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Merged
dweindl merged 5 commits into from
Oct 15, 2026
Merged

Add PEtab v2 encoding of Boehm_JProteomeRes2014 #335

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99ad4d0
Add PEtab v2 encoding of Boehm_JProteomeRes2014
dweindl Oct 14, 2026
c516946
Lint v2 encodings in `bmp-petablint`
dweindl Oct 14, 2026
2f7170c
Apply suggestion from @dilpath
dweindl Oct 14, 2026
9538ac9
Update problems/Boehm_JProteomeRes2014/v2/problem.yaml
dweindl Oct 15, 2026
32f79f1
Address review comments
dweindl Oct 15, 2026
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26 changes: 21 additions & 5 deletions CONTRIBUTING.md
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Original file line number Diff line number Diff line change
Expand Up @@ -31,9 +31,25 @@ This is a constraint on the math, not the encoding: `v2/` files may (and often s
structurally different from a mechanical v1-to-v2 port to make idiomatic use of v2 features
(experiments table, v2 prior/distribution syntax, mapping tables, ...) where they fit better.
They just have to evaluate to the same log-likelihood / log-posterior / gradient at nominal
parameters as `v1/`.
parameters as `v1/`, up to the reparametrization described below.

For problems with priors (maximum a posteriori estimation), the policy is not decided yet:
v1 priors on log-scaled parameters cannot, in general, be expressed as v2 priors without
changing either the optimum or the prior distribution. See
[#333](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab/issues/333).
PEtab v2 has no `parameterScale`: priors and the objective are always expressed in terms of the
parameter itself. To preserve the v1 optimization problem (and, for maximum a posteriori problems,
the v1 prior distribution), estimated `log`/`log10`-scaled v1 parameters are reparametrized in
`v2/`: an estimated `log10_<id>` (or `log_<id>`) replaces `<id>`, and `10^log10_<id>`
(or `exp(log_<id>)`) is substituted everywhere `<id>` appeared (model, observable and noise
formulas, condition and measurement table overrides). A v1 `parameterScale*` prior becomes the
corresponding prior on the new parameter; the v1 default prior (uniform on parameter scale)
becomes the v2 default prior (uniform within the transformed bounds).
Parameters with a prior on linear scale (e.g. a v1 `objectivePriorType` of `normal`, `laplace`,
...) are not reparametrized; their prior is already the same in both encodings.

Measurement table overrides cannot be expressions. Therefore, for a reparametrized parameter
used as an override, the placeholder is transformed instead (`<placeholder>` becomes
`10^<placeholder>` in the observable or noise formula), and all overrides of that placeholder are
log-transformed. If the same placeholder also receives non-positive numbers or parameters that
stay on linear scale, the observable is split, so that each copy's placeholder only receives
overrides that can be log-transformed, or none at all.

See [#333](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab/issues/333) for
the rationale (option C there).
49 changes: 49 additions & 0 deletions problems/Boehm_JProteomeRes2014/v2/measurements.tsv
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@@ -0,0 +1,49 @@
observableId experimentId time measurement noiseParameters datasetId
pSTAT5A_rel 0.0 7.90107299873911 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 2.5 66.3634939682528 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 5.0 81.1713239165337 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 10.0 94.7303080627876 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 15.0 95.1164830493244 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 20.0 91.44171655392 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 30.0 91.2570992263545 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 40.0 93.672297840992 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 50.0 88.754232823019 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 60.0 85.2697032178279 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 80.0 81.1323953369608 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 100.0 76.135928475796 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 120.0 65.2480591250229 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 160.0 42.5996587097166 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 200.0 25.1577975445976 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5A_rel 240.0 15.4301823994994 log10_sd_pSTAT5A_rel model1_data1_pSTAT5A_rel
pSTAT5B_rel 0.0 4.59653334288927 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 2.5 29.6345459874189 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 5.0 46.0438064695701 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 10.0 81.9747336191685 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 15.0 80.5716092952489 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 20.0 79.0357196371442 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 30.0 75.6723803658298 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 40.0 71.6247198618303 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 50.0 69.0628632755742 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 60.0 67.1473843168934 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 80.0 60.8994762938824 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 100.0 54.8092577721492 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 120.0 43.9812899778025 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 160.0 29.7714581636675 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 200.0 20.089016562771 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
pSTAT5B_rel 240.0 10.961845166698 log10_sd_pSTAT5B_rel model1_data1_pSTAT5B_rel
rSTAT5A_rel 0.0 14.7231682180584 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 2.5 33.762342293107 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 5.0 36.7998512942493 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 10.0 49.7176022859654 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 15.0 46.9281200952955 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 20.0 47.8365745550135 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 30.0 46.9287272497385 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 40.0 40.5977529351181 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 50.0 43.783663885071 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 60.0 44.4573876523855 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 80.0 41.3271592618297 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 100.0 41.0627332148332 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 120.0 39.2358300289377 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 160.0 36.6194605442418 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 200.0 34.8937144010561 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
rSTAT5A_rel 240.0 32.2110771608676 log10_sd_rSTAT5A_rel model1_data1_rSTAT5A_rel
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