# Chemical Reaction Models

The biological models functionality is provided by `DiffEqBiological.jl`

and helps the user to build discrete stochastic and differential equation based systems biological models. These tools allow one to define the models at a high level by specifying reactions and rate constants, and the creation of the actual problems is then handled by the modelling package.

## Installation

This functionality does not come standard with DifferentialEquations.jl. To use this functionality, you must install DiffEqBiological.jl:

```
]add DiffEqBiological
using DiffEqBiological
```

## The Reaction DSL - Basic

This section covers some of the basic syntax for building chemical reaction network models. Examples showing how to both construct and solve network models are provided in Chemical Reaction Network Examples.

#### 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 reaction network object which can be used as input to `ODEProblem`

, `SteadyStateProblem`

, `SDEProblem`

and `JumpProblem`

constructors. The `@min_reaction_network`

macro constructs a more simplified reaction network, deferring construction of all the various functions needed for each of these problem types. It can then be incrementally filled in for specific problem types as needed, which reduces network construction time for very large networks (see The Min Reaction Network Object for a detailed description).

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 variable, do not need to be called `rn`

). The macro generates a differential equation model according to the law of mass action, in the above example the ODEs become:

#### Arrow variants

Several types of arrows are accepted by the DSL and works instead of `-->`

. All of these works: `>`

, `→`

`↣`

, `↦`

, `⇾`

, `⟶`

, `⟼`

, `⥟`

, `⥟`

, `⇀`

, `⇁`

. Backwards arrows can also be used to write the reaction in the opposite direction. Hence all of these three reactions are equivalent:

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

(note that due to technical reasons `<--`

cannot be used)

#### Using bi-directional arrows

Bi-directional arrows can be used to designate a reaction that goes two ways. 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, X + Y ↔ XY
end
```

If the reaction rate in the backwards and forwards 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 much 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 separately. 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
```

#### Production and 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
```

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

forms the dimer `X2`

:

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

this corresponds to the differential equation:

Other numbers than 2 can be used and parenthesises can be used to use the same stoichiometry for several reactants:

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

#### Variable reaction rates

Reaction rates do not need to be constant, but can also depend on the current concentration of the various reactants (when e.g. one reactant activate the production of another one). E.g. this is a valid notation:

```
rn = @reaction_network begin
X, Y → ∅
end
```

and will have `Y`

degraded at rate

Note that this is actually equivalent to the reaction

```
rn = @reaction_network begin
1.0, X + Y → X
end
```

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

```
rn = @reaction_network begin
2.0*X^2, 0 → X + Y
gamma(Y)/5, X → ∅
pi*X/Y, Y → ∅
end
```

please note that user defined functions cannot be used directly (see later section "User defined functions in reaction rates").

#### Defining parameters

Just as when defining normal differential equations using `DifferentialEquations`

parameter values does not need to be set when the model is created. Components can be designated as 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 at the end will be considered a reactant.

#### 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
```

## Model Simulation

Once created, a reaction network can be used as input to various problem types which can be solved by `DifferentialEquations.jl`

.

#### Deterministic simulations using ODEs

A reaction network can be used as input to an `ODEProblem`

instead of a function, using `probODE = ODEProblem(rn, args...; kwargs...)`

E.g. a model can be created and simulated using:

```
rn = @reaction_network begin
p, ∅ → X
d, X → ∅
end p d
p = [1.0,2.0]
u0 = [0.1]
tspan = (0.,1.)
prob = ODEProblem(rn,u0,tspan,p)
sol = solve(prob)
```

(if no parameters are given `p`

does not need to be provided)

To solve for a steady-state starting from the guess `u0`

, one can use

```
prob = SteadyStateProblem(rn,u0,p)
sol = solve(prob, SSRootfind())
```

or

```
prob = SteadyStateProblem(rn,u0,p)
sol = solve(prob, DynamicSS(Tsit5()))
```

#### Stochastic simulations using SDEs

In a similar way a SDE can be created using `probSDE = SDEProblem(rn, args...; kwargs...)`

. In this case the chemical Langevin equations (as derived in Gillespie 2000) will be used to generate stochastic differential equations.

#### Stochastic simulations using discrete stochastic simulation algorithms

Instead of solving SDEs one can create a stochastic jump process model using integer copy numbers and a discrete stochastic simulation algorithm. This can be done using:

```
rn = @reaction_network begin
p, ∅ → X
d, X → ∅
end p d
p = [1.0,2.0]
u0 = [10]
tspan = (0.,1.)
discrete_prob = DiscreteProblem(u0,tspan,p)
jump_prob = JumpProblem(discrete_prob,Direct(),rn)
sol = solve(jump_prob,SSAStepper())
```

Here we used Gillespie's `Direct`

method as the underlying stochastic simulation algorithm.

## The Reaction DSL - Advanced

This section covers some of the more advanced syntax for building chemical reaction network models (still not very complicated!).

#### User defined functions in reaction rates

The reaction network DSL cannot "see" user defined functions. E.g. this is not correct syntax:

```
myHill(x) = 2.0*x^3/(x^3+1.5^3)
rn = @reaction_network begin
myHill(X), ∅ → X
end
```

However, it is possible to define functions in such a way that the DSL can see them using the `@reaction_func`

macro:

```
@reaction_func myHill(x) = 2.0*x^3/(x^3+1.5^3)
rn = @reaction_network begin
myHill(X), ∅ → X
end
```

#### Defining a custom reaction network type

While the default type of a reaction network is `reaction_network`

(which inherits from `AbstractReactionNetwork`

) it is possible to define a custom type (which also will inherit from `AbstractReactionNetwork`

) by adding the type name as a first argument to the `@reaction_network`

macro:

```
rn = @reaction_network my_custom_type begin
1.0, ∅ → X
end
```

#### Scaling noise in the chemical Langevin equations

When making stochastic simulations using SDEs it is possible to scale the amount of noise in the simulations by declaring a noise scaling parameter. This parameter is declared as a second argument to the `@reaction_network`

macro (when scaling the noise one have to declare a custom type).

```
rn = @reaction_network my_custom_type ns begin
1.0, ∅ → X
end
```

The noise scaling parameter is automatically added as a last argument to the parameter array (even if not declared at the end). E.g. this is correct syntax:

```
rn = @reaction_network my_custom_type ns begin
1.0, ∅ → X
end
p = [0.1,]
u0 = [0.1]
tspan = (0.,1.)
prob = SDEProblem(rn,u0,tspan,p)
sol = solve(prob)
```

Here the amount of noise in the stochastic simulation will be reduced by a factor 10.

#### Ignoring mass kinetics

While one in almost all cases want the reaction rate to take the law of mass action into account, so the reaction

```
rn = @reaction_network my_custom_type ns begin
k, X → ∅
end k
```

occur at the rate $d[X]/dt = -k[X]$, it is possible to ignore this by using any of the following non-filled arrows when declaring the reaction: `⇐`

, `⟽`

, `⇒`

, `⟾`

, `⇔`

, `⟺`

. This means that the reaction

```
rn = @reaction_network my_custom_type ns begin
k, X ⇒ ∅
end k
```

will occur at rate $d[X]/dt = -k$ (which might become a problem since $[X]$ will be degraded at a constant rate even when very small or equal to 0.

## The Reaction Network Object

The `@reaction_network`

macro generates a `reaction_network`

object, which has a number of fields which can be accessed.

`rn.f`

is a function encoding the right hand side of the ODEs (i.e. the time derivatives of the chemical species).`rn.f_func`

is a vector of expressions corresponding to the time derivatives of the chemical species.`rn.f_symfuncs`

is a vector of`SymEngine`

expressions corresponding to the time derivatives of the chemical species.`rn.g`

is a function encoding the noise terms for the SDEs (see`rn.g_func`

for details).`rn.g_func`

is a vector containing expressions corresponding to the noise terms used when creating the SDEs (n*m elements when there are n reactants and m reactions. The first m elements correspond to the noise terms for the first reactant and each reaction, the next m elements for the second reactant and all reactions, and so on).`rn.jump_affect_expr`

is a vector of expressions for how each reaction causes the species populations to change.`rn.jump_rate_expr`

is a vector of expressions for how the transition rate (i.e. propensity) of each reaction is calculated from the species populations.`rn.jumps`

is a vector storing a jump corresponding to each reaction (i.e.`ConstantRateJump`

,`VariableRateJump`

, etc...)`rn.odefun`

stores an`ODEFunction`

that can be used to create an`ODEProblem`

corresponding to the reaction network.`rn.p_matrix`

is a prototype matrix with the same size as the noise term.`rn.params`

is a vector containing symbols corresponding to all the parameters of the network.`rn.params_to_ints`

provides a mapping from parameter symbol to the integer id of the parameter (i.e. where it is stored in the parameter vector passed to`ODEProblem`

,`SDEProblem`

, etc...)`rn.reactions`

stores a vector of`DiffEqBiological.ReactionStruct`

s, which collect info for their corresponding reaction (such as stoichiometric coefficients).`rn.regular_jumps`

stores a`RegularJump`

representation of the network, for use in $\tau$-leaping methods.`rn.scale_noise`

is the noise scaling parameter symbol (if provided).`rn.sdefun`

is a`SDEFunction`

that can be used to create an`SDEProblem`

corresponding to the reaction network.`rn.symjac`

is the symbolically calculated Jacobian of the ODEs corresponding to the model.`rn.syms`

is a vector containing symbols for all species of the network.`rn.syms_to_ints`

is a map from the symbol of a species to its integer index within the solution vector.

## The Min Reaction Network Object

The `@min_reaction_network`

macro works similarly to the `@reaction_network`

macro, but initially only fills in fields corresponding to basic reaction network properties (i.e. `rn.params`

, `rn.params_to_ints`

, `rn.scale_noise`

, `rn.reactions`

, `rn.syms`

, and `rn.syms_to_ints`

). To fill in the remaining fields call

`addodes!(rn)`

to complete ODE-related fields`addsdes!(rn)`

to complete SDE-related fields`addjumps!(rn)`

to complete jump-related fields.`addjumps!`

accepts several keyword arguments to control which jumps get created ([] gives the default value for the keyword).`build_jumps=[true]`

is`true`

if`rn.jumps`

should be constructed. This can be set to`false`

for regular jump problems, where only`rn.regular_jumps`

is needed.`build_regular_jumps=[true]`

is`true`

if`rn.regular_jumps`

should be constructed. This can be set to`false`

for Gillespie-type jump problems, where`regular_jumps`

are not used.`minimal_jumps=[false]`

is`false`

if`rn.jumps`

should contain a jump for each possible reaction. If set to`true`

jumps are only added to`rn.jumps`

for non-mass action jumps. (Note, mass action jumps are still resolved within any jump simulation. This option simply speeds up the construction of the jump problem since entries in`rn.jumps`

that correspond to mass action jumps are never directly called within jump simulations.)

For example, to simulate a jump process (i.e. Gillespie) simulation without constructing any `RegularJump`

s, and only constructing a minimal set of jumps:

```
rs = @min_reaction_network begin
c1, X --> 2X
c2, X --> 0
c3, 0 --> X
end c1 c2 c3
p = (2.0,1.0,0.5)
addjumps!(rs; build_regular_jumps=false, minimal_jumps=true)
prob = DiscreteProblem([5], (0.0, 4.0), p)
jump_prob = JumpProblem(prob, Direct(), rs)
sol = solve(jump_prob, SSAStepper())
```

## Chemical Reaction Network Examples

#### Example: Birth-Death Process

```
rs = @reaction_network begin
c1, X --> 2X
c2, X --> 0
c3, 0 --> X
end c1 c2 c3
p = (1.0,2.0,50.)
tspan = (0.,4.)
u0 = [5.]
# solve ODEs
oprob = ODEProblem(rs, u0, tspan, p)
osol = solve(oprob, Tsit5())
# solve for Steady-States
ssprob = SteadyStateProblem(rs, u0, p)
sssol = solve(ssprob, SSRootfind())
# solve SDEs
sprob = SDEProblem(rs, u0, tspan, p)
ssol = solve(sprob, EM(), dt=.01)
# solve JumpProblem
u0 = [5]
dprob = DiscreteProblem(u0, tspan, p)
jprob = JumpProblem(dprob, Direct(), rs)
jsol = solve(jprob, SSAStepper())
```

#### Example: Michaelis-Menten Enzyme Kinetics

```
rs = @reaction_network begin
c1, S + E --> SE
c2, SE --> S + E
c3, SE --> P + E
end c1 c2 c3
p = (0.00166,0.0001,0.1)
tspan = (0., 100.)
u0 = [301., 100., 0., 0.] # S = 301, E = 100, SE = 0, P = 0
# solve ODEs
oprob = ODEProblem(rs, u0, tspan, p)
osol = solve(oprob, Tsit5())
# solve JumpProblem
u0 = [301, 100, 0, 0]
dprob = DiscreteProblem(u0, tspan, p)
jprob = JumpProblem(dprob, Direct(), rs)
jsol = solve(jprob, SSAStepper())
```