it’s great to be here Minneapolis my hometown
hometown
hometown [Applause]
movies we live in an amazing digital
movies we live in an amazing digital technology age mobile devices and online
technology age mobile devices and online
technology age mobile devices and online retail are changing the way we live and
retail are changing the way we live and
retail are changing the way we live and work we’re seeing the emergence of
work we’re seeing the emergence of
work we’re seeing the emergence of artificial intelligence and with it the
artificial intelligence and with it the
artificial intelligence and with it the potential for self-driving cars but most
potential for self-driving cars but most
potential for self-driving cars but most importantly we can make cat videos this
importantly we can make cat videos this
importantly we can make cat videos this is my cat salt being cute and I shot
is my cat salt being cute and I shot
is my cat salt being cute and I shot this movie using my phone very proud of
this movie using my phone very proud of
this movie using my phone very proud of that movies are powerful because they
that movies are powerful because they
that movies are powerful because they capture motion and they can tell a story
capture motion and they can tell a story
capture motion and they can tell a story all I could stop there and that would be
all I could stop there and that would be
all I could stop there and that would be the best TED talk ever thank you
the best TED talk ever thank you
the best TED talk ever thank you I didn’t want to tell you about tonight
I didn’t want to tell you about tonight
I didn’t want to tell you about tonight is a story about how we are missing a
is a story about how we are missing a
is a story about how we are missing a great opportunity for using movies to
great opportunity for using movies to
great opportunity for using movies to diagnose and treat cancer and I remember
diagnose and treat cancer and I remember
diagnose and treat cancer and I remember growing up in Minneapolis and having the
growing up in Minneapolis and having the
growing up in Minneapolis and having the opportunity through a summer school
opportunity through a summer school
opportunity through a summer school program to see through a microscope
program to see through a microscope
program to see through a microscope micro organism swimming in pond water
micro organism swimming in pond water
micro organism swimming in pond water that I had collected I became fascinated
that I had collected I became fascinated
that I had collected I became fascinated by biological motion and guess what I
by biological motion and guess what I
by biological motion and guess what I still study it today in my laboratory at
still study it today in my laboratory at
still study it today in my laboratory at the University of Minnesota we study
the University of Minnesota we study
the University of Minnesota we study cancer and in recent years have come to
cancer and in recent years have come to
cancer and in recent years have come to focus on the aggressive brain tumor
focus on the aggressive brain tumor
focus on the aggressive brain tumor known as glioblastoma glioblastoma is a
known as glioblastoma glioblastoma is a
known as glioblastoma glioblastoma is a devastating disease median survival is
devastating disease median survival is
devastating disease median survival is about 15 months despite surgery
about 15 months despite surgery
about 15 months despite surgery chemotherapy radiation and most recently
chemotherapy radiation and most recently
chemotherapy radiation and most recently tumor treating electrical fields Ted
tumor treating electrical fields Ted
tumor treating electrical fields Ted Kennedy Beau Biden Eleanor Mondale all
Kennedy Beau Biden Eleanor Mondale all
Kennedy Beau Biden Eleanor Mondale all succumbed to brain tumors and just this
succumbed to brain tumors and just this
succumbed to brain tumors and just this past summer Senator John McCain was
past summer Senator John McCain was
past summer Senator John McCain was diagnosed with glioblastoma so you might
diagnosed with glioblastoma so you might
diagnosed with glioblastoma so you might think as I did that since cancer is
think as I did that since cancer is
think as I did that since cancer is driven by the dynamic growth and
driven by the dynamic growth and
driven by the dynamic growth and movement of cancer cells within the body
movement of cancer cells within the body
movement of cancer cells within the body and since we have such amazing digital
and since we have such amazing digital
and since we have such amazing digital imaging technology that clinics would be
imaging technology that clinics would be
imaging technology that clinics would be routinely filming cancer patient cells
routinely filming cancer patient cells
routinely filming cancer patient cells surprisingly we don’t instead clinical
surprisingly we don’t instead clinical
surprisingly we don’t instead clinical diagnosis relies on a combination of MRI
diagnosis relies on a combination of MRI
diagnosis relies on a combination of MRI and lab pathology now MRI is incredibly
and lab pathology now MRI is incredibly
and lab pathology now MRI is incredibly powerful in providing spatial detail and
powerful in providing spatial detail and
powerful in providing spatial detail and guiding surgical removal of tumors this
guiding surgical removal of tumors this
guiding surgical removal of tumors this individual was diagnosed with
individual was diagnosed with
individual was diagnosed with glioblastoma underwent surgery chemo
glioblastoma underwent surgery chemo
glioblastoma underwent surgery chemo radiation and entered a Phase two
radiation and entered a Phase two
radiation and entered a Phase two clinical trial which didn’t work and
clinical trial which didn’t work and
clinical trial which didn’t work and within six months had passed away from
within six months had passed away from
within six months had passed away from the disease typically for such patients
the disease typically for such patients
the disease typically for such patients only one or a few scans are collected
only one or a few scans are collected
only one or a few scans are collected because there the scans are quite
because there the scans are quite
because there the scans are quite expensive typically costing thousands of
expensive typically costing thousands of
expensive typically costing thousands of dollars per scan compare that to $800
dollars per scan compare that to $800
dollars per scan compare that to $800 smartphone that can capture thousands of
smartphone that can capture thousands of
smartphone that can capture thousands of movies each having thousands of frames
movies each having thousands of frames
movies each having thousands of frames and in addition can be used for texting
and in addition can be used for texting
and in addition can be used for texting web browsing movie watching gaming and
web browsing movie watching gaming and
web browsing movie watching gaming and even phoning
even phoning
even phoning people still do that MRI in addition to
people still do that MRI in addition to
people still do that MRI in addition to being expensive and capturing pictures
being expensive and capturing pictures
being expensive and capturing pictures not movies does not capture cellular
not movies does not capture cellular
not movies does not capture cellular level information cellular information
level information cellular information
level information cellular information is obtained by so-called histopathology
is obtained by so-called histopathology
is obtained by so-called histopathology where tumor tissue is chemically fixed
where tumor tissue is chemically fixed
where tumor tissue is chemically fixed and stained and viewed through a
and stained and viewed through a
and stained and viewed through a microscope by a highly trained
microscope by a highly trained
microscope by a highly trained pathologist however the method that’s
pathologist however the method that’s
pathologist however the method that’s used today is essentially the same
used today is essentially the same
used today is essentially the same method that existed nearly a hundred
method that existed nearly a hundred
method that existed nearly a hundred years ago meaning that Senator McCain’s
years ago meaning that Senator McCain’s
years ago meaning that Senator McCain’s tumor was diagnosed using the same
tumor was diagnosed using the same
tumor was diagnosed using the same technology that existed when he was born
technology that existed when he was born
technology that existed when he was born in 1936 so I began to wonder might live
in 1936 so I began to wonder might live
in 1936 so I began to wonder might live sell movies be the weapon we’ve been
sell movies be the weapon we’ve been
sell movies be the weapon we’ve been missing in our fight against cancer
missing in our fight against cancer
missing in our fight against cancer might they enable dramatic improvements
might they enable dramatic improvements
might they enable dramatic improvements in our ability to understand the
in our ability to understand the
in our ability to understand the aggressiveness of an individual cancer
aggressiveness of an individual cancer
aggressiveness of an individual cancer patients tumor and enable us to better
patients tumor and enable us to better
patients tumor and enable us to better identify the chemo therapies that will
identify the chemo therapies that will
identify the chemo therapies that will work for that patient in fact we can
work for that patient in fact we can
work for that patient in fact we can make these lifestyle movies this is what
make these lifestyle movies this is what
make these lifestyle movies this is what brain cancer cell invasion looks like
brain cancer cell invasion looks like
brain cancer cell invasion looks like these are proliferating glioblastoma
these are proliferating glioblastoma
these are proliferating glioblastoma cells migrating into small micro
cells migrating into small micro
cells migrating into small micro channels that we’ve fabricated to mimic
channels that we’ve fabricated to mimic
channels that we’ve fabricated to mimic the tight confines of the brain that
the tight confines of the brain that
the tight confines of the brain that cells have to squeeze through in order
cells have to squeeze through in order
cells have to squeeze through in order to invade and we imaged it using a
to invade and we imaged it using a
to invade and we imaged it using a camera chip that’s essentially the same
camera chip that’s essentially the same
camera chip that’s essentially the same as that in my phone directly observing
as that in my phone directly observing
as that in my phone directly observing these dynamics allows us to see cancer
these dynamics allows us to see cancer
these dynamics allows us to see cancer cells and thereby biomechanical outputs
cells and thereby biomechanical outputs
cells and thereby biomechanical outputs so that we can target our therapies
so that we can target our therapies
so that we can target our therapies directly towards those we can even see
directly towards those we can even see
directly towards those we can even see the tracks that cancer cells used to
the tracks that cancer cells used to
the tracks that cancer cells used to invade these green cancer cells are
invade these green cancer cells are
invade these green cancer cells are tracking along red blood vessels as they
tracking along red blood vessels as they
tracking along red blood vessels as they invade into the brain and these dynamics
invade into the brain and these dynamics
invade into the brain and these dynamics are not unique to brain cancer cell
are not unique to brain cancer cell
are not unique to brain cancer cell migration is important for the invasion
migration is important for the invasion
migration is important for the invasion and metastasis of a wide range of
and metastasis of a wide range of
and metastasis of a wide range of cancers when we look at cancer cell
cancers when we look at cancer cell
cancers when we look at cancer cell migration through the microscope we see
migration through the microscope we see
migration through the microscope we see them dynamically extending and
them dynamically extending and
them dynamically extending and retracting protrusions these
retracting protrusions these
retracting protrusions these tentacle-like protrusions formed by
tentacle-like protrusions formed by
tentacle-like protrusions formed by protein self-assembly into filaments and
protein self-assembly into filaments and
protein self-assembly into filaments and that self-assembly pushes the membrane
that self-assembly pushes the membrane
that self-assembly pushes the membrane forward but at the same time elec
forward but at the same time elec
forward but at the same time elec Motors pull the filaments backwards away
Motors pull the filaments backwards away
Motors pull the filaments backwards away from the membrane and toward the cell
from the membrane and toward the cell
from the membrane and toward the cell body those motor forces can be
body those motor forces can be
body those motor forces can be transmitted to the environment through a
transmitted to the environment through a
transmitted to the environment through a set of protein bonds that I will refer
set of protein bonds that I will refer
set of protein bonds that I will refer to as sticky proteins together the
to as sticky proteins together the
to as sticky proteins together the molecular motors act as tiny muscles and
molecular motors act as tiny muscles and
molecular motors act as tiny muscles and the sticky proteins as tiny shoes that
the sticky proteins as tiny shoes that
the sticky proteins as tiny shoes that allow cells to get traction in their
allow cells to get traction in their
allow cells to get traction in their environment to move through it and we’re
environment to move through it and we’re
environment to move through it and we’re now trying to determine what the sticky
now trying to determine what the sticky
now trying to determine what the sticky proteins are in brain cancer so that we
proteins are in brain cancer so that we
proteins are in brain cancer so that we can control them working with my
can control them working with my
can control them working with my University of Minnesota colleague the
University of Minnesota colleague the
University of Minnesota colleague the late John O fest who developed a mouse
late John O fest who developed a mouse
late John O fest who developed a mouse model for glioblastoma we found that the
model for glioblastoma we found that the
model for glioblastoma we found that the level of sticky protein influenced Mouse
level of sticky protein influenced Mouse
level of sticky protein influenced Mouse survival what we found was that mice at
survival what we found was that mice at
survival what we found was that mice at either low or high levels of sticky
either low or high levels of sticky
either low or high levels of sticky protein live longer than mice at middle
protein live longer than mice at middle
protein live longer than mice at middle levels of sticky protein kind of valley
levels of sticky protein kind of valley
levels of sticky protein kind of valley of survival when we looked at how fast
of survival when we looked at how fast
of survival when we looked at how fast cells proliferate we didn’t see any
cells proliferate we didn’t see any
cells proliferate we didn’t see any dependence on sticky protein it was
dependence on sticky protein it was
dependence on sticky protein it was independent of it then we looked at cell
independent of it then we looked at cell
independent of it then we looked at cell migration and this is what we saw you
migration and this is what we saw you
migration and this is what we saw you can see the tumor mass in the lower left
can see the tumor mass in the lower left
can see the tumor mass in the lower left and individual cancer cells migrating
and individual cancer cells migrating
and individual cancer cells migrating away from the tumor into the surrounding
away from the tumor into the surrounding
away from the tumor into the surrounding brain tissue when we quantified the rate
brain tissue when we quantified the rate
brain tissue when we quantified the rate at which they’re moving
at which they’re moving
at which they’re moving we found an inverse correlation fastest
we found an inverse correlation fastest
we found an inverse correlation fastest migration where there was poor survival
migration where there was poor survival
migration where there was poor survival you can see that there’s a peak in
you can see that there’s a peak in
you can see that there’s a peak in migration that coincides with a valley
migration that coincides with a valley
migration that coincides with a valley of survival as indicated by the red
of survival as indicated by the red
of survival as indicated by the red arrow then we looked at human data each
arrow then we looked at human data each
arrow then we looked at human data each point on this graph represents a single
point on this graph represents a single
point on this graph represents a single cancer patient and they’re plotted on
cancer patient and they’re plotted on
cancer patient and they’re plotted on the graph by the stickiness level they
the graph by the stickiness level they
the graph by the stickiness level they had and how long they survived after
had and how long they survived after
had and how long they survived after diagnosis and you can see how variable
diagnosis and you can see how variable
diagnosis and you can see how variable stickiness is amongst the patients some
stickiness is amongst the patients some
stickiness is amongst the patients some are low some are medium some are high so
are low some are medium some are high so
are low some are medium some are high so stickiness good or bad well if we start
stickiness good or bad well if we start
stickiness good or bad well if we start at the low level of stickiness on the
at the low level of stickiness on the
at the low level of stickiness on the left and follow the black curve survival
left and follow the black curve survival
left and follow the black curve survival gets worse as stickiness increases but
gets worse as stickiness increases but
gets worse as stickiness increases but then it bottoms out and rises again it
then it bottoms out and rises again it
then it bottoms out and rises again it gets better again it means that we’re
gets better again it means that we’re
gets better again it means that we’re survival is at the middle level of
survival is at the middle level of
survival is at the middle level of stickiness just like the mouse then we
stickiness just like the mouse then we
stickiness just like the mouse then we looked at another human data set
looked at another human data set
looked at another human data set bigger than this one same result middle
bigger than this one same result middle
bigger than this one same result middle stickiness worse survival so together
stickiness worse survival so together
stickiness worse survival so together the mouse data and the human data told
the mouse data and the human data told
the mouse data and the human data told us that the worst survival is at the
us that the worst survival is at the
us that the worst survival is at the middle stickiness level and in the mouse
middle stickiness level and in the mouse
middle stickiness level and in the mouse that’s where cell migration was fastest
that’s where cell migration was fastest
that’s where cell migration was fastest in fact this is what our theory had
in fact this is what our theory had
in fact this is what our theory had predicted built we built a cell
predicted built we built a cell
predicted built we built a cell migration simulator using a simple
migration simulator using a simple
migration simulator using a simple biophysical rules and we built from
biophysical rules and we built from
biophysical rules and we built from earlier seminal work by Doug Laughlin
earlier seminal work by Doug Laughlin
earlier seminal work by Doug Laughlin Berger now at MIT and his students and
Berger now at MIT and his students and
Berger now at MIT and his students and this is what the model looks like here
this is what the model looks like here
this is what the model looks like here you can see individual protrusions
you can see individual protrusions
you can see individual protrusions extend and retract according to Newton’s
extend and retract according to Newton’s
extend and retract according to Newton’s laws of motion and the individual sticky
laws of motion and the individual sticky
laws of motion and the individual sticky bond proteins engage and break under
bond proteins engage and break under
bond proteins engage and break under load as determined by random number
load as determined by random number
load as determined by random number generator on the computer and with this
generator on the computer and with this
generator on the computer and with this we could predict how fast cells migrate
we could predict how fast cells migrate
we could predict how fast cells migrate as a function of the stickiness level on
as a function of the stickiness level on
as a function of the stickiness level on the left at low stickiness cells send
the left at low stickiness cells send
the left at low stickiness cells send out protrusions but they don’t stick
out protrusions but they don’t stick
out protrusions but they don’t stick very well they slip as if they’re on ice
very well they slip as if they’re on ice
very well they slip as if they’re on ice in a Minneapolis winter tough break kid
in a Minneapolis winter tough break kid
in a Minneapolis winter tough break kid it’s happened to me on the right you can
it’s happened to me on the right you can
it’s happened to me on the right you can see the protrusions go out it’s as if
see the protrusions go out it’s as if
see the protrusions go out it’s as if they’re stuck they can’t let go in any
they’re stuck they can’t let go in any
they’re stuck they can’t let go in any direction it’s as if they’re bogged down
direction it’s as if they’re bogged down
direction it’s as if they’re bogged down in mud in all directions too bad for mud
in mud in all directions too bad for mud
in mud in all directions too bad for mud guy but in the middle these protrusions
guy but in the middle these protrusions
guy but in the middle these protrusions are just can get enough grip but not so
are just can get enough grip but not so
are just can get enough grip but not so much that they can’t also let go which
much that they can’t also let go which
much that they can’t also let go which allows them to move fast
allows them to move fast
allows them to move fast kind of like Fitness Man here so we’ve
kind of like Fitness Man here so we’ve
kind of like Fitness Man here so we’ve got three cases low medium and high but
got three cases low medium and high but
got three cases low medium and high but only in the middle case is fast and
only in the middle case is fast and
only in the middle case is fast and that’s exactly what we saw with the
that’s exactly what we saw with the
that’s exactly what we saw with the mouse data to move fast you need to have
mouse data to move fast you need to have
mouse data to move fast you need to have both the muscles to power you and the
both the muscles to power you and the
both the muscles to power you and the right Footwear to match the track that
right Footwear to match the track that
right Footwear to match the track that you’re on so what does all that mean for
you’re on so what does all that mean for
you’re on so what does all that mean for cancer patients our solid migration
cancer patients our solid migration
cancer patients our solid migration simulator would predict for the clinical
simulator would predict for the clinical
simulator would predict for the clinical trial of a stickiness protein blocking
trial of a stickiness protein blocking
trial of a stickiness protein blocking drug that patients in the middle level
drug that patients in the middle level
drug that patients in the middle level would actually do well with this drug
would actually do well with this drug
would actually do well with this drug the reason is they’re at their peak
the reason is they’re at their peak
the reason is they’re at their peak performance optimal stickiness and if we
performance optimal stickiness and if we
performance optimal stickiness and if we block it we knock them off their game
block it we knock them off their game
block it we knock them off their game and they start to slip as if there are
and they start to slip as if there are
and they start to slip as if there are nice so that’s good but if we take the
nice so that’s good but if we take the
nice so that’s good but if we take the patients throughout low stickiness to
patients throughout low stickiness to
patients throughout low stickiness to begin with
begin with
begin with and treat them they were already
and treat them they were already
and treat them they were already slipping on ice so probably no effect
slipping on ice so probably no effect
slipping on ice so probably no effect but think about the third case the
but think about the third case the
but think about the third case the patients at high stickiness level if we
patients at high stickiness level if we
patients at high stickiness level if we don’t completely block them and drive
don’t completely block them and drive
don’t completely block them and drive all the way through that peak they might
all the way through that peak they might
all the way through that peak they might end up in the middle and get worse and
end up in the middle and get worse and
end up in the middle and get worse and now if you take all three of those cases
now if you take all three of those cases
now if you take all three of those cases together no effect got better got worse
together no effect got better got worse
together no effect got better got worse an average them no effect overall in
an average them no effect overall in
an average them no effect overall in fact recently a phase three clinical
fact recently a phase three clinical
fact recently a phase three clinical trial was completed for a stickiness
trial was completed for a stickiness
trial was completed for a stickiness protein blocking drug for glioblastoma
protein blocking drug for glioblastoma
protein blocking drug for glioblastoma patients now phase three clinical trial
patients now phase three clinical trial
patients now phase three clinical trial is the most expensive trial you can do
is the most expensive trial you can do
is the most expensive trial you can do it builds on years of research and
it builds on years of research and
it builds on years of research and typically costs altogether on the order
typically costs altogether on the order
typically costs altogether on the order of 1 billion dollars that’s for
of 1 billion dollars that’s for
of 1 billion dollars that’s for reference equal to all of the budget
reference equal to all of the budget
reference equal to all of the budget that’s been allocated towards the cancer
that’s been allocated towards the cancer
that’s been allocated towards the cancer moonshot that trial failed and sadly we
moonshot that trial failed and sadly we
moonshot that trial failed and sadly we would have predicted it would have
would have predicted it would have
would have predicted it would have failed and the reason is the trial did
failed and the reason is the trial did
failed and the reason is the trial did not discriminate between low medium and
not discriminate between low medium and
not discriminate between low medium and high stickiness levels instead they
high stickiness levels instead they
high stickiness levels instead they treated all the patients of the same we
treated all the patients of the same we
treated all the patients of the same we would have recommended treating the
would have recommended treating the
would have recommended treating the middle group mean that patients need to
middle group mean that patients need to
middle group mean that patients need to be classified and treatments
be classified and treatments
be classified and treatments personalized based on the predictions of
personalized based on the predictions of
personalized based on the predictions of the simulator in recent years simulators
the simulator in recent years simulators
the simulator in recent years simulators have become commonplace airplanes
have become commonplace airplanes
have become commonplace airplanes chemical plants computer chips they’re
chemical plants computer chips they’re
chemical plants computer chips they’re all extensively simulated according to
all extensively simulated according to
all extensively simulated according to known physical chemical rules before the
known physical chemical rules before the
known physical chemical rules before the risk is taken and the investments made
risk is taken and the investments made
risk is taken and the investments made to send up a test plane to actually
to send up a test plane to actually
to send up a test plane to actually build the chemical plant to actually
build the chemical plant to actually
build the chemical plant to actually fabricate the computer chips even
fabricate the computer chips even
fabricate the computer chips even golfers have simulators and this guy’s
golfers have simulators and this guy’s
golfers have simulators and this guy’s grip looks a little wonky to me but
grip looks a little wonky to me but
grip looks a little wonky to me but that’s the beauty the simulator he can
that’s the beauty the simulator he can
that’s the beauty the simulator he can play around with his grip it can be
play around with his grip it can be
play around with his grip it can be wonky he can figure out which clubs work
wonky he can figure out which clubs work
wonky he can figure out which clubs work for him that’s not what is happening in
for him that’s not what is happening in
for him that’s not what is happening in cancer and drug discovery instead a
cancer and drug discovery instead a
cancer and drug discovery instead a trial and error approach has been used
trial and error approach has been used
trial and error approach has been used instead of simulating and the result has
instead of simulating and the result has
instead of simulating and the result has been low success in the clinic about 25
been low success in the clinic about 25
been low success in the clinic about 25 percent success rate in Phase two
percent success rate in Phase two
percent success rate in Phase two clinical trials
clinical trials
clinical trials five percent success rate in brain
five percent success rate in brain
five percent success rate in brain cancer trials and then correspondingly
cancer trials and then correspondingly
cancer trials and then correspondingly high drug costs to pay for the cost of
high drug costs to pay for the cost of
high drug costs to pay for the cost of failure in each and every one of those
failure in each and every one of those
failure in each and every one of those failures so we’ve left not one but two
failures so we’ve left not one but two
failures so we’ve left not one but two major weapons against cancer sitting on
major weapons against cancer sitting on
major weapons against cancer sitting on the table one live cell dynamics from
the table one live cell dynamics from
the table one live cell dynamics from cancer from cancer patients tissue and
cancer from cancer patients tissue and
cancer from cancer patients tissue and two mathematical computational models
two mathematical computational models
two mathematical computational models such as the cell migration simulator
such as the cell migration simulator
such as the cell migration simulator informed by these movies with which to
informed by these movies with which to
informed by these movies with which to predict individual patient prognosis and
predict individual patient prognosis and
predict individual patient prognosis and potential therapeutic strategies the
potential therapeutic strategies the
potential therapeutic strategies the good news is the technology to do that
good news is the technology to do that
good news is the technology to do that exists right now I mentioned my phone
exists right now I mentioned my phone
exists right now I mentioned my phone and the camera chip my lab group
and the camera chip my lab group
and the camera chip my lab group routinely stores and processes terabytes
routinely stores and processes terabytes
routinely stores and processes terabytes of live cell movie data high-intensity
of live cell movie data high-intensity
of live cell movie data high-intensity illuminators such as LEDs and lasers a
illuminators such as LEDs and lasers a
illuminators such as LEDs and lasers a relatively cheap thanks to consumer
relatively cheap thanks to consumer
relatively cheap thanks to consumer demand for video graphic displays and
demand for video graphic displays and
demand for video graphic displays and also for telecom and we use them to make
also for telecom and we use them to make
also for telecom and we use them to make high quality fluorescence movies and
high quality fluorescence movies and
high quality fluorescence movies and what about the simulations they require
what about the simulations they require
what about the simulations they require high performance computing here again
high performance computing here again
high performance computing here again the consumer market comes to the rescue
the consumer market comes to the rescue
the consumer market comes to the rescue thanks to video gaming Thank You gamers
thanks to video gaming Thank You gamers
thanks to video gaming Thank You gamers yes
yes
yes graphical processing units or GPUs were
graphical processing units or GPUs were
graphical processing units or GPUs were built for gamers so you get high-speed
built for gamers so you get high-speed
built for gamers so you get high-speed graphics from highly parallel processing
graphics from highly parallel processing
graphics from highly parallel processing well guess what that technology is now
well guess what that technology is now
well guess what that technology is now driving self-driving cars and
driving self-driving cars and
driving self-driving cars and self-driving cars rely on the two things
self-driving cars rely on the two things
self-driving cars rely on the two things I mentioned for cancer movies plus
I mentioned for cancer movies plus
I mentioned for cancer movies plus models to predict and control car
models to predict and control car
models to predict and control car behavior and complex dynamic
behavior and complex dynamic
behavior and complex dynamic environments this is exactly the
environments this is exactly the
environments this is exactly the approach that is not happening in cancer
approach that is not happening in cancer
approach that is not happening in cancer and drug discovery and development
and drug discovery and development
and drug discovery and development without even realizing it by making our
without even realizing it by making our
without even realizing it by making our cat videos by watching movies online by
cat videos by watching movies online by
cat videos by watching movies online by playing video games you’ve helped set
playing video games you’ve helped set
playing video games you’ve helped set the stage for a new era of cancer when
the stage for a new era of cancer when
the stage for a new era of cancer when driven by live cell movies and
driven by live cell movies and
driven by live cell movies and mathematical computational modeling so
mathematical computational modeling so
mathematical computational modeling so here in Minneapolis
here in Minneapolis
here in Minneapolis at the University of Minnesota we are
at the University of Minnesota we are
at the University of Minnesota we are doing it with funding from the NIH s
doing it with funding from the NIH s
doing it with funding from the NIH s National Cancer Institute for new
National Cancer Institute for new
National Cancer Institute for new physical sciences in oncology Center we
physical sciences in oncology Center we
physical sciences in oncology Center we are collecting live cell data and using
are collecting live cell data and using
are collecting live cell data and using it to inform our biophysical models
it to inform our biophysical models
it to inform our biophysical models working with surgeons and oncologists we
working with surgeons and oncologists we
working with surgeons and oncologists we are currently enrolling patients at the
are currently enrolling patients at the
are currently enrolling patients at the University of Minnesota Hospital to
University of Minnesota Hospital to
University of Minnesota Hospital to collect their tumor tissue and use it
collect their tumor tissue and use it
collect their tumor tissue and use it for our for our research but the thing
for our for our research but the thing
for our for our research but the thing we do that’s different that’s not
we do that’s different that’s not
we do that’s different that’s not standard in the clinic where the first
standard in the clinic where the first
standard in the clinic where the first step is to either kill or discard that
step is to either kill or discard that
step is to either kill or discard that tumor tissue we try to keep it alive as
tumor tissue we try to keep it alive as
tumor tissue we try to keep it alive as best we can so here we’re more like an
best we can so here we’re more like an
best we can so here we’re more like an organ transplant surgeon but instead of
organ transplant surgeon but instead of
organ transplant surgeon but instead of transplanting to a recipient patient we
transplanting to a recipient patient we
transplanting to a recipient patient we transplant the tissue from the operating
transplant the tissue from the operating
transplant the tissue from the operating room to our microscope where we collect
room to our microscope where we collect
room to our microscope where we collect live cell movies overnight in
live cell movies overnight in
live cell movies overnight in biologically realistic environments in
biologically realistic environments in
biologically realistic environments in many cases while the patient is still in
many cases while the patient is still in
many cases while the patient is still in recovery from surgery we view tumor
recovery from surgery we view tumor
recovery from surgery we view tumor tissue as a vital precious living entity
tissue as a vital precious living entity
tissue as a vital precious living entity that potentially contains valuable
that potentially contains valuable
that potentially contains valuable dynamic information within it finally we
dynamic information within it finally we
dynamic information within it finally we use that live cell data to inform our
use that live cell data to inform our
use that live cell data to inform our simulators to predict patient outcomes
simulators to predict patient outcomes
simulators to predict patient outcomes and in the process we aim to transform
and in the process we aim to transform
and in the process we aim to transform oncology driven by an engineering
oncology driven by an engineering
oncology driven by an engineering approach that fully leverages the
approach that fully leverages the
approach that fully leverages the exponential growth of digital imaging
exponential growth of digital imaging
exponential growth of digital imaging computing and physics based modeling to
computing and physics based modeling to
computing and physics based modeling to stop cancer in its tracks
stop cancer in its tracks
stop cancer in its tracks thank you
thank you
thank you [Applause]
”
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