Reconstructing Waddington Landscape from Cell Migration and Proliferation
Interdisciplinary Sciences Computational Life Sciences,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 7, 2025
Язык: Английский
Dynamical systems of fate and form in development
Seminars in Cell and Developmental Biology,
Год журнала:
2025,
Номер
172, С. 103620 - 103620
Опубликована: Июнь 3, 2025
Developmental
biology
has
long
drawn
on
dynamical
systems
to
understand
the
diverging
fates
and
emerging
form
of
developing
embryo.
Cell
differentiation
morphogenesis
unfold
in
high-dimensional
gene-expression
spaces
position
spaces.
Yet,
their
stable
reproducible
outcomes
suggest
low-dimensional
geometric
structures-e.g.,
fixed
points,
manifolds,
dynamic
attracting
repelling
structures-that
organize
cell
trajectories
both
This
review
surveys
history
recent
advances
frameworks
for
development.
We
focus
techniques
extracting
organizing
structures
fate
decisions
morphogenetic
movements
from
experiments,
as
well
interconnections.
unifying,
perspective
aids
rationalizing
increasingly
complex
experimental
datasets,
facilitating
principled
dimensionality
reduction
an
integrated
understanding
development,
bridging
typically
distinct
domains.
Язык: Английский
Information content and optimization of self-organized developmental systems
Proceedings of the National Academy of Sciences,
Год журнала:
2024,
Номер
121(23)
Опубликована: Май 31, 2024
A
key
feature
of
many
developmental
systems
is
their
ability
to
self-organize
spatial
patterns
functionally
distinct
cell
fates.
To
ensure
proper
biological
function,
such
must
be
established
reproducibly,
by
controlling
and
even
harnessing
intrinsic
extrinsic
fluctuations.
While
the
relevant
molecular
processes
are
increasingly
well
understood,
we
lack
a
principled
framework
quantify
performance
stochastic
self-organizing
systems.
that
end,
introduce
an
information-theoretic
measure
for
self-organized
fate
specification
during
embryonic
development.
We
show
proposed
assesses
total
information
content
decomposes
it
into
interpretable
contributions
corresponding
positional
correlational
information.
By
optimizing
measure,
our
provides
normative
theory
circuits,
which
demonstrate
on
lateral
inhibition,
type
proportioning,
reaction-diffusion
models
self-organization.
This
paves
way
toward
classification
based
common
language,
thereby
organizing
zoo
implicated
chemical
mechanical
signaling
processes.
Язык: Английский
FateNet: an integration of dynamical systems and deep learning for cell fate prediction
Bioinformatics,
Год журнала:
2024,
Номер
40(9)
Опубликована: Авг. 23, 2024
Abstract
Motivation
Understanding
cellular
decision-making,
particularly
its
timing
and
impact
on
the
biological
system
such
as
tissue
health
function,
is
a
fundamental
challenge
in
biology
medicine.
Existing
methods
for
inferring
fate
decisions
state
dynamics
from
single-cell
RNA
sequencing
data
lack
precision
regarding
decision
points
broader
implications.
Addressing
this
gap,
we
present
FateNet,
computational
approach
integrating
dynamical
systems
theory
deep
learning
to
probe
cell
decision-making
process
using
scRNA-seq
data.
Results
By
leveraging
information
about
normal
forms
scaling
behavior
near
bifurcations
common
many
systems,
FateNet
predicts
occurrence
with
higher
accuracy
than
conventional
offers
qualitative
insights
into
new
of
system.
Also,
through
in-silico
perturbation
experiments,
identifies
key
genes
pathways
governing
differentiation
hematopoiesis.
Validated
different
data,
emerges
user-friendly
valuable
tool
predicting
critical
processes,
providing
complex
trajectories.
Availability
implementation
github.com/ThomasMBury/fatenet.
Язык: Английский
Constructing a holistic map of cell fate decision by hyper solution landscape
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 3, 2024
Summary
The
Waddington
landscape
metaphor
has
inspired
extensive
quantitative
studies
of
cell
fate
decisions
using
dynamical
systems.
While
these
approaches
provide
valuable
insights,
the
intrinsic
nonlinear
complexity
and
parameter
dependence
limits
systematic
analysis
transitions.
Here,
we
introduce
Hyper
Solution
Landscape
(HSL),
a
parameter-independent
methodology
showing
comprehensive
structure
all
possible
configurations
for
gene
regulatory
networks.
Building
on
concept
solution
that
primarily
captures
complete
stationary
points
in
static
landscape,
HSL
connects
different
landscapes
to
reflect
dynamic
changes
associated
with
bifurcations.
Applied
Cross-Inhibition
Self-activation
motif,
identifies
key
hyperparameters
driving
distinct
directional
propensity.
Importantly,
routes
through
between
same
initial
final
states
can
produce
markedly
distributions.
This
enables
rational
design
transition
strategies.
We
validate
HSL’s
utility
seesaw
model
cellular
reprogramming,
establishing
powerful
framework
understanding
engineering
decisions.
Язык: Английский