fluxPredict: Make predictions from seahorse data by sampling and...

In jdreyf/sybilxf: R package for integration of Seahorse extracellular flux (XF) data with metabolic modeling.

Description

This function integrates the sampled seahorse measurements as constraints into the specified model, optionally maximizes an objective reaction, minimizes total flux for each sample, and returns a matrix of reactions-by-samples where the entries are predicted fluxes.

Usage

fluxPredict(model, seahorse_data, biomass_est = 0, alg = c("mtf", "fba"),
  low.wt.rxns = NULL, obj_rxn = NULL, model.nm = "2.1A",
  solver = c("gurobi", "glpk"))

Arguments

model	An object of class modelorg.
seahorse_data	A data.frame returned by map_seahorse
biomass_est	Estimated biomass flux. Default = 0.
alg	Either "fba" (default) to optimize a reaction or "mtf" to only minimize total flux (and not do fba).
obj_rxn	Reaction in model to optimize if alg="fba". Default NULL optimizes ATP demand reaction.
model.nm	The metabolic model name. One of "2.1A", "2.1x", or "2.2".
solver	The solver to use; "gurobi" (default) or "glpk".

Details

For parallel computing, a parallel backend must be registered. See foreach for details.

Author(s)

Alfred Ramirez, Jonathan Dreyfuss

References

Ramirez AK, Lynes MD, Shamsi F, Xue R, Tseng YH, Kahn CR, Kasif S, Dreyfuss JM. Integrating Extracellular Flux Measurements and Genome-Scale Modeling Reveals Differences between Brown and White Adipocytes. Cell Rep 2017 Dec; 21(11): 3040-3048.

jdreyf/sybilxf documentation built on May 22, 2019, 4:41 p.m.