https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI/Head https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI/assertion https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI/provenance https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI/pubinfo https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAxA4sPzUS5deiS6yD5dkou6kUGQblaEf9X8rtw5PZcBI/assertion https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://purl.org/dc/terms/description analysis.py fetches each Table S1 SBML file from EBI BioModels and, for clamp scales 10, 50 and 100, counts UCS-based memories plus associative memories. A UCS memory is a factor-of-2 regulation during a 50-time-unit clamp that is still at least halfway from the baseline mean after a 50-time-unit release. An associative memory is a node that does not regulate the response, then does so by a factor of 2 after a paired clamp with a node that does. Integration is scipy BDF on libroadrunner rates, one model per subprocess, 240 second limit, models outside 3–12 floating species excluded before scoring. decide() compared the fractions same and lost and the count gained with the pre-registered thresholds. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/delphi/ontology/Assessment https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://www.w3.org/2000/01/rdf-schema#comment Measured: 0.6086956521739131 fraction of completed models with fewer memories at scale 10 or scale 50 than at scale 100 (n = 23, deterministic given the SBML bytes, the BDF tolerances, and the pre-registered memory rules). Interpretation: The claim is a simulation result about memory counts in the surveyed ODE pathway models at training amplitudes 10×, 50× and 100× baseline (Results 2.2). It is not a statement that pathway memory in cells is dose-robust. A UCS-based memory follows Test_US_Memory.m and DetectMem.m: the clamped node regulates the response by a factor of at least 2 during a 50-time-unit clamp (RscaleUp = 2 in MEMORY_EVALUATOR_ODE_SLURM1.m), and after a 50-time-unit release the response mean stays at least halfway from the baseline mean to the clamped mean. An associative memory follows UCS_NS_Asso.m: the neutral node does not regulate the response before pairing, the unconditioned and neutral nodes are clamped together for 50 time units, and after a 50-time-unit release the neutral node alone regulates the response by a factor of 2 relative to that release. Baseline max and min are the maximum and minimum of each floating species over the first 50 time units of an unforced trajectory from the SBML initial state, including zeros, as in the memory script. Up-clamp is scale times that maximum; down-clamp is the minimum divided by scale. A direction with a zero clamp level is skipped. Counts use every ordered pair of distinct floating species and both directions. The same adaptive one-integration-per-epoch clamp used for levin-2022-grn-learning-18 is used here; it is part of this registration. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://www.w3.org/2000/01/rdf-schema#label Claim test of levin-2022-grn-learning-09: refutes, confidence 0.75 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://www.w3.org/2000/01/rdf-schema#seeAlso https://github.com/fractastical/delphisci/blob/bf2af6ee0a84e23ffa6414ad7a0a50a8c276506e/test-data/claim-tests/levin-papers/levin-2022-grn-learning-09/result.json https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test http://www.w3.org/ns/prov#wasAttributedTo https://w3id.org/delphi/resource/agent/delphisci-claim-tester-grok-4-7 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/admissibleDatasets The pathway models whose BioModels identifiers are listed in Table S1 of Biswas, Clawson and Levin, Int. J. Mol. Sci. 2023, 24, 285 (supplement ijms-24-00285-s001), simulated from the public SBML with topology and parameters left at the deposited values. Generated random gene circuits do not count for this claim. Models with fewer than 3 or more than 12 floating species are outside the pre-registered computational bound and are excluded before scoring. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/assessmentType claim-test/0.1.0 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/dataKind computational https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/disconfirmingObservation At least 40% of models lose memories when the clamp is weakened from 100×, or no model gains memories at a weaker clamp and fewer than half keep the same count at all three amplitudes. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst A simulation of deposited pathway models does not show that stimulus amplitude leaves memory intact in cells. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst Four of 27 size-eligible models failed to integrate, below the pre-registered half-failure cutoff. Assignment-rule species and a collapsed solver step are the failures. libroadrunner getRatesOfChange does not fire SBML events. Each epoch is one adaptive integration with the clamped species held fixed. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst One completed model, BIOMD0000000483, has a memory count of zero at all three scales and is included in the same fraction. The registration did not drop zero counts. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst The count is UCS-based memories plus associative memories only. Pairing, transfer and consolidation memories are not included. The paper's pooled percentages can use a wider set of memory phenotypes. This exclusion was stated before the run. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst The pre-registered rule refutes the claim: N = 23 completed models, same = 0.2608695652173913, lost = 0.6086956521739131, gained = 6. Refutation requires N >= 12 and lost >= 0.40. That holds. The pass rule also required same >= 0.50 and lost <= 0.20. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/evidenceAgainst gained = 6 means six models had a higher memory count at scale 10 or 50 than at scale 100. That is the direction of the claim's remark that some models formed more memories under weaker stimulation. The registered rule still refutes, because the lost fraction is above 0.40. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/expectedObservation At least half of the completed surveyed pathway models have an identical memory count at 10×, 50× and 100× baseline clamps, no more than 20% have a lower count at a weaker clamp than at 100×, and at least one model has a higher count at a weaker clamp. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/failCriterion Pre-registered. Refutes if and only if N >= 12 and either lost >= 0.40, or gained = 0 and same < 0.50. If N >= 12 but neither the pass nor the fail rule holds, the verdict is mixed. If the Table S1 identifier list or the SBML files cannot be retrieved, or no ODE engine can be imported, the verdict is inconclusive with reason data-unavailable. If N < 12, or more than half of the size-eligible models fail to integrate or exceed the per-model time limit, the verdict is inconclusive with reason underpowered. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/hasConfidence 0.75 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/hasEvidence https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/hasEvidence https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/hasVerdict refutes https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/hypothesis If the published claim holds for the surveyed BioModels pathway ODEs, memory counts are stable across clamp amplitudes of 10, 50 and 100 times baseline: most models keep the same count at all three amplitudes, at most a small minority have fewer memories at a weaker amplitude than at 100 times, and at least one model has more memories at a weaker amplitude. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/inferenceMethod Memory count at a scale is the number of UCS-based memories plus the number of associative memories, defined in protocol. Fractions same and lost and the count gained are compared with the fixed thresholds in pass_criterion and fail_criterion. No assessor score is used. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/passCriterion Pre-registered. For each completed model let c10, c50 and c100 be the memory counts at clamp scales 10, 50 and 100. Let N be the number of models with all three counts. Let same be the fraction of those N with c10 = c50 = c100. Let lost be the fraction with c10 < c100 or c50 < c100. Let gained be the number with c10 > c100 or c50 > c100. Supports if and only if N >= 12, same >= 0.50, lost <= 0.20, and gained >= 1. https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/ontology/preregistered false https://w3id.org/delphi/resource/claim/levin-2022-grn-learning-09 https://w3id.org/delphi/ontology/hasAssessment https://w3id.org/delphi/resource/assessment/levin-2022-grn-learning-09--claim-test https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://purl.org/dc/terms/description Table S1 lists the BioModels identifiers. The zip is not committed. Result figures in the supplement were not used as measurements. https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://purl.org/dc/terms/license https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/delphi/ontology/EvidenceItem https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://www.w3.org/2000/01/rdf-schema#label Dataset: Biswas, Clawson and Levin, Int. J. Mol. Sci. 2023, 24, 285, supplement ijms-24-00285-s001, Table S1 model identifiers https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://www.w3.org/ns/prov#atLocation https://mdpi-res.com/d_attachment/ijms/ijms-24-00285/article_deploy/ijms-24-00285-s001.zip?version=1672829351 https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 http://www.w3.org/ns/prov#wasDerivedFrom https://www.mdpi.com/1422-0067/24/1/285 https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-1 https://w3id.org/delphi/ontology/hasChecksum sha256:292a7f33bcb3a9589614507114de8c1a7a5f5d8bc11e382d546c3d08da381e39 https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://purl.org/dc/terms/description Deposited pathway ODEs. Memory counts were computed from these files. The authors' .mat result files were not opened. https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://purl.org/dc/terms/license CC0 1.0 on files whose SBML text contains CC0; otherwise unstated in the file https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/delphi/ontology/EvidenceItem https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://www.w3.org/2000/01/rdf-schema#label Dataset: EBI BioModels SBML for the Table S1 identifiers https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://www.w3.org/ns/prov#atLocation https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000001?filename=BIOMD0000000001_url.xml https://w3id.org/delphi/resource/evidence/levin-2022-grn-learning-09--claim-test--dataset-2 http://www.w3.org/ns/prov#atLocation 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