library(ggplot2)
library(dplyr)
library(tidyr)
library(deSolve)


# Chemical concentrations across a 2 step reaction
enzyme <- function(t, n, parameters){   
  with(as.list(c(t, n, parameters)),{
    
    dE = -k1*E*S + k1r*ES + k2*ES - k2r*E*P1 - k3*E*P1 +k3r*EP +k4*EP -k4r*E*P2
    dES = k1*E*S - k1r*ES - k2*ES +k2r*E*P1
    dEP = k3*E*P1 -k3r*EP - k4*EP +k4r*E*P2 
    
    dS = -k1*E*S +k1r*ES
    dP1 = k2*ES -k2r*E*P1 - k3*E*P1 +k3r*EP
    dP2 = k4*EP - k4r*E*P2
    
    dStot = dS + dES
    dP1tot = dP1 + dEP
    
    return(list(c(dE,dS, dES, dEP, dP1, dP2, dStot, dP1tot)))
  })
}



parameters = c(k1 = 1208,     #/sec
               k2 = 0.0087,    #/sec
               k3 = 1208,      #/sec - This changes across simulations
               k4 = 0.087,    #/sec
               
               k1r = 0.01208,
               k2r = 0,
               k3r = 0.001208,
               k4r = 0)    
               

start_density = c(E = 0.000000, S = 0.0000757, ES = 0.0000043, EP = 0.0000007, P1 = 0.0000103, P2 = 0.000009, Stot=0.00008, P1tot=0.000011) 
time_to_sim = seq(from = 0 , to = 3600, by = 0.1)
out1 = ode(y = start_density,
          times = time_to_sim,
          func = enzyme,
          parms = parameters)

out1dataframe <-data.frame(out1)

ggplot(out1dataframe, aes(x=time)) + 
  geom_line(aes(y=Stot, color="Stot")) + 
  geom_line(aes(y=P1tot, color="P1tot")) +
  geom_line(aes(y=P2, color="P2")) +
  ggtitle("Condition 1")


Sim <-out1dataframe[seq(1,nrow(out1dataframe),600),]

write.csv(Sim,"data.csv")