Unverified Commit 57f8adb4 authored by St. Elmo's avatar St. Elmo
Browse files

added references to all functions

parent 0081b9b2
......@@ -42,6 +42,9 @@ max cᵀx
s.t. S x = b
xₗ ≤ x ≤ xᵤ
See "Orth, J., Thiele, I. & Palsson, B. What is flux balance analysis?. Nat
Biotechnol 28, 245–248 (2010). https://doi.org/10.1038/nbt.1614" for more
The `optimizer` must be set to a `JuMP`-compatible optimizer, such as
`GLPK.Optimizer` or `Tulip.Optimizer`
......@@ -19,7 +19,9 @@ s.t. S x = b
cᵀx ≤ bounds(Z₀)[2]
where Z₀:= cᵀx₀ is the objective value of an optimal solution of the associated
FBA problem (see [`flux_balance_analysis`](@ref)).
FBA problem (see [`flux_balance_analysis`](@ref)). See "Gudmundsson, S., Thiele,
I. Computationally efficient flux variability analysis. BMC Bioinformatics 11,
489 (2010). https://doi.org/10.1186/1471-2105-11-489" for more information.
The `bounds` is a user-supplied function that specifies the objective bounds
for the variability optimizations, by default it restricts the flux objective
......@@ -7,7 +7,28 @@
relax_bounds=[1.0, 0.999999, 0.99999, 0.9999, 0.999, 0.99],
Run parsimonious flux balance analysis (pFBA) on the `model`.
Run parsimonious flux balance analysis (pFBA) on the `model`. In short, pFBA
runs two consecutive optimization problems. The first is traditional FBA:
max cᵀx = μ
s.t. S x = b
xₗ ≤ x ≤ xᵤ
And the second is a quadratic optimization problem:
min Σᵢ xᵢ²
s.t. S x = b
xₗ ≤ x ≤ xᵤ
μ = μ⁰
Where the optimal solution of the FBA problem, μ⁰, has been added as an
additional constraint. See "Lewis, Nathan E, Hixson, Kim K, Conrad, Tom M,
Lerman, Joshua A, Charusanti, Pep, Polpitiya, Ashoka D, Adkins, Joshua N,
Schramm, Gunnar, Purvine, Samuel O, Lopez‐Ferrer, Daniel, Weitz, Karl K, Eils,
Roland, König, Rainer, Smith, Richard D, Palsson, Bernhard Ø, (2010) Omic data
from evolved E. coli are consistent with computed optimal growth from
genome‐scale models. Molecular Systems Biology, 6. 390. doi:
accession:10.1038/msb.2010.47" for more details.
pFBA gets the model optimum by standard FBA (using
[`flux_balance_analysis`](@ref) with `optimizer` and `modifications`), then
......@@ -8,7 +8,10 @@
Perform a basic hit and run sampling for `N` iterations on a constrained JuMP
model in `opt_model`.
model in `opt_model`. See "Robert L. Smith Efficient Monte Carlo Procedures for
Generating Points Uniformly Distributed over Bounded Regions. Operations
Research 32 (6) 1296-1308 https://doi.org/10.1287/opre.32.6.1296" for more
The process generates `samplesize` samples, and logs the sample state each
`keepevery` iterations.
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