3.4 Define the biomass composition

Metabolism must be able to synthesise the macromolecules that make up biomass. GEMs encode this in biomass pseudoreactions that specify which macromolecules (DNA, RNA, protein, lipid, carbohydrate) are required and in what amounts. Defining the biomass composition early ensures the model can biosynthesise everything it needs.

In the template models the biomass is split over several pseudoreactions, e.g. the DNA pseudoreaction:

\[ x\;\text{dAMP} + y\;\text{dCMP} + y\;\text{dGMP} + x\;\text{dTMP} \rightarrow \text{DNA} \]

which then combine into biomass:

\[ \text{DNA} + \text{RNA} + \text{protein} + \text{lipids} + \text{carbohydrate} \rightarrow \text{biomass} \]

Take the pseudoreactions from a template

H. polymorpha has poly-unsaturated fatty acids, similar to R. toruloides, so the rhto-GEM biomass pseudoreactions are used as the starting point. They are recognised by reaction names ending in pseudoreaction:

biomassRxns = modelRhto.rxns(endsWith(modelRhto.rxnNames, 'pseudoreaction'));
model = addRxnsGenesMets(model, modelRhto, biomassRxns);

Update the stoichiometric coefficients

A spreadsheet in the repository's data/biomass/ folder details the calculations behind the coefficients; the script loads them and applies each group with changeRxns. The DNA pseudoreaction (r_4050), for example:

DNA.mets        = {'s_0584', 's_0589', 's_0615', 's_0649', 's_3720'};
DNA.stoichCoeffs = [-0.00189, -0.00174, -0.00174, -0.00189, 1];
model = changeRxns(model, 'r_4050', DNA, 1);

The same pattern updates RNA (r_4049), protein (r_4047), carbohydrate (r_4048) and the lipid backbone/chain pseudoreactions (r_4063, r_4065).

Where the coefficients come from

Determining a coefficient combines a measured total fraction of a macromolecule with the ratio of its constituents:

Component	Total fraction from	Constituent ratio from
DNA	scaled from yeast-GEM (0.24% for the smaller genome)	DNA FASTA nucleotide frequencies
RNA	6% of biomass (as in yeast-GEM)	coding-sequence nucleotide frequencies (from the GenBank file)
Protein	measured total protein content	amino-acid frequencies in the protein FASTA
Carbohydrate	measured glucan / mannan / trehalose / glycogen	reported levels for H. polymorpha
Lipids	measured lipid classes and acyl chains	the SLIME approach (next section)

For nucleotides, the DNA FASTA gives the frequency of each base — readily counted with sed/grep:

sed 's/>.*$//' hpo.fna > hanpo_modified.fa
for i in {A,T,G,C}; do
    echo -n "$i "; grep -oi $i hanpo_modified.fa | wc -l;
done

Together with the paired nature of A·T and C·G and the nucleotide molecular weights, this gives the amount of each deoxyribonucleotide needed per gram of biomass. Ribonucleotide and amino-acid ratios are derived analogously from the coding sequences and the protein FASTA.

Lipids use SLIME

Lipid representation depends on two measurement types — lipid classes (e.g. triacylglycerol, phosphatidylinositol) and acyl chains (e.g. 16:0, 18:1). This model uses the SLIME formalism (Split Lipids Into Measurable Entities), set up in the next section.

Next: Curation of lipid reactions.