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DYNAMIC and STATIC Simulation

Note that, in the previous example, the DYNAMIC option is specified in the SOLVE statement. The DYNAMIC and STATIC options work the same for differential equations as they do for dynamic systems. In the differential equation case, the DYNAMIC option makes the initial value needed at each observation the computed value from the previous iteration. For a static simulation, the data set must contain values for the integrated variables. For example, if DERT.Y and DERT.Z are the differential variables, you must include Y and Z in the input data set in order to do a static simulation of the model.

If the simulation is dynamic, the initial values for the differential equations are obtained from the data set, if they are available. If the variable is not in the data set, you can specify the initial value in a declaration statement. If you do not specify an initial value, the value of 0.0 is used.

A dynamic solution is obtained by solving one initial value problem for all the data. A graph of a simple dynamic simulation is shown in Figure 18.45. If the time variable for the current observation is less than the time variable for the previous observation, the integration is restarted from this point. This allows for multiple samples in one data file.

Figure 18.45 Dynamic Solution

In a static solution, n–1 initial value problems are solved using the first n–1 data values as initial values. The equations are integrated using the th data value as an initial value to the i+1 data value. Figure 18.46 displays a static simulation of noisy data from a simple differential equation. The static solution does not propagate errors in initial values as the dynamic solution does.

Figure 18.46 Static Solution

For estimation, the DYNAMIC and STATIC options in the FIT statement perform the same functions as they do in the SOLVE statement. Components of differential systems that have missing values or are not in the data set are simulated dynamically. For example, often in multiple compartment kinetic models, only one compartment is monitored. The differential equations that describe the unmonitored compartments are simulated dynamically.

For estimation, it is important to have accurate initial values for ODEs that are not in the data set. If an accurate initial value is not known, the initial value can be made an unknown parameter and estimated. This allows for errors in the initial values but increases the number of parameters to estimate by the number of equations.

Estimation of Differential Equations

Consider the kinetic model for the accumulation of mercury (Hg) in mosquito fish (Matis, Miller, and Allen 1991, p. 177). The model for this process is the one-compartment constant infusion model shown in Figure 18.47.

Figure 18.47 One-Compartment Constant Infusion Model

The differential equation that models this process is

The analytical solution to the model is

The data for the model are

data fish;
   input day conc;
datalines;
0.0    0.0
1.0    0.15
2.0    0.2
3.0    0.26
4.0    0.32
6.0    0.33
;

To fit this model in differential form, use the following statements:

proc model data=fish;
   parm ku ke;

   dert.conc = ku - ke * conc;

   fit conc / time=day;
run;

The results from this estimation are shown in Figure 18.48.

Auxiliary Equations

Auxiliary equations can be used with differential equations. These are equations that need to be satisfied with the differential equations at each point between each data value. They are automatically added to the system, so you do not need to specify them in the SOLVE or FIT statement.

Consider the following example.

The Michaelis-Menten equations describe the kinetics of an enzyme-catalyzed reaction. The enzyme is E, and S is called the substrate. The enzyme first reacts with the substrate to form the enzyme-substrate complex ES, which then breaks down in a second step to form enzyme and products P.

The reaction rates are described by the following system of differential equations:

The first equation describes the rate of formation of ES from E + S. The rate of formation of ES from E + P is very small and can be ignored. The enzyme is in either the complexed or the uncomplexed form. So if the total () concentration of enzyme and the amount bound to the substrate is known, can be obtained by conservation.

In this example, the conservation equation is an auxiliary equation and is coupled with the differential equations for integration.

Time Variable

You must provide a time variable in the data set. The name of the time variable defaults to TIME. You can use other variables as the time variable by specifying the TIME= option in the FIT or SOLVE statement. The time intervals need not be evenly spaced. If the time variable for the current observation is less than the time variable for the previous observation, the integration is restarted.

Differential Equations and Goal Seeking

Consider the following differential equation

and the data set

data t2;
   y=0; time=0; output;
   y=2; time=1; output;
   y=3; time=2; output;
run;

The problem is to find values for X that satisfy the differential equation and the data in the data set. Problems of this kind are sometimes referred to as goal-seeking problems because they require you to search for values of X that satisfy the goal of Y.

This problem is solved with the following statements:

proc model data=t2;
   independent x 0;
   dependent y;
   parm a 5;
   dert.y = a * x;
   solve x / out=goaldata;
run;

proc print data=goaldata;
run;

The output from the PROC PRINT statement is shown in Figure 18.52.

Note that an initial value of 0 is provided for the X variable because it is undetermined at TIME = 0.

In the preceding goal-seeking example, X is treated as a linear function between each set of data points (see Figure 18.53).

Figure 18.53 Form of X Used for Integration in Goal Seeking

If you integrate manually, you have

For observation 2, this reduces to

So for this observation.

Goal seeking for the TIME variable is not allowed.

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