fillEnzConcs

 fillEnzConcs
   Uses the protein concentrations from protData to fill model.ec.concs.
   Protein levels should be provided in mg/gDCW. If no data is provided
   a particular protein, its level is NaN. Existing entries in
   model.ec.concs are overwritten.

 Input:
   model       an ecModel in GECKO 3 format (with ecModel.ec structure)
   protData    structure with proteome data, from loadProtFluxData
               uniprotIDs      cell arrray with Uniprot IDs matching
                               protData.abundances
               abundances      matrix of proteomics data
   dataCol     number indicating the column in protData.abundances that
               contains the relevant protein concentrations (protData may
               contain data from multiple conditions/samples/experiments,
               each with their own column in protData.abundances.
               Optional, default = 1.

 Output:
   model       an ecModel where model.ec.concs is populated with protein
               concentrations.

 Note: to also constrain the model with the content of model.ec.concs, you
 should run constrainEnzConcs.

 Usage:
   model = fillEnzConcs(model, protData)

0001 function model = fillEnzConcs(model, protData, dataCol)
0002 % fillEnzConcs
0003 %   Uses the protein concentrations from protData to fill model.ec.concs.
0004 %   Protein levels should be provided in mg/gDCW. If no data is provided
0005 %   a particular protein, its level is NaN. Existing entries in
0006 %   model.ec.concs are overwritten.
0007 %
0008 % Input:
0009 %   model       an ecModel in GECKO 3 format (with ecModel.ec structure)
0010 %   protData    structure with proteome data, from loadProtFluxData
0011 %               uniprotIDs      cell arrray with Uniprot IDs matching
0012 %                               protData.abundances
0013 %               abundances      matrix of proteomics data
0014 %   dataCol     number indicating the column in protData.abundances that
0015 %               contains the relevant protein concentrations (protData may
0016 %               contain data from multiple conditions/samples/experiments,
0017 %               each with their own column in protData.abundances.
0018 %               Optional, default = 1.
0019 %
0020 % Output:
0021 %   model       an ecModel where model.ec.concs is populated with protein
0022 %               concentrations.
0023 %
0024 % Note: to also constrain the model with the content of model.ec.concs, you
0025 % should run constrainEnzConcs.
0026 %
0027 % Usage:
0028 %   model = fillEnzConcs(model, protData)
0029 
0030 if nargin < 3 || isempty(dataCol)
0031     dataCol = 1;
0032 end
0033 
0034 uniprotIDs = protData.uniprotIDs;
0035 abundances = protData.abundances(:,dataCol);
0036 
0037 %Redefine an empty model.ec.concs vector
0038 model.ec.concs=nan(numel(model.ec.enzymes),1);
0039 
0040 [a,b] = ismember(uniprotIDs, model.ec.enzymes);
0041 model.ec.concs(b(a)) = abundances(a);
0042 end