From ae7c7a1da7d614cb8b922592dfc4ea1354bd2e88 Mon Sep 17 00:00:00 2001 From: edkerk Date: Mon, 6 Mar 2023 00:18:18 +0100 Subject: [PATCH] chore: remove old files --- .travis.yml | 53 -------- GECKO.png | Bin 97253 -> 0 bytes README.rst | 4 +- protocol.m | 19 --- tutorials/.keep | 0 tutorials/protocol.m | 306 ------------------------------------------- 6 files changed, 2 insertions(+), 380 deletions(-) delete mode 100644 .travis.yml delete mode 100644 GECKO.png delete mode 100644 tutorials/.keep delete mode 100644 tutorials/protocol.m diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index c862e3c06..000000000 --- a/.travis.yml +++ /dev/null @@ -1,53 +0,0 @@ -language: python -sudo: false -git: - depth: 2 -cache: - pip: true -branches: - only: - - master - - devel - - "/v\\d+\\.\\d+\\.\\d+/" -matrix: - fast_finish: true - include: - - os: linux - python: '3.5' - env: - - TOXENV=flake8 - - os: linux - python: '3.6' - env: - - TOXENV=py36 - - os: linux - python: '3.7' - env: - - TOXENV=py37 - - os: linux - python: '3.8' - env: - - TOXENV=py38 -before_install: -- cd geckopy -- make copy-models -install: -- pip install --upgrade pip setuptools wheel tox -script: -- travis_wait tox -notifications: - email: - on_success: never - on_failure: always -deploy: - provider: pypi - skip_cleanup: true - distributions: sdist bdist_wheel - user: BenjaSanchez - password: - secure: 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b/README.rst index df6317904..0929afcb6 100644 --- a/README.rst +++ b/README.rst @@ -1,4 +1,4 @@ -.. image:: GECKO.png +.. image:: docs/GECKO.png :align: center |Current Version| |Tests passing| |Gitter| |Zenodo| @@ -63,7 +63,7 @@ Getting started In the GECKO folder, ``protocols.m`` contains instructions on how to reconstruct and analyze an ecModel for *S. cerevisiae*. -Notes on GECKO 1 and 2 +GECKO 1 and 2 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Due to significant refactoring of the code, GECKO version 3 is generally not backwards compatible with earlier GECKO versions. diff --git a/protocol.m b/protocol.m index 636831a0a..4d0e6f51a 100644 --- a/protocol.m +++ b/protocol.m @@ -1,10 +1,3 @@ -%% This script sketches the blueprint for the GECKO3 protocol. -% At a later stage, it can likely be converted into a script that is -% referenced in the protocol paper. Right now, this script might contain -% comments and clarifications that should rather be included in the -% protocol than in here, while other comments might be best kept in this -% script. At the moment it is just a sketchbook. - %% Prepare for ec model reconstruction % - Prepare a folder in userData, or select another path whether all model- % specific files and scripts will be kept @@ -31,17 +24,7 @@ % - Modify the ModelAdapter file in the appropriate userData subfolder % with some of the above information and more. -%% Summary -% The pipeline can look different dependent on user preferences. Getting an -% all-DLKcat model would look something like: -% makeEcModel -> findMetSmiles -> writeDLKcatInput -> readDLKcatOutput -> -% selectKcatValue -> applyKcatConstraints -> ... -% while a fuzzy matching to BRENDA approach looks like: -% makeEcModel -> getECfromDatabase -> fuzzyMatching -> applyKcatConstraints -% -> ... - %% Initiate model reconstruction - % Set the ModelAdapter correctly. This loads the ModelAdapter file that is % in userData/ecYeastGEM/. ModelAdapterManager.setDefaultAdapterFromPath(fullfile(findGECKOroot,'userData','ecYeastGEM'), 'true'); @@ -184,7 +167,6 @@ [ecModelFlex, flexProt] = flexibilizeProtConcs(ecModelProtFlux,0.08885,10); % Growth is reached! -%ecModelProtFlux = bayesianSensitivityTuning(ecModelProtFlux); %% Perform simulations % Constrain with the same conditions to model and ecModel @@ -200,4 +182,3 @@ % Perform FVA [minFlux, maxFlux] = ecFVA(ecModelProtFlux, modelY); - diff --git a/tutorials/.keep b/tutorials/.keep deleted file mode 100644 index e69de29bb..000000000 diff --git a/tutorials/protocol.m b/tutorials/protocol.m deleted file mode 100644 index 8c787d471..000000000 --- a/tutorials/protocol.m +++ /dev/null @@ -1,306 +0,0 @@ -%% This file accompanies the GECKO3 protocol (DOI TO BE ADDED) -% Where the commands shown in the protocol paper refer to generic -% functionality, these functions are used here to reconstruct, simulate and -% analyze ecYeastGEM, the ecModel of Saccharomyces cerevisiae. -% -% NOTE: this file might have more up-to-date information about the -% functions, and should always work with the most recent GECKO3 release. -% This file will have additional commands and analysis, that are not -% included as such in the protocol. - -%% Preparation stage for ecModel reconstruction -% - Install GECKO % RAVEN -% - GECKO can be installed via cloning or direct download of ZIP file. -% See installation instructions #here (TO BE ADDED) -% - Add the appropriate GECKO (sub)folders to MATLAB path: -GECKOInstaller.install % -% - Install RAVEN by following the installation instructions: -% https://github.com/SysBioChalmers/RAVEN/wiki/Installation -% - The installation of Gurobi as LP solver is highly recommended -checkInstallation % Confirm that RAVEN is functional, should be 2.7.12 or later. - -% - Prepare a folder in userData, or select another path whether all model- -% specific files and scripts will be kept. This is already done for -% ecYeastGEM. -% - Find a high-quality GEM of your species of interest. ecYeastGEM is -% based on yeast-GEM https://github.com/SysBioChalmers/yeast-GEM/releases -% - Release v8.6.2 of yeast-GEM is also distributed with GECKO at -% userData/ecYeastGEM/model/yeast-GEM.xml -% - Modify the model adapter (at userData/ecYeastGEM/ecYeastGEMadapter.m) -% to contain organism- and model-specific parameters. - -%% STAGE 1: expansion from a starting metabolic model to an ecModel structure - -% STEP 1 Set modelAdapter -geckoRoot = findGECKOroot; -ModelAdapterManager.setDefaultAdapterFromPath(fullfile(geckoRoot,'userData','ecYeastGEM')); -% If you explicitly want to set the ModelAdapter as input parameter for -% many of the GECKO functions, you can get the ModelAdapter structure with -% the following command that should be run after the above command. -ModelAdapter = ModelAdapterManager.getDefaultAdapter(); -% Make it easy to check what value the parameters are. -params = ModelAdapter.getParameters(); - -% STEP 2 Load conventional yeast-GEM -% If the location to the conventional GEM was already set in the modelAdapter, -% as obj.param.convGEM, then loadConventionalGEM can directly be used. If -% the model is stored somewhere else (and not specified in obj.param.convGEM), -% you can also use RAVEN's importModel(). In that case you will never use -% loadConventionalGEM and the obj.param.convGEM never has to be specified. -modelY = loadConventionalGEM(); -% modelY = importModel(fullfile(modelRoot,'models','yeast-GEM.xml')); %Alternative - -% STEP 3 Prepare ecModel -% We will make a full and a light GECKO model -[ecModelFull, noUniprot] = makeEcModel(modelY,false); -% Note that noUniprot is empty: for all genes a match could be find in the -% Uniprot dataset -[ecModelLight, noUniprot] = makeEcModel(modelY,true); -% Whil we initiate a light model here, for the remainder of this protocol.m -% file we will continue with the full version -ecModel = ecModelFull; -doc makeEcModel -% Read makeEcModel documentation to get a list of all it does: it prepare -% the new model.ec structure and prepares the S-matrix by splitting -% reversible reactions, isozymes etc. - -% STEP 4 Annotate with complex data -% As Saccharomyces cerevisiae is available on Complex Portal -% (https://www.ebi.ac.uk/complexportal/), its data is included in -% ecYeastGEM. -complexInfo = getComplexData(); % No need to run, as data is already in adapter folder -[ecModel, foundComplex, proposedComplex] = applyComplexData(ecModel, complexInfo); - -% STEP 5 Store model in YAML format -saveEcModel(ecModel,ModelAdapter,'yml','ecYeastGEMfull'); -ecModel=loadEcModel('ecYeastGEMfull.yml'); - -%% STAGE 2: integration of kcat into the ecModel structure -% STEP 6 Decide which kcat source to use -% In the steps below, all options are shown. Not all are required, it is up -% to the user to decide which ones they want to use. - -% STEP 7 Fuzzy matching with BRENDA -% Requires EC numbers, which are here first taken from the starting model, -% with the missing ones taken from Uniprot & KEGG databases. -ecModel = getECfromGEM(ecModel); -noEC = cellfun(@isempty, ecModel.ec.eccodes); -ecModel = getECfromDatabase(ecModel); -% Do the actual fuzzy matching with BRENDA -kcatList_fuzzy = fuzzyKcatMatching(ecModel); -% Now we have a kcatList, which will be used to update ecModel in a later -% step. - -% STEP 8 DLKcat prediction through machine learning -% Requires metabolite SMILES, which are gathered from PubChem -ecModel = findMetSmiles(ecModel); -% DLKcat runs in Python. An input file is written, which is then used by -% DLKcat, while the output file is read back into MATLAB. -writeDLKcatInput(ecModel); -% runDLKcat will attempt to install and run DLKcat, but this might not work -% for all systems. In that case, the user should install Docker Desktop -% (https://docs.docker.com/get-docker/) and use runDLKcatFromDocker() -% instead. -runDLKcat(); -%runDLKcatFromDocker(); % Only uncomment and run if required. -kcatList_DLKcat = readDLKcatOutput(ecModel); - -% STEP 9 Combine kcat from BRENDA and DLKcat -kcatList_merged = mergeDLKcatAndFuzzyKcats(kcatList_DLKcat, kcatList_fuzzy); - -% STEP 10 Take kcatList and populate edModel.ec.kcat -ecModel = selectKcatValue(ecModel, kcatList_merged); - -% STEP 11 Apply custom kcat values -% During the development of yeast-GEM ecModels (through GECKO 1 & 2), -% various kcat values have been manually curated, to result in a model that -% is able to validate a wide range of phenotypes. These curations are -% summarized under /data/customKcats.tsv, and applied here. -[ecModel, rxnUpdated, notMatch] = applyCustomKcats(ecModel); -% To modify the S-matrix, to actually implement the kcat/MW constraints, -% you run applyKcatConstraints. -ecModel = applyKcatConstraints(ecModel); - -% STEP 12 Get kcat values across isoenzymes -ecModel = getKcatAcrossIsoenzymes(ecModel); - -% STEP 13 Get standard kcat -% Assign an enzyme cost to reactions without gene assocation (except -% exchange, transport and pseudoreactions) -[ecModel, rxnsMissingGPR, standardMW, standardKcat] = getStandardKcat(ecModel); - -% STEP 14 Apply kcat constraints from ecModel.ec.kcats to ecModel.S -% This function can be run at any point to re-apply the kcat contraints on -% the model. It also considers the complex data -ecModel = applyKcatConstraints(ecModel); - -% STEP 15 Set upper bound of protein pool -ecModel = setProtPoolSize(ecModel); - -%% STAGE 3: model tuning -% Test whether the model is able to reach maximum growth if glucose uptake -% is unlimited. First set glucose uptake unconstraint -ecModel = setParam(ecModel,'lb','r_1714',-1000); -% Run FBA -sol = solveLP(ecModel,1) -% It reaches growth rate 0.0936, while it should be able to reach 0.41. -% We can also look at the exchange fluxes, but it does not inform use too -% much at this point. Interesting to see that there is quite some ethanol -% fermentation going on. -printFluxes(ecModel, sol.x) - -% STEP 16 Relax protein pool constraint -protPoolIdx = strcmp(ecModel.rxns, 'prot_pool_exchange'); -ecModel.ub(protPoolIdx) = 1000; -% Important to perform parsimonious FBA by setting minFlux to 1: -sol = solveLP(ecModel,1); -ecModel.ub(protPoolIdx) = sol.x(protPoolIdx); - -% STEP 17 Sensitivity tuning -% First reset the protein pool constraint to a more realistic value, -% reverting STEP 16. -ecModel = setProtPoolSize(ecModel); -[ecModel, tunedKcats] = sensitivityTuning(ecModel); - -% STEP 18 Bayesian tuning -% Alternative to STEP 17. -%ecModel = bayesianSensitivityTuning(ecModel); - -%% STAGE 4 integration of proteomics data into the ecModel -% STEP 19 Load proteomics data and constrain ecModel -protData = loadProtData(3); %Number of replicates -ecModel = fillProtConcs(ecModel,protData); -ecModel = constrainProtConcs(ecModel); - -% STEP 20 Update protein pool -ecModel = updateProtPool(ecModel); - -% STEP 21 Load flux data -fluxData = loadFluxData(); -ecModel = constrainFluxData(ecModel,fluxData,1,'max','loose'); % Use first condition -sol = solveLP(ecModel); % To observe if growth was reached -disp(['Growth rate that is reached: ' num2str(abs(sol.f))]) - -% Growth rate of 0.1 is by far not reached, flexibilize protein -% concentrations -[ecModel, flexProt] = flexibilizeProtConcs(ecModel,0.1,10); - -% It gets stuck at 0.0889. It seems like protein abundances are not -% preventing the model to reach 0.1. First look if the conventional GEM is -% able to reach 0.1 with the same constraints: - -modelY = constrainFluxData(modelY,fluxData); -sol = solveLP(modelY) - -% It also only reaches 0.0889! So the metabolic network would not be able -% to adhere to all measured constraints. Perhaps there is something -% incorrect with the measurements? For now, we will limit the growth rate -% to 0.08885 in our search: -[ecModel, flexProt] = flexibilizeProtConcs(ecModel,0.08885,10); - -% Growth is reached! Let's make sure we store this functional model -saveEcModel(ecModel,ModelAdapter,'yml','ecYeastGEMfull'); - -%% STAGE 5: simulation and analysis -% If starting from here, load some basic assets -ecModel = loadEcModel('ecYeastGEMfull.yml',ModelAdapter); -modelY = loadConventionalGEM(); -fluxData = loadFluxData; - -% STEP 23 Example of various useful RAVEN functions -% % Set the upper bound of reaction r_0001 to 10. -% ecModel = setParam(ecModel,'ub','r_0001',10); -% % Set the lower bound of reaction r_0001 to 0. -% ecModel = setParam(ecModel,'lb','r_0001',0); -% % Set the objective function to maximize reaction biomassRxn -% ecModel = setParam(ecModel,'obj','r_4041',1); -% % Set the objective function to minimize protein usage -% ecModel = setParam(ecModel,'obj','prot_pool_exchange',-1); -% % Perform flux balance analysis (FBA) -% sol = solveLP(ecModel); -% % Perform parsimonious FBA (minimum total flux) -% sol = solveLP(ecModel,1); -% % Inspect exchange fluxes from FBA solution -% printFluxes(ecModel,sol.x) -% % Inspect all (non-zero) fluxes from FBA solution -% printFluxes(ecModel,sol.x,false) -% % Export to Excel file (will not contain potential model.ec content) -% exportToExcelFormat(ecModel,'filename.xlsx'); - -% STEP 24 Selecting objective functions -ecModel = setParam(ecModel,'obj',params.bioRxn,1); -sol = solveLP(ecModel) -disp(['Growth rate reached: ' num2str(abs(sol.f))]) -% Set growth lower bound to 99% of the previous value -ecModel = setParam(ecModel,'lb',params.bioRxn,0.99*abs(sol.f)); -ecModel = setParam(ecModel,'obj','prot_pool_exchange',-1); -sol = solveLP(ecModel) -disp(['Minimum protein pool usage: ' num2str(abs(sol.f)) ' ug/gDCW']) - -% STEP 25 Compare fluxes from ecModel and starting model -% Constrain with the same conditions to model and ecModel. We now fix the -% observed growth as lower bound ('min' in constrainFluxData) and allow 5% -% variability around the other measured fluxes. -% We know that growth can only reach 0.088, so use this instead of 0.1. -fluxData.grRate(1) = 0.088; -ecModel = constrainFluxData(ecModel,fluxData,1,'min',5); -% Minimize protein pool usage. -ecModel = setParam(ecModel,'obj','prot_pool_exchange',-1); -solEC = solveLP(ecModel,1) - -% Apply (almost) the same to non-ecModel. Same constraints on fluxes, but -% objective function remains growth (cannot minimize protein usage). -modelY = constrainFluxData(modelY,fluxData,1,'min',5); -sol = solveLP(modelY,1) -% Note that with the 5% of variability, the model can reach higher growth -% rate than 0.0885, as it is allowed to take up a little bit more glucose -% this time. - -% Map the ecModel fluxes back to the non-ecModel -[mappedFlux, enzUsageFlux, usageEnz] = mapRxnsToConv(ecModel, modelY, solEC.x); - -% Print the fluxes next to each other, showing only exchange reactions -printFluxes(modelY,[sol.x mappedFlux]) -% Print the fluxes next to each other, showing all reactions -printFluxes(modelY,[sol.x mappedFlux],false) -% Look at ratio of ecModel / non-ecModel -ratioFlux = mappedFlux ./ sol.x; -ratioFlux(isnan(ratioFlux)) = 0; % Divisions by zero give NaN, reset to zero. -printFluxes(modelY,ratioFlux,false) - -% STEP 26 Inspect enzyme usage -% Show the result from the earlier simulation, without mapping to -% non-ecModel. -usageData = enzymeUsage(ecModel,solEC.x); -usageReport = reportEnzymeUsage(ecModel,usageData,0.90); - -% STEP 27 Perform (ec)FVA -% Perform FVA -[minFluxEc, maxFluxEc] = ecFVA(ecModel, modelY); -[minFluxY, maxFluxY] = ecFVA(modelY, modelY); - -% Write results to output file -output = [modelY.rxns, modelY.rxnNames, num2cell([minFluxY, maxFluxY, minFlux, maxFlux])]'; -fID = fopen(fullfile(params.path,'output','ecFVA.tsv'),'w'); -fprintf(fID,'%s %s %s %s %s %s\n','rxnIDs', 'rxnNames', 'minFlux', ... - 'maxFlux', 'ec-minFlux', 'ec-maxFlux'); -fprintf(fID,'%s %s %g %g %g %g\n',output{:}); -fclose(fID); - -% Look at flux ranges to indicate reaction-level variability -fluxRange = maxFlux - minFlux; -fluxRangeY = maxFluxY - minFluxY; - -% Plot variability distributions of both models in 1 plot -hold on -cdfplot(fluxRange) -cdfplot(fluxRangeY) -set(gca, 'XScale', 'log', 'Xlim', [1e-8 1e4]) -set(findall(gca, 'Type', 'Line'), 'LineWidth', 2) -legend(['ecYeastGEM (mean: ' num2str(mean(fluxRange)) ')'],... - ['yeast-GEM (mean: ' num2str(mean(fluxRangeY)) ')'],... - 'Location','northwest') -title('Flux variability (cumulative distribution)'); -xlabel('Variability range [mmol/gDw h]'); -ylabel('Cumulative distribution'); -saveas(gca, fullfile(params.path,'output','ecFVA.pdf')) \ No newline at end of file