Chrr

chrrExpSampler(model, numSkip, numSamples, lambda, toRound, roundedPolytope, useFastFVA)[source]

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)[source]

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)[source]

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