c13solver

Combination(n, k)

Produces the array of combinations possible picking k from n adapted from Combinadics http://msdn.microsoft.com/en-us/library/aa289166(VS.71).aspx

Usage

[out] = Combination(n, k)

Inputs

• n – number of elements in the pool
• k – number of elements to pick from n

Output

• out – array of combinations

cdv2idv(n)

Transformation matrix to transform cumomers to idv’s. idv = cdv2idv(log2(length(cdv)))*cdv; Employs memoization.

Usage

[out] = cdv2idv(n)

Input

• n – cdv

Output

• out – idv

generateIsotopomerSolver(model, inputMet, experiment, FVAflag)

Prints a file which looks like BiosyntheticMappingFile except that it has the indexes of every reaction in there as well. After that it calls converter.pl, optimizer.pl and validator.pl but I can take care of that.

Usage

generateIsotopomerSolver(model, inputMet, experiment, FVAflag)

Inputs

• model – model structure with .isotopomer filed
• inputMet – input metabolites
• experiment – structure
• FVAflag – default = false, if true then additinoal operations involving fluxVariability involved

Prints a file to /isotopomer/solver/ directory

idv2cdv(n)

Returns transformation to go from idv to cumomers. cdv = idv2cdv(log2(length(idv)))*idv;

Usage

[out] = idv2cdv(n)

Input

• n – idv

Output

• out – cdv

idv2idv(n)

Outputs a transformation matrix for changing from forward to reverse order.

order 1 (Jennie’s)

000, 001, 010, 011, 100, 101, 110, 111

order 2 (mine)

000, 100, 010, 110, 001, 101, 011, 111

Usage

[out] = idv2idv(n)

Input

• n – matrix, size of matrix (2^n x 2^n)

Output

• out – transforamtion matrix

idv2mdv(n, fragment)

Returns transofmation matrix from idv’s (either Jennie’s or Jan’s order). MDV = idv2mdv(log2(length(idv)))*idv;

Usage

[out] = idv2mdv(n, fragment)

Input

• n – matrix

Optional input

• fragment – a vector of carbons to be included. [ 0, 0, 1, 1, 1]’ = last 3 carbons.

Output

• out – transformation matrix

scoreC13Fit(flux, expdata, model, namesset, method)

This function (1) computes the theoretical mdv distribution vector for a given flux vector, v, (2) and then computes an error score by taking a running sum of the squared difference between the theortical and experimental mdv vectors.

Usage

[output] = scoreC13Fit(flux, expdata, model, namesset, method)

Inputs

• flux – flux vector
• expdata – experimental data structure
• model – model structure
• namesset – set of names
• method – method 1 = cumomer, method 2 = CMU

Output

• output – contains fields:
  • error - the calculated error sum value
  • theory - theoretical mdv vector
  • experimental - experimental mdv vector

Example

v - flux vector array
expdata - experimental data structure
     e.g.
       ala57
         - met = xalaL
         - fragment = [1,1,1]'
         - data = [0.238,0.098,0.017]'
glc_cdv is a sugar distribution in cumomer format (see idv2cdv).