minimal_perturbation_lp: 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_lp(ma_terms, samples, S, v_min = 0, v_max = 1000, 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.
`S`	The stoichiometric matrix or a list containing one matrix for each sample.
`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'.
`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.

A list with the following components:

coefficients: The coefficients for the equalities/inequalities in the LP. Each row denotes one equation.
type: The type of the (in)equalities. A character vector with as many entries as rows in 'coefficients' being either "equal", "less" or "larger"
bounds: A matrix with two columns containing the upper and lower variable (flux) bounds.
row_bounds: Vector containing the right sides to the (in)equalities.
obj_coefs: The objective coefficients. Containing one number for each variable.