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Bug: Fix compatibility running ecFSEOF with GECKO3 models #301

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merged 13 commits into from
May 11, 2023
Merged

Bug: Fix compatibility running ecFSEOF with GECKO3 models #301

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22ee651
reverse: simulateGrowth for ecFSEOF
ae-tafur Apr 4, 2023
85cc8af
fix: consistency in terminology
ae-tafur Apr 4, 2023
4e9fae1
refactor: fluxes for a target
ae-tafur Apr 4, 2023
855e213
refactor: compatibility to GECKO3 ecFlux_scanning
ae-tafur Apr 4, 2023
d3e233e
refactor: add compatibility to GECKO3
ae-tafur Apr 4, 2023
04e6724
fix: typo
ae-tafur Apr 5, 2023
bc86f71
fix: save file type file
ae-tafur Apr 5, 2023
8851279
fix: filter tolerance
ae-tafur Apr 5, 2023
d2f2fca
fix: if not carbon uptake have been defined
ae-tafur Apr 5, 2023
dd5d295
fix: add toleracne to protein pool
ae-tafur Apr 5, 2023
7c605e0
refactor: spaces instead of tabs
edkerk Apr 18, 2023
d382f1d
fix: function input description
ae-tafur Apr 24, 2023
35c50ba
Update src/geckomat/utilities/ecFSEOF/run_ecFSEOF.m
edkerk May 11, 2023
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155 changes: 80 additions & 75 deletions src/geckomat/utilities/ecFSEOF/ecFlux_scanning.m
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Original file line number Diff line number Diff line change
@@ -1,141 +1,146 @@
function FC = ecFlux_scanning(model,target,C_source,alpha,tol,filterG)
function FC = ecFlux_scanning(ecModel,target,cSource,alpha,tolerance,filterG)
%ecFlux_scanning
%
% model (struct) ecModel with total protein pool constraint.
% the model should come with growth pseudoreaction as
% an objective to maximize.
% target (string) Rxn ID for the production target reaction,
% a exchange reaction is recommended.
% cSource (string) Rxn ID for the main carbon source uptake
% reaction
% alpha (dobule) scalling factor for production yield
% for enforced objective limits
% tol (double, optional) numerical tolerance for fixing
% bounds
% filterG (logical) TRUE if genes K_scores results should
% be filtered according to the alpha vector distribution
%
% Usage: FC = compare_substrate(model,target,C_source,alpha,tol,filterG)
% Input:
% ecModel an ecModel in GECKO 3 format (with ecModel.ec structure).
% rxnTarget rxn ID for the production target reaction, a exchange
% reaction is recommended.
% cSource rxn ID for the main carbon source uptake reaction.
% alpha scalling factor for production yield for enforced objective
% limits
% tolerance numerical tolerance for fixing bounds.
% (Optional, defaul 1E-4)
% filterG logical value. TRUE if genes K_scores results should be
% filtered according to the alpha vector distribution
% (Optional, defaul false)
%
% Usage:
% FC = ecFlux_scanning(model,target,cSource,alpha,tolerance,filterG)

if nargin < 6 || isempty(filterG)
filterG = false;
end

if nargin<6
filterG = false;
if nargin < 5 || isempty(tolerance)
tolerance = 1E-4;
end

%Simulate WT (100% growth):
cd ..
FC.flux_WT = simulateGrowth(model,target,C_source,1,1);
%Simulate forced (X% growth and the rest towards product) based on yield:
% Simulate WT (100% growth):
[~, FC.flux_WT] = getFluxTarget(ecModel,target,cSource);

% Simulate forced (X% growth and the rest towards product) based on yield:
FC.alpha = alpha;
%initialize fluxes and K_scores matrices
v_matrix = zeros(length(model.rxns),length(alpha));
k_matrix = zeros(length(model.rxns),length(alpha));

% Initialize fluxes and K_scores matrices
v_matrix = zeros(length(ecModel.rxns),length(alpha));
k_matrix = zeros(length(ecModel.rxns),length(alpha));
for i = 1:length(alpha)
%disp(['Iteration #' num2str(i)])
FC.flux_MAX = simulateGrowth(model,target,C_source,1,alpha(i));
[~, FC.flux_MAX] = getFluxTarget(ecModel,target,cSource,alpha(i));
v_matrix(:,i) = FC.flux_MAX;
k_matrix(:,i) = FC.flux_MAX./FC.flux_WT;
end
cd ecFSEOF
%Take out rxns with no grRule:
withGR = ~cellfun(@isempty,model.grRules);
%Generate rxn equations:
rxnEqs = constructEquations(model,model.rxns(withGR),true);

% Take out rxns with no grRule:
withGR = ~cellfun(@isempty,ecModel.grRules);

% Generate rxn equations:
rxnEqs = constructEquations(ecModel,ecModel.rxns(withGR),true);
v_matrix = v_matrix(withGR,:);
k_matrix = k_matrix(withGR,:);
rxnGeneM = model.rxnGeneMat(withGR,:);
FC.rxns = [model.rxns(withGR),model.rxnNames(withGR),model.grRules(withGR),rxnEqs];
rxnGeneM = ecModel.rxnGeneMat(withGR,:);
FC.rxns = [ecModel.rxns(withGR),ecModel.rxnNames(withGR),ecModel.grRules(withGR),rxnEqs];

%Filter out rxns that are always zero -> k=0/0=NaN:
% Filter out rxns that are always zero -> k=0/0=NaN:
non_nan = sum(~isnan(k_matrix),2) > 0;
v_matrix = v_matrix(non_nan,:);
k_matrix = k_matrix(non_nan,:);
rxnGeneM = rxnGeneM(non_nan,:);
FC.rxns = FC.rxns(non_nan,:);

%Replace remaining NaNs with 1s:
% Replace remaining NaNs with 1s:
k_matrix(isnan(k_matrix)) = 1;
%Replace any Inf value with 1000 (maximum value is ~700):

% Replace any Inf value with 1000 (maximum value is ~700):
k_matrix(k_matrix>1000) = 1000;

%Filter out values that are inconsistent at different alphas:
always_down = sum(k_matrix <= (1-tol),2) == length(alpha);
always_up = sum(k_matrix >= (1+tol),2) == length(alpha);
%Identify those reactions with mixed patterns
% Filter out values that are inconsistent at different alphas:
always_down = sum(k_matrix <= 1,2) == length(alpha);
always_up = sum(k_matrix >= 1,2) == length(alpha);

% Identify those reactions with mixed patterns
incons_rxns = always_down + always_up == 0;
%Identify genes that are linked to "inconsistent rxns"

% Identify genes that are linked to "inconsistent rxns"
incons_genes = sum(rxnGeneM(incons_rxns,:),1) > 0;
%Finally, inconsistent reactions are those that are not conected
%to "inconsistent genes" from the original "inconsistent rxns" set

% Finally, inconsistent reactions are those that are not conected
% to "inconsistent genes" from the original "inconsistent rxns" set
incons_rxns = sum(rxnGeneM(:,incons_genes),2) > 0;
%Keep results for the consistent rxns exclusively

% Keep results for the consistent rxns exclusively
v_matrix = v_matrix(~incons_rxns,:);
k_matrix = k_matrix(~incons_rxns,:);
rxnGeneM = rxnGeneM(~incons_rxns,:);
FC.rxns = FC.rxns(~incons_rxns,:);
%Get median k score across steps

% Get median k score across steps
FC.k_rxns = mean(k_matrix,2);
%Remove arm_rxns from the list of rxns with K_score>1
armUP = startsWith(FC.rxns(:,1),'arm_') & FC.k_rxns>1;
FC.k_rxns = FC.k_rxns(~armUP,:);
FC.v_matrix = v_matrix(~armUP,:);
FC.k_matrix = k_matrix(~armUP,:);
rxnGeneM = rxnGeneM(~armUP,:);
FC.rxns = FC.rxns(~armUP,:);

%Order from highest to lowest median k_score (across alphas)

% Order from highest to lowest median k_score (across alphas)
[~,order] = sort(FC.k_rxns,'descend');
FC.k_rxns = FC.k_rxns(order,:);
FC.v_matrix = v_matrix(order,:);
FC.k_matrix = k_matrix(order,:);
rxnGeneM = rxnGeneM(order,:);
FC.rxns = FC.rxns(order,:);
%Create list of remaining genes and filter out any inconsistent score:
%Just those genes that are connected to the remaining rxns are
FC.genes = model.genes(sum(rxnGeneM,1) > 0);
FC.geneNames = model.geneShortNames(sum(rxnGeneM,1) > 0);

% Create list of remaining genes and filter out any inconsistent score:
% Just those genes that are connected to the remaining rxns are
FC.genes = ecModel.genes(sum(rxnGeneM,1) > 0);
FC.geneNames = ecModel.geneShortNames(sum(rxnGeneM,1) > 0);
FC.k_genes = zeros(size(FC.genes));
cons_genes = false(size(FC.genes));
rxnGeneM = rxnGeneM(:,sum(rxnGeneM,1) > 0);

for i = 1:length(FC.genes)
%Extract all the K_scores (from rxns across alphas) conected to
%each remaining gene
% Extract all the K_scores (from rxns across alphas) conected to
% each remaining gene
k_set = FC.k_rxns(rxnGeneM(:,i) > 0);
%Check the kind of control that gene i-th exerts over its reactions
always_down = sum(k_set <= (1-tol)) == length(k_set);
always_up = sum(k_set >= (1+tol)) == length(k_set);
%Evaluate if gene is always exerting either a positive or negative
%control
% Check the kind of control that gene i-th exerts over its reactions
always_down = sum(k_set <= (1-tolerance)) == length(k_set);
always_up = sum(k_set >= (1+tolerance)) == length(k_set);
% Evaluate if gene is always exerting either a positive or negative
% control
cons_genes(i) = always_down + always_up == 1;
FC.k_genes(i) = mean(k_set);
end
%Keep "consistent genes"
% Keep "consistent genes"
FC.genes = FC.genes(cons_genes);
FC.geneNames = FC.geneNames(cons_genes);
FC.k_genes = FC.k_genes(cons_genes);
rxnGeneM = rxnGeneM(:,cons_genes);

if filterG
%Filter any value between mean(alpha) and 1:
unchanged = (FC.k_genes >= mean(alpha) - tol) + (FC.k_genes <= 1 + tol) == 2;
FC.genes = FC.genes(~unchanged);
FC.geneNames = FC.geneNames(~unchanged);
FC.k_genes = FC.k_genes(~unchanged);
% Filter any value between mean(alpha) and 1:
unchanged = (FC.k_genes >= mean(alpha) - tolerance) + (FC.k_genes <= 1 + tolerance) == 2;
FC.genes = FC.genes(~unchanged);
FC.geneNames = FC.geneNames(~unchanged);
FC.k_genes = FC.k_genes(~unchanged);
rxnGeneM = rxnGeneM(:,~unchanged);
%Update results for rxns-related fields (remove remaining reactions
%without any associated gene in rxnGeneM)
% Update results for rxns-related fields (remove remaining reactions
% without any associated gene in rxnGeneM)
toKeep = (sum(rxnGeneM,2) > 0);
FC.k_rxns = FC.k_rxns(toKeep,:);
FC.v_matrix = v_matrix(toKeep,:);
FC.k_matrix = k_matrix(toKeep,:);
FC.rxns = FC.rxns(toKeep,:);
end

%Order from highest to lowest k:
% Order from highest to lowest k:
[~,order] = sort(FC.k_genes,'descend');
FC.genes = FC.genes(order,:);
FC.geneNames = FC.geneNames(order,:);
FC.k_genes = FC.k_genes(order,:);

end
end
132 changes: 77 additions & 55 deletions src/geckomat/utilities/ecFSEOF/run_ecFSEOF.m
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Original file line number Diff line number Diff line change
@@ -1,53 +1,72 @@
function results = run_ecFSEOF(model,rxnTarget,cSource,alphaLims,Nsteps,file1,file2)
%
% run_ecFSEOF
function results = run_ecFSEOF(ecModel,rxnTarget,cSource,alphaLims,Nsteps,file_genes,file_rxns,modelAdapter)
%run_ecFSEOF
%
% Function that runs Flux-scanning with Enforced Objective Function
% for a specified production target.
%
% model (struct) ecModel with total protein pool constraint.
% rxnTarget (string) Rxn ID for the production target reaction,
% a exchange reaction is recommended.
% cSource (string) Rxn ID for the main carbon source uptake
% reaction (make sure that the correct directionality is
% indicated).
% alphaLims (vector) Minimum and maximum biomass yield [gDw/mmol Csource]
% for enforced objective limits
% Nsteps (integer) Number of steps for suboptimal objective
% in FSEOF
% file1 (string) opt, file name for an output .txt tab-separated
% file for the results at the genes level
% file2 (string) opt, file name for an output .txt tab-separated
% file for the results at the reactions level
% Function that runs Flux-scanning with Enforced Objective Function
% for a specified production target.
%
% Usage: run_ecFSEOF(ecModel,rxnTarget,cSource,alphaLims,Nsteps)
% Input:
% ecModel an ecModel in GECKO 3 format (with ecModel.ec structure).
% rxnTarget rxn ID for the production target reaction, a exchange
% reaction is recommended.
% cSource rxn ID for the main carbon source uptake reaction.
% alphaLims vector of Minimum and maximum biomass scalling factors for
% enforced objective limits (e.g. [0.5 1]). Max value: 1.
% Nsteps number of steps for suboptimal objective in FSEOF.
% (Optional, default 16)
% file_genes file name for results output at the genes level.
% (Optional, default output in the model-specific 'output'
% sub-folder taken from modelAdapter,
% e.g. GECKO/userData/ecYeastGEM/output/ecFSEOF_genes.tsv)
% file_rxns file name for results output at the reactions level.
% (Optional, default output in the model-specific 'output'
% sub-folder taken from modelAdapter,
% e.g. GECKO/userData/ecYeastGEM/output/ecFSEOF_rxns.tsv)
% modelAdapter a loaded model adapter. (Optional, will otherwise use
% the default model adapter)
%
% Usage:
% results = run_ecFSEOF(ecModel,rxnTarget,cSource,alphaLims)
%

if nargin < 8 || isempty(modelAdapter)
modelAdapter = ModelAdapterManager.getDefaultAdapter();
if isempty(modelAdapter)
error('Either send in a modelAdapter or set the default model adapter in the ModelAdapterManager.')
end
end
params = modelAdapter.getParameters();

if nargin < 7 || isempty(file_rxns)
file_rxns = fullfile(params.path,'output','ecFSEOF_rxns.tsv');
end

if nargin < 7
file2 = [];
if nargin < 6
file1 = [];
end
if nargin < 6 || isempty(file_genes)
file_genes = fullfile(params.path,'output','ecFSEOF_genes.tsv');
end
%Check model format, if COBRA model then convert to RAVEN format
if isfield('model','rules') && ~isfield('model','metComps')
model = ravenCobraWrapper(model);

if nargin < 5 || isempty(Nsteps)
Nsteps = 16;
end
%Define alpha vector for suboptimal enforced objective values

% Define alpha vector for suboptimal enforced objective values
alphaV = alphaLims(1):((alphaLims(2)-alphaLims(1))/(Nsteps-1)):alphaLims(2);
%Standardize grRules and rxnGeneMat in model
[grRules,rxnGeneMat] = standardizeGrRules(model,true);
model.grRules = grRules;
model.rxnGeneMat = rxnGeneMat;
%run FSEOF analysis
results = ecFlux_scanning(model,rxnTarget,cSource,alphaV,1E-4,true);
%Create gene table:

% Standardize grRules and rxnGeneMat in model
[grRules,rxnGeneMat] = standardizeGrRules(ecModel,true);
ecModel.grRules = grRules;
ecModel.rxnGeneMat = rxnGeneMat;

% run FSEOF analysis
results = ecFlux_scanning(ecModel,rxnTarget,cSource,alphaV,1E-4,true);

% Create gene table:
results.geneTable = cell(length(results.genes),3);
results.geneTable(:,1) = results.genes;
results.geneTable(:,2) = results.geneNames;
results.geneTable(:,3) = num2cell(results.k_genes);
%Create rxns table (exclude enzyme usage reactions):
toKeep = find(~startsWith(results.rxns(:,1),'draw_prot_'));

% Create rxns table (exclude enzyme usage reactions):
toKeep = find(~startsWith(results.rxns(:,1),'usage_prot_'));
results.k_rxns = results.k_rxns(toKeep);
results.k_matrix = results.k_matrix(toKeep,:);
results.v_matrix = results.v_matrix(toKeep,:);
Expand All @@ -57,24 +76,27 @@
results.rxnsTable(:,3) = num2cell(results.k_rxns);
results.rxnsTable(:,4) = results.rxns(toKeep,3);
results.rxnsTable(:,5) = results.rxns(toKeep,4);
try
if ~isempty(file1)
varNames = {'gene_IDs' 'gene_names' 'K_score'};
T = cell2table(results.geneTable,'VariableNames',varNames);
writetable(T,file1,'Delimiter','\t','QuoteStrings',false)
end
if ~isempty(file2)
varNames = {'rxn_IDs' 'rxnNames' 'K_score' 'grRules' 'rxn_formula'};
T = cell2table(results.rxnsTable,'VariableNames',varNames);
writetable(T,file2,'Delimiter','\t','QuoteStrings',false)
end
catch
warning('Output files directory not found');
end
%Remove redundant output fields

writetable(cell2table(results.geneTable, ...
'VariableNames', {'gene_IDs' 'gene_names' 'K_score'}), ...
file_genes, ...
'FileType', 'text', ...
'Delimiter', '\t', ...
'QuoteStrings', false);

writetable(cell2table(results.rxnsTable, ...
'VariableNames', {'rxn_IDs' 'rxnNames' 'K_score' 'grRules' 'rxn_formula'}), ...
file_rxns, ...
'FileType', 'text', ...
'Delimiter', '\t', ...
'QuoteStrings', false);

disp(['ecFSEOF results stored at: ' newline fileparts(file_genes)]);

% Remove redundant output fields
results = rmfield(results,'k_rxns');
results = rmfield(results,'rxns');
results = rmfield(results,'genes');
results = rmfield(results,'geneNames');
results = rmfield(results,'k_genes');
end
end
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