Hello, and welcome to the Physics World weekly
podcast.
I'm Hamish Johnston.
This episode
features
Amanda Randalls,
who is a computer scientist and biomedical
engineer
at Duke University
in The
US. Duke is also where she began her
scientific career
by studying for an undergraduate degree in physics.
For the past few years, she's been using
physics based
computationally
intense
simulations
of the circulatory
system
to identify signs of heart disease
before symptoms start.
These are signs that show up not only
in clinical tests, but also in data from
wearable devices,
such as smartwatches
that record information
while patients are going about their daily lives.
More recently, Randalls has also become interested
in understanding
how cancer cells
move through the bloodstream
and initiate
metastasis,
which is the process where cancer that started
in one location within the body
spreads to other organs.
In 2024,
the Association for Computing Machinery
awarded Randalls its ACM prize in computing
for her groundbreaking
contributions to algorithms
and high performance
computing methods
aimed at diagnosing
and treating
human diseases.
As well as scientific
prestige
and $250,000
in cash,
This prize comes with an invitation
to the annual
Heidelberg
Laureate Forum,
or HLF,
which brings prize winning researchers
and early career researchers
in computer science and mathematics
to Heidelberg,
Germany
for a week of talks and networking.
My colleague Margaret Harris caught up with Randalls
at last year's h l f.
But interestingly,
this wasn't Randalls first trip to the forum.
She'd previously attended
when she was an early career researcher
herself.
You'll hear her talk about this experience later
in the podcast,
But
first, let's hear about her early interest in
physics
and how it fed into a concept
that's known as a digital twin.
My first question is, how did you get
interested in physics in the first place?
Yeah. I I think basic things of, like,
you know, I was always a kid, you
know, who was always asking lots of questions.
And my parents ended up buying me, like,
that whole set of the, like, ask me
why books to, like, you know, try to
address that.
In high school, I went to a math
and science center for half the day every
day for, the high school programming. And that
was where I, you know, first took some
physics classes and really kind of fell in
love with it and and really liked it.
I had like, the the physics teacher there
was fantastic and and just, I don't know,
made everything really exciting.
So I knew when I went to Duke,
I really wanted to study some kinda like
biophysics or or something in that space.
And then, yeah, I just I I really
liked it when I when I was there.
I I I like the, you know, the
approach of really, you know, how do you
understand something from first principles and and really
get to the answer. And And you're continuing
to apply that sort of physics based approach
to your work now, which is very much
on the board between
computer science, biomedical engineering,
and particularly this concept of the digital twin.
I wonder if you could just tell our
listeners what a digital twin is.
Yeah. So so, basically, like, we're we're reviewing
a digital twin is where it's a virtual
replica of a physical entity that's gonna be
continuously updated with information driving, like, from that
physical entity. So you have, you know, potentially
sensors are gonna be giving continuous feedback to
that entity and then allowing you to draw
something that may, you know, then influence that
entity in the future.
I think this is an idea that actually
has a quite long history in engineering. So,
yeah. This this idea is it's very popular
right now, but it's not it's it's not
anything new. They've been doing it for years.
It's been really popular in civil engineering for
a long time. You see it a lot
in mechanical engineering. So there there's constantly
you know, we see strong examples of using,
you know, digital twins of bridges where you
have sensors all over the bridges to see,
like, what the load is like and getting
that information about, like, that specific bridge. They
had the same thing. You know, many companies
have been doing this for their jet engines
for years.
We're even starting to see it for, like,
supercomputing centers. We have a digital twin at
the supercomputing center to, you know, watch, you
know, the power consumption, cooling, and that that
side of things. So it's pretty pervasive. We're
just we're we're trying to kind of make
use of these advances and use it for
the health side and start trying to make
this digital twin of the human. It it's
been around for a while. Could you give
me an example of what's a digital twin
might how it might work in practice in
your field?
So we're we're really starting with just focusing
on a digital twin of vascular of a
like a vascular digital twin. So we're looking
at your circulatory system, and we're building a
replica. So we need medical imaging. We get
a three d model of your, you know,
coronary arteries, for example.
And
in that case, we then can extract you
know, we get the three d geometry representing
that specific patient's coronary arteries, and then we
get information from, like, wearable devices. So we
can use that to understand, you know, what
is your heart rate, kind of try to
figure out what is what are the flow
properties, and try to assess, you know, what
would be the velocity going into those coronary
arteries and drive the flow simulations from the
data that we're able to get from the
wearables. And then you could imagine as someone's,
you know, going through their daily life,
typically, what a doctor would look at is,
you know, information they can get in the
doctor's office, what they can measure in real
time over just a couple heartbeats.
And what we're trying to do is understand
how is someone's three d blood flow changing
as they go about, you know, a week
or a month to see, you know, not
just, like, did you enter a vulnerable state,
but, you know, how long did you spend
in these vulnerable states, and is that helping
us predict or identify
identify any kind of disease progressions? You could
imagine, you know, someone wearing this for heart
failure and before they ever have symptoms of,
like, you know, shortness of breath or kind
of chest pain, there may be a change
in their hemodynamics
that the digital twin can identify.
And then the doctor might be able to,
like, you know, see that that change has
happened and either, you know, change their medication,
ask them to come in, do something that's
a bit more proactive. So kind of use
it as a way of monitoring the patient
while they're at home. And I guess one
of the things that connects
the examples you gave in civil engineering and
what you're now trying to do with humans
and health care
is that you can't do
infillimen
experiments
on other system, and you can't do things
to the bridge that's gonna destroy it, just
like you can't do anything to a human
that's going to in the severely hormone, unless
there's a commensurate benefit, like you're doing in
face of test, for example. Right. So how
does it work to sort of link
what what's the time scales on which you're
linking the information you're gathering from wearables, you're
gathering from scans, and how are you up
using it to update the digital twin so
you get information about the the actual work?
Yeah. I know. That's a great question. So
the way the way we're doing it is
that we really see it as, you know,
a blending of data from things like the
wearable sensors with the physics based modeling. What
we're trying to set up right now is
we're we've developed this method. We call it
longitudinal hemodynamic mapping framework, and it allows you
to really kind of take a parallel on
time approach to simulating the hemodynamics.
And it allowing like, basically, we're we're we're
getting at, you know, a finite number of
hemodynamic units that we can identify that if
you can piece those back together and and,
you know, figure out it's almost like a
lookup table of, you know, you figure out
what these hemodynamic units are. You use your
wearable data and you figure out which hemodynamic
unit you should be pulling from. What we
need to do then is simulate all of
those hemodynamic units when we first get the
medical imaging. So we're trying to minimize the
amount of time it takes, you know, the
number of hemodynamic units and how efficiently we
can calculate each of those. But by doing
that, you know, it might take
somewhere in the order of, like, fifteen minutes
to half an hour. If it's a really
complicated geometry, maybe a little bit longer to
segment the geometry
and get the data, like, you know, get
that geometry from the medical imaging. Once you
have that, then it can take, you know,
a few hours to maybe a day or
two,
to run all of those hemodynamic units. But
then the goal is then in practice is
once you've run the simulations,
it works like a lookup table. You can
then use that. So then, you know, it
could almost run on the on your devices
or on edge computing,
setups
to have, you know, draw driven from your
wearable device to identify what are those hemodynamic
units and then have algorithms on that that
can can kind of track and see what
the the markers are. And that's, you know,
how we would see it for diagnostics or
for tracking.
But then there's, you know, the other side
of things where, you know, that's assuming we
know what the marker is that we're looking
for. There's a whole another piece where it's
trying to just, you know, do a discovery
phase where we wanna create, save all of
the three d blood flow over six months
of time, do a large scale, you know,
analysis to identify what is that marker that
is changing before you have shortness of breath,
and can we identify what that is? So,
you know, that takes much longer and actually
needs, you know, full simulations, all of the
data retained.
But that allows you to then, you know,
create potential AI surrogates to go on the
edge and pieces like that. So there's there's
kinda two pieces of when you would wanna
deploy it and how you'd really use it
to find new markers.
And I can see so you're talking about
simulating these little chunks of a bloodstream effectively.
It's not not quite sort of individual red
blood cells, but are are are chunks of
the bloodstream.
How much computing power does that currently take
and what challenges does that
present? Like, I mean, a lot of our
goal is to reduce the computing power. So
a lot of this is an interesting,
we have to do, like, the brute force
simulation
to make sure we're, like, you know, we're
keeping everything in that we need to. So
we're it requires a lot of times, like,
the world's largest supercomputers,
but that's only to run, like, the validation
ground truth simulation that we're then using to
test our algorithms against. So we're we're working
you know, we have a lot of different
models to go, you know, longer in time
or deeper,
spatially
that, you know, we run the huge simulation
on the large supercomputer, and then we'll run,
you know, on several nodes in the cloud
and compare against that simulation.
So to to really, you know, put it
into use, you would only need the few
nodes on the cloud. But it's, you know,
critically important to do the testing. And should
note, like, we're pairing that testing with, you
know, testing with Doppler ultrasound and testing with,
you know, implantable devices. So also trying to
make sure it's actually matching the patients as
well in the experimental side, but I think
having that verification with, like, the ground truth
has been important. So, yeah, we we we
do use a lot of the, yeah, like,
the worst biggest supercomputers to run some of
these simulations. And our goal is to try
to, you know, put absolutely everything into the
model so we can figure out what we
can take out and how to pair it
down. And so you do that book out
table every time. Yes.
Yeah. What are some principles that's
that you're using in your work you're looking
for in when you're simulating,
the circulatory system? Yeah. So I guess, fundamentally,
I think the the key piece is, like,
it's not just we're not just looking at,
like, random data points. There's not, like, just
a data driven,
approach. Like, this really truly is a physics
informed,
like, we are running physics based modeling,
where you are getting less, like, three d
blood flow model.
We are taking that three d model that
you're getting out of the the medical imaging
from segmentation.
We use a discretized approach. So we put,
like, a regular like, just a regular Cartesian
grid across that mesh.
We figure out, like, what's an inlet node,
what's an outlet node, and then you're just
solving, you know, very basic fluid dynamics equations
at each of those grid points.
It's really, really, you know, very fine mesh,
so you need, like, you know, so many
grid points you end up needing large scale
supercomputers.
But at the end of the day, you're
just solving you know, we use a lot
of Boltzmann, but it's a way of solving
Navier Stokes. So we're really just solving, you
know, basic fluid equations at each of these
at each of these grid points. And that
allows us to recover,
you know, the three d float profile.
So we can get quantities like vorticity, velocity,
we need pressure, and we can get that
in a three d format. And then we're
combining that with cellular models. So we have
finite element models of, you know, red blood
cells as they're moving through the geometry, how
they're interacting with that fluid.
But it is it's important to have, like,
a physics understanding and, like, a physics based
model of, like, how is that red blood
cell deforming as it's moving through? Where is
it going to adhere? How is it interacting?
And it it it's all coming from the
physics.
I think that's been your your focus for
for several years now. Yeah. And but you're
now actually starting to shift as well while
still working on that to
understanding, like, the biology and the physics impact
of of cancer and what that looks like
in a digital twin sort of simulation.
What's that look like for you? Yeah. So
everything we've done on, like, the heart disease
side, you really don't need to have individual
cells in that case. Like, having bulk fluid,
you know, is enough to recover, like, the
values that we're interested in that are known
biomarkers for heart disease. But by having this
large scale,
kind of framework, you know, we started adding
in red blood cells probably I don't know.
I think, like, 2,009, 2,010.
And, we realized you could use this model
for studying cancer and trying to understand
we we don't look at tumors themselves. We
tend to look at when a cancer cell
has ripped off a primary tumor cell and
is moving through the bloodstream.
We wanna understand what it is about that
cancer cell that is making it choose one
path or the other path. Why is it
spending so much time at certain locations on
the wall? What's gonna make it more likely
to adhere to that location on the wall?
So from that end, it's, you know, it
it's an incredibly computationally intense but very physics
based question. So we, you know, we're trying
to build
accurate models of the cell membrane so we
can, you know, compare to microfluidic devices and
make sure, you know, we're getting those specific
cancer cells accurately.
We're trying to add we have models of,
like, ligand receptor interactions. So we have you
know, one one of the projects we have
going right now is,
having the large scale fluid model to understand
the wall shear stress on, like, the vascular
like, on the endothelium and on the walls,
and then using that to drive these the
receptor expression and understand how that's, you know,
influencing the endothelium and causing different receptors. Like,
trying to, like, predict what that pattern is
going to be based on that wall shear
stress interaction.
And then you have the computational model of
the cancer cell moving through to see where
is it going to here and really try
to, you know, find a way of parsing
the dynamics of, you know, the fluid combination
and how it's affecting the cells and then
how the cells are interacting in that space
and giving you this way. The computational tools
are fantastic and that, you know, we can
control absolutely everything. You can leave everything exactly
the same and tweak just, you know, the
velocity slightly or the bond strength slightly. And
then you can really isolate, you know, how
did that bond strength change
the amount of time you spent at the
wall or your residence time for that cancer
cell. And it really lets us
fine tune test these items that it's harder
to do in an experiment when, you know,
humidity might be changing. Or, like, you know,
there are many other quantities that are changing
that you can't control. So it offers, you
know, a nice compliment to the experiments in
that way. And are you exclusively interested in
this
metastasis process whereby a a cell or a
cancer cell breaks off from the initial tumor
then travels to somewhere else in the bloodstream
to produce a cancer somewhere else? Is that
is that the main how they're focusing it?
Yeah. That that's really what we've looked at
at this point. We're we're starting to branch
a little bit into like, you know, we
we model all the red blood cells. We're
kind of branching a little bit into, you
know, how to what changes with sickle cell
anemia,
like, you know, changes to the red blood
cells themselves.
And even just as you age, how do
the mechanical properties of the red blood cells
change, and how does that affect, you know,
your response to diseases? So we're kind of
branching into, like, the red blood cell side
as well, but a a lot of the
focus has been just on the can we
understand what it is about the cancer cell
that's driving like, the mechanics of the cancer
cell that's driving cancer metastasis or the likelihood
of disease progression.
Thinking about challenges now as you try to
move forward in research, what would you say
the biggest
physics related challenges
are?
Yeah. I think a lot of it has
been, trying to make stable simulations that can
cross spatial and temporal boundaries. So
for the cancer project,
we want to understand
it's not just the deformation of the cancer
cell or the nucleus which requires a high
resolution.
But then we really want these ligand receptor
pairings, but we wanna understand how it how
those, you know, adhesion is changing the behavior
over a centimeter scale or, you know, it'd
be great if we get a mill like,
a a meter scale. But by doing that,
you you know, we've been using, like, this
approach we call, like, adaptive physics refinement. It's
very similar in spirit to, like, adaptive mesh
refinement.
But as soon as you go in that
direction, it's like, you know, how do you
stably couple these different levels of different mesh
refinements and how do you get you know,
in our case, we have one physics model
being used at the the fine grain model,
the coarse grain model, a different physics unit.
And how do you have that couple in
a stable way that is, you know, accurate
and continuing?
We have, like, in the in the coarse
grain model, it's the bulk fluid, which has
a very viscous fluid. So from the even
just the fluid side, it's coupling to, like,
the plasma, which is more like a water.
So you're you're introducing it just a lot
of points of potential instability, and it's, it's
requiring a lot more on the physics and
the algorithm side versus just, you know, we
know the equation. Let's try to make it
faster. And there's there are current limitations in
what the resolution gap between that fine grain
and the coarse grain model can be. And
the more we can push that boundary and
allow that to widen would really help us.
And in terms of computational challenges, what are
the big issues there? I think at the
point at this point, like, data is a
huge issue of, you know, we I keep
saying, like, we're really great at, you know,
the input, the STL file that you're getting
at the triangulated mesh is
probably a couple of kilobytes.
You're getting, you know, a few your input
file is, like, 10 numbers in it. It's,
you know, it's very small data. And then
we are running on the world's biggest supercomputers
and using all of the data, which can
be on the order of a petabyte of
data for one time step. And then you're
trying to run for millions and millions of
time steps. So, you know,
we're generating
petabytes and petabytes of data. Obviously, we can't
store all of that data. So it's, you
know, how do you analyze that data? How
do you make use of it? What do
you store?
It's like data has really become
a a huge part of it. Like, we're
we're doing a lot of work on in
situ machine learning and in situ visualization and
what can we do while the data is
still in memory on the super computer. But
it just trying to figure out, you know,
and that's, you know, just for one patient,
if you wanna start looking across multiple patients,
how do you bring all this together and
actually analyze it in a way that that's
tractable and useful?
It's I think we we've had a lot
of success scaling the code, which is
is great, but that has then led to
this problem of now we have tons of
data, which is, you know, a good problem
to have, but it's it's definitely an issue.
Touch the part more businesses. I know. Yeah.
It's
So part about this, what is something that
you can't do now
that you see coming in the future and
you're really excited about?
Right now,
for the for the right now, for the
digital twins, especially even just on the heart
side,
we've done a lot of work
validating that, like, at the we can match
single time point measures of what you get
in the clinic. And then that's, you know,
a critical first step, really important.
But what we want to see happen is
that we are getting the right data as
you go out into the world and you're
wearing your wearable and we're getting the right
data. And it it looks promising, but we
have a lot of studies going on to,
like, validate that data. You know, we've been
doing a lot of comparisons against him for
heart failure for the an implantable device that
they it measures once a day when you're
laying down, when you're sedentary.
And we can match that very strongly, and
it looks really promising. But now we need
to do it while you're exercising, while you're
moving around and see can we get heart
rate recovery. Are we really capturing what's happening
throughout the day and dynamically? And I think
once we can do that,
it really opens up that's where you're gonna
find the new biomarkers, and that's where you're
gonna be able to identify something that goes
further than what we have now. And it's
you know, we have tons of collaborations in
place to get the data we need from
wearables to be able to make that jump,
But it's still an open question of, like,
what's gonna be the right way of measuring?
Like, you know, for instance, we need stroke
volume. That is an incredibly hard measurement to
get with wearables, and we have many projects
trying to figure out how to do that
accurately. But it's until you really have that
estimation in there, it it's incredibly promising, but
there's still work to be done. So stroke
volume is how much blood gets pulse when
this with a single heartbeat. Yes. Yep. So
we can get heart rate easily, but we
really need cardiac output. And for that, you
need heart rate and stroke volume. And, Usually,
like, a tiny robot inside of the body,
kind of measuring things inside the the artery
or something. Yes. Yep. Yeah.
That would be really helpful.
And talk a little bit about how the
physics that you use in your work. Which
aspect of physics would you say that you
use most? What lesson from your physics education
do you do to keep coming back to?
I feel like it's more just like the
approach of
really, you know, trying to under like, trying
to understand what's going on from the fundamental.
Like, how do you describe the problem?
I don't even know how to describe I
know I've had many conversations where I I
think everybody approaches a problem in that way,
and then you're like, oh, no. No. This
is definitely a physicist. Like, think of mindset.
And it's, I think it's a very systematic
way of breaking down problems and how you
approach it that
is there are so many things that is
like, in biology, there are so many things
that we can't account for or we don't
know. And I know you kind of always
think of the spherical cow of, like, okay.
How close can we get? What do we
need to take into our model? And having
that approach,
really helps us understand,
you know, we're not gonna be able to
take into account everything
for the human body and where can we
make those assumptions, but how do we do
that in an appropriate way? And I think
you kind of learn a lot of that
from the physics education.
So we're talking at the Heidelberg Law Firm
where you were initially a student. What do
you know now that you wish you'd known
back then? Not about the Heidelberg Law Firm,
specifically, but just about what would have helped
you in your career.
Oh, that's interesting.
Things have changed a lot. Yeah. It's like
the things have changed so much, and there's
been I I I think,
I I was always really excited about,
yeah, like, interdisciplinary
research and that side at the time. And
I think just seeing I don't know how
comfortable I would have been going at like,
you know, now we're doing a virtual reality
work. We're doing the wearable work, which I
had no experience with at that stage. So
I think just kind of knowing
to pay more attention to even, like, some
of the experimental data and some of the
talks on, you know, how do you how
like, you know, what where is where are
the drawbacks of these medical imaging platforms? And
I like,
I I I liked going to talks outside
of my field, but really understanding, like, how
useful it was to kind of embrace those
talks that might not be you might not
immediately see the connection to your research, but
it's worth going to and and can really
spark ideas. I think,
yeah, knowing that would have been helpful. And
even just being more comfortable, like, even like,
the students here,
I like, are so comfortable raising their hand
and asking questions. I think I I was
a lot shyer as the person at that
stage. I don't I don't know if I
I really ever like, I I would not
have been comfortable asking that. So I think
just, you know, knowing it's okay if you
don't know the perfect phrase or how to
do it or, like, you know, you don't
have to think through it. It doesn't have
to be perfect to ask the question. I
think it would have been useful to to
realize them.
And thinking about your job at the moment,
again, this is in the context of giving
careers advice to people who are starting out
in their careers,
what do you like best and least about
your your current work, your current job?
Like, I love that I I get to
work on interdisciplinary work. I get to work
with incredibly smart people. The students are amazing.
We're in an engineering building where the hospital
is across the street, so it is easy
to walk over and ask for help. And,
you know, as a physicist, like, going in
and saying, like, this is what we think
would be really useful and having them be
like sometimes they're like, you know, that's not
implementable, and it's cute, but never gonna happen.
So it's it's kind of fun where you
know? And I think a lot of people
complain about writing grants and things sort of
at the same time. It's like, you know,
by writing the grants and doing this, you
get to spend most of your time kind
of daydreaming about what could we do and
what are you know, dreaming big and thinking
about, you know, high risk projects and then
trying to figure out how to get there.
We now have a really big team, so
it's you know, we have enough people to
really try out.
We have a couple of really complicated problems
that you break down and you have, like,
five different people working on different pieces of
it at the same time, and you can
kinda see how that's all gonna come together
when they're done. And that's it's really fun
to kinda put that puzzle together and see
if we can make progress in each of
these pieces.
I I yeah. I I really like the
just getting to brainstorm the research projects and
get to see where we can go, and
they they kind of afford you that opportunity
there, which is is great. And, like, now
we have a new center where we have
we have a whole section on wearables. We
have another section of virtual reality and one
on AI and one on high performance computing.
We have other researchers and other faculty members
kind of bringing that experience in to all
really help push this idea of the digital
twin and kind of having it be bigger
than my lab is a lot more fun
where it's like, let's bring
in other people who have, you know, whole
new ideas and and can go and, like
and there there's people who are just amazing
on the wearable side that are adding whole
new dimensions you we wouldn't have
expected. So that's been really fun.
I'd say on the the negative side is
it is I joined this and kinda went
into this direction because I really love the
physics. I love the coding. I'm the person
who will stay up all night coding, and
I love it. And this is the and,
like, I do not write lines of code
as much anymore. I know some people today
were saying they still code every day.
I do more of you know, I get
to see code in our in our project
meetings.
So I'm I'm still in touch with the
code, but I'm not the person who gets
to run it. And I think that that
that part is a little, like, a little
bit of a change, and and there are
a lot of meetings. So let's say between
those those two things are the kind of
drawbacks.
I think you're the first person I ever
asked that question. You said they actually like
writing Grant post, but I can totally see
what you described as. How what they mean.
Yeah.
Amanda Randalls, thank you very much. It's been
a pleasure to talk with you. Thanks so
much.
That was Amanda Randalls of Duke University
in conversation
with Physics World's Margaret Harris.
Thanks to both of them for coming on
the podcast.
Physics World will be at the APS
Global Physics Summit,
which will take place on March
in Denver, Colorado,
and online.
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And when you're in Denver,
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I'm afraid that's all the time we have
for this week's podcast.
I'm Hamish Johnston, and our producer is Fred
Iles.
Our theme music is called one three seven,
and it was composed and performed
by the physicist
Philip Moriarty.
We'll be back again next week.