CHRR

chrrExpSampler(model, numSkip, numSamples, lambda, toRound, roundedPolytope, useFastFVA)

Generate random flux samples from an exponential distribution with CHRR Coordinate Hit-and-Run with Rounding. chrrExpSampler will generate numSamples samples from model, taking numSkip steps of a random walk between each sample. Rounding the polytope is a potentially expensive step. If you generate multiple rounds of samples from a single model, you can save roundedPolytope from the first round and input it for subsequent rounds. It is allowed to change lambda without recomputing roundedPolytope.

USAGE:

[samples, roundedPolytope, minFlux, maxFlux] = chrrExpSampler(model, numSkip, numSamples, lambda, toRound, roundedPolytope, useFastFVA)

INPUTS:

model: COBRA model structure with fields:

  • .S - The m x n stoichiometric matrix

  • .lb - n x 1 lower bounds on fluxes

  • .ub - n x 1 upper bounds on fluxes

  • .c - n x 1 linear objective

numSkip: Number of steps of coordinate hit-and-run between samples numSamples: Number of samples

OPTIONAL INPUTS:
lambda: the bias vector, i.e. generate samples from exp(-<lambda, x>)

restricted to the feasible region. OPTIONAL ONLY IF model.c is nonzero, in which case we set lambda=model.c.

toRound: {0, 1} Option to round the polytope before sampling. roundedPolytope: The rounded polytope from a previous round of

sampling the same model.

useFastFVA: Boolean to use fastFVA (default: false)

OUTPUTS:

samples: n x numSamples matrix of random flux samples roundedPolytope: The rounded polytope. Save for use in subsequent

rounds of sampling.

minFlux, maxFlux: flux minima and maxima

chrrParseModel(model)

Parse a COBRA model into the right format for the CHRR sampler

USAGE:

[P,model] = chrrParseModel(model);

We are trying to sample uniformly at random from the points v that satisfy:

\[\begin{split}Sv = b\\ ~~ l_b \leq v \leq u_b\end{split}\]
INPUTS:

model: COBRA model structure with fields:

  • .S - The m x n stoichiometric matrix

  • .lb - n x 1 lower bounds on fluxes

  • .ub - n x 1 upper bounds on fluxes

OPTIONAL INPUTS:

  • .C - ‘k x n’ matrix of additional inequality constraints

  • .d - ‘k x 1’ rhs of the above constraints

  • .dsense - ‘k x 1’ the sense of the above constraints (‘L’ or ‘G’)

OUTPUTS:

P: A structure with fields:

  • .A_eq - Equality constraint matrix (model.S)

  • .b_eq - Right hand side of equality constraints (model.b)

  • .A - Inequality constraint matrix ([I_n 0; 0 -I_n])

  • .b - Right hand side of inequality constraints ([lb; -ub])

chrrSampler(model, numSkip, numSamples, toRound, roundedPolytope, useFastFVA, optPercentage)

Generate uniform random flux samples with CHRR Coordinate Hit-and-Run with Rounding. chrrSampler will generate numSamples samples from model, taking numSkip steps of a random walk between each sample. Rounding the polytope is a potentially expensive step. If you generate multiple rounds of samples from a single model, you can save roundedPolytope from the first round and input it for subsequent rounds.

USAGE:

[samples, roundedPolytope] = chrrSampler(model, numSkip, numSamples, toRound, roundedPolytope, useFastFVA)

INPUTS:

model: COBRA model structure with fields:

  • .S - The m x n stoichiometric matrix

  • .lb - n x 1 lower bounds on fluxes

  • .ub - n x 1 upper bounds on fluxes

  • .c - n x 1 linear objective

numSkip: Number of steps of coordinate hit-and-run between samples numSamples: Number of samples

OPTIONAL INPUTS:
toRound: {0,(1)} Option to round the polytope before sampling.

0: no rounding 1 (default): round using max volume ellipsoid 2: round using isotropy (slower, more accurate) 3: Barrier rounding (the fastest among three & as efficient as isotropic rounding)

roundedPolytope: The rounded polytope from a previous round of

sampling the same model.

useFastFVA: Boolean to use fastFVA (default: false) optPercentage: Only consider solutions that give you at least a certain

percentage of the optimal solution (Default = 100) Can decrease optPercentage slightly if getting errors.

OUTPUTS:

samples: n x numSamples matrix of random flux samples roundedPolytope: The rounded polytope. Save for use in subsequent

rounds of sampling.

minFlux, maxFlux: flux minima and maxima