06	3.520845e-34
translocation_BSU41040_Plasma_Membrane	3.277003e-08	9.992851e-34

7758 rows × 2 columns

8.4.2. Biomass production

[5]:

fluxes[fluxes.index.str.contains("biomass")]

[5]:

	fluxes	reduced_costs
biomass_dilution	0.101279	-5.648980e-01
protein_biomass_to_biomass	0.034953	-2.375644e-34
mRNA_biomass_to_biomass	0.000116	-1.199677e-34
tRNA_biomass_to_biomass	0.000700	0.000000e+00
rRNA_biomass_to_biomass	0.005100	-7.874817e-35
ncRNA_biomass_to_biomass	0.000000	7.391429e-35
tmRNA_biomass_to_biomass	0.000000	7.391429e-35
DNA_biomass_to_biomass	0.006003	7.391429e-35
lipid_biomass_to_biomass	0.023282	7.391429e-35
constituent_biomass_to_biomass	0.011441	7.391429e-35
prosthetic_group_biomass_to_biomass	0.000023	-5.556140e-35
peptidoglycan_biomass_to_biomass	0.000000	7.391429e-35
biomass_constituent_demand	0.101279	1.554562e-02

8.4.3. Transcription rates

[6]:

fluxes[fluxes.index.str.contains("^transcription_")].head()

[6]:

	fluxes	reduced_costs
transcription_TU8J2-1243_from_BSU25200-MONOMER	0.000000e+00	-6.246011e-33
transcription_TU8J2-569_from_BSU25200-MONOMER	2.160400e-08	7.605084e-32
transcription_TU8J2-1577_from_BSU25200-MONOMER	0.000000e+00	-6.986124e-32
transcription_TU8J2-257_from_BSU25200-MONOMER	0.000000e+00	-1.127560e+03
transcription_TU8J2-677_from_BSU25200-MONOMER	1.736594e-08	-1.447121e-32

8.4.4. Translation rates

[7]:

fluxes[fluxes.index.str.contains("^translation_")].head()

[7]:

	fluxes	reduced_costs
translation_BSU00090	1.419685e-08	-1.712708e-32
translation_BSU00110	6.498762e-13	-6.375952e-33
translation_BSU00120	6.498762e-13	-6.911721e-33
translation_BSU00130	2.293786e-08	2.102613e-32
translation_BSU00140	5.327400e-11	9.486318e-33

8.5. Biomass profile

We can calculate the predicted biomass composition in this condition by using flux_based_reactions which outputs the mass balance of a metabolite

[8]:

flux_based_reactions(me,"biomass")

[8]:

	lb	ub	rxn_flux	met_flux	reaction
biomass_dilution	mu	mu	0.101279	-0.101279	1.0 biomass -->
protein_biomass_to_biomass	0.0	1000.0	0.034953	0.054615	1.0 protein_biomass + 0.5625 unmodeled_protein...
lipid_biomass_to_biomass	0.0	1000.0	0.023282	0.023282	1.0 lipid_biomass --> 1.0 biomass
constituent_biomass_to_biomass	0.0	1000.0	0.011441	0.011441	1.0 constituent_biomass --> 1.0 biomass
DNA_biomass_to_biomass	0.0	1000.0	0.006003	0.006003	1.0 DNA_biomass --> 1.0 biomass
rRNA_biomass_to_biomass	0.0	1000.0	0.005100	0.005100	1.0 rRNA_biomass --> 1.0 biomass
tRNA_biomass_to_biomass	0.0	1000.0	0.000700	0.000700	1.0 tRNA_biomass --> 1.0 biomass
mRNA_biomass_to_biomass	0.0	1000.0	0.000116	0.000116	1.0 mRNA_biomass --> 1.0 biomass
prosthetic_group_biomass_to_biomass	0.0	1000.0	0.000023	0.000023	1.0 prosthetic_group_biomass --> 1.0 biomass
tmRNA_biomass_to_biomass	0.0	1000.0	0.000000	0.000000	1.0 tmRNA_biomass --> 1.0 biomass
ncRNA_biomass_to_biomass	0.0	1000.0	0.000000	0.000000	1.0 ncRNA_biomass --> 1.0 biomass
peptidoglycan_biomass_to_biomass	0.0	1000.0	0.000000	0.000000	1.0 peptidoglycan_biomass --> 1.0 biomass

[9]:

tmp = flux_based_reactions(me,"biomass")["met_flux"]
BiomassComponents = tmp[tmp>0]
BiomassComponents.plot.pie()

[9]:

<Axes: ylabel='met_flux'>