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I have created a community model by stitching lots of iML1515 models together and while doing this found the reconstruction process to be slow and error prone, see #202 . Here are some wish list items that would've made it faster for me:
I don't think this is too hard, but would be super nice to have... |
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Replies: 2 comments
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So maybe have a community model that looks like this: struct CommunityModel
S :: SparseMat
b :: SparseVec
c :: SparseVec
lb :: SparseVec
ub :: SparseVec
mets :: Dict{String, Dict{String, Int}}
rxns :: Dict{String, Dict{String, Int}}
end The crucial difference between mets["ENV"] = Dict("env met 1" => 1, "env met 2" => 3, ... )
mets["organism a"] = Dict("met 1" => 2044, "met 2" => 4556, ... )
mets["organism b"] = Dict("met 1" => 8993, "met 2" => 10223, ... ) etc., and the same for the reactions. Then, after running vec = flux_balance_analysis_vec(community_model, Tulip.Optimizer) The results could very easily be queried in dictionary format, e.g. index = get(community_model.rxns["ENV"], "reaction i", nothing)
vec[index] # flux you care about I suppose the user could also construct something like this themselves but it would be nice to generate these types of linked dictionaries during automated community model construction... Another thought is that if there are If only the environmental exchanges and interactions between (not within) microbes are stored then it would also be easy to construct a species map/visualization of the community, and maybe superimpose those fluxes on that. |
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will be addressed by #270 |
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will be addressed by #270