Gene knockouts

FBA is classically very good at predicting the effect of knocking out genes in an organism. Here we demonstrate the ways of using the FBA to examine knockouts in COBREXA.

As usual, we need packages and models:

using COBREXA

download_model(
    "http://bigg.ucsd.edu/static/models/e_coli_core.json",
    "e_coli_core.json",
    "7bedec10576cfe935b19218dc881f3fb14f890a1871448fc19a9b4ee15b448d8",
)

import JSONFBCModels
import HiGHS

model = load_model("e_coli_core.json")

JSONFBCModels.JSONFBCModel(#= 95 reactions, 72 metabolites =#)

Single gene knockouts

Function gene_knockouts is a convenience wrapper for FBA that computes and optimizes the knockout biomass productions for all genes:

ko_objective_values = gene_knockouts(model, optimizer = HiGHS.Optimizer)

ko_dict = Dict(ko_objective_values)


ko_dict["b3919"]

ko_dict["b3738"]

0.3742298749331098

From the result, we can see e.g. how many genes are critical:

critical = count(isnothing, values(ko_dict))

Multiple gene knockouts

By default, gene_knockouts simply computes all gene knockouts. To examine multi-gene knockouts, we specify them manually as an array of tuples:

some_double_knockouts = gene_knockouts(
    model,
    [("b3919", "b3738"), ("b0118", "b0720")],
    optimizer = HiGHS.Optimizer,
)

2-element Vector{Pair{Tuple{String, String}, Float64}}:
 ("b3919", "b3738") => 0.13475540327382504
 ("b0118", "b0720") => 0.0

With the array processing functionality of Julia it is quite straightforward to generate the tuples for various specifications of knockout sets; for example here we specify all double knockout where the second knocked-out gene is b3919:

knockouts_with_b3919 =
    gene_knockouts(model, tuple.(keys(ko_dict), "b3919"), optimizer = HiGHS.Optimizer)

137-element Vector{Pair{Tuple{String, String}}}:
 ("b3236", "b3919") => 0.7040369478590238
 ("b1621", "b3919") => 0.7040369478590238
 ("b1241", "b3919") => 0.7040369478590238
 ("b2276", "b3919") => nothing
 ("b3925", "b3919") => 0.7040369478590238
 ("b0979", "b3919") => 0.7040369478590238
 ("b2296", "b3919") => 0.7040369478590238
 ("b4232", "b3919") => 0.7040369478590238
 ("b2282", "b3919") => nothing
 ("b2283", "b3919") => nothing
                    ⋮
 ("b0903", "b3919") => 0.7040369478590238
 ("b1723", "b3919") => 0.7040369478590238
 ("b0810", "b3919") => 0.7040369478590238
 ("b0356", "b3919") => 0.7040369478590238
 ("b2277", "b3919") => nothing
 ("b1276", "b3919") => 0.7040369478590238
 ("b4153", "b3919") => 0.7040369478590238
 ("b2279", "b3919") => nothing
 ("b0726", "b3919") => 0.7040369478590238

Now, how many genes are critical given b3919 is already missing?

critical_without_b3919 = count(isnothing, last.(knockouts_with_b3919))

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