README.md

---
license: cc-by-4.0
pretty_name: The Well for The Cell
tags:
- biology
- simulation
- time-series
- whole-cell
- E. coli
size_categories:
- 100K<n<1M
configs:
- config_name: index
  data_files: index.parquet
- config_name: summary
  data_files:
  - split: wildtype_media
    path: data/summary/wildtype_media/*.parquet
  - split: single_knockout
    path: data/summary/single_knockout/*.parquet
  - split: dynamic_media
    path: data/summary/dynamic_media/*.parquet
  - split: regulation
    path: data/summary/regulation/*.parquet
  - split: double_knockout
    path: data/summary/double_knockout/*.parquet
- config_name: core
  data_files:
  - split: wildtype_media
    path: data/core/wildtype_media/*.parquet
  - split: single_knockout
    path: data/core/single_knockout/*.parquet
  - split: dynamic_media
    path: data/core/dynamic_media/*.parquet
  - split: regulation
    path: data/core/regulation/*.parquet
  - split: double_knockout
    path: data/core/double_knockout/*.parquet
- config_name: expression
  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
- config_name: expression_1s
  data_files:
  - split: wildtype_media
    path: data/expression_1s/wildtype_media/*.parquet
  - split: single_knockout
    path: data/expression_1s/single_knockout/*.parquet
  - split: dynamic_media
    path: data/expression_1s/dynamic_media/*.parquet
  - split: regulation
    path: data/expression_1s/regulation/*.parquet
  - split: double_knockout
    path: data/expression_1s/double_knockout/*.parquet
- config_name: metabolism_1s
  data_files:
  - split: wildtype_media
    path: data/metabolism_1s/wildtype_media/*.parquet
  - split: single_knockout
    path: data/metabolism_1s/single_knockout/*.parquet
  - split: dynamic_media
    path: data/metabolism_1s/dynamic_media/*.parquet
  - split: regulation
    path: data/metabolism_1s/regulation/*.parquet
  - split: double_knockout
    path: data/metabolism_1s/double_knockout/*.parquet
---

# The Well for The Cell

Version **1.0.0-rc2-pre**, built 2026-10-30T22:50:30+00:00 by the well4cell builder at wcEcoli commit `db3415a72f007937b7ba463e05472b8e2a6255f4`. That is the code that built this repo, not a simulator commit; the simulations ran under the commits in `index.git_hash` (see Provenance), and `manifest.json` lists both (`builder_commit`, `simulator_commits`).

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.

| family | tier | what varies | cells | lineages | trajectories |
|---|---|---|---:|---:|---:|
| `wildtype_media` | T1 | wild-type cells in a range of static media | 21 | 168 | 641 |
| `single_knockout` | T2 | single-gene knockouts across media | 2,752 | 21,949 | 83,937 |
| `dynamic_media` | T3 | media shifts and sinusoidal media schedules | 684 | 5,472 | 18,599 |
| `regulation` | T4 | transcription-factor activity and ppGpp set-point perturbations | 64 | 512 | 2,046 |
| `double_knockout` | T5 | two-gene knockouts | 375 | 3,000 | 11,951 |
| **total** | | | 3,896 | 31,101 | 117,174 |

## Limitations

Read this before using the data. Every number below is computed from this release's index unless it says it was measured on 1.0.0-rc1; each limitation has an index column, so nothing needs a side table. Rows are flagged, never removed: filter with the columns named.

1. **Partial knockouts.** A knockout zeroes whole transcription units (TUs), and the campaign mapped each gene to one TU. A gene that sits in several TUs kept transcribing from the others: 1,145 knockout cells, 35,552 trajectories (770 single, 180 media-shift and 195 double-knockout cells), confirmed on protein counts (lpxD, ispH and pgk stay at wild-type level in all four generations). Columns: `partial_knockout`, `knockout_target_tus_missing`; these rows are `superseded` and are re-run over every TU of the target, and `superseded_by` will name the replacement.
2. **Collateral genes.** Of the 1,982 single knockouts that did silence their target, 913 also fully silence at least one other gene and 1,069 take at least one TU from another gene (up to 11 genes fully silenced: `T2/wzyE_KO/acetate` also silences rffC, rffG, rffH, rffM, wecA, wecB, wecC, wecE, wecF, wzxE, wzzE). A cell labelled `X_KO` is a knockout of X's TU, an operon knockout. Columns: `collateral_genes_full`, `collateral_genes_partial`.
3. **Duplicate designs.** Genes that share a TU give the same simulation: 369 single-knockout groups (918 cells) and 78 `dynamic_media` groups (204 cells) have the same zeroed TUs and the same condition label, and same-seed copies are bit-identical (447 pairs, 13,108 trajectories compared byte for byte on rc1; the rest are flagged by design identity). With the glucose doses folded (4) the 1,763 single-knockout designs cover 2,752 cells. Columns: `design_id`, and `canonical` on one cell per design (81,393 trajectories). 20 multi-cell designs mix parameter sets or simulator commits: those cells are the same design, not bit-identical twins.
4. **Splits.** 1.0.0-rc1's `split_by_lineage` was stratified by cell, so bit-identical twins sat on both sides (977 to 3,135 twin lineage pairs, depending on how the pairs are counted), and `split_by_gene` held out gene symbols rather than TUs (160 of 2,578 groups leaked, 18,502 trajectories). Both splits are now on designs: `split_by_lineage` assigns a design's seed as one unit and `split_by_tu` holds out TUs, so no twin crosses a split. `split_by_gene` is gone; its values remain at Hub revision `1.0.0-rc1`.
5. **Glucose doses.** `basal`, `glc_20mM`, `glc_5mM` and `glc_2mM` are the same cell: glucose uptake is a switch, not a saturable rate, so the dose has nothing to act on (knockout in `glc_20mM` equals `basal` in 16,498 of 16,498 trajectories compared on rc1). `condition_effective` folds them into `basal` (19,364 rows). It also replaces the label of regulation cells, whose campaign condition the simulator ignores (e.g. `T4/bglJ/active` and `T4/cytR/active`, labelled `acetate` and `succinate`, ran in `minimal`): 19,715 rows in all have `condition_effective` different from `condition`. The 384 sinusoidal-media trajectories have none (a continuous mixture; read `media_a`/`media_b`); the glucose sinusoid is probably static basal for the same reason, but that was not checked.
6. **Shifts, not adapted cells.** No wild-type or knockout run set an initial condition, so every one started from the cell fitted to `basal` (`medium_fitted_for` `minimal`) and a static medium is a switch at t = 0 (`shift_at_t0`, 61,660 trajectories). Every such lineage is a shift experiment, not an adapted cell (`unfitted_shift` `t0`, 61,660 trajectories in 1,992 cells): expression stays fitted to `basal` while replication initiation follows the new medium. In a carbon source generation 0 over-initiates and generations 1 and 2 divide early (acetate birth dry mass 354, 202, 146 fg in generations 0 to 2 on rc1); in `with_aa` (22,618 trajectories) generation 0 divides at the basal time (about 2,800 s) where a cell fitted to `with_aa` takes about 1,400 s (measured on rc1). The adapted range arrives only after this campaign's four generations. Cells fitted to their medium (`ppgpp_conc`, `tf_activity`, the amino-acid shift controls) are not shifts. A later switch into a medium the cell is not fitted for (`unfitted_shift` `mid_run`, 11,457 trajectories in 366 cells; placeholder media are `withdrawn` instead, 7) does the same after the switch: the 153 trajectories that divide one second after birth are all of this kind (`dynamic_media`, `0 minimal, 1200 minimal_acetate`, generation 2), not static acetate cells.
7. **Placeholder media.** `minimal_minus_phosphate` and `minimal_no_glucose` were added to the campaign as placeholders with invented doubling times; the model has no starvation response (on rc1, protein synthesis continued unchanged until ATP ran out). 418 trajectories are born into `minimal_minus_phosphate` (0 divide) and 1,547 into `minimal_no_glucose` (898 divide). `withdrawn` marks every generation in which one is in effect: 3,565 trajectories in 200 cells, with `withdrawn_reason`.
8. **Divided is not viable.** Division follows replication, not translation, so a cell can divide with its ribosomes stopped: 6,273 trajectories have `translation_collapse`, 4,188 of them divided, and in some lineages protein at birth halves every generation. 160 trajectories have 10 steps or fewer (`short_trajectory`), 153 of them divided (the one-second divisions of 6). `viable` = divided, not `translation_collapse` and not `short_trajectory`: 108,205 viable, 8,969 not.
9. **No tRNA charging channel.** `aa_pool_size`, `ntp_pool_size`, `trna_charged_fraction` were in the exporter's channel list but were dropped between the simulation and the export, and the raw output they could be recomputed from was pruned, so no tRNA or amino-acid pool question can be answered from these rows.
10. **Knockout versus wild type crosses parameter sets and code.** Single knockouts: 83,937 on `8ff93a58` / `c47683e1`. Wild type: 604 on `b1750cf0` / `c41efd75`, 29 on `8ff93a58` / `ddc88250`, 4 on `b1750cf0` / `e319d09f`, 4 on `b1750cf0` / `18a908d2`. Double knockouts: 11,776 on `b1750cf0` / `c41efd75`, 103 on `8ff93a58` / `c462ae99`, 58 on `8ff93a58` / `c47683e1`, 14 on `8ff93a58` / `ddc88250`. So the release's most obvious comparison, knockout against wild type, also compares two parameter sets (they differ by 11 keys added for the `cytR` fix, no changed values) and two code versions (the knockout commit changed amino-acid synthesis, FBA retries, equilibrium and lineage termination). Compare only rows with equal `kb_sha256` and `git_hash`; wild-type controls on the knockout commit and both parameter sets are being added.

**What that leaves.** The knockout rows (111,829 trajectories, 3,727 cells), each counted at its first step, in the order of the 1.0.0-rc1 review:

| step | trajectories | cells | review (rc1) |
|---|---:|---:|---:|
| the knockout did not happen (`superseded`): re-run | 35,552 | 1,145 | 35,552 / 1,145 |
| a twin of another cell with the same label (operon mates; not `canonical`) | 12,562 | 431 | 12,562 / 431 |
| a glucose-dose copy of a `basal` design (not `canonical`) | 10,723 | 360 | 10,723 / 360 |
| t = 0 switch into a medium the cell is not fitted for (`unfitted_shift` `t0`; the review: carbon sources only): a shift experiment | 34,889 | 1,141 | 22,007 / 722 |
| mid-run switch into a medium the cell is not fitted for (`unfitted_shift` `mid_run`; the review: acetate or no oxygen only): a shift experiment | 5,346 | 174 | 3,539 / 116 |
| placeholder medium (`withdrawn`) | 1,998 | 112 | 2,030 / 116 |
| usable: target silenced, no duplicate, no switch into a medium the cell is not fitted for, no placeholder medium | 10,759 | 364 | 25,416 / 837 |

Partition: usable 10,759, relabel as shift experiments 40,235, redundant copies 23,285, re-run 35,552, withdraw 1,998. The flags differ from the review in two ways. Every switch into a medium the cell is not fitted for is a shift, not only carbon sources and anoxia: static `with_aa` (12,882 trajectories) and mid-run switches into `with_aa` move from usable to relabel, so every usable row is `basal`. And `withdrawn` is per generation: a lineage's first generations that divided before the switch into a placeholder are not withdrawn (cells are counted in every step one of their rows reaches). Even usable rows need the collateral genes read (158 cells, 4,665 trajectories touch another gene) and the viability filter (9,879 of 10,759 are `viable`).

## What is in this repo

Which families of each config are uploaded here (size on the Hub):

| family | trajectories | `summary` | `core` | `expression` | `metabolism` | `expression_1s` | `metabolism_1s` | `charging` |
|---|---:|---|---|---|---|---|---|---|
| `wildtype_media` | 641 | yes (242.6 KB) | yes (103.0 MB) | yes (512.4 MB) | yes (502.3 MB) | yes (2.0 GB) | yes (4.9 GB) | no rows |
| `single_knockout` | 83,937 | yes (23.9 MB) | yes (13.5 GB) | yes (59.9 GB) | yes (65.6 GB) | yes (237.4 GB) | yes (639.3 GB) | no rows |
| `dynamic_media` | 18,599 | yes (6.2 MB) | yes (2.8 GB) | yes (11.7 GB) | yes (13.5 GB) | yes (46.4 GB) | yes (131.6 GB) | no rows |
| `regulation` | 2,046 | yes (736.0 KB) | yes (323.9 MB) | yes (1.7 GB) | yes (1.6 GB) | yes (6.5 GB) | yes (15.6 GB) | no rows |
| `double_knockout` | 11,951 | yes (4.0 MB) | yes (1.9 GB) | yes (8.8 GB) | yes (9.1 GB) | yes (35.0 GB) | yes (88.9 GB) | no rows |

Every config is complete: all five families are in this repo.

`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). 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 but `charging` (see tRNA charging) 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. `expression` and `metabolism` are exact row subsets of `expression_1s` and `metabolism_1s`; they are kept for loader compatibility and will be reconsidered at 1.0.0.

| config | rows every | matrices (active columns) | trajectories | rows | size |
|---|---|---|---:|---:|---:|
| `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) | 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) | 117,174 | 316,751,845 | 327.4 GB |
| `metabolism_1s` | 1 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | 117,174 | 316,751,845 | 880.3 GB |
| `charging` | 1 s | `aa_pool_size` (21), `ntp_pool_size` (4), `trna_charged_fraction` (21) | not in this release | | |

Matrix columns are pruned to the columns that are non-zero at least once anywhere in the campaign (active columns: mrna_counts 2,773 of 3,133, protein_counts 4,174 of 4,310, reaction_flux 3,974 of 9,612, exchange_flux 82 of 87). The full column lists, with an `active` mask and each active column's position in the product (`product_column`), are in `provenance/reference/<matrix>.parquet`.

## tRNA charging

The `charging` config has one row per 1 s step, like `core`, but no scalars: `sample_id`, `t`, `t_abs`, `aa_pool_size` (fixed_size_list<float32>[21], molecules), `ntp_pool_size` ([4], molecules) and `trna_charged_fraction` ([21], fraction of each amino acid's tRNAs that is charged). Join it to `core` on (`sample_id`, `t`). Column k of a list is the row with `column == k` of `provenance/reference/charging_<column>.parquet` (21 amino acids, ATP/CTP/GTP/UTP). It holds only trajectories with index `trna_channels` true: runs converted from rc2 on (0 trajectories); every rc1 row has none, and never will (their raw output is deleted). Coverage: none in this release. Row 0 (`t` = 0) of every list is NaN: the simulator's listener writes an all-zero first row before its first step (checked at export), like `growth_rate`, which is null there.

## index.parquet

One row per trajectory, sorted by family (tier order T1..T5), cell_id, seed, generation.

| column | type | meaning |
|---|---|---|
| `sample_id` | string | trajectory id, `lineage_id + "/g{generation}"` |
| `lineage_id` | string | `cell_id + "/s{seed}"`: one simulated lineage |
| `cell_id` | string | the experimental cell (design x condition), e.g. `T2/argG_KO/basal`; see Ids |
| `parent_id` | string | sample_id of the mother generation; null at generation 0 |
| `experiment_name` | string | the campaign's experiment name, verbatim (wrong for T4 transcription-factor cells; see Ids) |
| `family` | string | one of the five families (the split names of every data config) |
| `tier` | string | campaign tier, T1..T5 |
| `variant_type` | string | wcEcoli variant name |
| `variant_index` | int32 | wcEcoli variant index |
| `variant` | string | gene symbol(s), `wildtype`, or the variant type |
| `condition` | string | the campaign's condition as designed; ignored by the simulator for TF-activity and sinusoidal cells (read `media_schedule`) |
| `timeline` | string | the media schedule that actually ran, `"0 minimal, 1200 minimal_acetate"` (simulation seconds); null for sinusoidal media |
| `seed` | int8 | simulation seed |
| `generation` | int8 | generation number within the lineage (0 = first cell) |
| `knockout_genes` | list<element: string> | knocked-out gene symbols |
| `knockout_gene_ids` | list<element: string> | knocked-out gene ids (EcoCyc) |
| `knockout_tu_ids` | list<element: string> | transcription units whose expression was set to zero (a knockout removes a whole TU) |
| `media_schedule` | list<element: struct<t_abs_sec: int32, medium: string>> | media changes, lineage-absolute seconds (`t_abs_sec`) and medium |
| `medium_at_birth` | string | medium at the first row of this generation |
| `medium_at_division` | string | medium at the last row of this generation |
| `media_a` | string | first medium of a two-medium schedule |
| `media_b` | string | second medium of a two-medium schedule |
| `period_sec` | int32 | period of a sinusoidal schedule, s (null otherwise) |
| `tf_target` | string | symbol of the transcription factor that was actually perturbed (regulation family) |
| `tf_target_id` | string | EcoCyc id of that transcription factor |
| `tf_state` | string | imposed TF state: active, inactive, or control |
| `ppgpp_setpoint_um` | float | ppGpp set point, uM (regulation family) |
| `ppgpp_factor` | float | multiple of the basal ppGpp concentration (0.2..2.0) |
| `aa_shift` | string | amino-acid shift label, e.g. `+ARG`, `-ARG`, `rich->6aa`, `control` |
| `perturbation_text` | string | one-sentence plain-language description of the perturbation |
| `n_steps` | int32 | rows of this trajectory at 1 s |
| `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 |
| `trna_channels` | bool | the run's raw output recorded `aa_pool_size`, `ntp_pool_size` and `trna_charged_fraction` (false for every run converted before rc2; see Provenance) |
| `division_time_sec` | float | `n_steps - 1` when divided, else null |
| `final_mass_fg` | float | cell mass at the last row, fg |
| `growth_rate` | float | mean instantaneous growth rate over the generation, 1/s |
| `lineage_terminated` | bool | the lineage ended in some generation |
| `lineage_generations` | int8 | generations present for this lineage |
| `condition_effective` | string | the condition that actually ran: the campaign condition of the medium at t = 0, with `glc_20mM`/`glc_5mM`/`glc_2mM` folded into `basal`; null for sinusoidal media (see Limitations) |
| `medium_fitted_for` | string | medium of the condition the first cell was initialised (fitted) from; `minimal` for every wild-type and knockout cell whatever its label |
| `shift_at_t0` | bool | the medium at t = 0 is not `medium_fitted_for`: a cell fitted to one medium switched into another at t = 0 (null for sinusoidal media) |
| `unfitted_shift` | string | `t0` (= `shift_at_t0`) or `mid_run` (a later switch into a medium other than `medium_fitted_for`, glucose doses as minimal, placeholders excepted) when the cell meets a medium its expression was not fitted for; a shift experiment, not an adapted cell |
| `partial_knockout` | bool | a targeted gene kept a transcription unit: the knockout did not silence it |
| `knockout_target_tus_missing` | list<element: string> | transcription units of the targeted genes that were not zeroed (null without a knockout) |
| `collateral_genes_full` | list<element: string> | other genes every transcription unit of which was zeroed (silenced with the target) |
| `collateral_genes_partial` | list<element: string> | other genes that lost some but not all of their transcription units |
| `superseded` | bool | replaced by a re-run (every partial knockout); kept for the record |
| `superseded_by` | string | cell_id of the re-run that replaces this cell (null until it is published) |
| `withdrawn` | bool | a placeholder medium with no mechanism in the model is in effect in this generation |
| `withdrawn_reason` | string | why the row is withdrawn |
| `design_id` | string | hash of everything the simulator was given (zeroed TUs, fitted-for medium, schedule with glucose doses folded, sinusoid, TF state, ppGpp factor, amino-acid shift); twins share it |
| `canonical` | bool | true on one cell per design (keep `canonical` rows to count each experiment once) |
| `short_trajectory` | bool | `n_steps <= 10` |
| `viable` | bool | divided, not `translation_collapse` and not `short_trajectory`; null when that depends on a null `translation_collapse` |
| `split_by_lineage` | string | train/val/test over a design's seeds: every lineage of every cell of a design with one seed is in one split, so twins never cross |
| `split_by_tu` | string | train/val/test holding out whole transcription units; a row takes the most held-out split of its `knockout_tu_ids`; non-knockout rows are train |
| `job_id` | int32 | campaign job id |
| `attempt` | int8 | campaign job attempt |
| `started_at` | string | simulation start time |
| `run_dir` | string | campaign run directory |
| `h5_group` | string | group in the run's channels.h5, e.g. `seed0/gen0` |
| `git_hash` | string | wcEcoli commit that ran the simulation |
| `parca_cache` | string | Parca (fitted-parameter) cache the run used, by name; the cache itself is not distributed |
| `kb_sha256` | string | sha256 of the fitted parameter set (simData.cPickle) used; the set itself is not distributed |
| `sim_options_hash` | string | sha256 of the run's simulation options minus per-run keys and the keys carried as columns (git_hash, timeline, multi-knockout indices); key into `provenance/sim_options/` |
| `<config>_file`, `<config>_row_offset`, `<config>_n_rows` | string, int64, int32 | locator of this trajectory in each config: file path in the repo, first row, row count (null when the trajectory is not in that config) |

## Product schema

Rows of one trajectory are contiguous and in time order within one file. Units are also in each field's parquet metadata (`unit`).

| column | type | unit | meaning |
|---|---|---|---|
| `sample_id` | dictionary<values=string, indices=int32, ordered=0> | | joins to `index.parquet` |
| `t` | int32 | s | seconds since this cell's birth; row 0 is the birth state |
| `t_abs` | int32 | s | seconds since the lineage began (continues across generations) |
| `cell_mass` | float | fg | total cell mass |
| `dry_mass` | float | fg | dry mass |
| `protein_mass` | float | fg | protein mass |
| `rna_mass` | float | fg | RNA mass |
| `dna_mass` | float | fg | DNA mass |
| `small_molecule_mass` | float | fg | small-molecule mass |
| `cell_volume` | float | fL | cell volume |
| `growth_rate` | float | 1/s | instantaneous growth rate (null at row 0) |
| `ppgpp_conc` | float | uM | ppGpp concentration |
| `aa_supply_total` | float | counts/s | total amino-acid supply rate |
| `aa_synthesis_total` | float | counts/s | total amino-acid synthesis rate |
| `mrna_counts` | int32 | molecules | total mRNA molecules |
| `n_oric` | int32 | count | origins of replication |
| `fba_objective` | float | mmol/gDCW/h | FBA objective value |
| `exchange_flux_total` | float | mmol/gDCW/h | sum of exchange fluxes |
| `reaction_flux_total` | float | mmol/gDCW/h | sum of reaction fluxes |
| `ribosome_elongation_rate` | float | aa/s | ribosome elongation rate |
| `ribosome_actual_elongations` | float | aa | amino acids elongated in the step |
| `mrna_counts_matrix` | fixed_size_list<element: int32 not null>[2773] | molecules | mRNA counts per transcription unit (ids are mRNA TU ids); column k is the row of `provenance/reference/mrna_counts.parquet` with `product_column == k` |
| `protein_counts_matrix` | fixed_size_list<element: int32 not null>[4174] | molecules | protein counts per monomer; column k is the row of `provenance/reference/protein_counts.parquet` with `product_column == k` |
| `reaction_flux_matrix` | fixed_size_list<element: float not null>[3974] | mmol/gDCW/h | flux per metabolic reaction; column k is the row of `provenance/reference/reaction_flux.parquet` with `product_column == k` |
| `exchange_flux_matrix` | fixed_size_list<element: float not null>[82] | mmol/gDCW/h | flux per exchange reaction; column k is the row of `provenance/reference/exchange_flux.parquet` with `product_column == k` |

## Loading

```python
from datasets import load_dataset

index = load_dataset("cSynBioSys/well4cell", "index", split="train")
core = load_dataset("cSynBioSys/well4cell", "core", split="single_knockout", streaming=True)
expression = load_dataset("cSynBioSys/well4cell", "expression", split="wildtype_media")
```

DuckDB, one trajectory:

```sql
SELECT t, cell_mass, growth_rate
FROM 'hf://datasets/cSynBioSys/well4cell/data/core/*/*.parquet'
WHERE sample_id = 'T1/WT/acetate/s0/g0'
ORDER BY t;
```

One trajectory by its locator (reads only the row groups it spans):

```python
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

row = index.filter(lambda r: r["sample_id"] == "T1/WT/acetate/s0/g0")[0]
path = hf_hub_download("cSynBioSys/well4cell", row["expression_file"], repo_type="dataset")
table = pq.read_table(path, filters=[("sample_id", "=", row["sample_id"])])
assert table.num_rows == row["expression_n_rows"]
```

## Ids

- `cell_id` is the campaign experiment name, slugified, with the tier as a plain `T1`..`T5` prefix: `[T2_CORE] argG_KO/basal` becomes `T2/argG_KO/basal`. The raw name is in `experiment_name`.
- Regulation (T4) transcription-factor cells are the exception: the campaign named them from the sorted TF *symbols* while the simulator indexes the sorted TF *ids*, so their experiment names name the wrong TF (`[T4] araC/active` ran trpR). Their `cell_id`, `tf_target` and `tf_target_id` use the TF that actually ran (`T4/trpR/active`); `experiment_name` keeps the campaign's name.
- A knockout removes a whole transcription unit: the model sets the expression of one TU to zero, so other genes of that TU are silenced too. `knockout_genes` lists the targeted genes, `knockout_tu_ids` the TUs actually knocked out.
- `lineage_id = cell_id + "/s{seed}"`; `sample_id = lineage_id + "/g{generation}"`, e.g. `T1/WT/acetate/s0/g0`.
- `parent_id` is the previous generation's `sample_id` (null at generation 0).

## Time axis

- Steps are 1 s. `t` restarts at 0 for every generation; `t_abs` continues across the generations of a lineage, so generation g+1 starts where generation g ended.
- Row 0 is the birth state: the daughter at half the mother's mass. `growth_rate` is null at row 0 (no step has been taken yet).
- Only one daughter is followed at each division, so a lineage is a chain, not a tree.
- A generation stopped by the per-generation time limit (`TimeLimitReached`) has 10,801 steps (0..10,800 s).

## Outcomes

`divided` is true when the cell divided. `terminated` marks the generation in which the lineage ended, and `termination_class` says why:

| termination_class | meaning | trajectories |
|---|---|---:|
| `none` | divided normally | 112,545 |
| `time_limit` | reached the time limit without dividing: the only clean non-division (slow or arrested growth) | 1,973 |
| `negative_counts` | a molecule count went negative (numerical failure) | 2,647 |
| `fba_infeasible` | the metabolism FBA problem had no solution | 3 |
| `equilibrium_unstable` | the equilibrium solver failed | 6 |
| `other` | any other simulator exception | 0 |

`time_limit` is the only clean non-division; the other classes are solver failures, and the rows before the failure are kept as recorded.

The same outcomes in the `qc` vocabulary of the Cellarium corpus:

| well4cell `termination_class` | Cellarium `qc` | meaning |
|---|---|---|
| `none` | `ok` | the cell divided |
| `time_limit` | `no_division` | the generation ran to the time limit without dividing; the model's "did not divide" |
| `negative_counts` | `crashed` | simulator error (a molecule count went negative) |
| `fba_infeasible` | `fba_infeasible` | metabolism solver failure |
| `equilibrium_unstable` | `crashed` | equilibrium solver failure |
| `other` | `crashed` | any other simulator error |

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). 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

1. **Growth-rate regression from the perturbation**: predict `growth_rate` (index) from the perturbation columns (`knockout_genes`, media, `tf_target`, ...) and optionally the first rows of the trajectory.
2. **Viability classification**: predict whether a generation is *not* terminated by the time limit (`termination_class != 'time_limit'`), i.e. whether the perturbed cell still divides.
3. **Forecasting**: given the trajectory up to time t, predict the channels at t + h, with horizons h stated in seconds (e.g. 10, 60, 600 s) so 1 s and 10 s products compare directly.

Use `split_by_lineage` (train 93,703 / val 11,732 / test 11,739) to hold out whole replicate lineages of each design, or `split_by_tu` (train 90,837 / val 12,039 / test 14,298) to hold out whole transcription units; both are fixed columns of the index (seed 20261008) and both keep bit-identical twins on one side. For viability use `viable`, not `divided`; to count each experiment once keep `canonical`; leave out `superseded` and `withdrawn` rows (see Limitations).

## Provenance

- Simulator commits (`index.git_hash`): `18a908d28303c2feeba161e962c049e4f712454e`, `5f5fa5f4fe2302af12385528d176f4d73c4bf3a6`, `c41efd7561d40316e5e83b20e3fb27316845b0bc`, `c462ae99c47d24c327a0311edd785f664d8038bc`, `c47683e19614b17e9c022762a238a1b36aa12dd2`, `ddc88250ad5c3ab07872c46888cd18f0fd3d32e6`, `e319d09f3749168155d246b103b7d37ba492ec63`.
- Simulation options hashes (`index.sim_options_hash`): `2ba80aae18ba5904625753010432220257822ee442fe87c162cb651d35f5cc0f`.
- Fitted parameters (`index.parca_cache`, with the sha256 of its `simData.cPickle` in `index.kb_sha256`): `5558948af4ad806f21d7c80c` (sha256 `8ff93a58576f484edfd6fea641eeeb4fc554b52b97da49f4d8b275ca74496667`), `fa6a9ebd4b1c988a36ef9fa1` (sha256 `b1750cf04409c37ebce9d4d3009daeae0779dd66508812c2807744d83baf1ab7`). The Parca caches themselves are not included in this repo (see Licence).
- 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 export checks of every trajectory in every config (585,870 trajectory x config writes checked, 0 failed): rows landed in the part file, steps are contiguous, counts are valid. A trajectory that fails is left out of that config. These checks say the rows were **written** correctly, not that the cell was **healthy**: the cells' outcomes are in `termination_class`, `translation_collapse` and `viable` (`manifest.json`: `export_checks` versus `cell_health`).
- `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`, 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

```bibtex
@misc{well4cell,
  title  = {The Well for The Cell: whole-cell E. coli simulation trajectories},
  author = {Yermakov, Alexey},
  year   = {2026},
  url    = {https://huggingface.co/datasets/cSynBioSys/well4cell}
}
```

TODO(authors): the author list above is incomplete; it is completed before 1.0.0.