diff --git a/.JuliaFormatter.toml b/.JuliaFormatter.toml
index 453925c..9c79359 100644
--- a/.JuliaFormatter.toml
+++ b/.JuliaFormatter.toml
@@ -1 +1,2 @@
-style = "sciml"
\ No newline at end of file
+style = "sciml"
+format_markdown = true
\ No newline at end of file
diff --git a/README.md b/README.md
index 91e3e98..82fb7b1 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
[](https://codecov.io/gh/SciML/ReactionNetworkImporters.jl)
[](https://github.com/SciML/ReactionNetworkImporters.jl/actions?query=workflow%3ACI)
-[](https://github.com/SciML/ColPrac)
+[](https://github.com/SciML/ColPrac)
[](https://github.com/SciML/SciMLStyle)
This package provides importers to load reaction networks into
@@ -14,10 +14,11 @@ This package provides importers to load reaction networks into
[`ReactionSystem`s](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#ModelingToolkit.ReactionSystem)
from several file formats. Currently it supports loading networks in the
following formats:
-1. A *subset* of the BioNetGen .net file format.
-2. Networks represented by dense or sparse substrate and product stoichiometric
- matrices.
-3. Networks represented by dense or sparse complex stoichiometric and incidence matrices.
+
+ 1. A *subset* of the BioNetGen .net file format.
+ 2. Networks represented by dense or sparse substrate and product stoichiometric
+ matrices.
+ 3. Networks represented by dense or sparse complex stoichiometric and incidence matrices.
[SBMLToolkit.jl](https://docs.sciml.ai/SBMLToolkit/stable/) provides an
alternative for loading SBML files into Catalyst models, offering a much broader
@@ -34,42 +35,48 @@ the documentation which contains the unreleased features.
## Examples
### Loading a BioNetGen .net file
+
A simple network from the builtin BioNetGen bngl examples is the
[repressilator](data/repressilator/Repressilator.bngl). The `generate_network`
command in the bngl file outputs a reduced network description, i.e. a
[.net](data/repressilator/Repressilator.net) file, which can be loaded into a
Catalyst `ReactionSystem` as:
+
```julia
using ReactionNetworkImporters
fname = "PATH/TO/Repressilator.net"
prnbng = loadrxnetwork(BNGNetwork(), fname)
```
+
Here `BNGNetwork` is a type specifying the file format that is being loaded.
`prnbng` is a `ParsedReactionNetwork` structure with the following fields:
-- `rn`, a Catalyst `ReactionSystem`
-- `u₀`, a `Dict` mapping initial condition symbolic variables to numeric values
- and/or symbolic expressions.
-- `p`, a `Dict` mapping parameter symbolic variables to numeric values and/or
- symbolic expressions.
-- `varstonames`, a `Dict` mapping the internal symbolic variable of a species
- used in the generated `ReactionSystem` to a `String` generated from the name
- in the .net file. This is necessary as BioNetGen can generate exceptionally
- long species names, involving characters that lead to malformed species names
- when used with `Catalyst`.
-- `groupstosyms`, a `Dict` mapping the `String`s representing names for any
- groups defined in the BioNetGen file to the corresponding symbolic variable
- representing the `ModelingToolkit` symbolic observable associated with the
- group.
+
+ - `rn`, a Catalyst `ReactionSystem`
+ - `u₀`, a `Dict` mapping initial condition symbolic variables to numeric values
+ and/or symbolic expressions.
+ - `p`, a `Dict` mapping parameter symbolic variables to numeric values and/or
+ symbolic expressions.
+ - `varstonames`, a `Dict` mapping the internal symbolic variable of a species
+ used in the generated `ReactionSystem` to a `String` generated from the name
+ in the .net file. This is necessary as BioNetGen can generate exceptionally
+ long species names, involving characters that lead to malformed species names
+ when used with `Catalyst`.
+ - `groupstosyms`, a `Dict` mapping the `String`s representing names for any
+ groups defined in the BioNetGen file to the corresponding symbolic variable
+ representing the `ModelingToolkit` symbolic observable associated with the
+ group.
Given `prnbng`, we can construct and solve the corresponding ODE model for the
reaction system by
+
```julia
using OrdinaryDiffEq, Catalyst
rn = prnbng.rn
tf = 100000.0
-oprob = ODEProblem(rn, Float64[], (0.,tf), Float64[])
-sol = solve(oprob, Tsit5(), saveat=tf/1000.)
+oprob = ODEProblem(rn, Float64[], (0.0, tf), Float64[])
+sol = solve(oprob, Tsit5(), saveat = tf / 1000.0)
```
+
Note that we specify empty parameter and initial condition vectors as these are
already stored in the generated `ReactionSystem`, `rn`. A `Dict` mapping each
symbolic species and parameter to its initial value or symbolic expression can
@@ -79,13 +86,16 @@ See the [Catalyst documentation](https://docs.sciml.ai/Catalyst/stable/) for how
generate ODE, SDE, jump and other types of models.
### Loading a matrix representation
+
Catalyst `ReactionSystem`s can also be constructed from
-- substrate and product stoichiometric matrices.
-- complex stoichiometric and incidence matrices.
+
+ - substrate and product stoichiometric matrices.
+ - complex stoichiometric and incidence matrices.
For example, here we both directly build a Catalyst
network using the `@reaction_network` macro, and then show how to build the same
network from these matrices using `ReactionNetworkImporters`:
+
```julia
# Catalyst network from the macro:
rs = @reaction_network begin
@@ -99,33 +109,33 @@ end k1 k2 k3 k4 k5
# network from basic stoichiometry using ReactionNetworkImporters
@parameters k1 k2 k3 k4 k5
@variables t A(t) B(t) C(t)
-species = [A,B,C]
-pars = [k1,k2,k3,k4,k5]
-substoich =[ 2 0 1 0 0;
- 0 1 1 0 0;
- 0 0 0 1 3]
-prodstoich = [0 2 0 1 3;
- 1 0 0 1 0;
- 0 0 1 0 0]
-mn= MatrixNetwork(pars, substoich, prodstoich; species=species,
- params=pars) # a matrix network
+species = [A, B, C]
+pars = [k1, k2, k3, k4, k5]
+substoich = [2 0 1 0 0;
+ 0 1 1 0 0;
+ 0 0 0 1 3]
+prodstoich = [0 2 0 1 3;
+ 1 0 0 1 0;
+ 0 0 1 0 0]
+mn = MatrixNetwork(pars, substoich, prodstoich; species = species,
+ params = pars) # a matrix network
prn = loadrxnetwork(mn) # dense version
# test the two networks are the same
@assert rs == prn.rn
# network from reaction complex stoichiometry
-stoichmat =[2 0 1 0 0 3;
- 0 1 1 0 0 0;
- 0 0 0 1 3 0]
-incidencemat = [-1 1 0 0 0;
- 1 -1 0 0 0;
- 0 0 -1 1 0;
- 0 0 1 -1 0;
- 0 0 0 0 -1;
- 0 0 0 0 1]
-cmn= ComplexMatrixNetwork(pars, stoichmat, incidencemat; species=species,
- params=pars) # a complex matrix network
+stoichmat = [2 0 1 0 0 3;
+ 0 1 1 0 0 0;
+ 0 0 0 1 3 0]
+incidencemat = [-1 1 0 0 0;
+ 1 -1 0 0 0;
+ 0 0 -1 1 0;
+ 0 0 1 -1 0;
+ 0 0 0 0 -1;
+ 0 0 0 0 1]
+cmn = ComplexMatrixNetwork(pars, stoichmat, incidencemat; species = species,
+ params = pars) # a complex matrix network
prn = loadrxnetwork(cmn)
# test the two networks are the same
@@ -133,50 +143,58 @@ prn = loadrxnetwork(cmn)
```
The basic usages are
+
```julia
-mn = MatrixNetwork(rateexprs, substoich, prodstoich; species=Any[],
- params=Any[], t=nothing)
+mn = MatrixNetwork(rateexprs, substoich, prodstoich; species = Any[],
+ params = Any[], t = nothing)
prn = loadrxnetwork(mn::MatrixNetwork)
-cmn = ComplexMatrixNetwork(rateexprs, stoichmat, incidencemat; species=Any[],
- params=Any[], t=nothing)
+cmn = ComplexMatrixNetwork(rateexprs, stoichmat, incidencemat; species = Any[],
+ params = Any[], t = nothing)
prn = loadrxnetwork(cmn::ComplexMatrixNetwork)
```
+
Here `MatrixNetwork` and `ComplexMatrixNetwork` are the types, which select that
we are constructing a substrate/product stoichiometric matrix-based or a
reaction complex matrix-based stoichiometric representation as input. See the
[Catalyst.jl API](hhttps://docs.sciml.ai/Catalyst/stable/api/catalyst_api/) for more
discussion on these matrix representations, and how Catalyst handles symbolic
reaction rate expressions. These two types have the following fields:
-- `rateexprs`, any valid
- [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/) expression for
- the rates, or any basic number type. This can be a hardcoded rate constant
- like `1.0`, a parameter like `k1` above, or an general Symbolics expression
- involving parameters and species like `k*A`.
-- matrix inputs
- - For `MatrixNetwork`
- - `substoich`, a number of species by number of reactions matrix with entry
- `(i,j)` giving the stoichiometric coefficient of species `i` as a
- substrate in reaction `j`.
- - `prodstoich`, a number of species by number of reactions matrix with entry
- `(i,j)` giving the stoichiometric coefficient of species `i` as a product
- in reaction `j`.
- - For `ComplexMatrixNetwork`
- - `stoichmat`, the complex stoichiometry matrix [defined
- here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.complexstoichmat).
- - `incidencemat`, the complex incidence matrix [defined
- here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.reactioncomplexes).
-- `species`, an optional vector of symbolic variables representing each species
- in the network. Can be constructed using the Symbolics.jl `@variables` macro.
- Each species should be dependent on the same time variable (`t` in the example
- above).
-- `parameters`, a vector of symbolic variables representing each parameter in
- the network. Can be constructed with the
- [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/)
- `@parameters` macro. If no parameters are used it is an optional keyword.
-- `t`, an optional Symbolics.jl variable representing time as the independent
- variable of the reaction network. If not provided `Catalyst.DEFAULT_IV` is
- used to determine the default time variable.
+
+ - `rateexprs`, any valid
+ [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/) expression for
+ the rates, or any basic number type. This can be a hardcoded rate constant
+ like `1.0`, a parameter like `k1` above, or an general Symbolics expression
+ involving parameters and species like `k*A`.
+
+ - matrix inputs
+
+ + For `MatrixNetwork`
+
+ * `substoich`, a number of species by number of reactions matrix with entry
+ `(i,j)` giving the stoichiometric coefficient of species `i` as a
+ substrate in reaction `j`.
+ * `prodstoich`, a number of species by number of reactions matrix with entry
+ `(i,j)` giving the stoichiometric coefficient of species `i` as a product
+ in reaction `j`.
+
+ + For `ComplexMatrixNetwork`
+
+ * `stoichmat`, the complex stoichiometry matrix [defined
+ here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.complexstoichmat).
+ * `incidencemat`, the complex incidence matrix [defined
+ here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.reactioncomplexes).
+ - `species`, an optional vector of symbolic variables representing each species
+ in the network. Can be constructed using the Symbolics.jl `@variables` macro.
+ Each species should be dependent on the same time variable (`t` in the example
+ above).
+ - `parameters`, a vector of symbolic variables representing each parameter in
+ the network. Can be constructed with the
+ [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/)
+ `@parameters` macro. If no parameters are used it is an optional keyword.
+ - `t`, an optional Symbolics.jl variable representing time as the independent
+ variable of the reaction network. If not provided `Catalyst.DEFAULT_IV` is
+ used to determine the default time variable.
For both input types, `loadrxnetwork` returns a `ParsedReactionNetwork`, `prn`,
with only the field, `prn.rn`, filled in. `prn.rn` corresponds to the generated
diff --git a/docs/make.jl b/docs/make.jl
index 592c907..3ad5ead 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -24,8 +24,15 @@ makedocs(sitename = "ReactionNetworkImporters.jl",
prettyurls = (get(ENV, "CI", nothing) == "true"),
canonical = "https://docs.sciml.ai/ReactionNetworkImporters/stable/"),
modules = [ReactionNetworkImporters],
- doctest = false,
- clean = true,
+ clean = true, doctest = false, linkcheck = true,
+ strict = [
+ :doctest,
+ :linkcheck,
+ :parse_error,
+ :example_block,
+ # Other available options are
+ # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
+ ],
pages = pages)
deploydocs(repo = "github.com/SciML/ReactionNetworkImporters.jl.git";
diff --git a/docs/src/index.md b/docs/src/index.md
index 4788ba3..cd36ea2 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -3,12 +3,13 @@
This package provides importers to load reaction networks into
[Catalyst.jl](https://docs.sciml.ai/Catalyst/stable/)
[`ReactionSystem`s](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#ModelingToolkit.ReactionSystem)
-from several file formats. Currently it supports loading networks in the
+from several file formats. Currently, it supports loading networks in the
following formats:
-1. A *subset* of the BioNetGen .net file format.
-2. Networks represented by dense or sparse substrate and product stoichiometric
- matrices.
-3. Networks represented by dense or sparse complex stoichiometric and incidence matrices.
+
+ 1. A *subset* of the BioNetGen .net file format.
+ 2. Networks represented by dense or sparse substrate and product stoichiometric
+ matrices.
+ 3. Networks represented by dense or sparse complex stoichiometric and incidence matrices.
[SBMLToolkit.jl](https://docs.sciml.ai/SBMLToolkit/stable/) provides an
alternative for loading SBML files into Catalyst models, offering a much broader
@@ -17,48 +18,78 @@ such as constant species, boundary condition species, events, constraint
equations and more. SBML files can be generated from many standard modeling
tools, including BioNetGen, COPASI, and Virtual Cell.
-----
+## Installation
+
+To install ReactionNetworkImporters.jl, use the Julia package manager:
+
+```julia
+using Pkg
+Pkg.add("ReactionNetworkImporters")
+```
+
+## Contributing
+
+ - Please refer to the
+ [SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://github.com/SciML/ColPrac/blob/master/README.md)
+ for guidance on PRs, issues, and other matters relating to contributing to SciML.
+
+ - See the [SciML Style Guide](https://github.com/SciML/SciMLStyle) for common coding practices and other style decisions.
+ - There are a few community forums:
+
+ + The #diffeq-bridged and #sciml-bridged channels in the
+ [Julia Slack](https://julialang.org/slack/)
+ + The #diffeq-bridged and #sciml-bridged channels in the
+ [Julia Zulip](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
+ + On the [Julia Discourse forums](https://discourse.julialang.org)
+ + See also [SciML Community page](https://sciml.ai/community/)
+
## Examples
### Loading a BioNetGen .net file
-A simple network from the builtin BioNetGen bngl examples is the
+
+A simple network from the built-in BioNetGen bngl examples is the
[repressilator](https://github.com/SciML/ReactionNetworkImporters.jl/blob/65f5f23bc52a7eefe32ca2ccabef0298e8e5772d/data/repressilator/Repressilator.bngl).
The `generate_network` command in the bngl file outputs a reduced network
description, i.e. a
[.net](https://github.com/SciML/ReactionNetworkImporters.jl/blob/65f5f23bc52a7eefe32ca2ccabef0298e8e5772d/data/repressilator/Repressilator.net)
file, which can be loaded into a Catalyst `ReactionSystem` as:
+
```julia
using ReactionNetworkImporters
fname = "PATH/TO/Repressilator.net"
prnbng = loadrxnetwork(BNGNetwork(), fname)
```
+
Here `BNGNetwork` is a type specifying the file format that is being loaded.
`prnbng` is a `ParsedReactionNetwork` structure with the following fields:
-- `rn`, a Catalyst `ReactionSystem`
-- `u₀`, a `Dict` mapping initial condition symbolic variables to numeric values
- and/or symbolic expressions.
-- `p`, a `Dict` mapping parameter symbolic variables to numeric values and/or
- symbolic expressions.
-- `varstonames`, a `Dict` mapping the internal symbolic variable of a species
- used in the generated `ReactionSystem` to a `String` generated from the name
- in the .net file. This is necessary as BioNetGen can generate exceptionally
- long species names, involving characters that lead to malformed species names
- when used with `Catalyst`.
-- `groupstosyms`, a `Dict` mapping the `String`s representing names for any
- groups defined in the BioNetGen file to the corresponding symbolic variable
- representing the `ModelingToolkit` symbolic observable associated with the
- group.
+
+ - `rn`, a Catalyst `ReactionSystem`
+ - `u₀`, a `Dict` mapping initial condition symbolic variables to numeric values
+ and/or symbolic expressions.
+ - `p`, a `Dict` mapping parameter symbolic variables to numeric values and/or
+ symbolic expressions.
+ - `varstonames`, a `Dict` mapping the internal symbolic variable of a species
+ used in the generated `ReactionSystem` to a `String` generated from the name
+ in the .net file. This is necessary as BioNetGen can generate exceptionally
+ long species names, involving characters that lead to malformed species names
+ when used with `Catalyst`.
+ - `groupstosyms`, a `Dict` mapping the `String`s representing names for any
+ groups defined in the BioNetGen file to the corresponding symbolic variable
+ representing the `ModelingToolkit` symbolic observable associated with the
+ group.
Given `prnbng`, we can construct and solve the corresponding ODE model for the
reaction system by
+
```julia
using OrdinaryDiffEq, Catalyst
rn = prnbng.rn
tf = 100000.0
-oprob = ODEProblem(rn, Float64[], (0.,tf), Float64[])
-sol = solve(oprob, Tsit5(), saveat=tf/1000.)
+oprob = ODEProblem(rn, Float64[], (0.0, tf), Float64[])
+sol = solve(oprob, Tsit5(), saveat = tf / 1000.0)
```
-Note that we specify empty parameter and initial condition vectors as these are
+
+Note that we specify empty parameter and initial condition vectors, as these are
already stored in the generated `ReactionSystem`, `rn`. A `Dict` mapping each
symbolic species and parameter to its initial value or symbolic expression can
be obtained using `ModelingToolkit.defaults(rn)`.
@@ -67,13 +98,16 @@ See the [Catalyst documentation](https://docs.sciml.ai/Catalyst/stable/) for how
generate ODE, SDE, jump and other types of models.
### Loading a matrix representation
+
Catalyst `ReactionSystem`s can also be constructed from
-- substrate and product stoichiometric matrices.
-- complex stoichiometric and incidence matrices.
+
+ - substrate and product stoichiometric matrices.
+ - complex stoichiometric and incidence matrices.
For example, here we both directly build a Catalyst
network using the `@reaction_network` macro, and then show how to build the same
network from these matrices using `ReactionNetworkImporters`:
+
```julia
# Catalyst network from the macro:
rs = @reaction_network begin
@@ -87,33 +121,33 @@ end k1 k2 k3 k4 k5
# network from basic stoichiometry using ReactionNetworkImporters
@parameters k1 k2 k3 k4 k5
@variables t A(t) B(t) C(t)
-species = [A,B,C]
-pars = [k1,k2,k3,k4,k5]
-substoich =[ 2 0 1 0 0;
- 0 1 1 0 0;
- 0 0 0 1 3]
-prodstoich = [0 2 0 1 3;
- 1 0 0 1 0;
- 0 0 1 0 0]
-mn= MatrixNetwork(pars, substoich, prodstoich; species=species,
- params=pars) # a matrix network
+species = [A, B, C]
+pars = [k1, k2, k3, k4, k5]
+substoich = [2 0 1 0 0;
+ 0 1 1 0 0;
+ 0 0 0 1 3]
+prodstoich = [0 2 0 1 3;
+ 1 0 0 1 0;
+ 0 0 1 0 0]
+mn = MatrixNetwork(pars, substoich, prodstoich; species = species,
+ params = pars) # a matrix network
prn = loadrxnetwork(mn) # dense version
# test the two networks are the same
@assert rs == prn.rn
# network from reaction complex stoichiometry
-stoichmat =[2 0 1 0 0 3;
- 0 1 1 0 0 0;
- 0 0 0 1 3 0]
-incidencemat = [-1 1 0 0 0;
- 1 -1 0 0 0;
- 0 0 -1 1 0;
- 0 0 1 -1 0;
- 0 0 0 0 -1;
- 0 0 0 0 1]
-cmn= ComplexMatrixNetwork(pars, stoichmat, incidencemat; species=species,
- params=pars) # a complex matrix network
+stoichmat = [2 0 1 0 0 3;
+ 0 1 1 0 0 0;
+ 0 0 0 1 3 0]
+incidencemat = [-1 1 0 0 0;
+ 1 -1 0 0 0;
+ 0 0 -1 1 0;
+ 0 0 1 -1 0;
+ 0 0 0 0 -1;
+ 0 0 0 0 1]
+cmn = ComplexMatrixNetwork(pars, stoichmat, incidencemat; species = species,
+ params = pars) # a complex matrix network
prn = loadrxnetwork(cmn)
# test the two networks are the same
@@ -121,50 +155,58 @@ prn = loadrxnetwork(cmn)
```
The basic usages are
+
```julia
-mn = MatrixNetwork(rateexprs, substoich, prodstoich; species=Any[],
- params=Any[], t=nothing)
+mn = MatrixNetwork(rateexprs, substoich, prodstoich; species = Any[],
+ params = Any[], t = nothing)
prn = loadrxnetwork(mn::MatrixNetwork)
-cmn = ComplexMatrixNetwork(rateexprs, stoichmat, incidencemat; species=Any[],
- params=Any[], t=nothing)
+cmn = ComplexMatrixNetwork(rateexprs, stoichmat, incidencemat; species = Any[],
+ params = Any[], t = nothing)
prn = loadrxnetwork(cmn::ComplexMatrixNetwork)
```
+
Here `MatrixNetwork` and `ComplexMatrixNetwork` are the types, which select that
we are constructing a substrate/product stoichiometric matrix-based or a
reaction complex matrix-based stoichiometric representation as input. See the
[Catalyst.jl API](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/) for more
discussion on these matrix representations, and how Catalyst handles symbolic
reaction rate expressions. These two types have the following fields:
-- `rateexprs`, any valid
- [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/) expression for
- the rates, or any basic number type. This can be a hardcoded rate constant
- like `1.0`, a parameter like `k1` above, or an general Symbolics expression
- involving parameters and species like `k*A`.
-- matrix inputs
- - For `MatrixNetwork`
- - `substoich`, a number of species by number of reactions matrix with entry
- `(i,j)` giving the stoichiometric coefficient of species `i` as a
- substrate in reaction `j`.
- - `prodstoich`, a number of species by number of reactions matrix with entry
- `(i,j)` giving the stoichiometric coefficient of species `i` as a product
- in reaction `j`.
- - For `ComplexMatrixNetwork`
- - `stoichmat`, the complex stoichiometry matrix [defined
- here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.complexstoichmat).
- - `incidencemat`, the complex incidence matrix [defined
- here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.reactioncomplexes).
-- `species`, an optional vector of symbolic variables representing each species
- in the network. Can be constructed using the Symbolics.jl `@variables` macro.
- Each species should be dependent on the same time variable (`t` in the example
- above).
-- `parameters`, a vector of symbolic variables representing each parameter in
- the network. Can be constructed with the
- [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/)
- `@parameters` macro. If no parameters are used it is an optional keyword.
-- `t`, an optional Symbolics.jl variable representing time as the independent
- variable of the reaction network. If not provided `Catalyst.DEFAULT_IV` is
- used to determine the default time variable.
+
+ - `rateexprs`, any valid
+ [Symbolics.jl](https://docs.sciml.ai/Symbolics/stable/) expression for
+ the rates, or any basic number type. This can be a hard-coded rate constant
+ like `1.0`, a parameter like `k1` above, or a general Symbolics expression
+ involving parameters and species like `k*A`.
+
+ - matrix inputs
+
+ + For `MatrixNetwork`
+
+ * `substoich`, a number of species by number of reactions matrix, with entry
+ `(i,j)` giving the stoichiometric coefficient of species `i` as a
+ substrate in reaction `j`.
+ * `prodstoich`, a number of species by number of reactions matrix, with entry
+ `(i,j)` giving the stoichiometric coefficient of species `i` as a product
+ in reaction `j`.
+
+ + For `ComplexMatrixNetwork`
+
+ * `stoichmat`, the complex stoichiometry matrix [defined
+ here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.complexstoichmat).
+ * `incidencemat`, the complex incidence matrix [defined
+ here](https://docs.sciml.ai/Catalyst/stable/api/catalyst_api/#Catalyst.reactioncomplexes).
+ - `species`, an optional vector of symbolic variables representing each species
+ in the network. Can be constructed using the Symbolics.jl `@variables` macro.
+ Each species should be dependent on the same time variable (`t` in the example
+ above).
+ - `parameters`, a vector of symbolic variables representing each parameter in
+ the network. Can be constructed with the
+ [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/stable/)
+ `@parameters` macro. If no parameters are used, it is an optional keyword.
+ - `t`, an optional Symbolics.jl variable representing time as the independent
+ variable of the reaction network. If not provided `Catalyst.DEFAULT_IV` is
+ used to determine the default time variable.
For both input types, `loadrxnetwork` returns a `ParsedReactionNetwork`, `prn`,
with only the field, `prn.rn`, filled in. `prn.rn` corresponds to the generated
@@ -184,56 +226,72 @@ species. `params` defaults to an empty vector, so that it does not need to be
set for systems with no parameters.
## Reproducibility
+
```@raw html
The documentation of this SciML package was built using these direct dependencies,
```
+
```@example
using Pkg # hide
Pkg.status() # hide
```
+
```@raw html
and using this machine and Julia version.
```
+
```@example
using InteractiveUtils # hide
versioninfo() # hide
```
+
```@raw html
A more complete overview of all dependencies and their versions is also provided.
```
+
```@example
using Pkg # hide
-Pkg.status(;mode = PKGMODE_MANIFEST) # hide
+Pkg.status(; mode = PKGMODE_MANIFEST) # hide
```
+
```@raw html