Eflux

applyEFluxConstraints(model, expression, varargin)[source]

Implementation of the EFlux algorithm as described in: Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, et al. (2009) PLOS Computational Biology 5(8): e1000489. https://doi.org/10.1371/journal.pcbi.1000489

USAGE

constraintModel = eFlux (model,expression)

INPUTS

  • model – The model to Constrain.

  • expression – struct with two fields required and one optional field: * .target - the names of the target (rxns or genes) * .value - the value for the target. Positive values

    for all constraint reactions, negative values for unconstraint reactions.

    • .preprocessed - Indicator whether the provided

      targets are genes (false), or reactions (true) Default: false

OPTIONAL INPUTS

varargin – Parameters given as struct or parameter/value pairs: * minSum: Switch for the processing of Genetic data. If false, ORs in the GPR will be treated as min. If true(default), ORs will be treated as addition. * softBounds: Whether to use soft bounds for the infered constraints or to add flexibility variables (default: false). * weightFactor: The weight factor for soft bounds (default: 1)

Note

All Flux bounds will be reset by this function, i.e. any enforced fluxes (like ATP Maintenance) will be removed!

..Authors

  • Thomas Pfau

Note

Implementation of the EFlux algorithm as described in: Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, et al. (2009) PLOS Computational Biology 5(8): e1000489. https://doi.org/10.1371/journal.pcbi.1000489

eFlux(model, controlExpression, conditionExpression, varargin)[source]

Calculate the objective fold change according to the eFlux approach for expression integration as described in: Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, et al. (2009) PLOS Computational Biology 5(8): e1000489. https://doi.org/10.1371/journal.pcbi.1000489

USAGE

[foldChange,standardError] = eFlux (model,controlExpression,conditionExpression,varargin)

INPUTS

  • model – The COBRA model struct to use

  • controlExpression – struct for the control expression with two fields required and one optional field: * .target - the names of the target (rxns or genes) * .value - the value for the target. Positive values for all constraint reactions, negative values for unconstraint reactions. * .preprocessed - Indicator whether the provided targets are genes (false), or reactions (true) Default: false

  • conditionExpression – struct for the condition expression (fields are the same as controlExpression)

OPTIONAL INPUT

varargin – parameters given as struct or parameter/value pairs. * testNoise - indicator whether to run multiple calculations with added noise to get a significance of the fold change. Requires either a noise function and a standard deviation of noise (‘noiseFun’ and ‘noiseStd’ respectively) or a controlData struct and a noise function. * noiseCount - number of noisy controls to create if noise is tested (default: 10) * noiseFun - The noise function to use, has to be a function handle taking 2 arguments (mean and std) * noiseStd - The standard deviation(s) to use. Either a single value (used for all values, or a vector with length equal to controlExpression.value). * controlData - a struct (like controlExpression which has a value matrix with multiple values per controlExpression to determine the noise distribution. If provided with testNoise == false, the values from this struct will be used to determine the noise. * minSum: Switch for the processing of Genetic data. If false, ORs in the GPR will be treated as min. If true(default), ORs will be treated as addition. * softBounds: Whether to use soft bounds for the infered constraints or to add flexibility variables (default: false). * weightFactor: The weight factor for soft bounds (default: 1)

OUTPUTS

  • foldChange – The fold change between the objective of the condition and the objective of the control expression

  • standardError – The error if noise is being used.

  • solControl – The solution of the given Control expression;

  • solCondition – The solution of the given Condition expression;

..Author: Thomas Pfau OCt 2018

Note

This si an implementation of the eFlux concept as presented in: Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, et al. (2009) PLOS Computational Biology 5(8): e1000489. https://doi.org/10.1371/journal.pcbi.1000489 Please note, that this code does not perform any preprocessing expcept for that described in the above paper after array normalization.

verifyEFluxExpressionStruct(model, expression)[source]

Verify the expression struct structure for EFlux

USAGE

tf = verifyEFluxExpressionStruct (model,expression)

INPUTS

  • model – The COBRA model struct for the checked expression struct.

  • expression – The expression struct (fields: value, target, preprocessed)

OUTPUT

tf – Whether this struct is valid or not.