minimal_perturbation: Assemble the LP problem for minimal perturbation.
In cdiener/dycone: dycone - analyze the dynamic landscape of metabolome data

Assemble the LP problem for minimal perturbation.

1 2	minimal_perturbation(ma_terms, samples, reacts, permutations = 1000, v_min = 0, v_max = 1000, tradeoff = 0.95, min_obj = 100)

`ma_terms`	A matrix or data frame containing the metabolic terms in the columns.
`samples`	A factor or character string with either "normal" and "disease" entries.
`reacts`	The reactions for the respective model.
`permutations`	The maximum number of permutations. If this number is smaller than all possible permutation will check all permutations, otherwise will sample that many permutations with replacements.
`v_min`	The lower bounds for the fluxes. Either a single value for the min bounds for all reactions or a matrix/data.frame with two columns and as many rows as irreversible reactions where the columns contain the lower bounds for each factor level in 'samples'.
`v_max`	The upper bounds for the fluxes. Either a single value for the max bounds for all reactions or a matrix/data.frame with two columns and as many rows as irreversible reactions where the columns contain the upper bounds for each factor level in 'samples'.
`tradeoff`	Double in [0, 1] specifiying the equlibrium between minimizing differences in k changes versus flux changes.
`min_obj`	Constraints for growth rate. If a vector with two elements the first will be interpreted as the index of the objective reaction and the second value as its lower bound. If only a single value will constrain the absolute sum of fluxes (sum \|v_i\|) and take the value as the lower bound.