C13solver¶
- Combination(n, k)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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)[source]¶
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).