Rmta

MTA(model, rxnFBS, Vref, varargin)[source]

Calculate Metabolic Transformation Analysis (MTA) using the the solver CPLEX. Code was prepared to be able to be stopped and be launched again by using a temporary file called ‘temp_MTA.mat’. Outputs are cell array for each alpha (one simulation by alpha). It there is only one alpha, content of cell will be returned The code here has been based on:

Yizhak, K., Gabay, O., Cohen, H., & Ruppin, E. (2013). ‘Model-based identification of drug targets that revert disrupted metabolism and its application to ageing’. Nature communications, 4. http://www.nature.com/ncomms/2013/131024/ncomms3632/full/ncomms3632.html

USAGE

[TSscore,deletedGenes,Vout] = MTA (model, rxnFBS, Vref, alpha, epsilon, varargin)

INPUTS

  • model – Metabolic model structure (COBRA Toolbox format).

  • rxnFBS – Array that contains the desired change: Forward, Backward and Unchanged (+1;0;-1). This is calculated from the rules and differential expression analysis.

  • Vref – Reference flux of the source state.

OPTIONAL INPUTS

  • alpha – Numeric value or array. Parameter of the quadratic problem (default = 0.66)

  • epsilon – Numeric value or array. Minimun perturbation for each reaction (default = 0)

  • rxnKO – Binary value. Calculate knock outs at reaction level instead of gene level. (default = false)

  • listKO – Cell array containing the name of genes or reactions to knockout. (default = all)

  • timelimit – Time limit for the calculation of each knockout. (default = inf)

  • SeparateTranscript – Character used to separate different transcripts of a gene. (default = ‘’). .. rubric:: Examples

    • SeparateTranscript = ‘’

      • gene 10005.1 ==> gene 10005.1

      • gene 10005.2 ==> gene 10005.2

      • gene 10005.3 ==> gene 10005.3

    • SeparateTranscript = ‘.’

      • gene 10005.1

      • gene 10005.2 ==> gene 10005

      • gene 10005.3

  • numWorkers – Integer: is the maximun number of workers used by the solver. 0 = automatic, 1 = sequential, > 1 = parallel. (default = 0)

  • FORCE_CPLEX – 1 to force CPLEX solver, 0 (default) for COBRA solver.

  • printLevel – Integer. 1 if the process is wanted to be shown on the screen, 0 otherwise. (default = 1)

OUTPUTS

  • TSscore – Transformation score by each transformation

  • deletedGenes – The list of genes/reactions removed in each knock-out

  • Vres – Matrix of resulting fluxes

MTA_MIQP(OptimizationModel, KOrxn, varargin)[source]

Returns the OptimizationModel solution of a particular MTA problem and an specific model

USAGE

[v_res, success, unsuccess] = MTA_MIQP (OptimizationModel, KOrxn, numWorkers, timeLimit, printLevel)

INPUT

  • OptimizationModel – Cplex Model struct

  • KOrxn – perturbation in the model (reactions)

  • numWorkers – number of threads used by Cplex.

  • FORCE_CPLEX – 1 to force CPLEX solver, 0 (default) for COBRA solver.

  • printLevel – 1 if the process is wanted to be shown on the screen, 0 otherwise. Default: 1.

OUTPUTS

  • Vout – Solution flux of MIQP formulation for each case

  • solution – Cplex solution struct

MTA_TS(Vout, Vref, rxnFBS)[source]

Returns the TS score of a particular solution of the MTA perturbation algorithm.

USAGE

score = MTA_TS (v_res,vref,Model,success,unsuccess)

INPUT

  • Vout – Solution flux of MIQP formulation for each case

  • Vref – Reference flux of source state

  • rxnFBS – Array that contains the desired change: Forward, Backward and Unchanged (+1;0;-1).

OUTPUTS

score – TS score for each case

buildMTAproblemFromModel(model, rxnFBS, Vref, varargin)[source]

Returns the COBRA Optimization model needed to perform the MTA

USAGE

OptimizationModel = buildMTAproblemFromModel (model,rxnFBS,Vref,alpha,epsilon)

INPUT

  • model – Metabolic model (COBRA format)

  • rxnFBS – Forward, Backward and Unchanged (+1;0;-1) values corresponding to each reaction.

  • Vref – Reference flux of the source state.

  • alpha – parameter of the quadratic problem (default = 0.66)

  • epsilon minimun disturbance for each reaction, (default = 0)

OUTPUTS

OptimizationModel – COBRA model struct that includes the stoichiometric contrains, the thermodinamic constrains and the binary variables.

calculateEPSILON(samples, rxnFBS, varargin)[source]

Calculate the minimum required flux change (epsilon) for reactions to be considered as significantly changed with p-value of 0.05 The code below is based on the method presented in:

Yizhak, K., Gabay, O., Cohen, H., & Ruppin, E. (2013). Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nature communications, 4, 2632.

USAGE

epsilon = calculateEPSILON (samples, rxnFBS, ‘unique_epsilon’, false, ‘minimum’, 1e-3)

INPUT

  • samples – Matrix of sampled fluxes.

  • rxnFBS – Array that contains the desired change: Forward, Backward and Unchanged (+1;0;-1). This is calculated from the rules and differential expression analysis.

OPTIONAL INPUTS

varargin – ParameterName value pairs with the following options:

  • unique_epsilon: True = unique epsilon, False = each reaction has different epsilon (default=false)

  • minimum: Minimun value for epsilon requiered (default=1e-3)

OUTPUT

epsilon – Numeric value or array with the epsilon for the different reactions

calculateGeneKOMatrix(model, varargin)[source]

Build a rxn-gene matrix such that the i-th column indicates what reactions become inactive because of the i-th gene’s knock-out.

USAGE

geneKO = calculateGeneKOMatrix (model, SeparateTranscript, printLevel)

INPUT

model – The COBRA Model structure

OPTIONAL INPUTS

  • SeparateTranscript Character used to separate – different transcripts of a gene. (default = ‘’) .. rubric:: Examples

    • SeparateTranscript = ‘’

      • gene 10005.1 ==> gene 10005.1

      • gene 10005.2 ==> gene 10005.2

      • gene 10005.3 ==> gene 10005.3

    • SeparateTranscript = ‘.’

      • gene 10005.1

      • gene 10005.2 ==> gene 10005

      • gene 10005.3

  • printLevel – Integer. 1 if the process is wanted to be shown on the screen, 0 otherwise. (default = 1)

OUTPUT

geneKO – Struct which contains matrix with blocked reactions for each gene in the metabolic model, name of reactions and name of genes.

diffexprs2rxnFBS(model, diff_exprs, Vref, varargin)[source]

Returns Forward - Backward - Unchanged (+1;0;-1) for each reaction. (+1)R_f (-1)R_b (0)unchanged

USAGE

rxnFBS = diffexprs2rxnFBS (model, diff_exprs, Vref, ‘SeparateTranscript’, ‘’, ‘logFC’, 0, ‘pval’,0.05)

INPUT

  • model – The COBRA Model structure

  • diff_exprs – MATLAB Table including the information of the differentially expressed genes. Required columns (with theses names):

    • gene ( ID of gene, same as in the meabolic model)

    • logFC (SOURCE VS TARGET)

    • pval (p-value or adjusted-p-value)

  • Vref – Reference flux of the model

OPTIONAL INPUTS

varargin – ParameterName value pairs with the following options:

  • SeparateTranscript: Character used to separate different transcripts of a gene. (default: ‘’)

  • logFC: minimum log2 (fold change) requiered (default = 0)

  • pval: maximum p-value admited (default = 0.05)

OUTPUT

rxnFBS – array containting the information of altered reactions: Forward - Backward - Unchanged (+1;0;-1);

rMTA(model, rxnFBS, Vref, varargin)[source]

Calculate robust Metabolic Transformation Analysis (rMTA) using the solver CPLEX. Code was prepared to be able to be stopped and be launched again by using a temporally file called ‘temp_rMTA.mat’. Outputs are cell array for each alpha (one simulation by alpha). It there is only one alpha, content of cell will be returned

USAGE

[TSscore,deletedGenes,Vout] = rMTA (model, rxnFBS, Vref, alpha, epsilon, varargin)

INPUTS

  • model – Metabolic model structure (COBRA Toolbox format).

  • rxnFBS – Array that contains the desired change: Forward, Backward and Unchanged (+1;0;-1). This is calculated from the rules and differential expression analysis.

  • Vref – Reference flux of the source state.

OPTIONAL INPUTS

  • alpha – Numeric value or array. Parameter of the quadratic problem (default = 0.66)

  • epsilon – Numeric value or array. Minimun perturbation for each reaction (default = 0)

  • parameterK – Numeric value value. Parameter to calculate rTS score. (default = 100)

  • rxnKO – Binary value. Calculate knock outs at reaction level instead of gene level. (default = false)

  • listKO – Cell array containing the name of genes or reactions to knockout. (default = all)

  • timelimit – Time limit for the calculation of each knockout. (default = inf)

  • SeparateTranscript – Character used to separate different transcripts of a gene. (default = ‘’) .. rubric:: Examples

    • SeparateTranscript = ‘’

      • gene 10005.1 ==> gene 10005.1

      • gene 10005.2 ==> gene 10005.2

      • gene 10005.3 ==> gene 10005.3

    • SeparateTranscript = ‘.’

      • gene 10005.1

      • gene 10005.2 ==> gene 10005

      • gene 10005.3

  • numWorkers – Integer: is the maximun number of workers used by the solver. 0 = automatic, 1 = sequential, > 1 = parallel. (default = 0)

  • FORCE_CPLEX – 1 to force CPLEX solver, 0 (default) for COBRA solver.

  • printLevel – Integer. 1 if the process is wanted to be shown on the screen, 0 otherwise. (default = 1)

OUTPUTS

  • TSscore – Transformation score by each transformation

  • deletedGenes – The list of genes/reactions removed in each knock-out

  • Vref – Matrix of resulting fluxes

rMTAsaveInExcel(xls_filename, TSscore, deletedGenes, alpha_values, varargin)[source]

Save in Excel all the results calculated using rMTA.

USAGE

rMTAsaveInExcel (output_file, TSscore, deletedGenes, alpha_values, varargin)

INPUTS

  • xls_filename – name of the resulting excel file

  • TSscore – Transformation score by each transformation

  • deletedGenes – The list of genes/reactions removed in each knock-out

  • alpha_values – Numeric value or array. Parameter of the quadratic problem (default = alpha_1, alpha_2,…)

OPTIONAL INPUTS

  • gene_info – Table with the gene names and additional information. It needs a column called ‘gene’, which contains the same information as the deletedGenes variable.

  • differ_genes – Table with the gene differential expression information. It needs a column called ‘gene’, which contains the same information as the deletedGenes variable.

  • RankingGeneID – Caracter array with the ID of the gene which score is desired to know.