reorganizing for git import'

CHANGELOG

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+2.0.5
+ -Added in support for gurobi5 (Thanks Tim)
+ -Added in Turner's drawCbMap patch
+ -Changed default solver to gurobi5 (no longer requires gurobi_mex).
+ -Updated the SBMLToolbox to 4.1.0 (works with libSBML 5.5.0 - Thanks SBML.org).
+
+2.0.4
+ -Applied patch to allow for non-integer ids in createTissueSpecificModel
+ -Added support for solving QP's with gurobi / gurobi_mex
+
+
+2.0.5 TestAll Results
+passedCnt =
+
+    17
+
+Tests passed: 
+testBuildMPS
+testDeletionStudy
+testElementalBalance
+testFBA
+testFVA
+testGDLS
+testGrowthExpMatch
+testMOMA
+testMaps
+testModelManipulation
+testOptKnock
+testRobustnessAnalysis
+testSBML
+testSampleCbModel
+testgpSampler
+testTissueModel
+testpFBA
+
+
+Tests not passed: 
+testC13Fitting
+testSolvers
+IT IS NOT NECESSARY FOR THE COBRA TOOLBOX TO PASS ALL TESTS TO FUNCTION; HOWEVER, IT MUST PASS THE TESTS THAT ARE RELEVANT TO YOUR PARTICULAR PROBLEM!!!
+Tests may not pass for several reasons.  Some of the most common issues:
+1.  The correct solver is not installed.  Certain tests require LP, MILP, QP or NLP solvers.  See changeCobraSolvers.m for a complete list of supported solvers.
+These tests will fail when running testAll unless one has the tomlab suite installed.  If all of the functions that you require for your use function then do not worry about them: testC13Fitting, testGDLS, testMOMA, testOptKnock, testSolvers
+If a particular test fails, you can run that test individually for more information
+
+ans =
+
+17 of 19 tests completed successfully.
+

INSTALL.txt

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+For convenience, we provide the SBMLToolbox 3.1.2, and glpk_mex in external/toolboxes for Mac OS X 10.6 (64-bit), GNU/Linux Ubuntu 10.0 (64-bit), and Microsoft Windows 7 (64-bit).  If you do not wish to use these items please delete the directories.  If you require these external libraries for other architectures please ask google or visit their respective websites.
+
+To read / write SBML files you must install libSBML 5.0 or greater from here: http://sourceforge.net/projects/sbml/files/libsbml/
+
+From MATLAB, run
+  initCobraToolbox
+
+
+NOTE: If you do not have gurobi_mex and tomlab installed on your machine, you will get some warnings and some errors.  The COBRA Toolbox will try to use glpk if it cannot find gurobi for LP / MILP.  To solve any QP problems you need tomlab.
+
+After running initCobraToolbox, run
+  testAll
+to see what functions will work with your current configuration.  Note, it is likely that one or more functions will not work unless you have both gurobi and tomlab installed.

LEGAL.txt

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+The software provided by the openCOBRA Project will be distributed under the GNU GPLv3 or later.  However, this software is designed for scientific research and as such may contain algorithms that are associated with patents in the U.S. and abroad.  If the user so chooses to use the software provided by the openCOBRA project for commercial endeavors then it is solely the user’s responsibility to license any patents that may exist and respond in full to any legal actions taken by the patent holder.

MOMA.m

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+function [solutionDel,solutionWT,totalFluxDiff,solStatus] = ...
+    MOMA(modelWT,modelDel,osenseStr,verbFlag,minNormFlag)
+%MOMA Performs a quadratic version of the MOMA (minimization of
+%metabolic adjustment) approach 
+%
+% [solutionDel,solutionWT,totalFluxDiff,solStatus] = MOMA(modelWT,modelDel,osenseStr,verbFlag,minNormFlag)
+%
+%INPUTS
+% modelWT           Wild type model
+% modelDel          Deletion strain model
+%
+%OPTIONAL INPUTS
+% osenseStr         Maximize ('max')/minimize ('min') (Default = 'max')
+% verbFlag          Verbose output (Default = false)
+% minNormFlag       Work with minimum 1-norm flux distribution for the FBA
+%                   problem (Default = false)
+% 
+%OUTPUTS
+% solutionDel       Deletion solution structure
+% solutionWT        Wild-type solution structure
+% totalFluxDiff     Value of the linear MOMA objective, i.e.
+%                   sum(v_wt-v_del)^2
+% solStatus         Solution status
+%
+% Solves two different types of MOMA problems:
+%
+% 1) MOMA that avoids problems with alternative optima (this is the
+% default)
+%
+%    First solve:
+%    
+%    max c_wt'*v_wt0
+%     lb_wt <= v_wt0 <= ub_wt
+%     S_wt*v_wt0 = 0
+%    
+%    Then solve: 
+%
+%    min sum(v_wt - v_del)^2
+%     S_wt*v_wt = 0
+%     S_del*v_del = 0
+%     lb_wt <= v_wt <= ub_wt
+%     lb_del <= v_del <= ub_del
+%     c_wt'*v_wt = f_wt
+%
+%   Here f_wt is the optimal wild type objective value found by FBA in the
+%   first problem. Note that the FBA solution v_wt0 is not used in the second
+%   problem. This formulation avoids any problems with alternative optima
+%
+% 2) MOMA that uses a minimum 1-norm wild type FBA solution (this approach
+% is used if minNormFlag = true)
+%
+%    First solve
+%
+%    max c_wt'*v_wt0
+%     lb_wt <= v_wt0 <= ub_wt
+%     S_wt*v_wt0 = 0
+%
+%    Then solve
+%
+%    min |v_wt|
+%     S_wt*v_wt = b_wt
+%     c_wt'*v_wt = f_wt
+%     lb_wt <= v_wt <= ub_wt
+%    
+%    Here f_wt is the objective value obtained in the 1st optimization.
+%
+%    Finally solve:
+%
+%    min sum(v_wt - v_del)^2
+%     S_del*v_del = 0
+%     lb_del <= v_del <= ub_del
+%
+% Notes:
+%
+% 1) These formulation allows for selecting for more appropriate
+% optimal wild type FBA solutions as the starting point as opposed to
+% picking an arbitrary starting point (original MOMA implementation).
+%
+% 2) The reaction sets in the two models do not have to be equal as long as
+% there is at least one reaction in common
+%
+% Markus Herrgard 11/7/06
+
+if (nargin <3 || isempty(osenseStr))
+    osenseStr = 'max';
+end
+if (nargin < 4)
+    verbFlag = false;
+end
+if (nargin < 5)
+    minNormFlag = false;
+end
+
+% LP solution tolerance
+global CBT_LP_PARAMS
+if (exist('CBT_LP_PARAMS', 'var'))
+    if isfield(CBT_LP_PARAMS, 'objTol')
+        tol = CBT_LP_PARAMS.objTol;
+    else
+        tol = 1e-6;
+    end
+else
+    tol = 1e-6;
+end
+
+[nMets1,nRxns1] = size(modelWT.S);
+[nMets2,nRxns2] = size(modelDel.S);
+
+% Match model reaction sets
+selCommon1 = ismember(modelWT.rxns,modelDel.rxns);
+nCommon = sum(selCommon1);
+if (nCommon == 0)
+    error('No common rxns in the models');
+end
+
+solutionWT.f = [];
+solutionWT.x = [];
+solutionWT.stat = -1;
+solutionDel.f = [];
+solutionDel.x = [];
+solutionDel.stat = -1;
+
+if (verbFlag)
+    fprintf('Solving wild type FBA: %d constraints %d variables ',nMets1,nRxns1);
+end
+% Solve wt problem
+if minNormFlag
+    solutionWT = optimizeCbModel(modelWT,osenseStr,true);
+else
+    solutionWT = optimizeCbModel(modelWT,osenseStr);
+end
+
+if (verbFlag)
+    fprintf('%f seconds\n',solutionWT.time);
+end
+% Round off solution to avoid numerical problems
+
+if (strcmp(osenseStr,'max'))
+    objValWT = floor(solutionWT.f/tol)*tol;
+else
+    objValWT = ceil(solutionWT.f/tol)*tol;
+end
+
+% Variables in the following problem are
+% x = [v1;v2;delta]
+% where v1 = wild type flux vector
+%       v2 = deletion strain flux vector
+%       delta = v1 - v2
+
+if (solutionWT.stat > 0)
+
+    if minNormFlag
+
+        b = zeros(nMets2,1);
+        A = modelDel.S;
+        c = -2*solutionWT.x;
+        F = 2*eye(nRxns2);
+        lb = modelDel.lb;
+        ub = modelDel.ub;
+        csense(1:nMets2) = 'E';
+
+    else
+
+        % Construct the LHS matrix
+        % Rows:
+        % 1: Swt*v1 = 0 for the wild type
+        % 2: Sdel*v2 = 0 for the deletion strain
+        % 5: c'v1 = f1 (wild type)
+        deltaMat = createDeltaMatchMatrix(modelWT.rxns,modelDel.rxns);
+        deltaMat = deltaMat(1:nCommon,1:(nRxns1+nRxns2+nCommon));
+        A = [modelWT.S sparse(nMets1,nRxns2+nCommon);
+            sparse(nMets2,nRxns1) modelDel.S sparse(nMets2,nCommon);
+            deltaMat;
+            modelWT.c' sparse(1,nRxns2+nCommon)];
+
+        % Construct the RHS vector
+        b = [zeros(nMets1+nMets2+nCommon,1);objValWT];
+
+        % Linear objective = 0
+        c = zeros(nRxns1+nRxns2+nCommon,1);
+
+        % Construct the ub/lb
+        % delta [-10000 10000]
+        lb = [modelWT.lb;modelDel.lb;-10000*ones(nCommon,1)];
+        ub = [modelWT.ub;modelDel.ub;10000*ones(nCommon,1)];
+
+        % Construct the constraint direction vector (G for delta's, E for
+        % everything else)
+        csense(1:(nMets1+nMets2+nCommon)) = 'E';
+        if (strcmp(osenseStr,'max'))
+            csense(end+1) = 'G';
+        else
+            csense(end+1) = 'L';
+        end
+
+        % F matrix
+        F = [sparse(nRxns1+nRxns2,nRxns1+nRxns2+nCommon);
+            sparse(nCommon,nRxns1+nRxns2) 2*eye(nCommon)];
+
+    end
+
+    if (verbFlag)
+        fprintf('Solving MOMA: %d constraints %d variables ',size(A,1),size(A,2));
+    end
+
+    % Solve the linearMOMA problem
+    [QPproblem.A,QPproblem.b,QPproblem.F,QPproblem.c,QPproblem.lb,QPproblem.ub,QPproblem.csense,QPproblem.osense] = deal(A,b,F,c,lb,ub,csense,1);
+    %QPsolution = solveCobraQP(QPproblem,[],verbFlag-1);
+    QPsolution = solveCobraQP(QPproblem, 'printLevel', verbFlag-1);
+
+    if (verbFlag)
+        fprintf('%f seconds\n',QPsolution.time);
+    end
+
+    % Get the solution(s)
+    if (QPsolution.stat > 0)
+        if minNormFlag
+            solutionDel.x = QPsolution.full;
+        else
+            solutionDel.x = QPsolution.full((nRxns1+1):(nRxns1+nRxns2));
+            solutionWT.x = QPsolution.full(1:nRxns1);
+        end
+        solutionDel.f = sum(modelDel.c.*solutionDel.x);
+        totalFluxDiff = sum((solutionWT.x-solutionDel.x).^2);
+    end
+    solutionDel.stat = QPsolution.stat;
+    solStatus = QPsolution.stat;
+    solutionDel.solver = QPsolution.solver;
+    solutionDel.time = QPsolution.time;
+
+else
+    warning('Wild type FBA problem is infeasible or unconstrained');
+end
+
+