  # Introduction

Our goal is to simulate the birth and death of three cell species (A, B, C). The above frame is a result of this simulation: three different cells processes (light grey, grey and black) are being simulated.

# Empirical data

For each cell we know empirically that:

• Every 2 seconds a new cell of type A is born.
• Every 15 seconds a cell of type A dies.
• Every 5 seconds a new cell of type B is born.
• Every 10 seconds a cell of type B dies.
• Every 4 seconds a new cell of type C is born.
• Every 30 seconds a cell of type C dies.

# Assumptions

We consider that the birth of a cell is an event that occurs continuously and independently at a constant average rate. Continuously because we do not consider things like temperature and independently because the birth of one cell does not affect the birth of another cell.

# A tool: Poisson process

This birth and death processes of our cells are called stochastic processes.They can be modelised with a Poisson process.

Our assumptions defined previously are in fact conditions to use a Poisson process:

• Events occurs continuously.
• Events occurs independently.
• Events occurs at a constant average rate (homogeneous Poisson process).

In a Poisson process the time between two events is described with an exponential distribution of parameter lambda (λ). It is called the rate parameter and it is equal to 1 devided by the desired mean value.

So in the case of our cells λ can be valued as follows:

• Every 2 seconds a new cell of type A is born: λ = 1/2.
• Every 15 seconds a cell of type A dies: λ = 1/15.
• Every 5 seconds a new cell of type B is born: λ = 1/5.
• Every 10 seconds a cell of type B dies: λ = 1/10.
• Every 4 seconds a new cell of type C is born: λ = 1/4.
• Every 30 seconds a cell of type C dies: λ = 1/30.

## The exponential distribution

We said that our process is using an exponential distribution of parameter λ to describe the time between each pair of birth and each pair of death.

A probability distribution assign a probability to each outcomes of a random experiment. The outcome of an experiment is a random variable called x. We can define for a probability distribution a function F(x) that will output a probability for a given value of x. This function F is called a cumulative density function.

### Cumulative density function

The exponential distribution is characterized by its density function which is:

f(x) = λ * exp(-λ * x)

This cumulative density function is the sum (i.e. the integration) of the probability density function. It describes the probability that the random variable x will takes a values less or equal to a given X.

F(x) = 1 - exp(-λ * x)

This is a plot of the cumulative distribution function of the exponential distribution for λ = 1/30.

If we consider that the y-axis is the time we can intuitively see that the more we wait the more the probability of the outcome is higher.

### Reading the cumulative density function in reverse

Let’s say that the y-axis describes the time in seconds. The x-axis is valued between 0 and 1.

So each time we want to know how much time we have to wait before seeing a positive outcome for the experiment we can randomly choose a number between 0 and 1 that will be the value of F(x) and read the value of the corresponding x. In the end value of x will give us the time we have to wait before seeing an occurrence of the event.

We can use the cumulative density function equation to express x knowing the value of F(x):

F(x) = A = 1 - exp(-λ * x)

exp(-λ * x) = 1 - A

ln(exp(-λ * x)) = ln(1 - A)

- λ * x = ln(1 - A)

x = - ln(1 - A) / λ

With F(x) = random(0, 1), we have: x = - ln(1 - random(0,1)) / λ

# Testing

In order to test this we are going to generate a grand number of values for λ = 1/30. That means that the events on the Poisson process occurs at a constant average rate of 30 seconds. If we calculate the mean of the generated numbers we should get a value likely equal to 30.

To do so we are going to use Python which provide a built-in method in the random module which return exponentialy distributed number for a given λ parameter.

``````>>> import random
>>> lambda_parameter = 1/30.0
>>> random.expovariate(lambda_parameter)
20.401058019178667
>>> values = [random.expovariate(lambda_parameter) for i in xrange(100000)]
>>> mean = sum(values) / len(values)
>>> mean
>>> 29.821100014718574
``````

The mean is likely equal to 30! Our simulated process has events that occurs at the right mean rate.

# Building a cell simulation with Javascript

Javascript does not provide a expovariate function. Here is an implementation for it:

``````Random = {};
Random.expovariate = function(lambda) {
return (-Math.log(1 - Math.random())) / lambda;
};
``````

Given the born and death rate of the cell we can determine with the expovariate function a time: the time to the next birth of a cell and the time to live of this cell. In Javascript the setTimout function can execute a function after a specified amount of time. We can use it to call a birth function and a death function with the given calculated times. SetTimeout use milliseconds: we need to multiply by 1000 our rate values.

``````var makeCellProcess  = function(cellSymbol, bornRate, deathRate) {
var start = function() {
return function() {
var timeToNextBirth = Random.expovariate(1/bornRate),
timeToLive = Random.expovariate(1/deathRate),
cell = Grid.placeCell(cellSymbol);

setTimeout(end(cell), timeToLive);
setTimeout(start(), timeToNextBirth);
};
};

var end = function(cell) {
return function() {
cell.remove();
};
};

// Calling start to initialize the process.
start()();
};

makeCellProcess(cellASymbol, 1000, 15000);
``````

# Conclusion

• Have a look to the annotated source of the cell simulation.
• You can simply simulate events occurring with known mean interval with a Poison process.
• A probability distribution gives the probability of a random variable x for a given experiment.
• A cumulative distribution function output the probability that a random variable X will be found at a value less than or equal to x.

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