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LCSBBioCore
COBREXA.jl
Commits
40c47045
Unverified
Commit
40c47045
authored
Nov 29, 2021
by
St. Elmo
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implemented reviews
parent
4ae89cb2
Pipeline
#50138
passed with stages
in 9 minutes and 40 seconds
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docs/src/notebooks/9_max_min_driving_force_analysis.jl
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40c47045
...
...
@@ 99,15 +99,16 @@ reaction_standard_gibbs_free_energies = Dict( # kJ/mol
"FUM"
=>

3.42
,
);
# In general
you
cannot be certain that all fluxes will be positive for a given flux
# In general
we
cannot be certain that all fluxes will be positive for a given flux
# solution. This poses problems for systematically enforcing that ΔᵣG ≤ 0 for each reaction,
# because it implicitly assumes that all fluxes are positive, as done in the original
# formulation of MMDF. In COBREXA we instead enforce ΔᵣG ⋅ vᵢ ≤ 0, where vᵢ is the flux of
# reaction i. By default all fluxes are assumed to be positive, but by supplying
# thermodynamically consistent flux solution it is possible to drop this implicit assumption
# and makes it easier to directly incorporate the max min driving force into noncustomized
# models. Here, customized model means a model written such that a negative ΔᵣG is associated
# with each positive flux in the model, and only positive fluxes are used by the model.
# formulation of MMDF. In `max_min_driving_force` we instead enforce ΔᵣG ⋅ vᵢ ≤ 0, where vᵢ
# is the flux of reaction i. By default all fluxes are assumed to be positive, but by
# supplying thermodynamically consistent flux solution it is possible to drop this implicit
# assumption and makes it easier to directly incorporate the max min driving force into
# noncustomized models. Here, customized model means a model written such that a negative
# ΔᵣG is associated with each positive flux in the model, and only positive fluxes are used
# by the model.
flux_solution
=
flux_balance_analysis_dict
(
# find a thermodynamically consistent solution
model
,
...
...
@@ 116,11 +117,11 @@ flux_solution = flux_balance_analysis_dict( # find a thermodynamically consisten
)
# Run max min driving force analysis with some reasonable constraints on metabolite
# concentration bounds.
Note,
protons and water
are removed
from the concentration
#
calculation of the optimization problem, thus we specify their IDs in the model
#
explicitly. The reason for this is that
the Gibbs free energies of
biochemical reactions
# are measured at constant pH, so proton concentration is fixed
; likewise, we assume that
# reactions occur in aqueous environments, hence water
is excluded too
.
# concentration bounds.
To remove
protons and water from the concentration
calculations, we
#
explicitly specify their IDs. Note, protons and water need to be removed from the
#
concentration calculation of the optimization problem, because
the Gibbs free energies of
#
biochemical reactions
are measured at constant pH, so proton concentration is fixed
, and
# reactions occur in aqueous environments, hence water
concentration does not change
.
sol
=
max_min_driving_force
(
model
,
...
...
@@ 147,16 +148,16 @@ sol.mmdf
#md # Transporters can be included in MMDF analysis, however water and proton
#md # transporters must be excluded explicitly in `ignore_reaction_ids`. Due to
#md # the way the method is implemented, the ΔᵣG for these transport reactions
#md # will always be 0. If
they are
not excluded the MMDF will
be 0 (if these
#md # reactions are used in the flux solution).
#md # will always be 0. If not excluded
,
the MMDF will
only have a zero solution (if
#md #
these
reactions are used in the flux solution).
# N
E
xt, we plot the results to show how the concentrations can be used to ensure that
# N
e
xt, we plot the results to show how the concentrations can be used to ensure that
# each reach proceeds "down hill" (ΔᵣG < 0) and that the driving force is as
# large as possible across all the reactions in the model. Compare this to the
# driving forces at standard conditions. Note, we only plot glycolysis for simplicity.
# We additionally scale the fluxes according to their stoichiometry in the
# pathway. From the output,
it is clear
that that metabolite concentrations
# pathway. From the output,
we can clearly see
that that metabolite concentrations
# play a large role in ensuring the thermodynamic consistency of in vivo reactions.
rids
=
[
"GLCpts"
,
"PGI"
,
"PFK"
,
"FBA"
,
"TPI"
,
"GAPD"
,
"PGK"
,
"PGM"
,
"ENO"
,
"PYK"
]
# glycolysis
...
...
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