Dataintegration

Dataintegration

• Metabotools
• Metanetxmapper
• Metaboannotator
  • Analysemetstruct
  • Molfilefinder
  • Generatemetinfo
  • Connect2resources
  • Buildmetstruct
• Xomicstomodel
  • Metabolomics
  • Proteomics
  • Preparedata
  • Onerxnperactivegene
  • Analysis
  • Thermokernel
• Chemoinformatics
  • Metabolites
  • Molecularweight
  • Rxn
  • Reactions
  • Inchi
• Transcriptomics
  • Init
  • Gimme
  • Moomin
  • Mba
  • Imat
  • Fastcore
  • Preprocessing
  • Swiftcore
  • Rfastcormics
  • Eflux
• Fluxomics
  • C13solver
  • Atomtransition

Function list

modelMultipleObjectives(model, param)

Solves multiple flux balance analysis problems, and variants thereof

USAGE:

solutions = modelMultipleObjectives (model, param)

INPUT:

model – A generic COBRA model with fields:

• .rxns - n x 1 reaction identifiers

• .mets - m x 1 metabolite identifiers

• .SConsistentRxnBool - n x 1 boolean, true for stoichiometrically consistent reactions

• .bondsBF - n x 1 number of bonds broken and formed by each reaction (BBF-weighted objectives)

• .bondsE - n x 1 bond enthalpy of each reaction (BE-weighted objectives)

• .expressionRxns - n x 1 reaction expression mapped from gene expression

• .expressionRxns0norm - n x 1 reaction expression weights for the zero-norm objective

• .expressionRxns1norm - n x 1 reaction expression weights for the one-norm objective

• .expressionRxns2norm - n x 1 reaction expression weights for the two-norm objective

• .DfGt0 - m x 1 standard transformed Gibbs energy of formation

• .DfG0 - m x 1 standard Gibbs energy of formation

• .osenseStr - objective sense passed to entropic FBA (‘min’ or ‘max’)

• .cf - forward reaction linear weight for entropic FBA

• .cr - reverse reaction linear weight for entropic FBA

• .g - flux entropy weight for entropic FBA

• .u0 - reference chemical potential for entropic FBA

• .f - concentration entropy weight for entropic FBA

• .g0 - removed if present before solving

• .g1 - removed if present before solving

OPTIONAL INPUTS:

param –

a structure containing the parameters for the function:

• .modelType - type of predictions {‘sec’, ‘upt’}.

• .printLevel - Greater than zero to receive more output printed.

• .objectives -List of objective functions to be tested.

  objectives supported: ‘unWeighted0norm’, ‘Weighted0normGE’,

  ‘Weighted0normBBF’, ‘unWeighted1norm’, ‘Weighted1normGE’, ‘Weighted1normBBF’, ‘unWeighted2norm’, ‘Weighted2normGE’, ‘Weighted2normBBF’, ‘unWeightedTCBMflux’, ‘unWeightedTCBMfluxConc’.

entropicFluxBalanceAnalysis ‘unWeightedTCBMflux’ unweighted thermodynamic constraint based modelling for fluxes. ‘unWeightedTCBMfluxConc’ unweighted thermodynamic constraint based modelling for fluxes and concentrations.

minimize g.*vf’*(log(vf) -1) + (cf + ci)’*vf + g.*vr’*(log(vr) -1) + (cr - ci)’*vr vf,vr,x,x0 + f.*x’ *(log(x) -1) + u0’*x0 + f.*x0’*(log(x0) -1) + u0’*x0

subject to N*(vf - vr) - x + x0 <=> b : y_N C*(vf - vr) <=> d : y_C

vl <= vf - vr <= vu : z_v dxl <= x - x0 <= dxu : z_dx 0 <= vf <= ub : z_vf 0 <= vr <= -lb : z_vr xl <= x <= xu : z_x x0l <= x0 <= x0u : z_x0

A mosek solver is required.

zero-norm ‘unWeighted0norm’ min 0-norm unweighted. and formed.

Minimize the cardinality (zero-norm) of v

\[\begin{split}min ~& d.*||v||_0 \\ s.t. ~& S v = b \\ ~& c^T v = f \\ ~& lb \leq v \leq ub\end{split}\]

The zero-norm is approximated by a non-convex approximation Six approximations are available: capped-L1 norm, exponential function logarithmic function, SCAD function, L_p norm with p<0, L_p norm with 0<p<1 Note : capped-L1, exponential and logarithmic function often give the best result in term of sparsity.

European Journal of Operational Research, 2014” http://dx.doi.org/10.1016/j.ejor.2014.11.031 A LP solver is required.

1-norm ‘unWeighted1norm’ min 1-norm unweighted. ‘Weighted1normGE’ min 1-norm weighted by the gene expression. ‘Weighted1normBBF’ min 1-norm weighted by number of bonds broken and formed.

Minimise the Taxicab Norm using LP.

\[\begin{split}min ~& d.*|v| \\ s.t. ~& S v = b \\ ~& c^T v = f \\ ~& lb \leq v \leq ub\end{split}\]

A LP solver is required.

2-norm ‘unWeighted2norm’ min 2-norm unweighted. ‘Weighted2normGE’ min 2-norm weighted by the gene expression. ‘Weighted2normBBF’ min 2-norm weighted by the number of bonds broken and formed.

unweighted; minimises the squared Euclidean Norm of internal fluxes.

\[\begin{split}min ~& 1/2 v'*v \\ s.t. ~& S v = b \\ ~& c^T v = f \\ ~& lb \leq v \leq ub\end{split}\]

weighted; n x 1 Forms the diagonal of positive definite matrix F in the quadratic program.

\[\begin{split}min ~& 0.5 v^T F v \\ s.t. ~& S v = b \\ ~& c^T v = f \\ ~& lb \leq v \leq ub\end{split}\]

A QP solver is required.

OUTPUTS:

solutions – The solution for each objective defined in param.objectives * .unWeighted0norm: min 0-norm unweighted * .unWeighted1norm: min 1-norm unweighted. * .Weighted1normGE: min 1-norm weighted by the gene expression. * .Weighted1normBBF: min 1-norm weighted by number of bonds broken * .unWeighted2norm: min 2-norm unweighted. * .Weighted2normGE: min 2-norm weighted by the gene expression. * .Weighted2normBBF: min 2-norm weighted by the number of bonds * .weightedTCBMt * .weightedTCBM * .unWeightedTCBMfluxConcNorm * .unWeightedTCBMfluxConc: unweighted thermodynamic constraint based modelling for fluxes and concentrations * .weightedTCBMflux: * .unWeightedTCBMflux: unweighted thermodynamic constraint based

modelling for fluxes

modelPredictiveCapacity(model, param)

Test the model’s predictive capacity in terms of its ability to predict uptake and secretions, as well as the consistency of flux in reactions and metabolites.

USAGE:

[comparisonData, summary] = modelPredictiveCapacity (model, param)

INPUT:

• model – A COBRA model to be tested, with fields:

  • .rxns - n x 1 reaction identifiers

  • .mets - m x 1 metabolite identifiers

  • .lb - n x 1 lower flux bounds

  • .ub - n x 1 upper flux bounds

  • .XomicsToModelSpecificData - struct of XomicsToModel context-specific data; its .exoMet table is used as the training set when present

• param – Parameters used to test the model

  • .tests - Array with the tests run on the model (Default: ‘flux’).

  ‘fluxConsistent’: Flux consistency

  ‘thermoConsistentFlux’: Thermodynamic flux consistency ‘flux’: Objective function comparison based on a training set ‘all’: Do all tests

  • .activeInactiveRxn - nx1 with entries {1,-1,0} depending on whether a

    reaction must be active, inactive, or unspecified respectively (Required for tests ‘fluxConsistent’ and ‘thermoFluxConsistent’).

  • .presentAbsentMet - nx1 with entries {1,-1,0} depending on whether a

    metabolite must be present, absent, or unspecified respectively (Required for tests ‘fluxConsistent’ and ‘thermoFluxConsistent’).

  • .trainingSet - Table with the training set. It includes the reaction

    identifier, the reaction name, the measured mean flux, standard deviation of the flux, the flux units, and the platform used to measure it (Required for test ‘flux’).

  • .objectives - List of objective functions to be tested (Required for

    test ‘flux’; Default: ‘all’); supported objectives:

    • ‘unWeighted0norm’ min 0-norm unweighted.

    • ‘Weighted0normGE’ min 0-norm weighted by the gene expression.

    • ‘unWeighted1norm’ min 1-norm unweighted

    • ‘Weighted1normGE’ min 1-norm weighted by the gene expression

    • ‘unWeighted2norm’ min 2-norm unweighted

    • ‘Weighted2normGE’ min 2-norm weighted by the gene expression

    • {‘unWeightedTCBMflux’} unweighted thermodynamic constraint based modelling for fluxes

  • .printLevel - Greater than zero to receive more output printed.

  • .cobraSolver - COBRA solver used for the predictions (Default: ‘gurobi’).

  • .approach - prediction approach selecting which exchanges are tested

    (e.g. ‘UptSec’; Default: ‘UptSec’).

OUTPUT:

• comparisonData – A struct array with two tables in it. * .fullReport - A table comparing each tested reactions with columns:

  • .fullReport.model - the name of the model

  • .fullReport.objective - objective function

  • .fullReport.rxns - reaction identifier

  • .fullReport.mean - experimental mean flux

  • .fullReport.SD - experimental standard deviation in flux

  • .fullReport.target - sign of the exchange flux

  • .fullReport.v - predicted exchange flux

  • .fullReport.predict - sign of the predicted flux

  • .fullReport.agree - true if sign of measured and predicted flux agree

  • .fullReport.dv - difference between measured and estimated fluxes

  • .fullReport.messages - error message in the case that there was a problem

  • .comparisonStats: - A table containing predictive capacity statistics for each objective function tested.

    • .comparisonStats.objective - objective function

    • .comparisonStats.model - model compared (both, modelUpt or modelSec)

    • .comparisonStats.qualAccuracy - qualitative predictive capacity

    • .comparisonStats.Spearman - Spearman rank

    • .comparisonStats.SpearmanPval - P value

    • .comparisonStats.wEuclidNorm - Euclidean norm

• summary – The average of the objective function accuracies and the average

• of the euclidean distances.