ccl4model: The CCl4 Inhalation Model

In deSolve: Solvers for Initial Value Problems of Differential Equations ('ODE', 'DAE', 'DDE')

The CCl4 Inhalation Model

Description

The CCl4 inhalation model implemented in .Fortran

Usage

ccl4model(times, y, parms, ...)

Arguments

times	time sequence for which the model has to be integrated.
y	the initial values for the state variables ("AI", "AAM", "AT", "AF", "AL", "CLT" and "AM"), in that order.
parms	vector or list holding the ccl4 model parameters; see the example for the order in which these have to be defined.
...	any other parameters passed to the integrator ode (which solves the model).

Details

The model is implemented primarily to demonstrate the linking of FORTRAN with R-code.

The source can be found in the ‘/doc/examples/dynload’ subdirectory of the package.

Author(s)

R. Woodrow Setzer <[e-mail]>

See Also

Try demo(CCL4model) for how this model has been fitted to the dataset ccl4data,

aquaphy, another FORTRAN model, describing growth in aquatic phytoplankton.

Examples

## =================
## Parameter values
## =================

Pm <- c(
  ## Physiological parameters
  BW = 0.182,   # Body weight (kg)
  QP = 4.0  ,   # Alveolar ventilation rate (hr^-1)
  QC = 4.0  ,   # Cardiac output (hr^-1)
  VFC = 0.08,   # Fraction fat tissue (kg/(kg/BW))
  VLC = 0.04,   # Fraction liver tissue (kg/(kg/BW))
  VMC = 0.74,   # Fraction of muscle tissue (kg/(kg/BW))
  QFC = 0.05,   # Fractional blood flow to fat ((hr^-1)/QC
  QLC = 0.15,   # Fractional blood flow to liver ((hr^-1)/QC)
  QMC = 0.32,   # Fractional blood flow to muscle ((hr^-1)/QC)

  ## Chemical specific parameters for chemical
  PLA = 16.17,  # Liver/air partition coefficient
  PFA = 281.48, # Fat/air partition coefficient
  PMA = 13.3,   # Muscle/air partition coefficient
  PTA = 16.17,  # Viscera/air partition coefficient
  PB = 5.487,   # Blood/air partition coefficient
  MW = 153.8,   # Molecular weight (g/mol)
  VMAX = 0.04321671, # Max. velocity of metabolism (mg/hr) -calibrated
  KM = 0.4027255,    # Michaelis-Menten constant (mg/l) -calibrated

  ## Parameters for simulated experiment
  CONC = 1000,  # Inhaled concentration
  KL = 0.02,    # Loss rate from empty chamber /hr
  RATS = 1.0,   # Number of rats enclosed in chamber
  VCHC = 3.8    # Volume of closed chamber (l)
)

## ================
## State variables
## ================
y <- c(
  AI  = 21, # total mass , mg
  AAM = 0,
  AT  = 0,
  AF  = 0,
  AL  = 0,
  CLT = 0,  # area under the conc.-time curve in the liver
  AM  = 0   # the amount metabolized (AM)
)

## ==================
## Model application
## ==================

times <- seq(0, 6, by = 0.1)

## initial inhaled concentration-calibrated
conc <- c(26.496, 90.197, 245.15, 951.46) 

plot(ChamberConc ~ time, data = ccl4data, xlab = "Time (hours)",
       xlim = range(c(0, ccl4data$time)),
       ylab = "Chamber Concentration (ppm)",
       log = "y", main = "ccl4model")

for (cc in conc) {
  Pm["CONC"] <- cc

  VCH <- Pm[["VCHC"]] - Pm[["RATS"]] * Pm[["BW"]]
  AI0 <- VCH * Pm[["CONC"]] * Pm[["MW"]]/24450 
  y["AI"] <- AI0
  
  ## run the model:
  out <- as.data.frame(ccl4model(times, y, Pm))
  lines(out$time, out$CP, lwd = 2)
}

legend("topright", lty = c(NA, 1), pch = c(1, NA), lwd = c(NA, 2),
       legend = c("data", "model"))

## ================================== 
## An example with tracer injection
## ==================================
## every day, a conc of 2 is added to AI.
## 1. implemented as a data.frame
eventdat <- data.frame(var = rep("AI", 6), time = 1:6 ,
  value = rep(1, 6), method = rep("add", 6))

eventdat

print(system.time(
  out <-ccl4model(times, y, Pm, events = list(data = eventdat))
))

plot(out, mfrow = c(3, 4), type = "l", lwd = 2)


# 2. implemented as a function in a DLL!
print(system.time(
out2 <-ccl4model(times, y, Pm, events = list(func = "eventfun", time = 1:6))
))

plot(out2, mfrow=c(3, 4), type = "l", lwd = 2)

deSolve documentation built on Nov. 28, 2023, 1:11 a.m.