Once we have a description of a system, and a nice drawing to
represent it, we can answer some interesting questions. One of the most
common questions is *what is the behavior of the system?* In
other words, we usually want to know *what will happen?*.

To answer this, we can use the computer. It is not hard to write a
small program to simulate the system. The simulation will be an
*approximate* answer, good enough to see the important
characteristics of the system’s behavior.

In this document we will consider systems represented by graphs like in the last post “Drawing Systems”, and we will write our computer code using the R language.

## The nodes have values

**Figure 1.** A model of the *cell
growth system*.

For this example we will use the system represented by the graph of Figure 1.

In this graph each circle represents an item of the system. The label
of the circle is the name of the item. In our example we have two
circles, named *cells* and *food*. The quantity of each
item is represented by a vector with the same name. In this case, the
vectors are `cells`

and `food`

.

Each circle has also a value that can change through time, usually
the *quantity* or *concentration* of the item in the
system. This value has the same name as the circle label. In our example
an item as the value `cells`

and the other the value
`food`

.

In these kind of systems each item has a second value, corresponding
to the *change* of the item’s quantity in each time-step. The
name of this value starts with `d_`

, followed by the item
label. In our example these values are called `d_cells`

and
`d_food`

.

The *process* are represented by boxes and are much simpler.
They only have one constant value, which is a rate. The value name is
made with the label of the box and the suffix `_rate`

. So the
box called *replication* has a constant named
`replication_rate`

.

In summary, each circle has two vectors, and each box a fixed number.

## Discrete time

In the computer the time is represented by an integer that increases
step by step^{1}. The units are arbitrary, so we can
think of anything between microseconds and millennia, including years,
days, hours, seconds, weeks, etc. We only assume that each time-step has
the same duration.

For example in our case we can think that each time-step is one hour. Time 1 corresponds to the initial condition, that is, the beginning of the experiment. Time 2 is one hour later. Time 25 is one day later.

We can use any symbol to represent time. Most people uses letters
`i`

, `j`

or `k`

, since these are the
letters traditionally used as index of vectors and matrices. In this
example we are going to represent time with `i`

.

The items of the system, represented by circles in the graph, have
two values that change with time. We represent these variables by
vectors, one element for each time-step. Thus, the number of cells at
time `i`

is `cells[i]`

, and its change in the same
time is `d_cells[i]`

.

## Finding the equations

To simulate the system we need to find the formulas. We start with the boxes, because they are the easy ones.

For each box in the graph we get a single term: the
**multiplication** of the rate constant and each of the
*quantity* variables of the circles connected by incoming arrows.
If there are several incoming arrows from the same circle, then the
variable is multiplied several times. The outgoing arrows are not
important in this part.

In our example the formula for *replication* box is
`replication_rate * cells[i-i] * food[i-1]`

. Here we use the
index `i-1`

because we do not know yet the value of
`cells[i]`

or `food[i]`

. We will calculate them
later. Metaphorically, at the begin of *“today”* we only know
*“yesterday”*.

The formula for the circles is made with the **sum** of
all the terms of boxes connected with incoming arrows,
**minus** all the terms of boxes connected with outgoing
arrows. This value is assigned to the *change* variable of the
circle.

In our example the variable `d_food[i]`

gets assigned the
value `-replication_rate * cells[i-i] * food[i-1]`

, since the
*food* circle has only one outgoing arrow. The *cell*
circle has two incoming arrows from *replication* and one
outgoing arrow to *replication*. Therefore the variable
`d_cells[i]`

gets the value

```
replication_rate * cells[i-i] * food[i-1]
+ replication_rate * cells[i-i] * food[i-1]
- replication_rate * cells[i-i] * food[i-1]
```

which, after simplification, is
`replication_rate * cells[i-i] * food[i-1]`

resulting on a
final result of one positive incoming arrow. In summary, the formulas
for the *change* variables are:

```
<- -replication_rate * cells[i-i] * food[i-1]
d_food[i] <- replication_rate * cells[i-i] * food[i-1] d_cells[i]
```

Finally, the *quantity* variables have to be updated. Each
*quantity* variable is the cumulative sum of the change
variables. In our example we have

```
<- food[i-1] + d_food[i]
food[i] <- cells[i-1] + d_cells[i] cells[i]
```

## Initial conditions

The last missing piece necessary for the simulation are the initial
values of the circles’ variables. The value of
`cells[`

*today*`]`

depends on the value of
`cells[`

*yesterday*`]`

, and we can only
calculate that for time `i`

greater than or equal to 2.

In our case we will use the variables `cells_ini`

and
`food_ini`

to store the values corresponding to
`cells[1]`

and `food[1]`

. For the *change*
variables, we can assume that they are initially zero.

## Complete code

Our simulation will be a function. The inputs are:

`N`

, the number of simulation steps,`replication_rate`

, the rate of change for the given time`cells_init`

, the initial quantity of cells`food_ini`

, the initial quantity of food

and the output will be a *data frame* with the vectors
`cells`

and `food`

. Thus, we know that the code in
R should be something like this:

```
<- function(N, replication_rate, cells_init, food_ini) {
cell_culture # create empty vectors
# initialize values
for(i in 2:N) {
# update `d_cells` and `d_food`
# update `cells` and `food` as a cumulative sum
}return(data.frame(cells, food))
}
```

To create an empty vector in R we only need to know the vector size^{2}, which in this case is
`N`

. Therefore we ca write

```
# create empty vectors
<- d_cells <- rep(NA, N)
cells <- d_food <- rep(NA, N) food
```

Here we use a *trick* in R that allows us to assign the same
value to two variables. The line

`<- d_cells <- rep(NA, N) cells `

is equivalent to

```
<- rep(NA, N)
d_cells <- rep(NA, N) cells
```

The first component of each vector needs an initial value. Our
function receives `cells_ini`

and `food_ini`

as
inputs. To make thing simple we assume that the *change* values
start at zero, so we write

```
1] <- cells_init
cells[1] <- food_ini
food[1] <- d_food[1] <- 0 d_cells[
```

Putting all together, we get

```
<- function(N, replication_rate, cells_init, food_ini) {
cell_culture # create empty vectors
<- d_cells <- rep(NA, N)
cells <- d_food <- rep(NA, N)
food # initialize values
1] <- cells_init
cells[1] <- food_ini
food[1] <- d_food[1] <- 0
d_cells[for(i in 2:N) {
# update `d_cells` and `d_food`
<- +replication_rate * cells[i-1] * food[i-1]
d_cells[i] <- -replication_rate * cells[i-1] * food[i-1]
d_food[i] # update `cells` and `food` as a cumulative sum
<- cells[i-1] + d_cells[i]
cells[i] <- food[i-1] + d_food[i]
food[i]
}return(data.frame(cells, food))
}
```

# Other examples

This modeling and simulation technique is not limited to a particular field of science. The same rules apply to may other systems, small and big. That is why we can use examples from very different areas and still learn something useful for out own interest. Let’s see other examples

## Water formation

**Figure 2.** A model of the *water
formation reaction*.

A chemical reaction combines hydrogen and oxygen to produce water. To simplify we assume that the reaction is this: \[2H+O\leftrightarrow H_2O\]

This system has 3 items: Hydrogen, Oxygen and Water, represented with
the letters H, O and W. This reaction is reversible, so we represent it
with two irreversible reactions at the same time: water formation
(*r_1*) and water decomposition (*r_2*). We can represent
this system with the graph in Figure 2. The R code to simulate this
system is:

```
<- function(N, r1_rate=0.1, r2_rate=0.1,
water_formation H_ini=1, O_ini=1, W_ini=0) {
# first, create empty vectors to fill later
<- d_W <- rep(NA, N) # Water, quantity and change on each time
W <- d_H <- rep(NA, N) # Hydrogen
H <- d_O <- rep(NA, N) # Oxygen
O # fill the initial quantities of water, hydrogen and oxygen
1] <- W_ini
W[1] <- H_ini
H[1] <- O_ini
O[1] <- d_H[1] <- d_O[1] <- 0 # the initial change is zero
d_W[
for(i in 2:N) {
<- r1_rate*H[i-1]*H[i-1]*O[i-1] - r2_rate*W[i-1]
d_W[i] <- -r1_rate*H[i-1]*H[i-1]*O[i-1] + r2_rate*W[i-1]
d_O[i] <- -2*r1_rate*H[i-1]*H[i-1]*O[i-1] + 2*r2_rate*W[i-1]
d_H[i]
<- W[i-1] + d_W[i]
W[i] <- O[i-1] + d_O[i]
O[i] <- H[i-1] + d_H[i]
H[i]
}return(data.frame(W, H, O))
}
```

## Predator-Prey population dynamics

**Figure 3.** A model of the
*Lotka-Volterra system*.

This is a system with several applications, known as
“*Lotka-Volterra system*”. It was discovered and first analyzed
by Alfred J. Lotka, and Vito Volterra, and appears again and again in
ecology and population dynamics.

Here we have two items: cats and mice, and three processes: birth (of mice), catch (of mice, also reproduction of cats) and death (of cats). The system is represented by the graph in Figure 3 and simulated by the following R code:

```
<- function(N, birth_rate, catch_rate, death_rate) {
cat_and_mouse <- d_mice <- rep(NA, N)
mice <- d_cats <- rep(NA, N)
cats 1] <- 1
mice[1] <- 3
cats[1] <- d_cats[1] <- 0
d_mice[
for(i in 2:N){
<- birth_rate*mice[i-1] - cats[i-1]*mice[i-1]*catch_rate
d_mice[i] <- -death_rate*cats[i-1] + cats[i-1]*mice[i-1]*catch_rate
d_cats[i] <- mice[i-1] + d_mice[i]
mice[i] <- cats[i-1] + d_cats[i]
cats[i]
}return(data.frame(mice, cats))
}
```

## Money in the bank

This one is an exercise. Consider the following system.

What will be the money in the bank after 12 month if the interest rate is 1/12? What if the interest is 1/100 and the total time is 100 months?

# References

Koch, Ina. *“Petri Nets - A Mathematical Formalism to Analyze
Chemical Reaction Networks.”* Molecular Informatics 29, no. 12
(December 17, 2010): 838–43. doi:10.1002/minf.201000086.

Baez, John C., and Jacob Biamonte. *“Quantum Techniques for
Stochastic Mechanics,”* September 17, 2012.
http://arxiv.org/abs/1209.3632.