Tuned parameter defaults

Each default listed here has one of three origins: a literature value from the original paper of the method, a measurement on real or synthetic models, or a convention taken over from a widely used tool or from another part of RAVEN. The tables are grouped by capability and cover the MATLAB implementation (RAVEN) and the Python implementation (raven-toolbox). Each row gives the default and the basis for it, and the linked studies give the measurements and methods.

Differences between the two implementations

The MATLAB implementation dates from 2013 and the Python implementation from 2026. The homology and KEGG cut-offs were measured and then set to the same values in both. Defaults that were not measured were not assumed correct, and where the two implementations disagreed, the default was measured. A row gives both values where they differ. Cross-toolbox parity decisions lists the values that are equal and the values that differ on purpose because the implementations use different solvers or algorithms.

Flux sampling

RAVEN MATLAB: randomSampling (method='achr'|'chrr'|'randomObjective'), sampleACHR, sampleCHRR, sampleMaxVolEllipse, sampleChebyshevCenter. raven-toolbox: random_sampling, find_good_reactions, max_volume_ellipsoid. Both implementations expose all three sampling methods through one entry point.

Parameter	Default	How determined
method	'achr' (Python); 'achr' (RAVEN 3, randomSampling)	Literature: ACHR gives near-uniform interior sampling, and the random-objective method draws polytope vertices. All three methods are selectable in both implementations ('randomObjective' in MATLAB, 'random_objective' in Python). RAVEN 2.11.3 had the random-objective method alone.
thinning	100	Upstream convention (cobrapy's own ACHR default). Measured insufficient at genome scale: on yeast-GEM one ACHR chain (seed 0) had an effective sample size (ESS) of about 10 from 300 stored samples (9.9 to 63.1 over four seeds), and 4 chains disagreed (median Gelman-Rubin R-hat 1.1020, 50.7 % of scored reactions above 1.1). At thinning=500 the lag-1 autocorrelation of one chain was 0.825 for seed 0 (0.644 to 0.825 over four seeds, ESS 28.8 to 64.9). Agreement between chains was tested at one other setting, thinning=300 with n_samples=100 (the same 30,000 steps per chain), and did not improve. See Flux sampling convergence and settings.
warmup	1000	Used by method='chrr' (the steps discarded before the first stored sample); the ACHR sampler of cobrapy has no such parameter. Same as nBurnin of RAVEN's sampleCHRR and randomSampling, which also applies to CHRR. Not measured.
n_objectives	2	Literature: Bordel et al. 2010.
replace_max_bound	False	Used by method='random_objective'. With True the run fails with an unbounded solver status: one probe of 50 objectives gave 49 optimal results and 1 unbounded result. From the cobra code, one unbounded objective stops all draws; the sample at which the run aborted was not recorded. With False, 200 samples completed. Kept different from MATLAB on purpose. Whether MATLAB's own solver path handles True was not tested.
loopless_good_reactions	True	Loopless FVA excludes reactions that take part in infeasible cycles, at higher cost; the alternative fixed-threshold (±999) heuristic also excludes reactions that legitimately reach capacity. The loopless technique is implemented in Python; porting it is a larger project than changing a default.
n_samples, seed, min_flux, max_attempts	1000, None, False, 100	Same in both implementations or no evidence favoured a different value.
flux_tol (in find_good_reactions)	1e-9	Python helper (supports target selection of the random-objective method); no cross-implementation comparison.
maxiter (in max_volume_ellipsoid)	150	Literature: Zhang & Gao 2003.
tol, reg (in max_volume_ellipsoid)	1e-6, 1e-8	Same in both implementations.

Full detail: Flux sampling convergence and settings (the primary measurement)

FSEOF

RAVEN MATLAB: FSEOF. raven-toolbox: fseof.

Parameter	Default	How determined
n_steps	10	Literature: Choi et al. 2010; measured: on iJO1366, 5 and 20 steps change the target set by 4 and 6 targets (Benchmarks).
max_fraction	0.9	Literature: Choi et al. 2010; measured: on iJO1366, 0.5 and 0.99 change the target set by 11 and 2 targets, so the sweep does not select 0.9 (Benchmarks).
correlation_threshold	0.9	Literature: Choi et al. 2010; measured: on iJO1366, 0.7 adds 4 amplification and 8 knockdown or knockout targets and 0.95 drops 2 and 2 (Benchmarks).
flux_eps	1e-6	Measured on iJO1366: the classification uses the fixed thresholds 1e-10 and 1e-8, as RAVEN does; flux_eps gates the check against the theoretical maximum, and the target counts are identical at 1e-8, 1e-7 and 1e-6.

Full detail: Benchmarks of fseof, remove_genes and other defaults

INIT / ftINIT

RAVEN MATLAB: runINIT, scoreModel, getINITModel, ftINIT, prepINITModel, ftINITInternalAlg, getINITSteps. raven-toolbox: score_reactions_from_genes, gene_scores_from_expression, ftinit, prep_init_model, run_ftinit, get_init_steps, classify_reactions.

Only MATLAB has the legacy tINIT method (runINIT, getINITModel), which ftINIT replaces; raven-toolbox implements ftINIT alone (see Context-specific models with ftINIT). Rows measured across both algorithms say so.

Parameter	Default	How determined
prod_weight	0.5	Literature: Ågren et al. 2012 (the original INIT paper).
allow_excretion	False	Recorded, without a measurement, as having no effect at the default prod_weight; no measurement is recorded on this site.
mip_gap	None in Python (solver default); 0.0004 in MATLAB	Acts on the first-step solve and on any step without a schedule of MILP runs; a named series such as 1+1 ignores it, because every step runs its own schedule. Steps are counted from 1 here; the logs count from 0, so the logged step 1 is the second step. Measured on Human-GEM (HCT116) in the first-step solve: the time is flat at 24 to 25 s, the Jaccard index to the result at the tightest gap (0.0002) is 0.9976 at 0.001 and 0.9908 at 0.01, and falls below 0.97 from 0.03. The Python default None needs no change.
time_limit	None in Python; 5000 seconds in MATLAB	RAVEN's ftINIT sets the Gurobi TimeLimit of a MILP run without its own limit to 5000 seconds, and the step schedules of getINITSteps use runs of 120 s, 5000 s and 5 s. In Python, ftinit with a named series caps each scheduled run by its own limit and time_limit lowers that cap further; time_limit=None leaves an ad-hoc step or a run_ftinit call uncapped, but no scheduled run. The task gap-fill of ftinit is a separate MILP with a fixed limit of 300 s per task and does not read time_limit. Measured on Human-GEM HCT116 (single runs): first-step solves took 23 to 30 s, and full pipelines 81 to 119 s with scaling and 157 s without. In the robustness runs of ftINIT parameter calibration and input robustness (degraded input, with tasks), the gap-fill of one 90 %-dropout model ran longer than 75 minutes; the time limit of that run is not recorded. None is the default. The study ran its full pipelines at time_limit=600, and its robustness script sets time_limit=120; the limit of the recorded robustness runs is not stated. The docstring of run_ftinit recommends 120 to 600 s per step for hard or noisy input; no measurement on this site tests that range.
series	'1+1'	Literature: Gustafsson et al. 2023 (the ftINIT paper).
force_on	0.1	RAVEN's value; measured near-insensitive across the range 0.02–0.5.
big_m	1000.0	The cap on each scored reaction's own big-M, as in ftINITInternalAlg: the big-M of a reaction is max(min(ub, big_m), min(-lb, big_m), 0), raised to force_on where the "on" floor needs it, and an infinite bound counts as big_m. The cap limits the flux a reaction can carry when it is on (it is the largest big-M of a scored reaction in its on/off constraint) and sets no bound on the reaction in the model.
resolve_ties, prove_abs_gap	False, None, both opt-in; MATLAB has them as resolveTies and proveAbsGap at the same defaults	resolve_ties fixes the answer among equal-score optima in phases: it holds the score objective at its optimum, minimises the count of kept removable (negative-score) reactions, then among those minimises the summed id rank of the kept ones (a merged reaction is ranked by the smallest original id of its group), then takes the fluxes with the least total absolute flux (relative and absolute gap 1e-6), then those with the least id-rank-weighted backward flux. The last ftINIT step skips the last two phases, because no later step reads its fluxes. Measured on Human-GEM v2.0.0-38 (one cell line, seeds 1234 and 7): with the default escalation the seed-to-seed difference is 110 reactions and 17 predicted essential genes. resolve_ties=True gives identical reaction sets at a fixed seed and reduces the seed-to-seed difference to 41 reactions and 1 gene, at about 3.6 times the run time (about 14 min against 4 min at time_limit=1800). With prove_abs_gap=1.0 as well, the difference is 1 reaction and 0 genes, at about 26 times the baseline run time (about 100 min); in the four sweep builds with prove_abs_gap=1.0, step 1 took 3305 to 3650 s (59.0 to 66.3 % of a build), mostly in its two flux phases, and step 2 another 1650 to 2500 s; no build without resolve_ties was run with prove_abs_gap. The flux phases (phase2c-minflux, phase2d-direction) reach the time limit in most builds, and a warning names each phase adopted at the limit. A tie-break that finds no incumbent in phase 2a or 2b is dropped without a warning. The escalation raises no warning either: in the four resolve_ties=True runs without prove_abs_gap of the prove_abs_gap sweep, its step 1 objective ended 4.0 to 12.0 below the proven optimum. A build without a warning is therefore not necessarily proven or tie-broken.
factor, max_score, min_score (in gene_scores_from_expression)	5.0, 10.0, -5.0	RAVEN's own formula. Wang et al. 2012 (mCADRE) use categorical scoring, and no source in either implementation cites a paper for these constants.
score_reactions_from_genes and classify_reactions arguments	various	Same in both implementations or standard practice.

Full detail: ftINIT parameter calibration and input robustness (the primary measurement, genome-scale Human-GEM) · ftINIT on different solvers · ftINIT reproducibility with resolve_ties and prove_abs_gap (resolve_ties=True gives identical reaction sets at a fixed seed and a seed-to-seed difference of 41 reactions and 1 gene; adding prove_abs_gap=1.0 reduces it to 1 reaction and 0 genes at about 26 times the run time) · Human-GEM cell-type model validation against RAVEN (Jaccard index against RAVEN across 5 cell lines of 0.964 to 0.982 for models extracted without tasks and 0.981 to 0.988 for task-constrained models)

Gap-filling

RAVEN MATLAB: fillGaps (connectivity mode), ported as connect_blocked_reactions. fill_gaps_fast_lp and fill_gaps_milp are the counterparts of RAVEN's gapFillFastLP and gapFillMILP.

Parameter	Default	How determined
epsilon (in fill_gaps_fast_lp)	0.0001	Literature: Thiele et al. 2014 (the fastGapFill paper).
threshold (in connect_blocked_reactions)	1.0	The flux a reaction must be able to exceed to count as unblocked. Same as the fixed threshold of 1 in RAVEN's fillGaps, and suited to models in the RAVEN convention, where exchange reactions have bounds of ±1000. Measured on one synthetic network with a maximum flux of 0.5, one run per value: 0.01 and 0.1 filled the gap, 0.5 and 1.0 did not (Benchmarks).
penalty, allow_net_production (in connect_blocked_reactions)	1.0, False	No measurement recorded. penalty=1.0 makes the MILP minimise the number of added reactions. allow_net_production=False keeps the steady-state balance S @ v == 0, the stricter test; True relaxes it to S @ v >= 0.
big_m (in fill_gaps_milp)	1000.0	Matches RAVEN's ±1000 bound convention. It caps an unbounded template reaction when the merged model has no finite bound; otherwise the largest finite bound does.

Full detail: Benchmarks of fseof, remove_genes and other defaults

Homology-based reconstruction

RAVEN MATLAB: getModelFromHomology, getBlast, getDiamond. raven-toolbox: get_model_from_homology, run_blast, run_diamond.

Parameter	Default	How determined
evalue (run_blast)	1e-4	Matches MATLAB's getBlast (hardcodes -evalue 10e-5). On H. polymorpha against S. cerevisiae, loosening it from 1e-5 to 1e-4 added 5,200 raw hits (8.9 % of the table); 5,310 of the 5,314 rows found at 1e-4 alone fail max_evalue=1e-30 of get_model_from_homology; 4 have an E-value below 1e-30.
evalue (run_diamond)	1e-3	Matches MATLAB's getDiamond, which passes no -evalue flag and so inherits DIAMOND's own default. The value is deliberately different from run_blast; each aligner follows its own MATLAB call.
threads	every CPU the process may run on (Python); the cores assigned to MATLAB (MATLAB)	A 500-query BLAST subset returned the same 2,746 hits at one thread and at four, in 20.4 s and 5.9 s (3.5 times faster; H. polymorpha against S. cerevisiae, BLAST+ 2.17.0, 2026-11-01). Both sides use all available cores.
max_evalue	1e-30	At identity 40 % and length 200 the score was identical from 1e-4 to 1e-30 in four organisms (BLAST, F0.5, KEGG reference). 1e-50 lowered it by 0.002 to 0.006 in three organisms and left it unchanged in K. lactis, and 1e-100 lowered it by 0.020 to 0.063.
min_align_len	100; RAVEN 2.11.3 used 200	Measured against the KEGG ortholog reference for 4 organisms of increasing distance from S. cerevisiae: compared with 200, a length of 100 raised the score (F0.5) by 0.010 to 0.020, with 2.2 to 3.8 percentage points more recall and at most 0.6 percentage points less precision; 50 and 100 gave the same score. No OMA scores were reported for length. RAVEN 3's getModelFromHomology uses minLen 100 too.
min_identity	40	Same study: over 25 to 50 % the score changed by 0.062 to 0.275, against 0.047 to 0.055 over alignment lengths 50 to 300 and 0.020 to 0.063 over E-values 1e-4 to 1e-100. With a wrongly transferred reaction weighted as worse than a missing one (F0.5, BLAST), the optimum was 35 or 40 % against KEGG and 35 or 45 % against OMA, depending on the organism; with DIAMOND against OMA the optimum for E. coli is 30 %.
bidirectional	True	Same in both implementations.

Scoring against curated GEMs (hanpo-GEM, rhto-GEM) built by the same reconstruction method is not part of the 2026-11-01 measurement.

Full detail: Homology cut-off calibration

KEGG-based reconstruction

RAVEN MATLAB: getKEGGModelForOrganism (the exact internal name of the KO-assignment sub-step has not been independently confirmed). raven-toolbox: assign_kos, run_hmmsearch, build_ko_hmm, and the get_kegg_model_* family.

Parameter	Default	How determined
cutoff (in assign_kos)	1e-30; RAVEN 2.11.3 used 1e-50	Measured against real KEGG gene→KO annotations across 4 organisms of varying study depth: 1e-50 lies inside the E-value distribution of the matched pairs and removes 1 to 10 % of them in three organisms and 10 to 50 % in M. genitalium. Precision rose with every stricter cut-off (S. cerevisiae 0.73 at 1e-30, 0.78 at 1e-50); recall fell by at most 0.03 down to 1e-30 and fell further at stricter cut-offs. Together with min_score_ratio_g 0.9, the default raised recall in all four organisms and changed F1 by at most 0.01 in three organisms and raised it by 0.02 in E. coli. RAVEN 3's getKEGGModelForOrganism uses cutOff 10^-30 too.
min_score_ratio_g (in assign_kos)	0.9; RAVEN 2.11.3 used 0.8	Same study: from 0.80 to 0.95 it raised precision by 0.05 to 0.10 at a recall loss of 0.00 to 0.02, and from 0.50 to 0.80 by 0.15 to 0.27; 0.9 itself was not swept. RAVEN 3's getKEGGModelForOrganism uses minScoreRatioG 0.9 too.
min_score_ratio_ko (in assign_kos)	0.3	Same study: at min_score_ratio_g 0.80 and cutoff 1e-50, raising it from 0.0 to 0.5 raised precision by 0.00 to 0.03 and lowered recall by 0.00 to 0.01; other combinations were not reported. Kept at the RAVEN value.
seq_identity (in build_ko_hmm)	0.9	Literature: the CD-HIT recommendation for protein clustering.
threads (run_hmmsearch, build_ko_hmm)	every CPU the process may run on	hmmsearch on S. cerevisiae gave identical parsed hit tables on 16, 12, 4 and 1 threads, with run times of 89.7, 88.6, 96.4 and 242.5 s (KEGG HMM cut-off calibration). build_ko_hmm was not measured.
keep_spontaneous, keep_undefined_stoich, keep_incomplete, keep_general (get_kegg_model_*)	True, True, True, False	Same in both implementations or standard draft-reconstruction practice.

Full detail: KEGG HMM cut-off calibration (the primary measurement, 4 organisms against real KEGG annotations)

Sub-cellular localisation

RAVEN MATLAB: predictLocalization. raven-toolbox: predict_localization. The two use different algorithms: MATLAB uses simulated annealing, a stochastic heuristic with no optimality guarantee, and Python solves a deterministic MILP. For several parameters the question of which value is right does not arise, because the same number means something different in the search budget of a heuristic than in the optimality gap of a solver.

Compartment assignment is in development in Python and is not at parity with MATLAB. The defaults are current, and the method they configure is changing.

Parameter	Default	How determined
default_compartment	'c' in Python; MATLAB requires the argument every call	Convenience default: cytosol is correct for most reactions. MATLAB has no default, so no values disagree.
transport_cost	0.5	Not independently benchmarked; same in both implementations. For real (non-integer-scale) predictor scores the value needs lowering in the studies that measured it; see the linked study.
time_limit (Python) and maxTime (MATLAB)	None (uncapped) in Python; MATLAB's maxTime defaults to 15 minutes	The values are not unified: the MATLAB number is a simulated-annealing search budget (more time can improve the heuristic answer, without a proof of optimality), and the Python number is a MILP solver cutoff (returns a proven-bounded incumbent). Python's None was validated at ~2.5 minutes on yeast-GEM, the primary development-scale model. Whether the MILP needs a cap for harder cases has not been stress-tested.
mip_gap	None	Python; the MATLAB heuristic has no optimality gap.

Full detail: the measurements behind these values (the yeast-GEM benchmark, the head-to-head against MATLAB's predictLocalization, the predictor benchmarks across four organisms, and the design documents) are in raven-gecko-parity, with the rest of the work in progress on this method.

Model manipulation

RAVEN MATLAB: removeGenes, mergeModels, addRxns, addTransport, simplifyModel (reversibility-constraining and duplicate-removal modes). raven-toolbox: remove_genes, merge_models, add_reactions_from_equations, add_transport_reactions, constrain_reversible_reactions, find_duplicate_reactions.

Parameter	Default	How determined
blocked_reactions (in remove_genes)	'constrain' in Python; MATLAB's removeGenes with removeBlockedRxns false does the same	Measured on e_coli_core: keeping a reaction unchanged after it has lost its last gene gives false-positive growth after an essential gene is removed (it survives with an empty gene rule). 'constrain' sets the bounds of such a reaction to 0 and keeps the reaction; 'remove' deletes it and is opt-in, for gene-essentiality or metabolic-engineering use where the reaction is to disappear; 'keep' leaves it unchanged.
eps (in constrain_reversible_reactions)	1e-10	The threshold of constrainReversible in RAVEN's simplifyModel.
match_by (in merge_models)	'name' (Python) and 'metNames' (MATLAB)	Same semantic field (metabolite display name) under a different schema field name; no values disagree.
remove_orphans, mets_by, ignore_direction, reversible, only_to_existing	various	Python additions and refinements; no direct MATLAB equivalent exists for comparison.

Full detail: Benchmarks of fseof, remove_genes and other defaults

Tasks

RAVEN MATLAB: checkTasks. raven-toolbox: check_tasks, find_task_essential_reactions.

Parameter	Default	How determined
close_boundaries (both functions)	False	Matches RAVEN's checkTasks, which leaves the model's own exchange reactions open; published task lists were curated against that. True makes the task the whole boundary, as closeModel does in RAVEN.

Full detail: Benchmarks of fseof, remove_genes and other defaults

Cross-toolbox parity decisions

Where the Python and MATLAB implementations disagree on a default, one value is used for both. The exception is a difference between the implementations that requires different values (solver stack, algorithm or data schema), where identical values would give incorrect behaviour. The tables state which side changed, or why the values differ.

Unified values

Parameter	Python	MATLAB	Common value	Side that changed	Confidence	Evidence
run_blast.evalue	1e-4	1e-4	1e-4	Python	High	No downstream effect measured either way (get_model_from_homology's 1e-30 dominates); matches develop's independent PR #91 call
get_model_from_homology.min_align_len	100	100 (200 in RAVEN 2.11.3)	100	Python and MATLAB	High	Directly measured: Homology cut-off calibration
assign_kos.cutoff	1e-30	1e-30 (1e-50 in RAVEN 2.11.3)	1e-30	MATLAB	High	Directly measured: KEGG HMM cut-off calibration
assign_kos.min_score_ratio_g	0.9	0.9 (0.8 in RAVEN 2.11.3)	0.9	MATLAB	High	Directly measured, same study
run_ftinit.allow_excretion	False	False	False	Python	High	Recorded, without a measurement, as having no effect at the default prod_weight; no measurement is recorded on a page of Parameter tuning. The values were unified to remove the inconsistency. Within ftinit, the step schedule sets the value (allow_met_secr)

Settings resolved without unification

The INIT parameters mip_gap and time_limit of run_init and run_ftinit are resolved by the genome-scale test with real expression data in ftINIT parameter calibration and input robustness (Human-GEM HCT116 and Hart2015), without matching the MATLAB numbers:

• mip_gap: solve time is flat across the gap in the single first-step solve, so a tight gap is nearly free there. The docstring of run_ftinit attributes the flat time to model construction; the study did not measure the share of model construction. A named series such as 1+1 ignores mip_gap and mip_gap_abs, because every step runs its own schedule of MILP runs, so the full pipeline gives the same result at 0.001, 0.003 and 0.01. Neither 0.001 nor 0.01 is MATLAB's 0.0004. Keep None (Gurobi's own ~1e-4 default is at least as tight as the measured-good 0.001).

• time_limit: RAVEN's ftINIT limits a MILP run to 5000 seconds when the run sets none, and its schedules use 120 s, 5000 s and 5 s runs. Python's ftinit with a named series caps each scheduled run by the same limits, and time_limit lowers the cap; time_limit=None leaves an ad-hoc step or a run_ftinit call uncapped, but no scheduled run. Measured first-step solves took 23 to 30 s, and full pipelines 81 to 119 s with scaling and 157 s without. In the robustness setup, the gap-fill of one 90 %-dropout model ran longer than 75 minutes; the gap-fill has its own fixed limit of 300 s per task and does not read time_limit. The study ran its full pipelines at 600 s; its robustness script sets 120 s, and the limit of the recorded robustness runs is not stated. Keep None as the code default. The docstring of run_ftinit recommends 120-600 s/step, a range with no measured basis on this site.

ftINIT on different solvers measured GLPK honouring the 900 s limit: both steps of the full pipeline hit it, the run took 2182 s and kept 6758 reactions (Jaccard 0.892 against Gurobi). The scipy interface (HiGHS) fails because it supports continuous variables. Gurobi is the backend the genome-scale numbers above were measured on. It completed the run within the overall limit; one scheduled solve stopped at a limit (the warning in the big_m=1000 run).

Values kept different

Parameter	Python	MATLAB	Why
random_sampling.replace_max_bound	False	True	With every bound of 1000 replaced by infinity, the random-objective LP becomes unbounded, because the objective can grow without limit through any reaction that is then unconstrained (Flux sampling convergence and settings gives this mechanism, read from the cobra code; the run with True failed with an unbounded status, the sample of the abort was not recorded, and the run with False completed 200 samples). Whether MATLAB's own solver path handles True was not tested.
random_sampling.loopless_good_reactions	True (loopless FVA)	heuristic (exclude FVA ≥ 0.999 times the loop bound, 999 for a bound of 1000)	The two are different techniques. Loopless FVA excludes reactions that take part in infeasible cycles, and the MATLAB heuristic also excludes reactions that legitimately reach capacity. Porting the loopless technique to MATLAB is a larger project than changing a default.
merge_models.match_by	'name'	'metNames'	Same semantic field (metabolite display name) under a different field name, which results from the cobrapy and COBRA Toolbox schemas; the field name is not tunable.
run_diamond.evalue	1e-3	1e-3 (implicit)	Both agree at 1e-3, matching the DIAMOND default. getDiamond passes no -evalue flag, so it inherits the DIAMOND default and does not follow the explicit 1e-4 of getBlast. run_blast and run_diamond deliberately stay unpaired: each follows the MATLAB call of its own aligner.
predict_localization.time_limit	None	maxTime=15 (minutes)	localization/predictLocalization.m shows that MATLAB solves the problem with simulated annealing (maxTime is its search budget; more time can improve the heuristic answer, with no optimality guarantee), and Python solves a deterministic MILP (time_limit is a solver cutoff; returns a proven-bounded incumbent). The same number has a different role in each, so the values are not unified. A common value of None would assume that both sides solve the same kind of problem, which they do not.
predict_localization.default_compartment	'c'	required arg (no default)	MATLAB has no default; Python's 'c' is a convenience default for the near-universal correct choice and produces no output difference (a MATLAB user must already supply 'c' explicitly in the common case). Optional, low priority: MATLAB could add the same default.

Defaults that differ between RAVEN 2.11.3 and RAVEN 3

The RAVEN 3 defaults of getKEGGModelForOrganism (cutOff, minScoreRatioG) and getModelFromHomology (minLen) equal the raven-toolbox defaults. The values come from src/ on RAVEN develop:

Function	RAVEN 2.11.3	RAVEN 3
getKEGGModelForOrganism, cutOff	10^-50	10^-30
getKEGGModelForOrganism, minScoreRatioG	0.8	0.9
getModelFromHomology, minLen	200	100

The time-budget parameter of the localisation is not unified: predictLocalization.m solves the problem with simulated annealing (maxTime, default 15 minutes, a search budget), and raven-toolbox uses a deterministic MILP (time_limit, a solver cutoff). See the table of values kept different.

Open items

• Genome-scale ACHR sampling convergence has no cheap fix. At genome scale (yeast-GEM, default ACHR settings) the median reaction fails the R-hat>1.1 threshold. Neither follow-up fixes it cheaply: reallocating the same step budget onto a larger thinning makes no measurable difference, and method='chrr' converges on a small model, but its genome-scale cost makes it impractical as a drop-in fix: a probe of 2 chains × 20 samples on yeast-GEM took 136 min (8147.9 s) in total, and the share of the rounding step in that time was not measured. Flux sampling convergence and settings describes the investigation, including a possible future unblock (caching the CHRR rounding transform across calls, an engineering change, so out of scope here).

• The gene_scores_from_expression scoring constants (factor=5.0, max_score=10.0, min_score=-5.0) have no confirmed literature source on either side; see the INIT section.

References

Choi HS, Lee SY, Kim TY, Woo HM (2010). In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol 76(10):3097–3105. https://doi.org/10.1128/AEM.00115-10

Bordel S, Ågren R, Nielsen J (2010). Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 6(7):e1000859. https://doi.org/10.1371/journal.pcbi.1000859

Zhang Y, Gao L (2003). On numerical solution of the maximum volume ellipsoid problem. SIAM J Optim 14(1):53–76. https://doi.org/10.1137/S1052623401397230

Ågren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J (2012). Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8(5):e1002518. https://doi.org/10.1371/journal.pcbi.1002518

Gustafsson J, Anton M, Roshanzamir F, et al. (2023). Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data. Proc Natl Acad Sci USA 120(6):e2217868120. https://doi.org/10.1073/pnas.2217868120

Thiele I, Vlassis N, Fleming RMT (2014). fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30(17):2529–2531. https://doi.org/10.1093/bioinformatics/btu321

Not listed: the gene_scores_from_expression constants (factor, max_score, min_score). Neither the source of RAVEN nor that of raven-toolbox cites a paper for them. Wang et al. 2012 (mCADRE) use categorical scoring. The constants are a choice of RAVEN with no confirmed literature source.