cSynBioSys/well4cell

The Well for The Cell

Version 1.0.0-rc1, built 2026-10-21T23:05:48+00:00 from wcEcoli commit 46667c5237081d72ed1195ec9f7332cd21b3be5d.

Time series from the 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	summary	core	expression	metabolism	expression_1s	metabolism_1s
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)
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)
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)
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)
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)

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 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; 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

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
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
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

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:

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):

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 93,698 / 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 (585,870 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, 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

@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}
}