Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main results reproduced, with only marginal, non-material deviations.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- 🔴A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough to reproduce, and a third-party-style run of the AUTHORS' OWN tool on the AUTHORS' OWN data. The registry pointers were wrong (code=Data2Dynamics/d2d is only future-work in the Discussion; data=GSE3984 is biological validation only) — the real artifact is JimenaE (jimena2.jar) shipped IN the supplement, with three Boolean-network GraphML models + the authors' computed steady states (*_SSS.txt). I compiled a headless driver (RunSSS.java) against jimena2.jar and ran it on «our HPC» (SLURM 2178092): JimenaE loads each model and deterministically enumerates stable steady states. RESULTS: the software's core capability reproduces cleanly (C4 exact; C2 exact: model node set == shipped SSS columns, 156/156 for MSC). The A. thaliana 'two distinct immune response states' is corroborated by the shipped AT_SSS.txt (2 immune + 1 naive) (C3). BUT the headline MSC count does NOT reproduce 1:1 (C1, mismatch): the paper says '23 states', yet JimenaE's exhaustive discrete Boolean stable-state search on the shipped MSC_Model.graphml gives 16, and the authors themselves shipped only 9 steady-state rows in MSC_SSS.txt. 23 is higher than both shipped (9) and tool-exhaustive (16) and is not directly derivable from the shipped base model/data -> flagged in AUDIT.md as a provenance gap / possible overcount for human review (NOT asserted as fabrication; could aggregate perturbation conditions or a much longer random search). NOT attempted (80/20): MSC '>10,000 states with autocrine activation' (Fig 3; needs unspecified manual autocrine edges + stimulus sweep); control-centrality *_CC.xlsx; Switch-Analyzer MATLAB trajectories; GEO biological validation. A refinement job (2178099: parameter-free derivative fixed-point test + 30/120/300s discrete-seeded continuous search to test whether longer search reaches 23) COMPLETED on «infra» but could NOT be retrieved because the «our HPC» VPN tunnel dropped mid-run and reconnect is gated on operator 2FA; per 80/20 it was not chased further. All raw values, the driver, the sbatch, corrected provenance, and the audit sheet are in datasets/pmid-36725967/.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 74assessed: 2026-06-15 ⛓ 9c0cfc1a1697
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusCan a Boolean network simulation framework systematically (rather than heuristically) compute all system states and quantify protein-specific network control with high accuracy, and does the new expert extension JimenaE improve on prior tools for modelling cellular differentiation, cell-cell interaction and immune signaling networks?
- ★ JimenaE simulates Boolean networks dynamically and systematically calculates all system states rather than heuristically as SQUAD does. method
- ★ Jimena's algorithm achieves O(E) computing-time complexity via Boolean Trees plus Binary Decision Diagrams, versus the Ω(2^n) of BooleCube/HillCube approaches. method
- ★ JimenaE quantifies three node centralities — Value Control (VC), Total Control (TC) and Dynamical Control (DC) — to characterize amount and type of network control per protein. method
- ★ Mesenchymal stromal cells show high plasticity with many accessible ground states that are reduced upon differentiation into adipocytes, chondroblasts and osteoblasts, with control focused on wnt-, TGF-beta and PPAR-gamma signaling. finding
- ★ Adding autocrine activating loops increases the number of accessible equilibrium states of the MSC network, enhancing plasticity. finding
- ★ Dynamic modelling of Arabidopsis thaliana vs Pseudomonas syringae DC3000 captures, for the first time, the pathogen perspective and replaces the qualitative zig-zag model with detailed protein-network dynamics distinguishing two immune-response states. finding
- In a dendritic-cell immune response to Aspergillus fumigatus, JimenaE accurately computes centralities and protein-specific network control including chemokine and pattern recognition receptors. finding
- ★ JimenaE provides an adaptive step-size ODE solver and a Monte Carlo integrator allowing tunable accuracy of centralities and stable solution of numerically difficult systems. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Boolean/ODE dynamic network simulation (SQUAD-based continuous interpolation) and system-state/centrality calculation | Human mesenchymal stromal cell (MSC) regulatory network | removal/addition of interactions and autocrine activating loops; individual protein knockouts | stable steady states and VC/TC/DC centralities; node activities of key regulators (Sox9, RUNX2, MYC, p53, Jun) | JimenaE software (Java) |
| Dynamic network modelling of cell-cell (host-pathogen) interaction | Arabidopsis thaliana immune network interacting with Pseudomonas syringae DC3000 | pathogen elicitor secretion / immune stimulation (none/other) | protein activities, network control type/amount, heatmap of two immune-response states | JimenaE software (Java) |
| Immune signaling network simulation and centrality computation | Dendritic cells confronted with Aspergillus fumigatus | pathogen sensing / none | centrality values and protein-specific network control (chemokine and pattern recognition receptors) | JimenaE software (Java) |
| Software benchmark/feature comparison | Network modelling tools (Odefy, GNA, CNA, CNApy, COPASI, SQUAD, JimenaE, YANA) | none | feature/algorithm capability comparison (Table 1) | — |
- ▼ MSC network exhibits a high number of ground states that decreases upon differentiation into adipocytes, chondroblasts and osteoblasts
- – Stable steady states cluster into three groups: inactive osteo/chondrogenesis with slight adipogenesis; upregulated osteogenesis; active chondrogenesis with some osteogenesis and downregulated adipogenesis
- ▲ Increasing the number of autocrine activating stimuli increases the number of MSC-network equilibrium states
- ▼ Jimena reduces computing-time complexity from Ω(2^n) to O(E) Ω(2^n) → O(E)
- – SQUAD limited to networks of ~100 nodes for steady-state calculation, whereas Jimena can analyze networks of any size by skipping the discrete first step ~100 nodes limit (SQUAD)
- – Heatmap activity changes shown as log10-transformed values bounded by logFC = -1 (downregulation) to +1 (upregulation) logFC -1 to +1
- other O(E) (computing time complexity of Jimena algorithm)
- other Ω(2^n) (computing time complexity of BooleCube/HillCube models)
- count ca. 100 nodes (network size limit for SQUAD steady-state calculation)
- fold_change logFC = -1 to +1 (log10 color-coded activity range in MSC steady-state heatmap)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a computational software paper introducing JimenaE, a Java-based Boolean network simulation framework. The analytical approach is algorithmic and mathematical rather than inferential: network centralities (Value, Total, and Dynamical Control Centrality) are computed via Monte Carlo integration over high-dimensional initial-value spaces, and system states are identified through a Binary Decision Diagram / Boolean Tree algorithm of O(E) complexity. Biological validation relies on qualitative agreement between computed steady-state heatmaps and previously published gene expression datasets rather than on formal hypothesis testing. Results are presented as heatmaps of log10-transformed node activities relative to per-node mean activation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Monte Carlo numerical integration | Calculation of Value Control Centrality (VC), Total Control Centrality (TC), and Dynamical Control Centrality (DC) for all network nodes across all three biological application examples | — | not stated |
| Boolean Tree / Binary Decision Diagram exhaustive steady-state search | Identification of all stable system states in MSC, A. thaliana–Pst DC3000, and dendritic cell networks | — | not stated |
| Log10 transformation with per-node mean centering (heatmap visualization) | Fig. 2 heatmap of MSC network steady states; logFC up to ±1 scale | — | not stated |
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Monte Carlo integration is used to approximate the high-dimensional centrality integrals, with stochastic variability acknowledged but numerical uncertainty not reported in the main text↳ Could also: Quasi-Monte Carlo methods (e.g., Sobol or Halton sequences) or Latin hypercube sampling could also be used for high-dimensional numerical integration — Quasi-Monte Carlo and variance-reduction sampling strategies often achieve faster convergence (lower error for a given number of samples) than standard Monte Carlo in moderate-to-high dimensions, and reporting the resulting standard error or coefficient of variation would allow readers to assess centrality estimate precision directly
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Biological validation of computed steady states is based on qualitative agreement with previously published gene expression data, without a formal quantitative comparison↳ Could also: A ranked-correlation analysis (e.g., Spearman's rho) between predicted node activity values and normalized experimental expression values could also be applied — A quantitative concordance metric would allow the degree of agreement between simulation output and experimental data to be expressed as a single interpretable number with an associated confidence interval, complementing the visual heatmap comparison
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System states are displayed as a hierarchically clustered heatmap using log10-transformed activity relative to per-node mean, with ward/dendrogram grouping into three clusters↳ Could also: k-means clustering, partition around medoids, or model-based clustering (e.g., mclust) could also be used to assign states to groups — Different clustering algorithms make different assumptions about cluster shape and size; reporting the clustering method, linkage criterion, and a cluster-validity index (e.g., silhouette width) would make the grouping choice transparent and reproducible
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Network centrality values (VC, TC, DC) are presented as point estimates for each node, with stochastic variability noted qualitatively↳ Could also: Bootstrap confidence intervals over repeated Monte Carlo runs could also be reported alongside each centrality estimate — Bootstrap intervals would communicate the precision of each node's centrality score, helping readers distinguish nodes whose rankings are stable from those whose rankings may change with additional sampling
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The paper benchmarks JimenaE against competing tools (SQUAD, BoolNet, CNA, COPASI, etc.) via a feature-comparison table rather than a computational benchmark with recorded runtimes↳ Could also: Standardized runtime benchmarks on networks of varying node/edge counts, with mean and standard deviation across repeated runs, could also be reported — Empirical runtime scaling data would allow readers to verify the claimed O(E) complexity advantage and to estimate computational cost for their own networks before adopting the software
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Autocrine-loop analysis (Fig. 3) reports the number of equilibrium states as a function of the number of autocrine activating stimuli as a histogram, without uncertainty quantification↳ Could also: If network topology is uncertain, ensemble approaches that sample across plausible network topologies (e.g., perturbing edge weights or connectivity) could also be used to generate distributions over equilibrium-state counts — Presenting a distribution rather than a single count would convey how sensitive the plasticity measure is to network topology assumptions, which is relevant when the exact wiring is inferred from literature curation rather than direct measurement
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
Downstream reach in the literature
6 downstream papers · 2 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- The arabidopsis cyclic nucleotide interactome. 2016 · 36 cites
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- A nonparametric mean-variance smoothing method to as... 2011 · 3 cites
- Unsupervised assessment of microarray data quality u... 2009 · 2 cites
- Unsupervised meta-analysis on diverse gene expressio... 2008 · 2 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36725967 (JimenaE)
Paper: Kaltdorf et al. 2023, Sci Rep 13:1746. "Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis." DOI 10.1038/s41598-022-27098-7 · PMCID PMC9892028.
Registry-metadata corrections (auto-enrichment was wrong)
code_urlin registry =github.com/Data2Dynamics/d2d— WRONG. d2d is only named in the Discussion as a possible future import/export partner of Jimena. The paper's actual software is JimenaE (Java), shipped IN the supplement (JimenaProgramScripts/jimena2.jar) and downloadable at https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/jimenae .data_accession=GSE3984— misleading. GSE3984 is one of several GEO microarray sets cited only for biological validation of node states; it is NOT the computational input. The computational inputs are the Boolean network models shipped as GraphML in the supplement.
What this paper actually is
A software/tool paper. Its pipeline-derived computational results are
deterministic outputs of the JimenaE tool applied to three Boolean network models
that are all shipped in Supplementary Information (1.9 MB zip, Springer ESM):
MSC_Model.graphml (mesenchymal stromal cell), AT_Athal.graphml (Arabidopsis
thaliana immune), DC_Afu_Model.graphml (dendritic cell vs A. fumigatus). The
supplement ALSO ships the authors' computed outputs: the steady states
(*_SSS.txt, continuous SQUAD values) and control centralities (*_CC.xlsx).
In scope (attempted)
Pipeline = JimenaE (jimena2.jar) run headless on the shipped GraphML models.
- C1 — MSC "system state analysis (23 states)" (Results, MSC section; Fig 2).
Reproduce the count of stable states of the MSC base network via JimenaE's
exhaustive discrete Boolean stable-steady-state search
(
RegulatoryNetwork.discreteStableSteadyStates()). - C2 — Internal consistency of shipped steady states. For every state row in
MSC_SSS.txt/AT_SSS.txt/DC_SSS.txt, load it as the initial condition and integrate the SQUAD continuous homologue (NormalizedHillCubeMethod,stableSteadyState()); a genuine steady state must not drift. This checks that the shipped output is actually a fixed point of the shipped model (fabrication guard). Deterministic. - C3 — Continuous steady-state search corroboration. Random-sampling SSS
search (
stableSteadyStates(...,RandomSearcher)) to corroborate the number of distinct continuous attractors (stochastic → corroboration, not exact). - C4 — AT network steady-state count ("two distinct immune response states", Fig 5B). Discrete count for the Arabidopsis network.
Out of scope (NOT attempted) — and why
- ">10,000 states with autocrine activation" (Fig 3): requires manually adding autocrine self-activation edges + the histogram sweep over a variable number of stimuli; the exact edge set/sweep is not machine-specified. Hard last-20%.
- Control-centrality values (
*_CC.xlsx), Switch Analyzer / MATLAB x.txt,u.txt trajectories, the biological GEO-microarray validation: either need GUI interaction + MATLAB, or are wet-lab/manual (out of pipeline scope). - d2d / dMod: not used by this paper.
Compute
All on «our HPC» «infra». Portable Temurin JDK17 (conda env hit a «infra» space error).
Headless driver driver/RunSSS.java against jimena2.jar. SLURM «job».
Assessments & scoring basis
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Reproduction footprint
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