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edkerk merged 2 commits into from
Oct 17, 2026
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docs(init): pass Human-GEM's spontaneous reactions to prep_init_model #86

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docs(init): pass Human-GEM's spontaneous reactions in the Python exam…
edkerk Sep 25, 2026
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docs(init): describe the current Human-GEM and RAVEN requirements only
edkerk Oct 17, 2026
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27 changes: 22 additions & 5 deletions docs/guide/init.md
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ Every other page in this guide is re-executed on every commit. This one is
not: preparing Human-GEM took **113 minutes** in MATLAB and **126 minutes**
in Python, the MATLAB run producing a **159 MB** artefact, which no
documentation build should attempt. The numbers below come
from one real run (Human-GEM `main`, RAVEN `develop3`, Gurobi 13.0.2) and
from one real run (Human-GEM `main`, RAVEN 3, Gurobi 13.0.2) and
are quoted with their wall-clock so you can plan around them.

For the same MATLAB workflow as the model's own documentation presents it,
Expand Down Expand Up @@ -133,9 +133,7 @@ prepData.mat: 159 MB
```

Human-GEM also has a wrapper, `prepHumanModelForftINIT`, which reads those
two files for you. It does **not** run against RAVEN `develop3`: its
`importTsvFile` returns the `spontaneous` column as text, so the `== 1`
inside it throws. Calling `prepINITModel` directly, as above, sidesteps that.
two files for you; it needs Human-GEM 2.0.1 or later.
:::
:::{tab-item} 🐍 Python
:sync: python
Expand All @@ -144,6 +142,7 @@ inside it throws. Calling `prepINITModel` directly, as above, sidesteps that.

```python
import cobra
import pandas as pd
from cobra.io import read_sbml_model

from raven_toolbox.init import prep_init_model
Expand All @@ -155,7 +154,11 @@ model = read_sbml_model("Human-GEM/model/Human-GEM.xml")
model.solver = "gurobi"
tasks = parse_task_list("Human-GEM/data/metabolicTasks/metabolicTasks_Essential.txt")

prep = prep_init_model(model, tasks, ext_comp="e")
# spontaneous reactions are flagged in the model's own annotation table
tsv = pd.read_csv("Human-GEM/model/reactions.tsv", sep="\t")
spont = tsv.loc[tsv["spontaneous"] == 1, "rxns"].tolist()

prep = prep_init_model(model, tasks, ext_comp="e", spontaneous=spont)
```

```text
Expand All @@ -176,6 +179,20 @@ same machine and solver. Two things affect the run:
:::
::::

:::{note} Pass the spontaneous reactions
ftINIT keeps every reaction passed as
spontaneous in every extracted model, whatever the data say. Other reactions
without a gene rule are kept only where the optimisation needs them, so they can
differ from sample to sample. In Human-GEM, `spontaneous` marks reactions that need
no enzyme. That includes diffusion: transport between cytosol and nucleus through
the nuclear pores, and gases and water (O2, CO2, H2O, NH3, H2O2, NO, H2S) crossing
membranes. Leaving out the list lets ftINIT drop such transports in some
samples. The model then
falls back on other routes. For example, without the dCTP transport into the nucleus,
deoxycytidine kinase (DCK) becomes essential for DNA synthesis, which is an artefact
of the extraction rather than biology.
:::

## 14.2 Bring in the expression data

RAVEN wants one struct: the genes, the sample names, and a genes × samples matrix
Expand Down
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