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separate quick start document to keep doc indexes tidy
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| # Quick start guide | ||
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| <!--insert_quickstart--> | ||
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| #### Model variant processing | ||
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| The main feature of COBREXA.jl is the ability to easily specify and process | ||
| many analyses in parallel. To demonstrate, let's see how the organism would perform if | ||
| some reactions were disabled independently: | ||
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| ```julia | ||
| # convert to a model type that is efficient to modify | ||
| m = convert(StandardModel, model) | ||
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| # find the model objective value if oxygen or carbon dioxide transports are disabled | ||
| screen(m, # the base model | ||
| variants=[ # this specifies how to generate the desired model variants | ||
| [], # one with no modifications, i.e. the base case | ||
| [with_changed_bound("R_O2t", lower=0.0, upper=0.0)], # disable oxygen | ||
| [with_changed_bound("R_CO2t", lower=0.0, upper=0.0)], # disable CO2 | ||
| [with_changed_bound("R_O2t", lower=0.0, upper=0.0), | ||
| with_changed_bound("R_CO2t", lower=0.0, upper=0.0)], # disable both | ||
| ], | ||
| # this specifies what to do with the model variants (received as the argument `x`) | ||
| analysis = x -> | ||
| flux_balance_analysis_dict(x, Tulip.Optimizer)["R_BIOMASS_Ecoli_core_w_GAM"], | ||
| ) | ||
| ``` | ||
| You should receive a result showing that missing oxygen transport makes the | ||
| biomass production much harder: | ||
| ```julia | ||
| 4-element Vector{Float64}: | ||
| 0.8739215022674809 | ||
| 0.21166294973372796 | ||
| 0.46166961413944896 | ||
| 0.21114065173865457 | ||
| ``` | ||
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| Most importantly, such analyses can be easily specified by automatically | ||
| generating long lists of modifications to be applied to the model, and | ||
| parallelized. | ||
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| Knocking out each reaction in the model is efficiently accomplished: | ||
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| ```julia | ||
| # load the task distribution package, add several worker nodes, and load | ||
| # COBREXA and the solver on the nodes | ||
| using Distributed | ||
| addprocs(4) | ||
| @everywhere using COBREXA, Tulip | ||
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| # get a list of the workers | ||
| worker_list = workers() | ||
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| # run the processing in parallel for many model variants | ||
| res = screen(m, | ||
| variants=[ | ||
| # create one variant for each reaction in the model, with that reaction knocked out | ||
| [with_changed_bound(reaction_id, lower=0.0, upper=0.0)] | ||
| for reaction_id in reactions(m) | ||
| ], | ||
| analysis = model -> begin | ||
| # we need to check if the optimizer even found a feasible solution, | ||
| # which may not be the case if we knock out important reactions | ||
| sol = flux_balance_analysis_dict(model, Tulip.Optimizer) | ||
| isnothing(sol) ? nothing : sol["R_BIOMASS_Ecoli_core_w_GAM"] | ||
| end, | ||
| # run the screening in parallel on all workers in the list | ||
| workers = worker_list, | ||
| ) | ||
| ``` | ||
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| In result, you should get a long list of the biomass production for each | ||
| reaction knockout. Let's decorate it with reaction names: | ||
| ```julia | ||
| Dict(reactions(m) .=> res) | ||
| ``` | ||
| ...which should output an easily accessible dictionary with all the objective | ||
| values named, giving a quick overview of which reactions are critical for the | ||
| model organism to create biomass: | ||
| ```julia | ||
| Dict{String, Union{Nothing, Float64}} with 95 entries: | ||
| "R_ACALD" => 0.873922 | ||
| "R_PTAr" => 0.873922 | ||
| "R_ALCD2x" => 0.873922 | ||
| "R_PDH" => 0.796696 | ||
| "R_PYK" => 0.864926 | ||
| "R_CO2t" => 0.46167 | ||
| "R_EX_nh4_e" => 1.44677e-15 | ||
| "R_MALt2_2" => 0.873922 | ||
| "R_CS" => 2.44779e-14 | ||
| "R_PGM" => 1.04221e-15 | ||
| "R_TKT1" => 0.864759 | ||
| ⋮ => ⋮ | ||
| ``` |