Protein constraints in genome‐scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes
Biotechnology and Bioengineering,
Journal Year:
2024,
Volume and Issue:
121(3), P. 915 - 930
Published: Jan. 4, 2024
Abstract
Genome‐scale
metabolic
models
provide
a
valuable
resource
to
study
metabolism
and
cell
physiology.
These
are
employed
with
approaches
from
the
constraint‐based
modeling
framework
predict
physiological
phenotypes.
The
prediction
performance
of
genome‐scale
can
be
improved
by
including
protein
constraints.
resulting
protein‐constrained
consider
data
on
turnover
numbers
(
k
cat
)
facilitate
integration
abundances.
In
this
systematic
review,
we
present
discuss
current
state‐of‐the‐art
regarding
estimation
kinetic
parameters
used
in
models.
We
also
highlight
how
data‐driven
aid
their
usage
improving
predictions
cellular
Finally,
identify
standing
challenges
perspective
future
improve
predictive
performance.
Language: Английский
Data integration across conditions improves turnover number estimates and metabolic predictions
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: March 17, 2023
Turnover
numbers
characterize
a
key
property
of
enzymes,
and
their
usage
in
constraint-based
metabolic
modeling
is
expected
to
increase
the
prediction
accuracy
diverse
cellular
phenotypes.
In
vivo
turnover
can
be
obtained
by
integrating
reaction
rate
enzyme
abundance
measurements
from
individual
experiments.
Yet,
contribution
improving
predictions
condition-specific
phenotypes
remains
elusive.
Here,
we
show
that
available
vitro
lead
poor
growth
rates
with
protein-constrained
models
Escherichia
coli
Saccharomyces
cerevisiae,
particularly
when
protein
abundances
are
considered.
We
demonstrate
correction
simultaneous
consideration
proteomics
physiological
data
leads
improved
rates.
Moreover,
estimates
more
precise
than
corresponding
numbers.
Therefore,
our
approach
provides
means
correct
paves
way
towards
cataloguing
kcatomes
other
organisms.
Language: Английский
Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective
Metabolites,
Journal Year:
2024,
Volume and Issue:
14(7), P. 365 - 365
Published: June 28, 2024
Stoichiometric
genome-scale
metabolic
models
(generally
abbreviated
GSM,
GSMM,
or
GEM)
have
had
many
applications
in
exploring
phenotypes
and
guiding
engineering
interventions.
Nevertheless,
these
predictions
thereof
can
become
limited
as
they
do
not
directly
account
for
protein
cost,
enzyme
kinetics,
cell
surface
volume
proteome
limitations.
Lack
of
such
mechanistic
detail
could
lead
to
overly
optimistic
engineered
strains.
Initial
efforts
correct
deficiencies
were
by
the
application
precursor
tools
GSMs,
flux
balance
analysis
with
molecular
crowding.
In
past
decade,
several
frameworks
been
introduced
incorporate
proteome-related
limitations
using
a
stoichiometric
model
reconstruction
basis,
which
herein
are
called
resource
allocation
(RAMs).
This
review
provides
broad
overview
representative
commonly
used
existing
RAM
frameworks.
discusses
increasingly
complex
models,
beginning
broadly
divided
into
two
categories:
coarse-grained
fine-grained,
different
strengths
challenges.
Discussion
includes
pinpointing
their
utility,
data
needs,
highlighting
framework
limitations,
appropriateness
various
research
endeavors,
largely
through
contrasting
mathematical
Finally,
promising
future
RAMs
discussed.
Language: Английский
Model-driven evaluation of microbial physiology: insights from protein allocation
Published: July 26, 2024
The
optimal
allocation
of
proteins
to
cellular
functions
is
crucial
for
cell
survival
and
growth.
However,
the
strategies
employed
by
are
still
elusive,
as
there
many
supposedly
conflicting
objectives
be
considered,
such
minimizing
expenditure
resources,
while
at
same
time
affording
produce
certain
enzymes
in
excess,
despite
lower
demand
enzyme
resources
maintain
a
amount
metabolic
flux.
Further,
phenotypes,
overflow
metabolism,
triggered
changes
resource
distribution.
In
order
tackle
these
problems,
thesis
focuses
on
usage
protein-constrained
models
combination
with
machine
learning
integration
multi-omics
data.
Based
approaches,
here
it
predicted
occurrence
metabolism
form
respiro-fermentative
yeast
Kluyveromyces
marxianus.
By
integrating
model
K.
marxianus
transcriptomics
data,
new
insights
genes,
metabolites
involved
ethanol
stress
were
obtained.
Next,
presented
approach
studying
redistribution,
PARROT,
which
minimizes
distance
between
an
initial
growth
condition
changing
condition,
based
principle
minimal
adjustment.
PARROT
was
able
predict
alternative
conditions
higher
accuracy
than
previous
methods.
While
this
useful
not
vivo
protein
concentrations,
given
that
limited
flux
catalytic
efficiency.
To
solve
problem,
combines
modelling
developed,
termed
CAMEL.
This
could
accurately
including
strains
metabolically
engineered.
Finally,
redistribution
evaluated
context
promiscuity,
from
network
reactions
“underground
metabolism”
can
arise.
end,
named
CORAL
developed
integrate
promiscuity
constraints
into
models.
It
found
promiscuous
important
maintaining
providing
robustness
disturbances
metabolism.
results
obtained
relevant
systems
engineering
endeavours,
tools
knowledge
design
microbial
more
suitable
industrial
applications.
Keywords:
Systems
biology;
Metabolic
engineering;
Microbial
physiology;
Machine
Language: Английский