Fluxomics¶

C13ConfidenceInterval(v0, expdata, model, max_score, directions, majorIterationLimit)[source]¶

Usage

[vs, output, v0] = C13ConfidenceInterval(v0, expdata, model, max_score, directions, majorIterationLimit)

Inputs

v0 – set of flux vectors to be used as initial guesses. They may be valid or not.

expdata – experimental data

model – the standard model. Additional field .N (= null(S)) should also be provided. This is a basis of the flux space.

max_score – maximum allowable data fit error

Optional inputs

directions – ones and zeros of which reactions to compute (size = n x 1) or numbers of reactions to use aka. [1; 5; 7; 8; 200] or reaction strings aka. {‘GPK’, ‘PGL’}. Ratios are possible with this input only. Default = [] meaning - do FVA with no ratios.

majorIterationLimit – default = 10000

Outputs

vs – matrix

output – structure

v0 – as in input

compareBinsOfFluxes(xglc, model, sammin, sammax, metabolites)[source]¶

Takes the overall sammin and sammax samples, bins them into separate bin sizes and compares them, then compares the results to the largest bin size. calls [totalz, zscore, mdv1, mdv2] = compareTwoSamp(xglc, model, samp1, samp2, measuredMetabolites) sammin and sammax each contain bins of fluxes in x.samps(r,1).points

Usage

[output] = compareBinsOfFluxes(xglc, model, sammin, sammax, metabolites)

Inputs

xglc – sugar distribution

model – model structure

sammin – samples containing bins of fluxes

sammax – samples containing bins of fluxes

Optional input

metabolites – list of metabolites

Output

output – result of comparison

compareMultSamp(xglc, model, samps, measuredMetabolites)[source]¶

Compare the multiple sets of samples

Usage

[totalz, zscore, mdvs] = compareMultSamp(xglc, model, samps, measuredMetabolites)

Inputs

xglc – sugar distribution, a random sugar distribution is calculated if empty

model – model structure, expects model.rxns to contain a list of rxn names

samps – samples, expects to have a field named points containing an array of sampled points

Optional input

measuredMetabolites – parameter fed to calcMDVfromSamp.m which only calculates the MDVs for the metabolites listed in this array

Outputs

totalz – sum of all zscores

zscore – calculated difference for each mdv element distributed across all the points

mdvs – contains fields:

mdv - the calculated mdv distribution converted from the idv solved from each point contained in their respective samples sampX

names - the names of the metabolites

ave - the average of each mdv element across all of the points

stdev - the standard dev for each mdv element across all points

compareTwoMDVs(mdv1, mdv2)[source]¶

Compares the 2 sets of mdvs

Usage

[totalz, zscore] = compareTwoMDVs(mdv1, mdv2)

Input

mdv1, mdv2 – contains fields:

mdv - the calculated mdv distribution converted from the idv solved from each point contained in their respective samples sampX

names - the names of the metabolites

ave - the average of each mdv element across all of the points

stdev - the standard dev for each mdv element across all points

Outputs

totalz – sum of all zscores

zscore – calculated difference for each mdv element distributed across all the points

compareTwoSamp(xglc, model, samp1, samp2, measuredMetabolites)[source]¶

Compare the 2 sets of samples

Usage

[totalz, zscore, mdvs] = compareMultSamp(xglc, model, samps, measuredMetabolites)

Inputs

xglc – sugar distribution, a random sugar distribution is calculated if empty

model – model structure, expects model.rxns to contain a list of rxn names

samp1, samp2 – samples, expects samp1 and samp2 to have a field named points containing an array of sampled points

Optional input

measuredMetabolites – parameter fed to calcMDVfromSamp.m which only calculates the MDVs for the metabolites listed in this array

Outputs

totalz – sum of all zscores

zscore – calculated difference for each mdv element distributed across all the points

mdv1, mdv2 – contains fields:

mdv - the calculated mdv distribution converted from the idv solved from each point contained in their respective samples sampX

names - the names of the metabolites

ave - the average of each mdv element across all of the points

stdev - the standard dev for each mdv element across all points

fitC13Data(v0, expdata, model, majorIterationLimit)[source]¶

Usage

[vout, rout] = fitC13Data(v0, expdata, model, majorIterationLimit)

Inputs

v0 – It will automatically be converted to alpha by solving N*alpha = v; if v0 is a matrix then it is assumed to be a multiple start situation and vout will also have this size.

expdata – either a data structure or a cell array of structures, in which case it is assumed that you wan to fit the sum of the scores

model – model structure

Optional input

majorIterationLimit – max number of iterations solver is allowed to take. Default = 1000

Outputs

vout – reflects size of v0, result of NLPsolution

rout – cell, result of NLPsolution

getBinsOfFluxes(samp, numfluxes, numbins)[source]¶

Takes a samp.points fluxes and bin them by numfluxes (remainder not used) or divide up in to bins of fluxes by numbins (remainder not used) sample each bin of fluxes and compare the differences between them.

Usage

[output] = getBinsOfFluxes(samp, numfluxes, numbins)

Inputs

samp – fluxes

numfluxes – default = 100

numbins – default = []

Output

output – structure with .samps field

getCompareBinsOfFluxes(xglc, model, samplo, samphi, metabolites)[source]¶

Compares the bins of fluxes between samplo and samphi, calls compareBinsOfFluxes(xglc, model, sammin, sammax, metabolites). `samplo and samphi each contain samples in x.points

Usage

[output] = getCompareBinsOfFluxes(xglc, model, samplo, samphi, metabolites)

Inputs

xglc – sugar distribution

model – model structure

samplo – samples containing bins of fluxes

samphi – samples containing bins of fluxes

Optional input

metabolites – list of metabolites

Output

output – result of comparison

getRandGlc()[source]¶

Generates random glucose in isotopomer format

Usage

[xGlc] = getRandGlc()

Output

xGlc – random glucose

goodInitialPoint(model, n)[source]¶

Generates 4*length(model.lb) random points, takes linear combinations of them so that all points are in the interior.

Usage

[out] = goodInitialPoint(model, n)

Inputs

model – model structure

n – number, default = 1

Output

out – random points with linear combinations

isotopomerViewer(mdv1, mdv2, names)[source]¶

Takes in an “experiment” and views the isotopomer as distributions between mdv1 and mdv2. No output.

Usage

isotopomerViewer(mdv1, mdv2, names)

Inputs

mdv1, mdv2 – structures

names – names in the plot

naturallabel(n)[source]¶

Returns a natural label idv of n carbons. Assumes 1.1% C13

Usage

[out] = naturallabel(n)

Input

n – size of label

Output

out – natural label idv of n carbons

runHiLoExp(experiment)[source]¶

Takes an experiment with the following structure and splits the sample space at the median of a target flux solves the two spaces with a given sugar and compares the resulting mdvs to provide a z-score.

Usage

[experiment] = runHiLoExp(experiment)

Inputs

experiment – contains:

model with fields:

S = the stoichiometric matrix

rxns = array of reaction names, corresponding the S

c = optimization target 1, or -1

ub,lb = upper and lower bounds of reactions

points = a #fluxes X #samples (~2000) array of the solution space if missing or empty, will generate a sample

glcs = an array of sugars in isotopomer format, each column a separate sugar. Should not be in MDV format. Conversion is done automatically. will default to generate 1 random sugar if set to []

targets = an array of cells with string for the reaction to split on the solution space, defaults to ‘PGL’

thresholds = #targets X 1 array of thresholds

metabolites = an optional parameter fed to calcMDVfromSamp.m which only calculates the MDVs for the metabolites listed in this array. e.g - optionally, metabolites can also be a structure of fragments

hilo = a #targets X #samples array of 0/1’s, 0 id’s the sample of fluxes as the lo side and 1 id’s the sample for the hi side. hilo will only be calculated/recalculated if it’s missing or if the targets have been replaced using the param list

mdvs = structure of mdv results:

(name) = name of the run = t + glc# e.g. t1, t2, glc# refers to the glc in the glcs array.

Note that the split of mdvs are not stored, also since the only time mdvs should be regen’d is when glcs has changed, but we have no way of knowing when this happens, the user will have to manually empty out mdvs to have it regenerated.

zscores = array of zscores from each run, targets X glcs

rscores = array of ridge scores from each run, targets X glcs

target – an optional string for a specific rxn to target. if supplied, it will override and replace the targets field in the experiment structure.

threshold – an optional number to apply on the solution space fluxes if supplied, it will be applied to the hilo field and replace the hilo splits.

Output

experiment – the experiment array.

This code will loop through one experiment per sugar, per target

score_KS(mdv, hilo, lambda)[source]¶

Calculates KS score

Usage

[out] = score_ridge(mdv, hilo, lambda, crossval)

Inputs

mdv – structure

hilo – (0’s and 1’s), ideally there will be a similar # of each.

Optional input

lambda – weighting, if the mean is less than lambda, the scores get weighted less, default = .02

Output

out – score

score_ridge(mdv, hilo, lambda, crossval)[source]¶

Calculates ridge score

Usage

[out] = score_ridge(mdv, hilo, lambda, crossval)

Inputs

mdv – structure

hilo – (0’s and 1’s), ideally there will be a similar # of each.

Optional inputs

lambda – ridge parameter, default = .01

crossval – whether to do cross validation. This severely slows down the computation, default is no.

Output

out – score