1. BOFdat step 1

1.1. Generate stoichiometric coefficients for the major macromolecules of the cell and calculate maintenance cost

from BOFdat import step1
from BOFdat.util import update
import pandas as pd
import cobra

CPLEX is available but could not load with error:
  Traceback (most recent call last):
    File "/home/jean-christophe/.local/lib/python3.5/site-packages/optlang/__init__.py", line 43, in <module>
      from optlang import cplex_interface
    File "/home/jean-christophe/.local/lib/python3.5/site-packages/optlang/cplex_interface.py", line 33, in <module>
      from cplex.exceptions import CplexSolverError
  ImportError: No module named 'cplex.exceptions'

1.2. Example using the E.coli genome-scale model iML1515

The weight percentage and abundance of each molecule in the cell may vary from an organism to another and vary between growth conditions for a given organism [1,2]. BOFdat allows to incorporate macromolecular cell composition obtained from literature or new experiments to generate new stoichiometric coefficients for your model’s biomass objective function (BOF). Once weight percentages are obtained, OMIC data can be incorporated to buff the coefficients and fit to experimental reality.

1.2.1. Steps

The following example will lead you through all the necessary steps for the generation of the BOF stoichiometric coefficients (BOFsc) for E.coli K12 MG1655 GEM iML1515 [3].

1. Obtain the macromolecular composition of the organism
2. Obtain OMICs experimental data
3. Generate BOFsc
4. Generate NGAM and GAM
5. Update BOF (BOFdat!)

1.5.1. Sources

[1] Dennis P. Patrick and Bremmer Hans. (1974) Macromolecular composition during steady-state growth of Escherichia coli B/r. Journal of bacteriology

[2] Benjamin Volkmer and Matthias Heinemann. (2011) Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling. PLoS One

[3] Jonathan M Monk, Colton J Lloyd, Elizabeth Brunk, Nathan Mih, Anand Sastry, Zachary King, Rikiya Takeuchi, Wataru Nomura, Zhen Zhang, Hirotada Mori, Adam M Feist and Bernhard O Palsson. (2017) iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotech.

1.3. Obtain the macromolecular compositon of the organism

E.coli has been characterized thoroughly in literature. The BOFsc used in iAF1260 [4] are the same in iML1515 [3] and were obtained from Neidhart et. al [5].

Note: The package also provides the option to include the percentage of each type of RNA molecule in the cell (ribosomal, transfer and messenger). The default values are rRNA: 0.9, tRNA 0.05 and mRNA: 0.05.

1.5.1. Sources

[4] Adam M Feist, Christopher S Henry, Jennifer L Reed, Markus Krummenacker, Andrew R Joyce, Peter D Karp, Linda J Broadbelt, Vassily Hatzimanikatis and Bernhard Ø Palsson. (2007) A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Bio.

[5] Neidhardt FC, Ingraham JL, Schaechter M (1990) Physiology of the Bacterial Cell: a Molecular Approach. Sinauer Associates: Sunderland, Mass

#Set parameters based on dry weight composition
dna_weight_fraction = 0.031
rna_weight_fraction = 0.205
protein_weight_fraction = 0.55
lipid_weight_fraction = 0.1
metabolite_weight_fraction = 0.1

1.4. Inputs

1.4.1. DNA

- Genome sequence (FASTA DNA)
- DNA macromolecular weight fraction
- Model (JSON or SBML)

1.4.2. RNA

- Genome annotation (GenBank)
- Transcriptomic data (2 column CSV)
- RNA macromolecular weight fraction
- Model (JSON or SBML)

1.4.3. Proteins

- Genome annotation (GenBank)
- Proteomic data (2 column CSV)
- Protein macromolecular weight fraction
- Model (JSON or SBML)

1.4.4. Lipids

- Lipidomic abundances (2 column CSV)
- Lipidomic conversion to model identifiers (2 column CSV)
- Lipid macromolecular weight fraction
- Model (JSON or SBML)

1.4.5. Maintenance costs

- Growth and uptake rates on different conditions (CSV)

#Give the path to each file as function parameters
#Genome file in BioPython supported format (.faa, .fna) and GenBank file
#also in BioPython supported format (.gb, .gbff)
genome = 'data/Ecoli_DNA.fna'
genbank = 'data/Ecoli_K12_MG1655.gbff'

#Omic datasets as a 2 column csv file, gene and abundance
transcriptomic = 'data/transcriptomic.csv'
proteomic = 'data/proteomic.csv'

#Lipidomic abundances and conversion to model identifier
lipidomic_abundances = 'data/lipidomic_abundances.csv'
lipidomic_conversion = 'data/lipidomic_conversion.csv'


#Growth data on different carbon sources, uptake and secretion rates
maintenance = 'data/maintenance.csv'

#The model for which the coefficients are generated
model = 'data/iML1515.json'

1.5. Obtain OMICs experimental data

Your genome should have a GenBank annotated file. This file should be provided in a BioPython supported format (.gb, .gbff).

Search in literature allowed to find multiple OMICs dataset for different macromolecules that can be used to generate stoichiometric coefficients [6,7,8]. The data should be converted into a 2 column csv file. The genome file should be provided in a standard BioPython supported format (.faa or .fna) and is used to calculate the abundance of each base in the genome.

Transcriptomic and proteomic files are 2 column csv files where the first column is the **gene identifier ** and the second column is the relative abundance of each of these genes in the cell.

Unlike DNA, RNA and proteins that are standard amongst every known life form, the lipid and metabolites in different organisms may vary. Hence a conversion file is required. This first column of this file is the original name of the compound and the second is the target identifier that this compound should have in your model. The first column of the abundance file gives the compound identifier in the model and the second column gives the abundance of that compound in the OMIC dataset.

1.5.1. Sources

[6] Sang Woo Seo, Donghyuk Kim, Haythem Latif, Edward J. O’Brien, Richard Szubin & Bernhard O. Palsson. (2014) Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in Escherichia coli. Nat. Comm.

[7] Alexander Schmidt, Karl Kochanowski, Silke Vedelaar, Erik Ahrné, Benjamin Volkmer, Luciano Callipo, Kèvin Knoops, Manuel Bauer, Ruedi Aebersold and Matthias Heinemann. (2016) The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotech.

[8] Kian-Kai Cheng, Baek-Seok Lee, Takeshi Masuda, Takuro Ito, Kazutaka Ikeda, Akiyoshi Hirayama, Lingli Deng, Jiyang Dong, Kazuyuki Shimizu, Tomoyoshi Soga, Masaru Tomita, Bernhard O. Palsson and Martin Robert. (2014) Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol. Nat Comm.

1.6. Generate BOFsc for macromolecules and generate maintenance costs

BOFdat operates with a single get_coefficient function for each macromolecule used. Input the parameters determined above as function parameters. Each function outputs a dictionary of metabolite and stoichiometric coefficients. The dictionary can be used to update the BOF (Step 5).

dna_coefficients = step1.generate_dna_coefficients(genome,model,DNA_WEIGHT_FRACTION=dna_weight_fraction)
dna_coefficients

{<Metabolite datp_c at 0x7f07c27c06d8>: -0.012215685700339098,
 <Metabolite dctp_c at 0x7f07c27b8c18>: -0.013659391084477245,
 <Metabolite dgtp_c at 0x7f07c272e2e8>: -0.011993613617708867,
 <Metabolite dttp_c at 0x7f07c27af710>: -0.012578844449428764,
 <Metabolite ppi_c at 0x7f07c27d1160>: 0.050447534851953975}

rna_coefficients = step1.generate_rna_coefficients(genbank,model,transcriptomic,RNA_WEIGHT_FRACTION=rna_weight_fraction)
rna_coefficients

/home/jean-christophe/.local/lib/python3.5/site-packages/BOFdat-0.1.7-py3.5.egg/BOFdat/core/rna.py:60 UserWarning: Some identifiers not found in provided annotation

{<Metabolite atp_c at 0x7f07c2502ba8>: -0.1556843351415502,
 <Metabolite ctp_c at 0x7f07c24af390>: -0.1671601704512458,
 <Metabolite gtp_c at 0x7f07c25957f0>: -0.16306640467110822,
 <Metabolite ppi_c at 0x7f07c2559b70>: 0.6397066800549615,
 <Metabolite utp_c at 0x7f07c24f2048>: -0.15379576979105725}

protein_coefficients = step1.generate_protein_coefficients(genbank,model,proteomic,PROTEIN_WEIGHT_FRACTION=protein_weight_fraction)
protein_coefficients

/home/jean-christophe/.local/lib/python3.5/site-packages/BOFdat-0.1.7-py3.5.egg/BOFdat/core/protein.py:64 UserWarning: Redundancy in dataset identifiers

{<Metabolite ala__L_c at 0x7f07c1ac6a20>: -0.7866865810122878,
 <Metabolite arg__L_c at 0x7f07c178b2e8>: -0.18048710436993537,
 <Metabolite asn__L_c at 0x7f07c1abf240>: -0.18757538475910354,
 <Metabolite asp__L_c at 0x7f07c1793208>: -0.286019444825156,
 <Metabolite cys__L_c at 0x7f07c172da90>: -0.03986681905592248,
 <Metabolite gln__L_c at 0x7f07c1ac9358>: -0.16176662571366865,
 <Metabolite glu__L_c at 0x7f07c1aca470>: -0.3088742159475492,
 <Metabolite gly_c at 0x7f07c1ac6160>: -0.8068327457540323,
 <Metabolite h2o_c at 0x7f07c1ac66d8>: 5.444549746608695,
 <Metabolite his__L_c at 0x7f07c1ac17f0>: -0.07909538580101926,
 <Metabolite ile__L_c at 0x7f07c1ac9ef0>: -0.2907964681752002,
 <Metabolite leu__L_c at 0x7f07c1abeeb8>: -0.41251515857770893,
 <Metabolite lys__L_c at 0x7f07c21fd2b0>: -0.2743566703314617,
 <Metabolite met__L_c at 0x7f07c1793a90>: -0.10723969052764211,
 <Metabolite phe__L_c at 0x7f07c1ac6ba8>: -0.1259235649707084,
 <Metabolite pro__L_c at 0x7f07c1abfb38>: -0.22635269540826172,
 <Metabolite ser__L_c at 0x7f07c17739e8>: -0.30382792752346605,
 <Metabolite thr__L_c at 0x7f07c1773908>: -0.30589419712897964,
 <Metabolite trp__L_c at 0x7f07c21fda20>: -0.024965954237879378,
 <Metabolite tyr__L_c at 0x7f07c172db38>: -0.08853072858049255,
 <Metabolite val__L_c at 0x7f07c1ac0390>: -0.44694238390821955}

lipid_coefficients = step1.generate_lipid_coefficients(lipidomic_abundances,lipidomic_conversion,model,LIPID_WEIGHT_FRACTION=lipid_weight_fraction)
lipid_coefficients

/home/jean-christophe/.local/lib/python3.5/site-packages/BOFdat-0.1.7-py3.5.egg/BOFdat/core/lipid.py:76 UserWarning: Redundancy in dataset identifiers

{<Metabolite pe160_p at 0x7f07bffc69b0>: -0.05984233670391253,
 <Metabolite pe161_p at 0x7f07c0010978>: -0.030710743257354293,
 <Metabolite pe181_p at 0x7f07bffbf128>: -0.013250952451946144,
 <Metabolite pg160_p at 0x7f07bffbc940>: -0.014826578855136979,
 <Metabolite pg161_p at 0x7f07c0019eb8>: -0.013732749319021109,
 <Metabolite pg181_p at 0x7f07bffb1198>: -0.009098089940190764}

1.7. Generate GAM and NGAM

Growth-associated maintenance (GAM) is the ATP cost related to growth. This includes the polymerization cost of each macromolecule. This cost is unaccounted for in the BOF because the model synthesizes the building blocks of each macromolecule in sufficient quantity to reflect the cell composition but not the cost of assembling those building blocks together. The GAM can be calculated experimentally by growing the bacteria on different sources of carbon at different starting concentrations. The carbon source should be the sole source of carbon in the media and its concentration should be measured after a given time. These remaining concentrations along with the excretion products are used by the package to constrain the model and calculate the ATP cost of growth.

maintenance_cost = step1.generate_maintenance_costs(maintenance,model)

cobra/util/solver.py:408 UserWarning: solver status is 'infeasible'

m 89.08201514974196 b 12.643976801582804
R2= 0.6714793285124082

1.8. Update BOF (BOFdat!)

All the dictionaries have been generated. Now it would be fun to start playing with the model. Actually BOFdat allows you to use the generated dictionaries to update and buff your BOF experimental data. Just buff that!

json_model = cobra.io.load_json_model(model)
bofdat_step1 = update.make_new_BOF(json_model,False,True,dna_coefficients,rna_coefficients,protein_coefficients,
                    lipid_coefficients,maintenance=maintenance_cost)

#Save the step1 objective function for use in step2
bofdat_step1.to_csv('data/bofdat_step1.csv')

That’s it!

The BOFsc for the major macromolecules and the maintenance cost of the cell have been updated in your model using BOFdat.