Molecular Systems Design & Engineering,
Год журнала:
2024,
Номер
10(2), С. 89 - 101
Опубликована: Дек. 24, 2024
We
develop
a
physics-informed
machine
learning
workflow
that
accelerates
multicomponent
phase-coexistence
calculations
on
the
number,
composition,
and
abundance
of
phases.
The
is
demonstrated
for
systems
described
by
Flory–Huggins
theory.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
15(45), С. 11428 - 11436
Опубликована: Ноя. 7, 2024
Enhancers
regulate
gene
expression
by
forming
contacts
with
distant
promoters.
Phase-separated
condensates
or
clusters
formed
transcription
factors
(TFs)
and
cofactors
are
thought
to
facilitate
these
enhancer-promoter
(E-P)
interactions.
Using
polymer
physics,
we
developed
distinct
coarse-grained
chromatin
models
that
produce
similar
ensemble-averaged
Hi-C
maps
but
"stable"
"dynamic"
characteristics.
Our
findings,
consistent
recent
experiments,
reveal
a
multistep
E-P
communication
process.
The
dynamic
model
facilitates
proximity
enhancing
TF
clustering
subsequently
promotes
direct
interactions
destabilizing
the
through
chain
flexibility.
study
physical
understanding
of
molecular
mechanisms
governing
in
transcriptional
regulation.
Digital Discovery,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
We
benchmark
the
performance
of
space-filling
and
active
learning
algorithms
on
classification
problems
in
materials
science,
revealing
trends
optimally
data-efficient
algorithms.
Molecular Systems Design & Engineering,
Год журнала:
2024,
Номер
10(2), С. 89 - 101
Опубликована: Дек. 24, 2024
We
develop
a
physics-informed
machine
learning
workflow
that
accelerates
multicomponent
phase-coexistence
calculations
on
the
number,
composition,
and
abundance
of
phases.
The
is
demonstrated
for
systems
described
by
Flory–Huggins
theory.