MC 5417
Candidate
Nathan Braniff | Applied Math, University of Waterloo
Title
Characterization of Biological Systems using Time-Varying Optogenetic Stimuli
Abstract
In
traditional
engineering
domains,
mathematical
modeling
has
been
used
to
guide
the
design
process
and
decrease
the
experimental
effort
required
to
realize
functioning
products.
Metabolic
engineers
and
synthetic
biologists
have
also
found
models
useful
in
engineering
increasingly
complex
biological
systems.
However,
predictive
models,
especially
of
dynamic
biological
behaviour,
are
difficult
to
build
and
are
limited
by
the
available
data.
Optogenetic
tools
provide
a
promising
approach
for
efficiently
characterizing
the
time-varying
behaviour
of
biological
systems.
Such
tools
enable
precise
temporal
manipulation
of
gene
expression,
and
can
produce
rich
datasets
needed
for
the
calibration
of
dynamic
models.
These
new
experimental
tools
raise
a
key
question:
What
is
the
best
way
to
vary
gene
expression
with
optical
signals
in
order
to
efficiently
obtain
the
data
needed
to
calibrate
a
predictive
model?
My
work
addresses
this
question
in
a
number
of
ways.
I
have
created
a
mechanistic
model
of
the
CcaS/CcaR
optogenetic
system
to
better
understand
and
predict
how
an
optical
signal
is
converted
into
gene
expression
changes.
I
have
also
reconstructed
this
system
in
an
E.
coli
strain
to
enable
further
experimental
work
in
the
lab.
My
ongoing
efforts
focus
on
implementing
Model-based
Design
of
Experiment
(MBDOE)
methods
which
can
select
the
most
informative
time-varying
gene
expression
profile
to
characterize
the
dynamics
of
downstream
systems
controlled
by
CcaS/CcaR.
As
part
of
this
effort,
I
have
constructed
models
of
a
metabolic
pathway
and
some
common
genetic
circuits,
which
we
will
target
with
optimized
experiments
using
CcaS/CcaR.
Together
CcaS/CcaR
and
MBDOE
tools
will
provide
an
improved
approach
to
understanding
these
downstream
systems,
and
allow
us
to
create
more
predictive
models
of
their
dynamic
behaviour.