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class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">User guide</a></li><li><a class="is-disabled">Advanced tutorials</a></li><li class="is-active"><a href>Exploring many model variants</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Exploring many model variants</a></li></ul></nav><div class="docs-right"><a class="docs-edit-link" href="https://github.com/lcsb-biocore/COBREXA.jl/blob/master/docs/src/advanced/1_variants.md" title="Edit on GitHub"><span class="docs-icon fab"></span><span class="docs-label is-hidden-touch">Edit on GitHub</span></a><a class="docs-settings-button fas fa-cog" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-sidebar-button fa fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a></div></header><article class="content" id="documenter-page"><h1 id="Exploring-many-model-variants"><a class="docs-heading-anchor" href="#Exploring-many-model-variants">Exploring many model variants</a><a id="Exploring-many-model-variants-1"></a><a class="docs-heading-anchor-permalink" href="#Exploring-many-model-variants" title="Permalink"></a></h1><p>A major goal of COBREXA.jl is to make exploring of many model variants easy and fast.</p><p>One main concept that can be utilized for doing that is implemented in the function <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a>, which takes your model, a list of model <em>variants</em> that you want to explore by some specified <em>analysis</em>, and schedules the analysis of the model variants parallely on the available distributed workers.</p><p>In its most basic form, the &quot;screening&quot; may use the slightly simplified variant of <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a> that is called <a href="../../functions/analysis/#COBREXA.screen_variants-Tuple{Any, Any, Any}"><code>screen_variants</code></a>, which works as follows:</p><pre><code class="language-julia">m = load_model(StandardModel, &quot;e_coli_core.json&quot;)
screen_variants(
m, # the model for screening
[
[], # a variant with no modifications
[with_changed_bound(&quot;CO2t&quot;, lb = 0, ub = 0)], # disable CO2 transport
[with_changed_bound(&quot;O2t&quot;, lb = 0, ub = 0)], # disable O2 transport
[with_changed_bound(&quot;CO2t&quot;, lb = 0, ub = 0), with_changed_bound(&quot;O2t&quot;, lb = 0, ub = 0)], # disable both transports
],
m -&gt; flux_balance_analysis_dict(m, Tulip.Optimizer)[&quot;BIOMASS_Ecoli_core_w_GAM&quot;],
)</code></pre><p>The call specifies a model (the <code>m</code> that we have loaded) that is being tested, then a vector of model variants to be created and tested, and then the analysis that is being run on each variant – in this case, we find an optimal steady state of each of the variants, and check out the biomass production rate at that state. In this particular case, we are checking what will be the effect of disabling combinations of CO2 transport and O2 transport in the cells. For that, we get the following result:</p><pre><code class="language-none">4-element Vector{Float64}:
0.8739215022678488
0.46166961413944896
0.21166294973372135
0.21114065173865518</code></pre><p>The numbers are the biomass production rates for the specified variants. We can see that disabling O2 transport really does not help the organism much.</p><h2 id="Variant-specification"><a class="docs-heading-anchor" href="#Variant-specification">Variant specification</a><a id="Variant-specification-1"></a><a class="docs-heading-anchor-permalink" href="#Variant-specification" title="Permalink"></a></h2><p>In the above example, we have specified 4 variants, thus the analysis returned 4 different results that correspond with the specifications. Let us have a look at the precise format of the specification and result.</p><p>Importantly, the <code>variants</code> argument is of type <code>Array{Vector{Any}}</code>, meaning that it can be an array of any dimensionality that contains vectors. Each of the vectors specifies precisely one variant, possibly with more modifications applied to the model in sequence.</p><p>For example:</p><ul><li><code>[]</code> specifies no modifications at all</li><li><code>[with_changed_bound(&quot;CO2t&quot;, lb=0, ub=10)]</code> limits the CO2 transport</li><li><code>[with_changed_bound(&quot;CO2t&quot;, lb=0, ub=2), with_changed_bound(&quot;O2t&quot;, lb=0, ub=100)]</code> severely limits the CO2 transport <em>and</em> slightly restricts the transport of O2</li></ul><div class="admonition is-info"><header class="admonition-header">Variants are single-parameter model-transforming functions</header><div class="admonition-body"><p>Because the variants are just generators of single parameter functions that take the model and return its modified version, you can also use <code>identity</code> to specify a variant that does nothing – <code>[identity]</code> is perfectly same as <code>[]</code></p></div></div><p>The shape of the variants array is important too, because it is precisely retained in the result (just as with <code>pmap</code>). If you pass in a matrix of variants, you will receive a matrix of analysis results of the same size. That can be exploited for easily exploring many combinations of possible model properties. Let&#39;s try exploring a &quot;cube&quot; of possible restricted reactions:</p><pre><code class="language-julia">using IterTools # for cartesian products
res = screen_variants(m,
[
# for each variant we restricts 2 reactions
[with_changed_bound(r1, lb=-3, ub=3), with_changed_bound(r2, lb=-1, ub=1)]
# the reaction pair will be chosen from a cartesian product
for (r1,r2) in product(
[&quot;H2Ot&quot;, &quot;CO2t&quot;, &quot;O2t&quot;, &quot;NH4t&quot;], # of this set of transport reactions
[&quot;EX_h2o_e&quot;, &quot;EX_co2_e&quot;, &quot;EX_o2_e&quot;, &quot;EX_nh4_e&quot;], # and this set of exchanges
)
],
m -&gt; flux_balance_analysis_dict(m, Tulip.Optimizer)[&quot;BIOMASS_Ecoli_core_w_GAM&quot;],
)</code></pre><p>As a result, we will receive a full matrix of the biomass productions:</p><pre><code class="language-none">4×4 Matrix{Float64}:
0.407666 0.454097 0.240106 0.183392
0.407666 0.485204 0.24766 0.183392
0.314923 0.319654 0.24766 0.183392
0.407666 0.485204 0.24766 0.183392</code></pre><p>Notably, this shows that O2 transport and NH4 exchange may be serious bottlenecks for biomass production.</p><p>For clarity, you may always annotate the result by zipping it with the specification structure you have used and collecting the data:</p><pre><code class="language-julia">collect(zip(
product(
[&quot;H2Ot&quot;, &quot;CO2t&quot;, &quot;O2t&quot;, &quot;NH4t&quot;],
[&quot;EX_h2o_e&quot;, &quot;EX_co2_e&quot;, &quot;EX_o2_e&quot;, &quot;EX_nh4_e&quot;],
),
res,
))</code></pre><p>...which gives the following annotated result:</p><pre><code class="language-none">4×4 Matrix{Tuple{Tuple{String, String}, Float64}}:
((&quot;H2Ot&quot;, &quot;EX_h2o_e&quot;), 0.407666) ((&quot;H2Ot&quot;, &quot;EX_co2_e&quot;), 0.454097) ((&quot;H2Ot&quot;, &quot;EX_o2_e&quot;), 0.240106) ((&quot;H2Ot&quot;, &quot;EX_nh4_e&quot;), 0.183392)
((&quot;CO2t&quot;, &quot;EX_h2o_e&quot;), 0.407666) ((&quot;CO2t&quot;, &quot;EX_co2_e&quot;), 0.485204) ((&quot;CO2t&quot;, &quot;EX_o2_e&quot;), 0.24766) ((&quot;CO2t&quot;, &quot;EX_nh4_e&quot;), 0.183392)
((&quot;O2t&quot;, &quot;EX_h2o_e&quot;), 0.314923) ((&quot;O2t&quot;, &quot;EX_co2_e&quot;), 0.319654) ((&quot;O2t&quot;, &quot;EX_o2_e&quot;), 0.24766) ((&quot;O2t&quot;, &quot;EX_nh4_e&quot;), 0.183392)
((&quot;NH4t&quot;, &quot;EX_h2o_e&quot;), 0.407666) ((&quot;NH4t&quot;, &quot;EX_co2_e&quot;), 0.485204) ((&quot;NH4t&quot;, &quot;EX_o2_e&quot;), 0.24766) ((&quot;NH4t&quot;, &quot;EX_nh4_e&quot;), 0.183392)</code></pre><p>This may be easily used for e.g. scrutinizing all possible reaction pairs, to find the ones that are redundant and not.</p><div class="admonition is-success"><header class="admonition-header">Notebook available</header><div class="admonition-body"><p>A notebook is available that demonstrates <a href="../../notebooks/6_screening/">the screening on a larger scale</a>.</p></div></div><p>There are many other variant &quot;specifications&quot; to choose from. You may use <a href="../../functions/reconstruction/#COBREXA.with_added_reactions-Tuple"><code>with_added_reactions</code></a>, <a href="../../functions/reconstruction/#COBREXA.with_removed_reactions-Tuple"><code>with_removed_reactions</code></a>, <a href="../../functions/reconstruction/#COBREXA.with_removed_metabolites-Tuple"><code>with_removed_metabolites</code></a>, and others. Function reference contains a complete list; as a convention, names of the specifications all start with <code>with_</code>.</p><h2 id="Writing-custom-variant-functions"><a class="docs-heading-anchor" href="#Writing-custom-variant-functions">Writing custom variant functions</a><a id="Writing-custom-variant-functions-1"></a><a class="docs-heading-anchor-permalink" href="#Writing-custom-variant-functions" title="Permalink"></a></h2><p>It is actually very easy to create custom specifications that do any modification that you can implement, to be later used with <a href="../../functions/analysis/#COBREXA.screen_variants-Tuple{Any, Any, Any}"><code>screen_variants</code></a> and <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a>.</p><p>Generally, the &quot;specifications&quot; are supposed to return a <em>function</em> that creates a modified copy of the model. The copy of the model may be shallow, but the functions should always prevent modifying the original model structure – <code>screen</code> is keeping a single copy of the original model at each worker to prevent unnecessary bulk data transport, and if that is changed in-place, all following analyses of the model will work on inconsistent data, usually returning wrong results (even randomly changing ones, because of the asynchronous nature of <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a> execution).</p><p>Despite of that, writing a modification is easy. The simplest modification that &quot;does nothing&quot; (isomorphic to standard <code>identity</code>) can be formatted as follows:</p><pre><code class="language-julia">with_no_change = model -&gt; model</code></pre><p>The modifications may change the model, provided it is copied properly. The following modification will remove a reaction called &quot;O2t&quot;, effectively removing the possibility to transport oxygen. We require a specific type of model where this change is easy to perform (generally, not all variants may be feasible on all model types).</p><pre><code class="language-julia">with_disabled_oxygen_transport = (model::StandardModel) -&gt; begin
# make &quot;as shallow as possible&quot; copy of the `model`.
# Utilizing `deepcopy` is also possible, but inefficient.
new_model = copy(model)
new_model.reactions = copy(model.reactions)
# remove the O2 transport from the model copy
delete!(new_model.reactions, &quot;O2t&quot;)
return new_model #return the newly created variant
end</code></pre><p>Finally, the whole definition may be parametrized as a normal function. The following variant removes any user-selected reaction:</p><pre><code class="language-julia">with_disabled_reaction(reaction_id) = (model::StandardModel) -&gt; begin
new_model = copy(model)
new_model.reactions = copy(model.reactions)
delete!(new_model.reactions, reaction_id) # use the parameter from the specification
return new_model
end</code></pre><p>In turn, these variants can be used in <a href="../../functions/analysis/#COBREXA.screen_variants-Tuple{Any, Any, Any}"><code>screen_variants</code></a> just as we used <a href="../../functions/reconstruction/#COBREXA.with_changed_bound-Tuple"><code>with_changed_bound</code></a> above:</p><pre><code class="language-julia">screen_variants(
m, # the model for screening
[
[with_no_change],
[with_disabled_oxygen_transport],
[with_disabled_reaction(&quot;NH4t&quot;)],
],
m -&gt; flux_balance_analysis_dict(m, Tulip.Optimizer)[&quot;BIOMASS_Ecoli_core_w_GAM&quot;],
)</code></pre><p>That should get you the results for all new variants of the model:</p><pre><code class="language-none">3-element Vector{Float64}:
0.8739215022674809
0.21166294865468896
1.2907224478973395e-15</code></pre><div class="admonition is-warning"><header class="admonition-header">Custom variants with distributed processing</header><div class="admonition-body"><p>If using distributed evaluation, remember the variant-generating functions need to be defined on all used workers (generating the variants in parallel on the workers allows COBREXA to run the screening process very efficiently, without unnecessary sending of bulk model data). Prefixing the definition with <code>@everywhere</code> is usually sufficient for that purpose.</p></div></div><h2 id="Passing-extra-arguments-to-the-analysis-function"><a class="docs-heading-anchor" href="#Passing-extra-arguments-to-the-analysis-function">Passing extra arguments to the analysis function</a><a id="Passing-extra-arguments-to-the-analysis-function-1"></a><a class="docs-heading-anchor-permalink" href="#Passing-extra-arguments-to-the-analysis-function" title="Permalink"></a></h2><p>Some analysis functions may take additional arguments, which you might want to vary for the analysis. <code>modifications</code> argument of <a href="../../functions/analysis/#COBREXA.flux_balance_analysis_dict-Tuple{MetabolicModel, Vararg{Any}}"><code>flux_balance_analysis_dict</code></a> is one example of such argument, allowing you to specify details of the optimization procedure.</p><p><a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a> function allows you to do precisely that – apart from <code>variants</code>, you may also specify an array of <code>args</code> of the same shape as <code>variants</code>, the entries of which will get passed together with the generated model variants to your specified analysis function. If either of the arguments is missing (or set to <code>nothing</code>), it is defaulted to &quot;no modifications&quot; or &quot;no arguments&quot;.</p><p>The arguments <em>must</em> be tuples; you may need to make 1-tuples from your data (e.g. using <code>(value,)</code>) if you want to pass just a single argument.</p><p>Let&#39;s try to use that functionality for trying to find a sufficient amount of iterations needed for Tulip solver to find a feasible solution:</p><pre><code class="language-julia">screen(m,
args = [(i,) for i in 5:15], # the iteration counts, packed in 1-tuples
analysis = (m,a) -&gt; # `args` elements get passed as the extra parameter here
flux_balance_analysis_vec(m,
Tulip.Optimizer;
modifications=[change_optimizer_attribute(&quot;IPM_IterationsLimit&quot;, a)],
),
)</code></pre><p>From the result, we can see that Tulip would need at least 14 iterations to find a feasible region:</p><pre><code class="language-none">11-element Vector{Union{Nothing, Vector{Float64}}}:
nothing
nothing
nothing
nothing
nothing
nothing
nothing
nothing
nothing
[7.47738193404817, 1.8840414375838503e-8, 4.860861010127701, -16.023526104614593, … ]
[7.47738193404817, 1.8840414375838503e-8, 4.860861010127701, -16.023526104614593, … ]</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« All advanced tutorials</a><a class="docs-footer-nextpage" href="../2_custom_model/">Working with custom models »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Monday 16 May 2022 15:20">Monday 16 May 2022</span>. Using Julia version 1.7.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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analysis</a></li></ul></li></ul></li><li><span class="tocitem">Types and functions</span><ul><li><a class="tocitem" href="../../functions/">Contents</a></li><li><a class="tocitem" href="../../functions/analysis/">Analysis functions</a></li><li><a class="tocitem" href="../../functions/base/">Base functions</a></li><li><a class="tocitem" href="../../functions/io/">Input and output</a></li><li><a class="tocitem" href="../../functions/reconstruction/">Model construction functions</a></li><li><a class="tocitem" href="../../functions/types/">Types</a></li><li><a class="tocitem" href="../../functions/utils/">Utilities</a></li></ul></li><li><a class="tocitem" href="../../howToContribute/">How to contribute</a></li></ul><div class="docs-version-selector field has-addons"><div class="control"><span class="docs-label button is-static is-size-7">Version</span></div><div class="docs-selector control is-expanded"><div class="select is-fullwidth is-size-7"><select id="documenter-version-selector"></select></div></div></div></nav><div class="docs-main"><header class="docs-navbar"><nav class="breadcrumb"><ul class="is-hidden-mobile"><li><a class="is-disabled">User guide</a></li><li><a class="is-disabled">Advanced tutorials</a></li><li class="is-active"><a href>Working with custom models</a></li></ul><ul class="is-hidden-tablet"><li class="is-active"><a href>Working with custom models</a></li></ul></nav><div class="docs-right"><a class="docs-edit-link" href="https://github.com/lcsb-biocore/COBREXA.jl/blob/master/docs/src/advanced/2_custom_model.md" title="Edit on GitHub"><span class="docs-icon fab"></span><span class="docs-label is-hidden-touch">Edit on GitHub</span></a><a class="docs-settings-button fas fa-cog" id="documenter-settings-button" href="#" title="Settings"></a><a class="docs-sidebar-button fa fa-bars is-hidden-desktop" id="documenter-sidebar-button" href="#"></a></div></header><article class="content" id="documenter-page"><h1 id="Working-with-custom-models"><a class="docs-heading-anchor" href="#Working-with-custom-models">Working with custom models</a><a id="Working-with-custom-models-1"></a><a class="docs-heading-anchor-permalink" href="#Working-with-custom-models" title="Permalink"></a></h1><p>It may happen that the intuitive representation of your data does not really match what is supported by a given COBRA package. COBREXA.jl attempts to avoid this problem by providing a flexible framework for containing any data structure that can, somehow, represent the constraint-based model.</p><p>The task of having such a polymorphic model definition can be split into 2 separate concerns:</p><ul><li>How to allow the analysis functions to gather the required information from any user-specified model data structure?</li><li>How to make the reconstruction functions (i.e., reaction or gene deletions) work properly on any data structure?</li></ul><p>To solve the first concern, COBREXA.jl specifies a set of generic accessors that work over the abstract type <a href="../../functions/types/#COBREXA.MetabolicModel"><code>MetabolicModel</code></a>. To use your data structure in a model, you just make it a subtype of <a href="../../functions/types/#COBREXA.MetabolicModel"><code>MetabolicModel</code></a> and overload the required accessors. The accessors are functions that extract some relevant information, such as <a href="../../functions/types/#COBREXA.stoichiometry-Tuple{CoreModel}"><code>stoichiometry</code></a> and <a href="../../functions/types/#COBREXA.bounds-Tuple{CoreModel}"><code>bounds</code></a>, returning a fixed simple data type that can be further used by COBREXA. You may see a complete list of accessors <a href="../../functions/#Base-Types">here</a>.</p><p>A good solution to the second concern is a slightly more involved, as writing generic data modifiers is notoriously hard. Still, there is support for easily making small changes to the model using the modifications system, with functions such as <a href="../../functions/reconstruction/#COBREXA.with_added_reactions-Tuple"><code>with_added_reactions</code></a> and <a href="../../functions/reconstruction/#COBREXA.with_changed_bound-Tuple"><code>with_changed_bound</code></a>.</p><div class="admonition is-success"><header class="admonition-header">Notebook available</header><div class="admonition-body"><p>A better example of using a custom model structure is available <a href="../../notebooks/8_custom_model/">in a separate notebook</a>.</p></div></div><h2 id="Writing-the-generic-accessors"><a class="docs-heading-anchor" href="#Writing-the-generic-accessors">Writing the generic accessors</a><a id="Writing-the-generic-accessors-1"></a><a class="docs-heading-anchor-permalink" href="#Writing-the-generic-accessors" title="Permalink"></a></h2><p>Let&#39;s write a data structure that represents a very small model that contains N metabolites that are converted in a circle through N linear, coupled reactions. (E.g., for N=3, we would have a conversion of metabolites A, B and C ordered as A → B → C → A.) This may be useful for testing purposes; we will use it for a simple demonstration.</p><p>The whole model can thus be specified with a single integer N that represents the length of the reaction cycle:</p><pre><code class="language-julia">struct CircularModel &lt;: MetabolicModel
size::Int
end</code></pre><p>First, define the reactions and metabolites:</p><pre><code class="language-julia">COBREXA.n_reactions(m::CircularModel) = m.size
COBREXA.n_metabolites(m::CircularModel) = m.size
COBREXA.reactions(m::CircularModel) = [&quot;rxn$i&quot; for i in 1:n_reactions(m)]
COBREXA.metabolites(m::CircularModel) = [&quot;met$i&quot; for i in 1:n_metabolites(m)]</code></pre><p>It is useful to re-use the already defined functions, as that improves the code maintainability.</p><p>We can continue with the actual linear model properties:</p><pre><code class="language-julia">function COBREXA.objective(m::CircularModel)
c = spzeros(n_reactions(m))
c[1] = 1 #optimize the first reaction
return c
end
COBREXA.bounds(m::CircularModel) = (
zeros(n_reactions(m)), # lower bounds
ones(n_reactions(m)), # upper bounds
)
function COBREXA.stoichiometry(m::CircularModel)
nr = n_reactions(m)
stoi(i,j) =
i == j ? 1.0 :
(i % nr + 1) == j ? -1.0 :
0.0
sparse([stoi(i,j) for i in 1:nr, j in 1:nr])
end</code></pre><p>You may check that the result now works just as with <a href="../../functions/types/#COBREXA.CoreModel"><code>CoreModel</code></a> and <a href="../../functions/types/#COBREXA.StandardModel"><code>StandardModel</code></a>:</p><pre><code class="language-julia">julia&gt; m = CircularModel(5)
Metabolic model of type CircularModel
1.0 -1.0 ⋅ ⋅ ⋅
⋅ 1.0 -1.0 ⋅ ⋅
⋅ ⋅ 1.0 -1.0 ⋅
⋅ ⋅ ⋅ 1.0 -1.0
-1.0 ⋅ ⋅ ⋅ 1.0
Number of reactions: 5
Number of metabolites: 5
</code></pre><p>This interface is sufficient to run most of the basic analyses, especially the flux balance finding ones:</p><pre><code class="language-julia">julia&gt; flux_balance_analysis_dict(m, Tulip.Optimizer)
Dict{String, Float64} with 5 entries:
&quot;rxn5&quot; =&gt; 1.0
&quot;rxn2&quot; =&gt; 1.0
&quot;rxn1&quot; =&gt; 1.0
&quot;rxn3&quot; =&gt; 1.0
&quot;rxn4&quot; =&gt; 1.0
</code></pre><h2 id="Writing-generic-model-modifications"><a class="docs-heading-anchor" href="#Writing-generic-model-modifications">Writing generic model modifications</a><a id="Writing-generic-model-modifications-1"></a><a class="docs-heading-anchor-permalink" href="#Writing-generic-model-modifications" title="Permalink"></a></h2><p>The custom model structure can also be made compatible with many of the existing variant-generating functions and analysis modifiers.</p><p>The functions prepared for use as &quot;variants&quot; in <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a>, usually prefixed by <code>with_</code>, have their generic variants that only call simpler, overloadable functions for each specific model. This choice is based on the overloading dispatch system of Julia. For example,<a href="../../functions/reconstruction/#COBREXA.with_removed_metabolites-Tuple"><code>with_removed_metabolites</code></a> is implemented very generically by reducing the problem to some specific <a href="../../functions/reconstruction/#COBREXA.remove_metabolites-Tuple{CoreModel, AbstractVector{Int64}}"><code>remove_metabolites</code></a> functions selected by the dispatch, as follows:</p><pre><code class="language-julia">with_removed_metabolites(args...; kwargs...) =
m -&gt; remove_metabolites(m, args...; kwargs...)</code></pre><p>To be able to use <a href="../../functions/reconstruction/#COBREXA.with_removed_metabolites-Tuple"><code>with_removed_metabolites</code></a> in your model, we can just overload the actual inner function. For the simple circular model, the modification might as well look like this:</p><pre><code class="language-julia">COBREXA.remove_metabolites(m::CircularModel, n::Int) =
return CircularModel(m.size - n)</code></pre><div class="admonition is-danger"><header class="admonition-header">Functions that generate model variants must be pure</header><div class="admonition-body"><p>Notice that the function is &quot;pure&quot;, i.e., does not make any in-place modifications to the original model structure. That property is required for <a href="../../functions/analysis/#COBREXA.screen-Tuple"><code>screen</code></a> and other functions to properly and predictably apply the modifications to the model. To expose potential in-place modifications to your codebase, you should instead overload the &quot;bang&quot; counterpart of remove metabolites, called <a href="../../functions/reconstruction/#COBREXA.remove_metabolites!-Tuple{CoreModel, AbstractVector{Int64}}"><code>remove_metabolites!</code></a>.</p></div></div></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../1_variants/">« Exploring many model variants</a><a class="docs-footer-nextpage" href="../../notebooks/">All notebooks »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Monday 16 May 2022 15:20">Monday 16 May 2022</span>. Using Julia version 1.7.0.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
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