well4cell 1.0.0-rc1: readme

README.md

@@ -40,15 +40,31 @@ configs:
   data_files:
   - split: wildtype_media
     path: data/expression/wildtype_media/*.parquet
+  - split: single_knockout
+    path: data/expression/single_knockout/*.parquet
+  - split: dynamic_media
+    path: data/expression/dynamic_media/*.parquet
+  - split: regulation
+    path: data/expression/regulation/*.parquet
+  - split: double_knockout
+    path: data/expression/double_knockout/*.parquet
 - config_name: metabolism
   data_files:
   - split: wildtype_media
     path: data/metabolism/wildtype_media/*.parquet
+  - split: single_knockout
+    path: data/metabolism/single_knockout/*.parquet
+  - split: dynamic_media
+    path: data/metabolism/dynamic_media/*.parquet
+  - split: regulation
+    path: data/metabolism/regulation/*.parquet
+  - split: double_knockout
+    path: data/metabolism/double_knockout/*.parquet
 ---
 
 # The Well for The Cell
 
-Version **1.0.0-rc1**, built 2026-10-09T17:33:51+00:00 from wcEcoli commit `60e68057b55926b0771cbb5c788e55a00805ef40`.
+Version **1.0.0-rc1**, built 2026-10-21T02:31:02+00:00 from wcEcoli commit `f3bdabc004a473964df62712469213bffde7ab90`.
 
 Time series from the [wcEcoli](https://github.com/CovertLab/wcEcoli) whole-cell model of *Escherichia coli*. Each sample is **one cell generation**: the cell from birth to division (or to the step at which the simulation stopped), recorded every **1 s** of simulated time. Lineages are simulated for several generations, and the samples of one lineage are linked by `parent_id`. The 117,174 trajectories come from 31,101 lineages of 3,896 distinct cells (design x condition), grouped into five families by campaign tier.
 
@@ -67,26 +83,26 @@ Which families of each config are uploaded here (size on the Hub):
 
 | family | trajectories | `summary` | `core` | `expression` | `metabolism` | `expression_1s` | `metabolism_1s` |
 |---|---:|---|---|---|---|---|---|
-| `wildtype_media` | 641 | yes (205.1 KB) | yes (103.0 MB) | yes (512.4 MB) | yes (502.3 MB) | not in this release | not in this release |
-| `single_knockout` | 83,937 | yes (20.2 MB) | yes (13.5 GB) | not yet | not yet | not in this release | not in this release |
-| `dynamic_media` | 18,599 | yes (5.2 MB) | yes (2.8 GB) | not yet | not yet | not in this release | not in this release |
-| `regulation` | 2,046 | yes (623.0 KB) | yes (323.9 MB) | not yet | not yet | not in this release | not in this release |
-| `double_knockout` | 11,951 | yes (3.4 MB) | yes (1.9 GB) | not yet | not yet | not in this release | not in this release |
+| `wildtype_media` | 641 | yes (242.6 KB) | yes (103.0 MB) | yes (512.4 MB) | yes (502.3 MB) | not in this release | not in this release |
+| `single_knockout` | 83,937 | yes (23.9 MB) | yes (13.5 GB) | yes (59.9 GB) | yes (65.6 GB) | not in this release | not in this release |
+| `dynamic_media` | 18,599 | yes (6.2 MB) | yes (2.8 GB) | yes (11.7 GB) | yes (13.5 GB) | not in this release | not in this release |
+| `regulation` | 2,046 | yes (736.0 KB) | yes (323.9 MB) | yes (1.7 GB) | yes (1.6 GB) | not in this release | not in this release |
+| `double_knockout` | 11,951 | yes (4.0 MB) | yes (1.9 GB) | yes (8.8 GB) | yes (9.1 GB) | not in this release | not in this release |
 
-`expression` and `metabolism` are currently available for `wildtype_media` only; `single_knockout`, `dynamic_media`, `regulation` and `double_knockout` will be added in a later upload. `expression_1s` and `metabolism_1s` (1 s matrices, ~1.3 TB estimated) are not in this release.
+`expression_1s` and `metabolism_1s` (1 s matrices, ~1.3 TB estimated) are not in this release.
 
 `index.parquet` lists every trajectory, uploaded or not. Its locator columns (`<config>_file`, `<config>_row_offset`, `<config>_n_rows`) are null for data not in this repo, so `index["<config>_file"].notnull()` (e.g. `index.expression_file.notnull()`) is the authoritative availability check. `load_dataset(..., "<config>", split="<family>")` fails for a family that has not been uploaded (e.g. `load_dataset(..., "expression", split="single_knockout")` until that family is uploaded).
 
 ## Configs
 
-Each config is one data product. `summary` is one row per trajectory and joins to `index.parquet` on `sample_id`: `n_steps` and, for each of the 18 scalar channels in the order of the product schema, `<channel>_mean`, `_min`, `_max`, `_first` and `_last` over the trajectory's 1 s `core` rows (int32 for `mrna_counts` and `n_oric` except `_mean`, float32 otherwise; the `growth_rate` statistics skip its null row 0, so `growth_rate_first` is the value at t = 1). Every other config carries `sample_id`, `t`, `t_abs` and the 18 scalar channels; the matrix configs add per-gene or per-reaction columns. `expression` and `metabolism` keep the rows with `t % 10 == 0` plus each trajectory's last row (point samples, not averages); `expression_1s` and `metabolism_1s` keep every row. The split of every config is the family.
+Each config is one data product. `summary` is one row per trajectory and joins to `index.parquet` on `sample_id`: `n_steps` and, for each of the 18 scalar channels in the order of the product schema, `<channel>_mean`, `_min`, `_max`, `_first` and `_last` over the trajectory's 1 s `core` rows (int32 for `mrna_counts` and `n_oric` except `_mean`, float32 otherwise; the `growth_rate` statistics skip its null row 0, so `growth_rate_first` is the value at t = 1). For every family it also has 13 pathway proteome fractions, `pathway_translation_ribosome`, `pathway_transcription_rnap`, `pathway_stringent_response`, `pathway_glycolysis`, `pathway_tca_cycle`, `pathway_pentose_phosphate`, `pathway_respiration_atp`, `pathway_aa_biosynthesis`, `pathway_amr_efflux`, `pathway_oxidative_stress`, `pathway_global_regulators`, `pathway_proteostasis` and `pathway_cell_division` (float32, unit `fraction`): for each pathway of the Cellarium pathway panel, the pathway's protein counts over all protein counts at each 10 s `expression` row, averaged over the trajectory (Cellarium's definition; the gene lists are in Cellarium's `pathway_panel.py`). Every other config carries `sample_id`, `t`, `t_abs` and the 18 scalar channels; the matrix configs add per-gene or per-reaction columns. `expression` and `metabolism` keep the rows with `t % 10 == 0` plus each trajectory's last row (point samples, not averages); `expression_1s` and `metabolism_1s` keep every row. The split of every config is the family.
 
 | config | rows every | matrices (active columns) | trajectories | rows | size |
 |---|---|---|---:|---:|---:|
-| `summary` | trajectory | none: `n_steps` and `_mean`, `_min`, `_max`, `_first`, `_last` of each scalar | 117,174 | 117,174 | 29.6 MB |
+| `summary` | trajectory | none: `n_steps` and `_mean`, `_min`, `_max`, `_first`, `_last` of each scalar; 13 `pathway_<name>` fractions | 117,174 | 117,174 | 35.0 MB |
 | `core` | 1 s | scalars only | 117,174 | 316,751,845 | 18.6 GB |
-| `expression` | 10 s | `mrna_counts_matrix` (2,773), `protein_counts_matrix` (4,174) | 641 (wildtype_media only) | 173,523 | 512.4 MB |
-| `metabolism` | 10 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | 641 (wildtype_media only) | 173,523 | 502.3 MB |
+| `expression` | 10 s | `mrna_counts_matrix` (2,773), `protein_counts_matrix` (4,174) | 117,174 | 31,830,394 | 82.7 GB |
+| `metabolism` | 10 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | 117,174 | 31,830,394 | 90.3 GB |
 | `expression_1s` | 1 s | `mrna_counts_matrix` (2,773), `protein_counts_matrix` (4,174) | not in this release | | |
 | `metabolism_1s` | 1 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | not in this release | | |
 
@@ -132,6 +148,7 @@ One row per trajectory, sorted by family (tier order T1..T5), cell_id, seed, gen
 | `divided` | bool | the cell divided (= not terminated) |
 | `terminated` | bool | the lineage ended in this generation |
 | `termination_class` | string | none, time_limit, negative_counts, fba_infeasible, equilibrium_unstable, other |
+| `translation_collapse` | bool | Cellarium's translation collapse, `ribosome_elongation_rate_mean < 1` (aa/s) in the `summary` config; null for a trajectory without a summary row (see Outcomes) |
 | `termination_reason` | string | the simulator's termination message |
 | `division_time_sec` | float | `n_steps - 1` when divided, else null |
 | `final_mass_fg` | float | cell mass at the last row, fg |
@@ -257,7 +274,7 @@ The same outcomes in the `qc` vocabulary of the Cellarium corpus:
 
 Only `time_limit` is a prediction of the model about the cell; the other non-`none` classes are numerical failures of the simulation, and a viability task should treat them as such. Cellarium's `implausible_channel` and `over_replicated` have no counterpart here because well4cell does not run those checks; its vocabulary is defined in the Cellarium repository's `docs/QC_STATUSES.md` (https://github.com/evanniko1/cellarium).
 
-A generation can divide while its ribosomes have effectively stopped, because chromosome replication does not require ongoing translation. The Cellarium corpus flags this as `translation_collapse` when the mean ribosome elongation rate over the generation is below 1 aa/s, an order of magnitude below the slowest elongation rate measured in *E. coli* (Dai et al. 2016). The same predicate on this dataset is `ribosome_elongation_rate_mean < 1` in the `summary` config. It flags 6,273 trajectories in this release, 4,188 of them with `termination_class` `none` (divided, but with translation collapsed). Treat them as inviable in any viability task.
+A generation can divide while its ribosomes have effectively stopped, because chromosome replication does not require ongoing translation. The Cellarium corpus flags this as `translation_collapse` when the mean ribosome elongation rate over the generation is below 1 aa/s, an order of magnitude below the slowest elongation rate measured in *E. coli* (Dai et al. 2016). Here the flag is the index column `translation_collapse`, the same predicate as `ribosome_elongation_rate_mean < 1` in the `summary` config (null for a trajectory without a summary row). It flags 6,273 trajectories in this release, 4,188 of them with `termination_class` `none` (divided, but with translation collapsed). Treat them as inviable in any viability task.
 
 ## Suggested tasks
 
@@ -275,15 +292,20 @@ Use `split_by_lineage` (train 93,698 / val 11,731 / test 11,745) to hold out who
 - Every trajectory ran with operons on and the steady-state tRNA charging model (`trna_charging` true, no kinetic charging), from one of two fitted parameter sets identified by `kb_sha256` and `parca_cache`; do not pool rows across the two parameter sets without checking `kb_sha256`.
 - `provenance/scan_runs.parquet`: every run directory read (32,072 runs), with its generation count and reference-id hash.
 - `provenance/failed_jobs.parquet`: 971 campaign jobs that failed, with the error message and the generations they left behind.
-- `provenance/export_qc.parquet`: the QC verdict of every trajectory in every config (118,456 checks, 0 failed). Trajectories that fail QC are left out of that config.
+- `provenance/export_qc.parquet`: the QC verdict of every trajectory in every config (351,522 checks, 0 failed). Trajectories that fail QC are left out of that config.
 - `stats/<family>.parquet`: per product, channel and matrix column: mean, population std, min, max and row count.
 - `manifest.json`: row and byte totals per config, counts per family.
 
+Degradation rates. Not every mRNA half-life in the fitted parameters is a fit to that transcript. The fitted rates are bounded below by the slowest measured mRNA decay (the longest measured half-life), and a unit whose rate sits on that bound is on the floor. Rates faster than the fastest measured decay (the shortest measured half-life) are clipped to it, and those units are on the ceiling. A unit none of whose genes has a measured half-life is imputed: it carries the mean measured half-life. Any claim about the stability of these transcripts, or that depends on it, rests on a bound or a population mean, not on a measurement of that transcript. Which units are in each class is derived from the fitted parameters, so like them it is kept with the campaign and not distributed (see Licence). This repo has the counts only.
+
+- `5558948af4ad806f21d7c80c`: of 3,133 mRNA transcription units, 2,293 have a fitted rate and 840, carrying 12.2% of basal mRNA expression, do not: 246 on the floor, 7 on the ceiling and 587 imputed.
+- `fa6a9ebd4b1c988a36ef9fa1`: of 3,133 mRNA transcription units, 2,293 have a fitted rate and 840, carrying 12.2% of basal mRNA expression, do not: 246 on the floor, 7 on the ceiling and 587 imputed.
+
 Never recorded: `aa_pool_size`, `ntp_pool_size`, `trna_charged_fraction` are in the exporter's channel list but were not written by any simulation, so they are absent from every product.
 
 ## Licence
 
-CC BY 4.0 for everything in this repo. The wcEcoli model is by the Covert lab (Stanford) and is licensed separately under the Stanford Academic Software License; it is not included here. The fitted parameter sets (Parca caches) are not distributed either and are identified only by `kb_sha256` / `parca_cache`; reproducing these simulations requires obtaining wcEcoli under its own licence.
+CC BY 4.0 for everything in this repo. The wcEcoli model is by the Covert lab (Stanford) and is licensed separately under the Stanford Academic Software License; it is not included here. The fitted parameter sets (Parca caches) are not distributed either and are identified only by `kb_sha256` / `parca_cache`, and the same applies to anything derived from them, such as the per-unit degradation-rate classification, of which only the counts are published (see Provenance); reproducing these simulations requires obtaining wcEcoli under its own licence.
 
 ## Citation