Replace figurative verbs and idioms in help text, comments and the README

databases/README.md

@@ -10,7 +10,7 @@
 
 ### BRENDA
 
-GECKO does not ship BRENDA data. `fetchBrendaAsset` downloads the files of the [toolbox-assets](https://github.com/CompMetEng/toolbox-assets) release `brenda-2026.1-1` ([BRENDA](https://www.brenda-enzymes.org) 2026.1, CC BY 4.0) on first use into `databases/brenda-2026.1-1`, checked against the manifest checksums. geckopy uses the same release. A project can use its own BRENDA files instead, in `data/brenda`, written by the `geckopy brenda-refresh` CLI of [geckopy](https://github.com/CompMetEng/geckopy).
+GECKO does not include BRENDA data. `fetchBrendaAsset` downloads the files of the [toolbox-assets](https://github.com/CompMetEng/toolbox-assets) release `brenda-2026.1-1` ([BRENDA](https://www.brenda-enzymes.org) 2026.1, CC BY 4.0) on first use into `databases/brenda-2026.1-1`, checked against the manifest checksums. geckopy uses the same release. A project can use its own BRENDA files instead, in `data/brenda`, written by the `geckopy brenda-refresh` CLI of [geckopy](https://github.com/CompMetEng/geckopy).
 
 - `kcat.tsv`: kcat values per organism, reaction and substrate. Both a per-triple maximum (`kcat_max`) and median (`kcat_median`) are recorded. Which one is used is set by `params.kcatAggregation` on the model adapter, or per call with `fuzzyKcatMatching(model,'kcatAggregation','median')`; the default is `max`.
 - `sa.tsv`: specific activities per organism, reaction and substrate, with a maximum (`sa_max`) and median (`sa_median`); `loadBRENDAdata` uses `sa_max`.

src/change_model/applyKcatConstraints.m

@@ -163,7 +163,7 @@
         % isozyme row without any enzyme has MWkcat zero. Drop those
         % isozymes *before* selecting the cheapest one: including them in
         % the min() below would let an isozyme with no kcat or no enzyme
-        % always win over a real kcat on another isozyme of the same reaction.
+        % always be selected over a real kcat on another isozyme of the same reaction.
         % A row with a kcat that is not positive or an enzyme mass that is not
         % positive has no usable cost either.
         hasEnzyme = full(sum(model.ec.rxnEnzMat(ecIdx,:) ~= 0,2)) > 0;

src/change_model/getSubsetEcModel.m

@@ -72,7 +72,7 @@
 smallEcModel = removeReactions(smallEcModel,~keepRxns, true, true, true);
 
 % Remove reactions from ec-structure
-% The reaction identifiers of a light ecModel carry a 4 character prefix
+% The reaction identifiers of a light ecModel have a 4 character prefix
 if smallEcModel.ec.geckoLight
     ecModelRxns = extractAfter(smallEcModel.ec.rxns,4);
 else

src/download_databases/fetchBrendaAsset.m

@@ -1,7 +1,7 @@
 function folder = fetchBrendaAsset(files)
 % fetchBrendaAsset  Download (once) GECKO's BRENDA files and return their folder.
 %
-% GECKO does not ship BRENDA data. The files of the toolbox-assets release
+% GECKO does not include BRENDA data. The files of the toolbox-assets release
 % brenda-2026.1-1 (BRENDA 2026.1, CC BY 4.0) are downloaded on first use
 % with RAVEN's fetchToolboxAsset, which checks them against the checksums
 % of the toolbox-assets manifest, and are unpacked into

src/download_databases/loadBRENDAdata.m

@@ -7,7 +7,7 @@
 % and specific activities (the latter converted to kcat values using the
 % molecular weights).
 %
-% kcat.tsv and sa.tsv each carry both a max and a median aggregate per
+% kcat.tsv and sa.tsv each have both a max and a median aggregate per
 % (EC, substrate, organism) triple; kcatAggregation selects which of the
 % two is read. Each file starts with a `#`-prefixed release-version line
 % followed by a tab-delimited column header, both skipped on read.

src/flux_analysis/getPfbaEnzymes.m

@@ -5,7 +5,7 @@
 % value of the current objective, finds the one that minimises total
 % enzyme usage: the smallest sum of usage_prot_* fluxes. This is the
 % enzyme-aware analogue of classical pFBA, which minimises total flux
-% instead --- for ecModels this usually picks the most parsimonious
+% instead --- for ecModels this usually selects the most parsimonious
 % *proteome*, not just the most parsimonious fluxes.
 %
 % Parameters
@@ -21,7 +21,7 @@
 %     (default: use model.c as given).
 % fractionOfOptimum : double
 %     fraction of the optimal objective value to fix before minimising
-%     enzyme usage (default 1.0). A value below 1.0 lets the objective fall
+%     enzyme usage (default 1.0). A value below 1.0 allows the objective to fall
 %     by (1 - fractionOfOptimum) times the magnitude of its optimum, so it
 %     also applies to a negative optimum (for example the exchange flux of
 %     a nutrient that is maximised).

src/gather_kcats/collectKcats.m

@@ -24,7 +24,7 @@
 %     [kcat_sources.okp]
 %     methods = ["CataPro", "UniKP"]   # the okp source group
 %
-% A stage decides the reactions that are still open:
+% A stage sets the kcat of the reactions that are still open:
 % - select: the first listed source with a value (see mergeKcats). Sources
 %   are the names of source files (custom, ec_tree, dlkcat, okp_catapro,
 %   ...) and the BRENDA quality tiers database_exact, database_top and

src/gather_kcats/convertCustomKcats.m

@@ -4,7 +4,7 @@
 % Writes the custom kcats of a GECKO 3 project as the curated kcat source
 % file data/kcats/custom.tsv, which collectKcats reads first. The kcats are
 % those applyCustomKcats gives the ecModel, including which of two entries
-% for the same reaction wins: one row per reaction, keyed by conventional
+% for the same reaction is kept: one row per reaction, keyed by conventional
 % reaction and enzyme set (see getEcRxnKeys), with the entry's notes in the
 % note column. The stoicho column is left out: complex stoichiometry comes
 % from applyComplexData. Unlike applyCustomKcats, custom.tsv also applies to

src/gather_kcats/getStandardKcat.m

@@ -145,7 +145,7 @@
     enzSubSystem_names = enzSubSystems(firstOccurrence);
     % Calculate the median kcat per subSystem, counting only reactions
     % with a real (nonzero, non-NaN) kcat toward both the median itself
-    % and the threshold that decides whether a subSystem-specific value
+    % and the threshold that determines whether a subSystem-specific value
     % is used at all.
     numSubSystem = numel(enzSubSystem_names);
     kcatsPerSubSystem = zeros(numSubSystem,1);

src/gather_kcats/mergeDLKcatAndFuzzyKcats.m

@@ -3,7 +3,7 @@
 %
 % Deprecated thin wrapper around mergeKcats, kept for the common DLKcat +
 % fuzzy-BRENDA case. New code should call mergeKcats directly, which also
-% handles a single list that mixes several sources (e.g. an
+% accepts a single list that mixes several sources (e.g. an
 % OpenKineticsPredictor result with BRENDA/Sabio-RK/CataPro rows).
 %
 % Merges the results from DLKcat and fuzzy matching to the BRENDA database.

src/gather_kcats/mergeKcats.m

@@ -5,9 +5,9 @@
 % Generalizes mergeDLKcatAndFuzzyKcats: instead of two fixed inputs (DLKcat
 % + fuzzy BRENDA) it accepts a cell array of kcatLists, each of which may
 % itself mix several sources (for example the output of
-% fetchOpenKineticsPredictor, carrying BRENDA, Sabio-RK and CataPro values
-% in its kcatSource field). A caller-supplied sourcePriority decides which
-% source wins for each reaction.
+% fetchOpenKineticsPredictor, holding BRENDA, Sabio-RK and CataPro values
+% in its kcatSource field). A caller-supplied sourcePriority determines which
+% source is kept for each reaction.
 %
 % Parameters
 % ----------
@@ -120,7 +120,7 @@
 
 dbSources = cellfun(@normalizeSource, databaseSources, 'UniformOutput', false);
 
-% Rank map: normalised priority token -> rank (1 = best). First wins.
+% Rank map: normalised priority token -> rank (1 = best). The first is kept.
 rankMap = containers.Map('KeyType','char','ValueType','double');
 for r = 1:numel(sourcePriority)
     key = normalizeSource(sourcePriority{r});

src/gather_kcats/readOpenKineticsPredictorOutput.m

@@ -87,7 +87,7 @@
     outFile = fullfile(fPath, fName);
 end
 
-%% Read OKP output file using readcell to correctly handle quoted fields
+%% Read OKP output file using readcell to correctly read quoted fields
 % that may contain commas in the 'Extra Info kcat' column.
 if ~exist(outFile,'file')
     error('Cannot find file: %s', outFile)

src/gather_kcats/submitOpenKineticsPredictor.m

@@ -5,7 +5,7 @@
 % SMILES, via writeOpenKineticsPredictorInput) and submits a kcat prediction
 % job to the OKP REST API (https://predictor.openkinetics.org/). The returned
 % job id is also stored in data/OKP_job.txt, so fetchOpenKineticsPredictor can
-% pick it up later.
+% read it later.
 %
 % Predictors available via OKP: CataPro, CatPred, CatRange, DLKcat, EITLEM,
 % KinForm-H, KinForm-L, OmniESI, OmniESI-O2DENet, TurNup, UniKP (see GET
@@ -19,7 +19,7 @@
 %     2. In the "API key generator" section, click "Generate".
 %     3. Copy the key shown (it is displayed only once). If you lose it,
 %        revoke it on the same page and generate a new one.
-%   Keys are tied to your IP and carry a daily prediction quota that resets
+%   Keys are tied to your IP and have a daily prediction quota that resets
 %   at midnight UTC.
 %
 %   The key is a secret; do NOT put it in the model adapter (which is

src/get_enzyme_data/findECInDB.m

@@ -192,7 +192,7 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 function tf = subsumes(general,specific)
 %True if the code general is equal to or a wildcard parent of the code
-%specific; both carry the "EC" prefix and need four levels.
+%specific; both have the "EC" prefix and need four levels.
 general  = strsplit(extractAfter(general,2),'.');
 specific = strsplit(extractAfter(specific,2),'.');
 tf = false;

src/kcat_tuning/evotune/bestParsimonious.m

@@ -5,7 +5,7 @@
 % Defaults to 2%, which is the scale of alternate-optimum noise typically
 % seen from re-solving an LP with a slightly different kcat vector --
 % below that, two vectors are not meaningfully different in fit, so the
-% sparser one wins.
+% sparser one is kept.
 %
 % Parameters
 % ----------

src/kcat_tuning/evotune/cmaesKcatTuning.m

@@ -7,7 +7,7 @@
 % maxGenerations/rmseThreshold are its stopping conditions.
 %
 % Vendors a separable (diagonal-covariance) CMA-ES -- MATLAB has no
-% built-in implementation and Global Optimization Toolbox doesn't ship
+% built-in implementation and Global Optimization Toolbox does not include
 % one either. Separable rather than full-covariance because that is what
 % geckopy's own port actually runs (cma's CMA_diagonal=True) and what its
 % defaults were validated against; it is also lower risk to implement
@@ -45,7 +45,7 @@
 % Returns
 % -------
 % ecModel : struct
-%     mutated: tunable rows carry the best kcat vector CMA-ES found, with
+%     mutated: tunable rows hold the best kcat vector CMA-ES found, with
 %     kcat constraints already applied. MATLAB structs are copied on
 %     assignment, so -- unlike geckopy's in-place mutation -- this output
 %     must be captured by the caller.
@@ -129,8 +129,8 @@
 
 expand = @(Xlog) exp(Xlog(assign,:));
 
-% Baseline: score the untouched prior, and pick up the excarbon-augmented
-% model to carry through the rest of the run (see evotuneScore's 4th
+% Baseline: score the untouched prior, and keep the excarbon-augmented
+% model to use in the rest of the run (see evotuneScore's 4th
 % output -- avoids rescanning metabolite formulas on every candidate).
 [obj0,rmse0,~,baseModel] = scoreCandidate(ecModel,tunableIdx,ecRxnIdsTunable,evoData,modelAdapter,params,kcat0,kcat0,sigma0log);
 bestObj = obj0; bestRmse = rmse0; bestVec = kcat0;

src/kcat_tuning/evotune/evotuneScore.m

@@ -46,7 +46,7 @@
 %     the input ecModel, with ec.excarbon cached on it if it wasn't
 %     already present. MATLAB structs are copied on assignment, not
 %     shared by reference like a Python object, so callers scoring many
-%     candidates in a loop should carry this output back in as the base
+%     candidates in a loop should pass this output back in as the base
 %     copy for the next candidate, rather than recomputing excarbon (an
 %     expensive metabolite-formula scan) on every single call.
 %
@@ -146,7 +146,7 @@
     sol = solveLP(model_tmp);
     if checkSolution(sol)
         bioRxn  = getIndexes(model_tmp,data.biomass,'rxns');
-        % Biomass carries an assumed 41 Cmmol/gDCW, matching fillCarbonNum.
+        % Biomass has an assumed 41 Cmmol/gDCW, matching fillCarbonNum.
         bioMeas = data.grRate(i) * 41;
         bioSim  = sol.x(bioRxn) * 41;
 

src/kcat_tuning/evotune/parsimonyFrontier.m

@@ -8,7 +8,7 @@
 % units of each kcat's own prior sigma) and reports how RMSE trades off
 % against how many kcats stayed at their tuned value, so a sparser
 % result within noise of the best fit can be preferred over the raw
-% tuner output. See bestParsimonious to pick a point off this frontier.
+% tuner output. See bestParsimonious to select a point on this frontier.
 %
 % Parameters
 % ----------

src/kcat_tuning/evotune/screenKcatLeverage.m

@@ -92,7 +92,7 @@
 rmses = zeros(nVec,1);
 
 if nProc == 1
-    % Serial path: carry the excarbon-augmented model from one call into
+    % Serial path: pass the excarbon-augmented model from one call to
     % the next, so the metabolite-formula scan behind it (evotuneScore's
     % 4th output) runs once for the whole screen rather than once per
     % probe vector.

src/kcat_tuning/evotune/tunePriorPenaltyWeight.m

@@ -1,7 +1,7 @@
 function sweepTable = tunePriorPenaltyWeight(ecModel,varargin)
 % tunePriorPenaltyWeight  Sweep priorPenaltyWeight and report the
-% fit/reproducibility trade-off, so the value doesn't have to be picked
-% blind.
+% fit/reproducibility trade-off, so the value can be selected from the reported
+% results.
 %
 % priorPenaltyWeight charges for moving a kcat away from its prior, and
 % mainly exists to keep large corrections reproducible across seeds

src/kcat_tuning/sigmaFitter.m

@@ -1,7 +1,7 @@
 function [model, sigma] = sigmaFitter(model, varargin)
 % sigmaFitter  Fit the average enzyme saturation factor of an ecModel.
 %
-% Scans candidate sigma-factors from 0.01 to 1 in steps of 0.01 and picks the
+% Scans candidate sigma-factors from 0.01 to 1 in steps of 0.01 and selects the
 % one whose simulated growth rate best matches the provided experimental
 % growth rate, then applies it to the model's protein pool constraint.
 %

src/limit_proteins/calculateFfactor.m

@@ -70,7 +70,7 @@
 if ischar(protData) && endsWith(protData,'paxDB.tsv')
     % Gather Uniprot database for finding MW. Only needed here, to map paxDB.tsv's gene
     % identifiers onto UniProt IDs and molecular weights --- a protData struct supplied by
-    % the caller already carries UniProt IDs and needs no lookup, so this stays inside the
+    % the caller already has UniProt IDs and needs no lookup, so this stays inside the
     % branch that actually uses it rather than running (and potentially downloading or
     % reading UniProt data) on every call.
     uniprotDB = loadDatabases('uniprot', modelAdapter);

src/limit_proteins/flexibilizeEnzConcs.m

@@ -44,7 +44,7 @@
 %     enzyme (default false). Enzymes and complexes are then ranked by the
 %     growth gained per foldChange of raising all their usage upper bounds
 %     together (a levelled complex binds on all subunits at once, so no
-%     single subunit shows a gain), every measured subunit of the picked
+%     single subunit shows a gain), every measured subunit of the selected
 %     complex is raised by the same factor, iterations are counted per
 %     complex, and one factor per complex is kept in the final pass, so that
 %     concentrations levelled by levelComplexConcs stay levelled. An upper

src/limit_proteins/relaxProteomicsGreedy.m

@@ -51,7 +51,7 @@
 % Different relaxation strategy from flexibilizeEnzConcs: this one is
 % shadow-price-ordered (one solve per step) and often converges in fewer
 % iterations when one or two enzymes dominate the infeasibility. The
-% trade-off is that it fully unconstrains each picked enzyme with no
+% trade-off is that it fully unconstrains each selected enzyme with no
 % tighten-back pass, so the result is looser (less proteomics-faithful).
 %
 % There are two distinct non-convergence outcomes: running out of

src/model_adapter/convertAdapterToToml.m

@@ -9,7 +9,7 @@
 % [matlab.params], so that loadAdapterParams gives back the same params.
 %
 % Methods that the adapter class overrides cannot be written to TOML.
-% They are listed in the report, with how to carry them over:
+% They are listed in the report, with how to convert them:
 % - getSpontaneousReactions: set [spontaneous] name_contains or file;
 % - makeModelAnaerobic, changeProteinBiomass: set [matlab]
 %   make_model_anaerobic or change_protein_biomass to a function name;

src/utilities/upgradeGECKOproject.m

@@ -1,7 +1,7 @@
 function report = upgradeGECKOproject(projectPath, varargin)
 % upgradeGECKOproject  Bring a GECKO 3 project to the GECKO 4 layout.
 %
-% Lists, and with 'dryRun', false carries out, the steps that bring a
+% Lists, and with 'dryRun', false performs, the steps that bring a
 % project made with GECKO 3 (or an earlier GECKO 4) to the current layout:
 %
 % 1. adapter: an adapter class without model_adapter.toml gets one