En_resistant_cells: En_resistant_cells

In Michorlab/ACESO: A Cancer Evolution Simulation Optimizer

Description

Expected number of resistant cells in the different clones over time after treatment initiation

Usage

En_resistant_cells(N, t, type_0, type_i, type_icr = NULL,
  approximation = TRUE)

Arguments

N	Number of different resistant cell types
t	Final time
type_0	Type-0 S4 object
type_i	Type-i S4 object
type_icr	Type-icr S4 object for the i cell type
approximation	logical argument indicating if an approximation of the numerical integration method must be used or not. Default to TRUE for faster computation.

Value

En_resistant_cells returns a matrix with the number of resistant cells over time in the different clones

Examples

## Not run: 
#Birth rate of type 0 cells as a function of time:
b0=function(time){0.05*sin(0.1*time)+0.1}
#Create Type-0 S4 object structure with the parameters of type 0 sensitive cells
Type0 <-define.Type0.cells(N0=100,birth_rate = b0,death_rate= 0.14)
#Birth rate of type 1 cells as a function of time:
b1=function(time){0.05*sin(0.1*time)+0.12}
#Create Type-i S4 object structure with the parameters of type i resistant cells
Type1 <- define.Typei.cells(Ni=0,birth_rate = b1,death_rate  = 0.09,mutation_rate=10^-4)
#Birth rate of type 2 cells as a function of time:
b2=function(time){0.05*sin(0.1*time)+0.12}
#Create Type-i S4 object structure with the parameters of type i resistant cells
Type2 <- define.Typei.cells(Ni=0,birth_rate = b2,death_rate  = 0.06,mutation_rate=10^-4)
Call En_resistant_cells function
En_resistant_cells(N=2,t=100,type_0=Type0,type_i=list(Type1,Type2))

## End(Not run)

Michorlab/ACESO documentation built on June 4, 2021, 4:57 p.m.