Datasets:

cSynBioSys
/

well4cell

+*.7z filter=lfs diff=lfs merge=lfs -text
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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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+# Image files - uncompressed
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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+# Video files - compressed
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+---
+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: 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
+- config_name: metabolism
+  data_files:
+  - split: wildtype_media
+    path: data/metabolism/wildtype_media/*.parquet
+---
+# The Well for The Cell
+Version **1.0.0-rc1**, built 2026-10-09T17:33:51+00:00 from wcEcoli commit `60e68057b55926b0771cbb5c788e55a00805ef40`.
+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 |
+## What is in this repo
+Which families of each config are uploaded here (size on the Hub):
+| family | trajectories | `core` | `expression` | `metabolism` | `expression_1s` | `metabolism_1s` |
+|---|---:|---|---|---|---|---|
+| `wildtype_media` | 641 | 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 (13.5 GB) | not yet | not yet | not in this release | not in this release |
+| `dynamic_media` | 18,599 | yes (2.8 GB) | not yet | not yet | not in this release | not in this release |
+| `regulation` | 2,046 | yes (323.9 MB) | not yet | not yet | not in this release | not in this release |
+| `double_knockout` | 11,951 | yes (1.9 GB) | not yet | not yet | 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.
+`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. Every 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 |
+|---|---|---|---:|---:|---:|
+| `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_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 | | |
+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`.
+## 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 |
+| `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 |
+| `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 |
+| `split_by_lineage` | string | train/val/test, whole lineages, stratified by cell within family |
+| `split_by_gene` | string | train/val/test holding out whole genes; 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.
+## 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 93,698 / val 11,731 / test 11,745) to hold out whole lineages, or `split_by_gene` (train 91,561 / val 12,627 / test 12,986) to hold out whole genes; both are fixed columns of the index (seed 20261008).
+## 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 QC verdict of every trajectory in every config (118,456 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.
+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.
+## Citation
+```bibtex
+@misc{well4cell,
+  title  = {The Well for The Cell: whole-cell E. coli simulation trajectories},
+  author = {{{TODO}}},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/cSynBioSys/well4cell}
+}
+```

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