The generic function simHum implements all neccessary functions for the individuals to update the complete environment.
simHum(
object,
arena,
j,
sublb,
bacnum,
sec_obj = "none",
cutoff = 1e-06,
pcut = 1e-06,
with_shadow = FALSE
)# S4 method for Human
simHum(
object,
arena,
j,
sublb,
bacnum,
sec_obj = "none",
cutoff = 1e-06,
pcut = 1e-06,
with_shadow = FALSE
)
An object of class Human.
An object of class Arena defining the environment.
The number of the iteration of interest.
A vector containing the substance concentrations in the current position of the individual of interest.
integer indicating the number of bacteria individuals per gridcell
character giving the secondary objective for a bi-level LP if wanted.
value used to define numeric accuracy.
A number giving the cutoff value by which value of objective function is considered greater than 0.
True if shadow cost should be stores (default off).
Returns the updated enivironment of the arena parameter with all new positions of individuals on the grid and all new substrate concentrations.
Human cell individuals undergo the step by step the following procedures: First the individuals are constrained with constrain to the substrate environment, then flux balance analysis is computed with optimizeLP, after this the substrate concentrations are updated with consume, then the cell growth is implemented with cellgrowth, the potential new phenotypes are added with checkPhen, finally the conditional function lysis is performed. Can be used as a wrapper for all important cell functions in a function similar to simEnv.
Human-class, Arena-class, simEnv, constrain, optimizeLP, consume, cellgrowth, checkPhen and lysis