# The Reaction DSL

This tutorial covers some of the basic syntax for building chemical reaction network models using Catalyst's domain specific language (DSL). Examples showing how to both construct and solve ODE, SDE, and jump models are provided in Basic Chemical Reaction Network Examples. To learn more about the symbolic `ReactionSystem`

s generated by the DSL, and how to use them directly, see the tutorial on Symbolic Reaction Systems.

## Basic syntax

The `@reaction_network`

macro allows the (symbolic) specification of reaction networks with a simple format. Its input is a set of chemical reactions, and from them it generates a symbolic `ReactionSystem`

reaction network object. The `ReactionSystem`

can be used as input to ModelingToolkit `ODEProblem`

, `NonlinearProblem`

, `SteadyStateProblem`

, `SDEProblem`

, `JumpProblem`

, and more. `ReactionSystem`

s can also be incrementally extended as needed, allowing for programmatic construction of networks and network composition.

The basic syntax is:

```
rn = @reaction_network begin
2.0, X + Y --> XY
1.0, XY --> Z1 + Z2
end
```

where each line corresponds to a chemical reaction. Each reaction consists of a reaction rate (the expression on the left hand side of `,`

), a set of substrates (the expression in-between `,`

and `-->`

), and a set of products (the expression on the right hand side of `-->`

). The substrates and the products may contain one or more reactants, separated by `+`

. The naming convention for these are the same as for normal variables in Julia.

The chemical reaction model is generated by the `@reaction_network`

macro and stored in the `rn`

variable (a normal Julia variable, which does not need to be called `rn`

). It corresponds to a `ReactionSystem`

, a symbolic representation of the chemical network. The generated `ReactionSystem`

can be converted to a symbolic differential equation model via

`osys = convert(ODESystem, rn)`

We can then convert the symbolic ODE model into a compiled, optimized representation for use in the SciML ODE solvers by constructing an `ODEProblem`

. Creating an `ODEProblem`

also requires our specifying the initial conditions for the model. We do this by creating a mapping from each symbolic variable representing a chemical species to its initial value

```
# define the symbolic variables
@variables t, X(t), Y(t), Z(t), XY(t), Z1(t), Z2(t)
# create the mapping
u0 = [X => 1.0, Y => 1.0, XY => 1.0, Z1 => 1.0, Z2 => 1.0]
```

Alternatively, we can create a mapping use Julia `Symbol`

s for each variable, and then convert them to a mapping involving symbolic variables like

`u0 = symmap_to_varmap(rn, [:X => 1.0, :Y => 1.0, :XY => 1.0, :Z1 => 1.0, :Z2 => 1.0]) `

Given the mapping, we can then create an `ODEProblem`

from our symbolic `ODESystem`

`oprob = ODEProblem(osys, u0, tspan, [])`

Catalyst provides a shortcut to avoid having to explicitly `convert`

to an `ODESystem`

and/or use `symmap_to_varmap`

, allowing direct construction of the `ODEProblem`

like

```
u0 = [:X => 1.0, :Y => 1.0, :XY => 1.0, :Z1 => 1.0, :Z2 => 1.0]
oprob = ODEProblem(rn, u0, tspan, [])
```

For more detailed examples, see the Basic Chemical Reaction Network Examples. The generated differential equations use the law of mass action. For the above example, the ODEs are then

\[\frac{d[X]}{dt} = -2 [X] [Y]\\ \frac{d[Y]}{dt} = -2 [X] [Y]\\ \frac{d[XY]}{dt} = 2 [X] [Y] - [XY]\\ \frac{d[Z1]}{dt}= [XY]\\ \frac{d[Z2]}{dt} = [XY]\]

## Defining parameters and species

Parameter values do not need to be set when the model is created. Components can be designated as symbolic parameters by declaring them at the end:

```
rn = @reaction_network begin
p, ∅ --> X
d, X --> ∅
end p d
```

Parameters can only exist in the reaction rates (where they can be mixed with reactants). All variables not declared after `end`

will be treated as a chemical species.

## Production, Destruction and Stoichiometry

Sometimes reactants are produced/destroyed from/to nothing. This can be designated using either `0`

or `∅`

:

```
rn = @reaction_network begin
2.0, 0 --> X
1.0, X --> ∅
end
```

If several molecules of the same reactant are involved in a reaction, the stoichiometry of a reactant in a reaction can be set using a number. Here, two molecules of species `X`

form the dimer `X2`

:

```
rn = @reaction_network begin
1.0, 2X --> X2
end
```

this corresponds to the differential equation:

\[\frac{d[X]}{dt} = -[X]^2\\ \frac{d[X2]}{dt} = \frac{1}{2!} [X]^2\]

Other numbers than 2 can be used, and parenthesis can be used to reuse the same stoichiometry for several reactants:

```
rn = @reaction_network begin
1.0, X + 2(Y + Z) --> XY2Z2
end
```

Note, one can explicitly multiply by integer coefficients too, i.e.

```
rn = @reaction_network begin
1.0, X + 2*(Y + Z) --> XY2Z2
end
```

## Arrow variants

A variety of unicode arrows are accepted by the DSL in addition to `-->`

. All of these work: `>`

, `→`

`↣`

, `↦`

, `⇾`

, `⟶`

, `⟼`

, `⥟`

, `⥟`

, `⇀`

, `⇁`

. Backwards arrows can also be used to write the reaction in the opposite direction. For example, these reactions are equivalent:

```
rn = @reaction_network begin
1.0, X + Y --> XY
1.0, X + Y → XY
1.0, XY ← X + Y
1.0, XY <-- X + Y
end
```

## Bi-directional arrows for reversible reactions

Bi-directional arrows, including bidirectional unicode arrows like ↔, can be used to designate a reversible reaction. For example, these two models are equivalent:

```
rn = @reaction_network begin
2.0, X + Y --> XY
2.0, X + Y <-- XY
end
rn = @reaction_network begin
(2.0,2.0), X + Y <--> XY
end
```

If the reaction rates in the backward and forward directions are different, they can be designated in the following way:

```
rn = @reaction_network begin
(2.0,1.0) X + Y <--> XY
end
```

which is identical to

```
rn = @reaction_network begin
2.0, X + Y --> XY
1.0, X + Y <-- XY
end
```

## Combining several reactions in one line

Several similar reactions can be combined in one line by providing a tuple of reaction rates and/or substrates and/or products. If several tuples are provided, they must all be of identical length. These pairs of reaction networks are all identical:

```
rn1 = @reaction_network begin
1.0, S --> (P1,P2)
end
rn2 = @reaction_network begin
1.0, S --> P1
1.0, S --> P2
end
```

```
rn1 = @reaction_network begin
(1.0,2.0), (S1,S2) --> P
end
rn2 = @reaction_network begin
1.0, S1 --> P
2.0, S2 --> P
end
```

```
rn1 = @reaction_network begin
(1.0,2.0,3.0), (S1,S2,S3) → (P1,P2,P3)
end
rn2 = @reaction_network begin
1.0, S1 --> P1
2.0, S2 --> P2
3.0, S3 --> P3
end
```

This can also be combined with bi-directional arrows, in which case separate tuples can be provided for the backward and forward reaction rates. These reaction networks are identical

```
rn1 = @reaction_network begin
(1.0,(1.0,2.0)), S <--> (P1,P2)
end
rn2 = @reaction_network begin
1.0, S --> P1
1.0, S --> P2
1.0, P1 --> S
2.0, P2 --> S
end
```

## Variable reaction rates

Reaction rates do not need to be a single parameter or a number, but can also be expressions depending on time or the current concentration of other species (when, for example, one species can activate the production of another). For instance, this is a valid notation:

```
rn = @reaction_network begin
k*X, Y --> ∅
end k
```

and will have `Y`

degraded at rate

\[\frac{d[Y]}{dt} = -k[X][Y].\]

Note, this is actually equivalent to the reaction

```
rn = @reaction_network begin
k, X + Y --> X
end k
```

in the resulting generated ODESystem, **however**, the latter `Reaction`

will be classified as `ismassaction`

and the former will not, which can impact optimizations used in generating `JumpSystem`

s. For this reason, it is recommended to use the latter representation when possible.

Most expressions and functions are valid reaction rates, e.g.:

```
using SpecialFunctions
rn = @reaction_network begin
2.0*X^2, 0 --> X + Y
t*gamma(Y), X --> ∅
pi*X/Y, Y → ∅
end
```

where here `t`

always denotes Catalyst's time variable. Please note that many user-defined functions can be called directly, but others will require registration with Symbolics.jl (see the faq).

## Naming the generated `ReactionSystem`

ModelingToolkit uses system names to allow for compositional and hierarchical models. To specify a name for the generated `ReactionSystem`

via the `reaction_network`

macro, just place the name before `begin`

:

```
rn = @reaction_network production_degradation begin
p, ∅ --> X
d, X --> ∅
end p d
ModelingToolkit.nameof(rn) == :production_degradation
```

## Pre-defined functions

Hill functions and a Michaelis-Menten function are pre-defined and can be used as rate laws. Below, the pair of reactions within `rn1`

are equivalent, as are the pair of reactions within `rn2`

:

```
rn1 = @reaction_network begin
hill(X,v,K,n), ∅ --> X
v*X^n/(X^n+K^n), ∅ --> X
end v K n
rn2 = @reaction_network begin
mm(X,v,K), ∅ --> X
v*X/(X+K), ∅ --> X
end v K
```

Repressor Hill (`hillr`

) and Michaelis-Menten (`mmr`

) functions are also provided:

```
rn1 = @reaction_network begin
hillr(X,v,K,n), ∅ --> X
v*K^n/(X^n+K^n), ∅ --> X
end v K n
rn2 = @reaction_network begin
mmr(X,v,K), ∅ --> X
v*K/(X+K), ∅ --> X
end v K
```

Please see the API Rate Laws section for more details.

## Interpolation of Julia Variables

The DSL allows Julia variables to be interpolated for the network name, within rate constant expressions, or for species within the reaction. For example,

```
@parameters k
@variables t, A(t)
spec = A
rate = k*A
name = :network
rn = @reaction_network $name begin
$rate*B, 2*$spec + B --> $spec + C
end
```

gives

```
Model network with 1 equations
States (3):
A(t)
B(t)
C(t)
Parameters (1):
k
```

with

`reactions(rn)`

giving

```
1-element Vector{Reaction}:
k*A(t)*B(t), 2A + B --> A + C
```

*As the parameter k was pre-defined and appears via interpolation, we did not need to declare it at the end of the @reaction_network macro.*

Note, when using interpolation expressions like `2$spec`

won't work; the multiplication symbol must be explicitly included like `2*$spec`

.