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).