# Discrete Stochastic (Gillespie) Equations

In this tutorial we will describe how to define and solve discrete stochastic simulations, also known in biological fields as Gillespie-type models. This tutorial assumes you have read the Ordinary Differential Equations tutorial. Discrete stochastic simulations are a form of jump equation with a "trivial" (non-existent) differential equation. We will first demonstrate how to build these types of models using the biological modeling functionality, and then describe how to build it directly and more generally using jumps, and finally show how to add discrete stochastic simulations to differential equation models.

## Defining a Model using Reactions

For our example, we will build an SIR model which matches the tutorial from Gillespie.jl. SIR stands for susceptible, infected, and recovered, and is a model is disease spread. When a susceptible person comes in contact with an infected person, the disease has a chance of infecting the susceptible person. This "chance" is determined by the number of susceptible persons and the number of infected persons, since when there are more people there is a greater chance that two come in contact. Normally, the rate is modeled as the amount

`rate_constant*num_of_susceptible_people*num_of_infected_people`

The `rate_constant`

is some constant determined by other factors like the type of the disease.

Let's build our model using a vector `u`

, and let `u[1]`

be the number of susceptible persons, `u[2]`

be the number of infected persons, and `u[3]`

be the number of recovered persons. In this case, we can re-write our rate as being:

`rate_constant*u[1]*u[2]`

Thus we have that our "reactants" are components 1 and 2. When this "reaction" occurs, the result is that one susceptible person turns into an infected person. We can think of this as doing:

```
u[1] -= 1
u[2] += 1
```

that is, we decrease the number of susceptible persons by 1 and increase the number of infected persons by 1.

These are the facts that are required to build a `Reaction`

. The constructor for a `Reaction`

is as follows:

`Reaction(rate_constant,reactants,stoichiometry)`

The first value is the rate constant. We will use `1e-4`

. Secondly, we pass in the indices for the reactants. In this case, since it uses the susceptible and infected persons, the indices are `[1,2]`

. Lastly, we detail the stoichometric changes. These are tuples `(i,j)`

where `i`

is the reactant and `j`

is the number to change by. Thus `(1,-1)`

means "decrease the number of susceptible persons by 1" and `(2,1)`

means "increase the number of infected persons by 1".

Therefore, in total, our reaction is:

`r1 = Reaction(1e-4,[1,2],[(1,-1),(2,1)])`

To finish the model, we define one more reaction. Over time, infected people become less infected. The chance that any one person heals during some time unit depends on the number of people who are infected. Thus the rate at which infected persons are turning into recovered persons is

`rate_constant*u[2]`

When this happens, we lose one infected person and gain a recovered person. This reaction is thus modeled as:

`r2 = Reaction(0.01,[2],[(2,-1),(3,1)])`

where we have chosen the rate constant `0.01`

.

## Building and Solving the Problem

First, we have to define some kind of differential equation. Since we do not want any continuous changes, we will build a `DiscreteProblem`

. We do this by giving the constructor `u0`

, the initial condition, and `tspan`

, the timespan. Here, we will start with `999`

susceptible people, `1`

infected person, and `0`

recovered people, and solve the problem from `t=0.0`

to `t=250.0`

. Thus we build the problem via:

`prob = DiscreteProblem([999,1,0],(0.0,250.0))`

Now we have to add the reactions/jumps to the problem. We do this using a `GillespieProblem`

. This takes in a differential equation problem `prob`

(which we just defined), a `ConstantJumpAggregator`

, and the reactions. The `ConstantJumpAggregator`

is the method by which the constant jumps are aggregated together and solved. In this case we will use the classic Direct method due to Gillespie, also known as GillespieSSA. This aggregator is denoted by `Direct()`

. Thus we build the jumps into the problem via:

`jump_prob = GillespieProblem(prob,Direct(),r1,r2)`

This is now a problem that can be solved using the differential equations solvers. Since our problem is discrete, we will use the `Discrete()`

method.

`sol = solve(jump_prob,Discrete())`

This solve command takes the standard commands of the common interface, and the solution object acts just like any other differential equation solution. Thus there exists a plot recipe, which we can plot with:

`using Plots; plot(sol)`

## Using the Reaction Network DSL

Also included as part of DiffEqBiological.jl is the reaction network DSL. We could define the previous problem via:

```
rs = @reaction_network begin
1e-4, S + I --> 2I
0.01, I --> R
end
prob = DiscreteProblem([999,1,0],(0.0,250.0))
jump_prob = GillespieProblem(prob,Direct(),rs)
sol = solve(jump_prob,Discrete())
```

## Defining the Jumps Directly

Instead of using the biological modeling functionality of `Reaction`

, we can directly define jumps. This allows for more general types of rates, at the cost of some modeling friendliness. The constructor for a `ConstantRateJump`

is:

`jump = ConstantRateJump(rate,affect!)`

where `rate`

is a function `rate(t,u)`

and `affect!`

is a function of the integrator `affect!(integrator)`

(for details on the integrator, see the integrator interface docs). Thus, to define the jump equivalents to the above reactions, we can use:

```
rate(t,u) = (0.1/1000.0)*u[1]*u[2]
function affect!(integrator)
integrator.u[1] -= 1
integrator.u[2] += 1
end
jump = ConstantRateJump(rate,affect!)
rate(t,u) = 0.01u[2]
function affect!(integrator)
integrator.u[2] -= 1
integrator.u[3] += 1
end
jump2 = ConstantRateJump(rate,affect!)
```

We can then use `JumpProblem`

to augment a problem with jumps. To add the jumps to the `DiscreteProblem`

and solve it, we would simply do:

```
jump_prob = JumpProblem(prob,Direct(),jump,jump2)
sol = solve(jump_prob,Discrete(apply_map=false))
```

## Adding Jumps to a Differential Equation

Notice that if we instead used some form of differential equation instead of a `DiscreteProblem`

, we would add the jumps/reactions to the differential equation. Let's define an ODE problem, where the continuous part only acts on some new 4th component:

```
function f(t,u,du)
du[4] = u[2]*u[3]/100000 - u[1]*u[2]/100000
end
prob = ODEProblem(f,[999.0,1.0,0.0,100.0],(0.0,250.0))
```

Notice we gave the 4th component a starting value of 100. The same steps as above will thus solve this hybrid equation. For example, we can solve it using the `Tsit5()`

method via:

```
jump_prob = GillespieProblem(prob,Direct(),r1,r2)
sol = solve(jump_prob,Tsit5())
```

### Caution about Constant Rate Jumps

Note that the assumption which is required for constant rate jumps is that their reaction rates must be constant on the interval between any constant rate jumps. Thus in the examples above,

```
rate(t,u) = (0.1/1000.0)*u[1]*u[2]
rate(t,u) = 0.01u[2]
```

both must be constant other than changes due to some constant rate jump (the same applies to reactions). Since these rates only change when `u[1]`

or `u[2]`

is changed, and `u[1]`

and `u[2]`

only change when one of the jumps occur, this setup is valid. However, `t*(0.1/1000.0)*u[1]*u[2]`

would not be valid because the rate would change during the interval, as would `(0.1/1000.0)*u[1]*u[4]`

. Thus one must be careful about to follow this rule when choosing rates.

(but note that it's okay for `u[4]`

to depend on the other variables because its updated in a continuous manner!)

If your problem must have the rates depend on a continuously changing quantity, you need to use the `VariableRateJump`

or `VariableRateReaction`

instead.

## Adding a VariableRateReaction

Now let's consider adding a reaction whose rate changes continuously with the differential equation. To continue our example, let's let there be a new reaction which has the same effect as `r2`

, but now is dependent on the amount of `u[4]`

.

`r3 = VariableRateReaction(1e-2,[4],[(2,-1),(3,1)])`

We would expect this reaction to increase the amount of transitions from state 2 to 3. Solving the equation is exactly the same:

```
prob = ODEProblem(f,[999.0,1.0,0.0,1.0],(0.0,250.0))
jump_prob = GillespieProblem(prob,Direct(),r1,r2,r3)
sol = solve(jump_prob,Tsit5())
```

Notice that this increases the amount of 3 at the end, reducing the falloff in the rate (though this model is kind of nonsensical).

Note that even if the problem is a `DiscreteProblem`

, `VariableRateJump`

s and `VariableRateReaction`

s require a continuous solver, like an ODE/SDE/DDE/DAE solver.

Lastly, we are not restricted to ODEs. For example, we can solve the same jump problem except with multiplicative noise on `u[4]`

by using an `SDEProblem`

instead:

```
function g(t,u,du)
du[4] = 0.1u[4]
end
prob = SDEProblem(f,g,[999.0,1.0,0.0,1.0],(0.0,250.0))
jump_prob = GillespieProblem(prob,Direct(),r1,r2,r3)
sol = solve(jump_prob,SRIW1())
```