Kind: captions
Language: en
the following is a conversation with
manolis kellis
he's a professor at mit and head of the
mit computational biology group
he's interested in understanding the
human genome from a computational
evolutionary biological and other
cross-disciplinary perspectives
he has more big impactful papers and
awards than i can list
but most importantly he's a kind curious
brilliant
human being and just someone i really
enjoy talking to
his passion for science and life in
general is contagious
the hours honestly flew by and i'm sure
we'll talk again on this podcast soon
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here's my conversation with manolis
kellis
what to use the most beautiful aspect of
the human genome
don't get me started
so we got time um the first answer is
that the beauty of genomes transcends
humanity so it's not just about the
human genome
genomes in general are amazingly
beautiful and again i'm obviously biased
so um in my view uh
the way that i like to introduce the
human genome and the way that i'd like
to introduce genomics to my class
is by telling them you know we're not
the inventors of the first digital
computer
we are the descendants of the first
digital computer
basically life is digital and that's
absolutely beautiful about life the fact
that
at every replication step you don't lose
any information because that information
is digital
if it was analog it was just protein
concentrations
you'd lose it after a few generations it
would just dissolve away
and that's what the ancients didn't
understand about inheritance
the first person to understand digital
inheritance was
mendel of course and his theory in fact
stayed in a bookshelf for like 50 years
while darwin was
getting famous about natural selection
but the missing component was this
digital inheritance
the mechanism of evolution that mendel
had discovered
so that aspect in my view is the most
beautiful aspect but it transcends all
of life
and can you elaborate maybe the
inheritance part
what was the what was the key thing that
the ancients didn't understand
so the very theory of inheritance uh
as discrete units you know throughout
the life
of mendel and well after his writing
people thought that
his p experiments were just a little
fluke that they were just
you know a little exception that would
normally not even apply
to humans that basically what they saw
is this continuum of eye color
this continuum of skin color this
continuum of hair color this continuum
of height
and all of these continuums did not fit
with a discrete type of inheritance that
mendel was describing
but what's unique about genomics and
what's unique about the genome is really
that there are
two copies and that you get a
combination of these
but for every trait there are dozens of
contributing variables
and it was only ronald fisher in the
20th century
that basically recognized that
even five mendelian traits would add up
to a continuum-like inheritance pattern
and he you know wrote a series of papers
that still are very relevant today
about sort of this mendelian inheritance
of continuum like traits
and i think that that was the missing
step in inheritance
so well before the discovery of the
structure of dna which is again another
amazingly beautiful aspect
the double helix what i like to call the
most noble molecule over time
is uh you know holds within it the
secret of that discrete inheritance
but the conceptualization of discrete
you know
elements is something that precedes that
so even though it's discrete
when it uh materializes itself
into actual traits that we see it can be
continuous it can
basically arbitrarily rich and complex
so if you have five
genes that contribute to human height
and there aren't five there's a thousand
if there's only five genes and you
inherit some combination of them
and everyone makes you two inches taller
or two inches shorter
it'll look like a continuum trait a
continuous trait
but instead of five there are thousands
and every one of them contributes to
less than one millimeter we change in
height
more during the day than each of these
genetic variants contributes
so by the evening you're shorter than
you were you woke up with isn't it weird
then
that we're not more different than we
are why are we all so similar
if there's so much possibility to be
different yeah
so so there are selective advantages
to being medium if you're extremely tall
or extremely short
you run into selective disadvantages so
you have trouble breathing you have
trouble running you have trouble
sitting if you're too tall if you're too
short you might i don't know
have other selective pressures are
acting against that if you look at
natural history of human population
there's actually selection for height
in northern europe and selection against
height in southern europe
so there might actually be advantages to
actually being
not not super tall and if you look
across the entire human population
you know for many many traits there's a
lot of push towards the middle
uh balancing selection is you know the
usual term
for selection that sort of seeks to not
be extreme
and to sort of have a combination of
alleles
that sort of you know keep recombining
and if you look at
you know mate selection super super tall
people
will not tend to sort of marry super
super tall people very often you see
these couples that are kind of
compensating for each other
and the best predictor of the kids age
is very often just take the average of
the
two parents and then adjust for sex and
boom you get it it's extremely heritable
let me ask uh
you kind of uh took a step back to the
genome
outside of just humans but is there
something that you find beautiful about
the human genome specifically
so i think that genome if more people
understood the beauty of the human
genome there would be so many fewer wars
so much less anger in the world i mean
what's really beautiful about the human
genome is
really the variation that teaches us
both about individuality
and about similarity so any two people
on the planet
are 99.9 identical
how can you fight with someone who's
99.9
identical to you it's just
counterintuitive
and yet any two siblings of the same
parent
differ in millions of locations so every
one of them
is basically two to the million unique
from any pair of parents
let alone any two random parents on the
planet so
that's i think something that teaches us
about sort of the nature of humanity in
many ways
that every one of us is as unique as any
star
and way more unique in actually many
ways and
uh yet we're all brothers and sisters
and yeah just like stars most of it is
just uh
fusion uh reactions yeah you only have a
few parameters to describe stars
you know mass size initial size and you
know stage of life
whereas for humans it's you know
thousands of parameters
scattered across our genome so the other
thing that makes humans unique
the other things that makes inheritance
unique in humans
is that most species
inherit things vertically basically
instinct
is a huge part of their behavior the way
that
you know i mean with my kids we've been
watching this
nest of birds with two little eggs
you know outside our window for the last
few months uh for the last few weeks as
they've been growing and there's so much
behavior
that's hard-coded birds don't just learn
as
they grow they don't you know there's no
culture like
a bird that's born in boston will be the
same as a bird that's born in california
so there's not as much um
inheritance of ideas of customs a lot of
it is hard-coded in their genome
what's really beautiful about the human
genome is that if you take a person from
today
and you place them back in ancient egypt
or if you take a person from ancient
egypt and you
place them here today they will grow up
to be completely normal
that is not genetics this is
the other type of inheritance in humans
so on one hand we have genetic
inheritance
which is vertical from your parents down
on the other hand we have horizontal
inheritance which is
the ideas that are built up at every
generation
are horizontally transmitted and the
huge amount of time that we spend
in educating ourselves a concept known
as
niotini neo for newborn and then tenney
for
holding so if you look at humans i mean
the little birds they were you know eggs
two weeks ago and that
now one of them has already flown off
the other one's ready to fly off
in two weeks they're ready to just fend
for themselves humans
16 years 18 years 24 getting out of
college
i'm still learning so so that's so
fascinating the this
picture of a vertical in the horizontal
i when you talk about the horizontal is
it in the realm of ideas
exactly okay so it's the actual social
interactions and that's exactly right
that's exactly right so basically the
concept of neotimi
is that you spend acquiring
characteristics from your environment
in an extremely malleable state of your
brain and the wiring of your brain
for a long period of your life compared
to primates
we are useless you take any primate at
seven weeks
and in human at seven weeks we lose the
battle
but at eighteen years you know all bets
are off like we
basically our brain continues to develop
in an extremely malleable form
until very late and this is what allows
education
this is what allows the person from
egypt to do extremely well
now and the reason for that is that
the wiring of our brain
and the development of that wiring is
actually delayed
so you know the longer you delay that
the more opportunity you have
to pass on knowledge to pass on concepts
ideals
ideas from the parents to the child
and what's really absolutely beautiful
about humans today is that that lateral
transfer of ideas and culture
is not just from uncles and aunts and
teachers at school
but it's from wikipedia and review
articles on the web
and thousands of journals that are sort
of putting out
information for free and podcasts and
videocasts
and all of that stuff where you can
basically learn
about any topic pretty much
everything that would be in any super
advanced textbook
in a matter of days instead of having to
go
to the library of alexandria and sail
there to read three books and then sail
for another few days to get to athens
and
et cetera et cetera so the
democratization of knowledge
and the spread the speed of spread of
knowledge is what
defines i think the human inheritance
pattern
so you sound excited about it about it
are you also
a little bit afraid or you're more
excited by the power of this kind of
distributed spread of information so you
put it very kindly that most people are
kind of using the internet
in uh you know looking wikipedia reading
articles reading papers and so on
but uh if we if we're honest most people
online especially when they're younger
probably looking at
five second clips on tick tock or
whatever the new social network is
are you um given this power of
horizontal inheritance
are you optimistic or a little bit
pessimistic
about the this new
effect of the internet and
democratization of knowledge on our
on our what would you call this this
geno like would you would you use the
term genome by the way
yeah i think um you know we use the
genome to talk about
dna but very often we say you know i
mean i'm greek so people ask me hey
what's in the greek genome
and i'm like well yeah what's in the
greek genome is both our genes and also
our ideas
and our ideals and our culture so the
poetic meaning of the word exactly
exactly yeah yeah
so i think that um
there's a beauty to the democratization
of knowledge
the fact that you can reach as many
people as
you know any other person on the planet
and it's not who you are
it's really your ideas that matter is
a beautiful aspect of the internet the
[Music]
i think there's of course a danger of my
ignorance
is as important as your expertise the
fact that
uh with this democratization comes the
abolishment
of respecting expertise just because
you've spent
you know 10 000 hours of your life
studying
i don't know human brain circuitry
why should i trust you i'm just going to
make up my own theories and they'll be
just as good as yours
it's an attitude that that sort of
counteracts the beauty
of the democratization and i think that
within our educational system
and within the upbringing of our
children we have to not only
teach them knowledge but we have to
teach them the means
to get to knowledge and that you know
it's very similar to sort of you
fish you catch a fish for a man for one
day you fed them for one day you teach
them how to fish
you fed them for the rest of their life
so instead of just gathering the
knowledge they need
for any one task we can just tell them
all right here's how you google it
here's how to figure out what's real and
what's not here's how you check the
sources
here's how you form a basic opinion for
yourself and i think that
inquisitive nature is paramount
to being able to sort through this huge
wealth of knowledge so you need a basic
educational foundation
based on which you can then add on
the sort of domain specific knowledge
but that basic educational foundation
should just
just not just be knowledge but it should
also be
epistemology the way to acquire
knowledge
i'm not sure any of us know how to do
that in this modern day we're
actually learning one of the big
surprising thing to me about the
the coronavirus for example is that
twitter has been
one of the best sources of information
basically like building your own network
of experts
of of uh you know as opposed to the
traditional
centralized expertise of the who and the
cdc and the
or um or maybe any one particular
respectable person at the top of a
department in some kind of institution
you instead look at a
you know 10 20 hundreds of people
some of whom are young kids with
just that are incredibly good at
aggregating data and plotting and
visualizing that data
that's been really surprising to me i
don't know what to make of it
i don't know i don't know how that
matures into something stable
you know i don't know if you have ideas
like what if you were to try to explain
to your kids
of how where should you go to learn
about the
about coronavirus what would you say
it's such a beautiful example and i
think uh the current pandemic and the
the
speed at which the scientific community
has moved in the current pandemic i
think exemplifies
this horizontal transfer and the speed
of horizontal transfer of information
the fact that you know the genome was
first sequenced
in early january the first sample was
obtained december 29
2019 a week after the publication of the
first
genome sequence moderna had already
finalized his vaccine design
and was moving to production i mean this
is
uh phenomenal the fact that we go from
not knowing what the heck is killing
people in wuhan to
wow it's starscore v2 and here's the set
of genes here's the genome
here's the sequence here the
polymorphisms et cetera in the matter of
weeks
is phenomenal in that incredible pace
of transfer of knowledge there have been
many mistakes
so you know some of those mistakes may
have been politically motivated our
other mistakes may have just been
innocuous errors
others may have been misleading the
public for the greater good
such as don't wear masks because we
don't want the mask to run out i mean
that was very silly in my view and
a very big mistake but the
the spread of knowledge from the
scientific community was phenomenal
and some people will point out to bogus
articles that snuck in and made the
front page
yeah they did but within 24 hours they
were debunked
and went out of the front page and i
think that's that's the beauty of
science today
the fact that it's not oh knowledge is
fixed
it's the ability to embrace that nothing
is permanent
when it comes to knowledge that
everything is the current best
hypothesis
and the current best model that best
fits the current data
and the willingness to be wrong the
expectation
that we're going to be wrong and the
celebration of success based on how long
was i not proven wrong for
rather than wow i was exactly right
because no one is going to be exactly
right with partial knowledge
but the arc towards perfection
i think so much more important than
how far you are on your first step and i
think that's what sort of
the current pandemic has taught us the
fact that yeah no of course we're gonna
make mistakes
but at least we're going to learn from
those mistakes and become better and
learn better and spread information
better so
if i were to answer the question of
where would you go to learn
about coronavirus first textbook
it all starts with a textbook just open
up a chapter on virology and how
coronaviruses work
then some basic epidemiology and sort of
how
pandemics have worked in the past what
are the basic principles surrounding
these first wave second wave why do they
even exist
then understanding about growth
understanding about the are not
and rt at you know various time points
and then understanding the means of
spread how it spreads from person to
person
then how does it get into your cells
from
when it gets into the cells what are the
paths that it takes what are the cell
types that express
the particular h2 receptor how is your
immune system interacting with the virus
and once your immune system launches
your defense how is that helping
or actually hurting your health what
about the cytokine storm what are most
people dying from
why are the comorbidities and these risk
factors
even applying what makes obese people
respond more or elderly people respond
more to the virus
while kids are completely you know
you know very often not even aware that
they're spreading it
so the you know i think there's some
basic
questions that you would start from and
then
i'm sorry to say but wikipedia is pretty
awesome yeah google is pretty awesome
so it used to be a time it used to be a
time maybe five years ago i forget
i forget when but people kind of made
fun of wikipedia
for being an unreliable source i never
quite understood it
i thought from the early days it was
pretty reliable or better than
a lot of the alternatives but at this
point it's kind of like
a solid accessible survey paper on every
subject ever
the there's an ascertainment bias and a
writing bias
so so i think this this is related to
sort of people saying oh
so many nature papers are wrong and
they're like why would you publish in
nature so many nature papers are wrong
and
my answer is no no no so many nature
papers are scrutinized
and just because more of them are being
proven wrong than in other articles
is actually evidence that they're
actually better papers overall because
they're being scrutinized at a rate
much higher than any other journal so if
you basically
uh judge wikipedia by
not the initial content by but by the
number of revisions
yeah then of course it's going to be the
best source of knowledge eventually
it's still very superficial you then
have to go into the review papers etc
etc but i mean for most scientific
project
topics it's extremely superficial but it
is quite authoritative because it is the
place that everybody likes to criticize
you as being wrong you say that it's
superficial on a lot of topics that
i'm i've studied a lot of i find it
i don't know if superficial is the right
word um
because superficial kind of implies that
it's not correct
no no i don't mean any implication of it
not being correct
it's just superficial it's basically
only scratching the surface
for depth you don't go to wikipedia you
go to the review articles but it can be
profound in the way that articles rarely
one of the frustrating things to me
about
like certain computer science like in
the machine learning world
articles they they don't as often take
the uh the bigger picture view you know
there's a
it's a kind of data set and you show
that it works and you kind of show that
here's an architectural thing that
creates an improvement
and so on and so forth but you don't say
well like what does this mean
for the nature of intelligence for
future data sets we haven't even thought
about
or if you were trying to implement this
like if we took this data set of uh
a hundred thousand examples and scaled
it to a hundred billion examples
with this method like like look at the
bigger picture which is what a wikipedia
article
would actually try to do which is like
what does this mean in the context
of computer the broad field of computer
vision or something like that yeah yeah
and no i i agree with you completely
like but it depends on the topic
i mean for some topics there's been a
huge amount of work for other topics
it's just a stub
so you know i got it yeah well yeah
actually the
uh which we'll talk on genomics was not
yeah it's great very shallow yeah yeah
it's not wrong it's just shallow
yeah every time i criticize something i
should feel partly responsible
basically if more people from my
community went there and edited
it would not be shallow it's just that
there's
different modes of communication in
different fields and in some fields
the experts have embraced wikipedia in
other fields
it's relegated and perhaps the reason is
that
if it was any better to start with
people would invest more time
but if it's not great to start with then
you need a few initial pioneers who will
basically go in and say
ah enough we're just going to fix that
and then i think it'll catch on much
more
so if it's okay before we go on to
genomics can we
linger a little bit longer on the beauty
of the human genome
you've given me a few notes what else
what else do you find beautiful about
the human genome
so the last aspect of what makes a human
genome unique
in addition to the you know
similarity and the differences and
individuality
is that so very early on
people would basically say oh you don't
do that experiment in human you have to
learn about that in fly
or you have to learn about that in yeast
first or in mouse first or in a prime at
first
and the human genome was in fact
relegated to sort of oh the last place
that you
you're going to go to learn something
new that has dramatically changed
and the reason that changed is human
genetics
we are these species
in the planet that's the most studied
right now it's embarrassing to say that
but this was not the case a few years
ago it used to be
you know first viruses then
bacteria then yeast then
the fruit fly and the worm then the
mouse
and eventually human was very far last
so it's embarrassing that it took us
this long to focus on it or the
uh it's embarrassing that the model
organisms have been taken over
because of the power of human genetics
that right now
it's actually simpler to figure out the
phenotype of something
by mining this massive amount of human
data
than by going back to any of the other
species and the reason for that is that
if you look at the natural variation
that happens in a population of 7
billion
you basically have a mutation in almost
every nucleotide
so every nucleotide you want to perturb
you can go find
a living breathing human being and go
test the function of that nucleotide by
sort of searching the database and
finding that person
wait why is that embarrassing it's a
beautiful data set
it's embarrassing for the for the model
organism for the flies
yeah exactly i i mean do you do you feel
on a small tangent is there something of
value
in um in the genome of a fly and other
these
model organisms that you miss that we
wish we
would have uh would be looking at deeper
so
directed perturbation of course so i
think the place where
the the place where humans are still
lagging is the fact that in an animal
model you can go and say well let me
knock out this gene completely
and let me knock out these three genes
completely and i said the moment you get
into combinatorics it's something you
can't do in the human because there just
simply aren't enough humans on the
planet and again let me be honest
we haven't sequenced all seven billion
people it's not like we have
every mutation but we know that there's
a carrier out there so if you look at
the trend
with and the speed with which human
genetics has progressed we can now
find thousands of genes involved
in human cognition in human psychology
in the emotions and the feelings that we
used to think are
uniquely learned turns out there's a
genetic basis to a lot of that
so the uh you know the
the human genome has continued to
elucidate
through these studies of genetic
variation so many different processes
that we previously thought were
you know something that like free will
free will is this beautiful concept that
humans have had for
a long time you know in the end it's
just a bunch of chemical reactions
happening in your brain
and the particular abundance of
receptors that you have
this day based on what you ate yesterday
or that you have been wired with
based on you know your parents and your
upbringing etc
determines a lot of that quote unquote
free will component to
you know sort of narrower and narrower
scale you know sort of
slices so how much uh on that point how
much freedom do you think we have
to escape the the constraints of
our genome you're making it sound like
more and more we're discovering that our
genome is actually has the
a lot of the story already encoded into
it how much freedom do we have
i uh so so
let me let me describe what that freedom
would look like that freedom would be
my saying oh i'm gonna resist the urge
to eat that apple
because i choose not to but
there are chemical receptors that made
me not resist the urge
to prove my individuality and my free
will by resisting the apple so then
the next question is well maybe now i'll
resist the urge to resist the apple and
i'll go for the chocolate instead to
prove my individuality but then
what about those other receptors that
you know
that that might be all encoded in there
so it's kicking the bucket down the road
and basically saying well your choice
will may have actually been driven by
other things that you actually are not
choosing
so that's why it's very hard to answer
that question well it's hard to know
what to do with that i mean if uh if the
genome has
if there's not much freedom it's uh
it's the butterfly effect it's basically
that in the short term
you can predict something extremely well
by knowing the current state of the
system
but a few steps down it's very hard to
predict based on the current knowledge
is that because the system is truly free
when i look at weather patterns i can
predict the next 10 days
is it because the weather it has a lot
of freedom and after 10 days
it chooses to do something else or is it
because in fact the system is fully
deterministic
and there's just a slightly different
magnetic feel of the earth slightly more
energy arriving from the sun a slightly
different spin of the gravitational pull
of jupiter
that is now causing you know all kinds
of tides and slight deviation of the
moon etc maybe all of that can be fully
modeled
maybe the fact that china is emitting a
little more carbon today
is actually going to affect the weather
in you know egypt in three weeks
and all of that could be fully modeled
in the same way
if you take a complete view of a human
being
now you know i model everything about
you
the question is can i predict your next
step probably
but at how far and if it's a little
further
is that because of stochasticity and
sort of chaos
properties of unpredictability of beyond
a certain level
or was that actually true free will yeah
then yeah
so the number of variables might might
be so you might need to uh build an
entire universe
to uh to be able to simulate a human and
then maybe that human will be fully
simulatable
but maybe aspects of free will will
exist and where's that free will coming
from it's still coming from the same
neurons
or maybe from a spirit inhabiting these
neurons but again
you know it's very difficult empirically
to sort of evaluate where does free will
begin
and sort of chemical reactions and
electric signals and you know and
so on that's on that topic let me ask
the most absurd question
uh that uh most mit faculty role their
eyes on but uh
do what do you think about the
simulation hypothesis and the idea that
we live in a simulation
i think it's complete bs
okay there's no empirical evidence no
it's not absolutely not
not in terms of empirical evidence or
not but uh in terms of a
thought experiment does it help you
think about the universe
i mean so if you look at the genome it's
encoding a lot of the information that
is required to create some of the
beautiful human complexity that we see
around us
it's an interesting thought experiment
how much
you know uh parameters do we need to um
have in order to model some you know
this
full human experience like if we were to
build a video game
yeah how hard it would be to build a
video game that's like convincing enough
and fun enough and you know uh
it has consistent laws of physics all
that stuff
it's not interesting to use the stock
experiment i i mean it's cute
but you know it's all comes razor i mean
what's what's more
realistic the fact that you're actually
a machine or that you're you know a
person what's what's
you know the fact that all of my
experiences exist inside the chemical
molecules that i have
or that somebody's actually you know
simulating all that i
mean well you did refer to humans as a
digital computer earlier so
of course of course but that's not kind
of a machine right i know
i know but i i think
the probability of all that is nil and
let the machines wake me up and just
terminate me now if it's not
i challenge your machines they're gonna
they're gonna wait a little bit
to see what you're gonna do next it's
fun it's fun to watch
especially the clever humans what's the
difference to you between the way
a computer stores information and uh the
human genome stores information
so you also have roots and your work
would you say you're when you introduce
yourself at a bar
um it depends who i'm talking
would you say it's computational biology
do you um
do you reveal uh your
expertise in computers it depends who
i'm talking to
truly i mean basically if i meet someone
who's in computers i'll say
oh i mean professor in computer science
if i meet someone who's in engineering i
say computer science and electrical
engineering
if i meet someone in biology i'll say
hey i work in genomics if i meet someone
in medicine i'm like hey i work on you
know
genetics so you're a fun person to meet
at a bar i got you but
so no no but i'm trying to say is that i
i don't
i mean there's no single attribute that
i will define myself as
you know there's a few things i know
there's a few things i study there's a
few things i have degrees on and there's
a few
things that i grant degrees in and
you know i i publish papers across the
whole gamut
you know the whole spectrum of
computation to biology etc i mean
i the complete answer is that
i use computer science to understand
biology
so i'm a you know i develop
methods in ai and machine learning
statistics and algorithms etc
but the ultimate goal of my career is to
really understand biology
if these things don't advance our
understanding of biology
i'm not as fascinated by them although
there are some
beautiful computational problems by
themselves
i've sort of made it my mission to apply
the power of computer science to truly
understand
the human genome health disease
you know and then the whole gamut of how
our brain works how our body works and
all of that
which is so fascinating
so the dream there's not an equivalent
sort of uh
complementary dream of understanding
human biology in order to create an
artificial life
an artificial brain artificial
intelligence that supersedes the
intelligence and the capabilities of us
humans
it's an interesting question it's a
fascinating question so
understanding the human brain is
undoubtedly coupled to how do we make
better ai
because so much of ai has in fact been
inspired
by the brain it may have taken 50 years
since the early days of neural networks
till we have you know all of these
amazing progress
that we've seen with uh
you know deep belief networks and uh
you know all of these advances in go
and chess in image synthesis and deep
vagues
in you name it and but but the
underlying architecture is very much
inspired
by the human brain which actually pauses
a very very interesting question
why are neural networks performing so
well
and they perform amazingly well is it
because they can simulate
any possible function and the answer is
no no they simulate a very small number
of functions
is it because they can simulate every
possible function in the universe
and that's where it gets interesting the
answer is actually yeah a little closer
to that
and here's where it gets really fun uh
if you look at human brain and human
cognition
it didn't evolve in a vacuum it evolved
in a world
with physical constraints
like the world that inhabits us it is
the world that we inhabit
and if you look at our senses
what do they perceive they perceive
different you know
parts of the electromagnetic spectrum
you know
the hearing is just different movements
in air
the the touch etc i mean all of these
things we've built intuitions
for the physical world that we inhabit
and our brains and the brains of all
animals
evolved for that world and
the ai systems that we have built happen
to work well with images
of the type that we encounter in the
physical world that we inhabit
whereas if you just take noise and you
add
random signal that doesn't match
anything in our world neural networks
will not do as well
and that actually um basically
has this whole loop around this which is
this was designed by studying our own
brain
which was evolved for our own world and
they happen to do well
in our own world and they happen to make
the same types of mistakes that humans
make
many times and of course you can
engineer images by adding just the right
amount
of you know sort of pixel deviations to
make a zebra look like a bamboo
and stuff like that or like a table
but ultimately the undoctored images at
least
are very often you know mistaken i don't
know between muffins and dogs for
example
in the same way that humans make those
mistakes so
it's it's on you know there's no doubt
in my view that the more we understand
about the tricks that our human brain
has evolved
to understand the physical world around
us the more we will be able to bring
new computational primitives in our ai
systems
to again better understand not just the
world around us
but maybe even the world inside us and
maybe even the computational problems
that arise from new types of data that
we haven't been exposed
to but are yet inhabiting the same
universe that we live in
with a very tiny little subset of
functions from all possible mathematical
functions
yeah and that small subset of functions
all that matters to us humans really
that's what makes
it's all that has mattered so far and
even within our scientific realm
it's all that seems to continue to
matter but
i mean i always like to think about our
senses
and how much of the physical world
around us
we perceive and if you look at the
um ligo experiment over the last
you know year and a half has been all
over the news what what did lago do
it created a new sense for human beings
a sense that has never been sensed in
the history of our planet
gravitational waves have been traversing
the earth
since its creation a few billion years
ago
life has evolved senses to sense things
that were never before sensed light
was not perceived by early life no one
cared
and eventually photoreceptors evolved
and you know the ability to sense colors
by sort of catching
different parts of that electromagnetic
spectrum
and hearing evolved and touch evolved
etc
but no organism evolved a way to sense
neutrinos floating through earth or
gravitational waves flowing through
earth etc
and i find it so beautiful in the
history of not just humanity but
life on the planet that we are now able
to capture
additional signals from the physical
world than we ever knew before
and axions for example have been all
over the news in the last few weeks
the concept that we can
capture and perceive more of that
physical world
is as exciting as the
fact that we are we were blind to it is
traumatizing
before right because that also tells us
how you know we're in 2020 picture
yourself in 30 20
or in 20 you know what new senses why
might we discover
is it you know could it be that we're
missing
physics that like there's a lot of
physics out there that we're just blind
to completely oblivious to it
yeah and yet they're permeating us all
the time yes it might be right in front
of us
so so when you're thinking about
premonitions
yeah yeah a lot of that is ascertainment
bias like yeah every you know every now
and then you're like oh i remember
my friend and then my friend doesn't
appear and i'll forget that i remember
my friend but every now and then my
friend will actually appear i'm like oh
my god
i thought about you a minute ago you
just called me that's amazing
so you know some of that is this but
some of that might be that
there are within our brain
sensors for waves that we emit
that we're not even aware of and this
whole
concept of when i hug my children
there's such an emotional
transfer there that we don't comprehend
i mean sure yeah of course we're all
like hardwired for all kinds of
touchy-feely things between parents and
kids it's beautiful between partners
it's beautiful etc
but then there are intangible aspects
of human communication that i don't
think it's
unfathomable that our brain has actually
evolved ways and sensors for it
that we just don't capture we don't
understand the function of the vast
majority
of our neurons and maybe our brain is
already sensing it but even worse
maybe our brain is not sensing it at all
and we're in
oblivious to this until we build a
machine that suddenly is able to sort of
capture
so much more of what's happening in the
natural world so what you're saying
is we're going physics is going to
discover a sensor for love
for and maybe maybe dogs are off scale
for that
and we've been oh you know we've been
oblivious to it the whole time because
we didn't have the right answer yeah
and now you're gonna have a little wrist
that says oh my god i feel all this love
in the house
i see i sense a disturbance in the force
all around us and dogs and cats will
have zero none
none
but let's take a step back to
our unfortunately one of the 400 topics
that we had actually planned
[Laughter]
but to our sad time in 2020 when we only
have
just a few sensors and uh very primitive
early computers so in your
you you have a foot in computer science
and a floating biology
in your sense how do
computers represent information
differently than like the genome or
biological systems
so first of all let me uh let me uh
correct
that no we're in an amazing time in 2020
computer science is totally awesome and
physics is totally awesome and we have
understood
so much of the natural world than ever
before
so i am extremely grateful
and feeling extremely lucky to be living
in the time that we are
because you know first of all who knows
when the asteroid will hit
[Laughter]
and second um you know of all times
in humanity this is probably the best
time to be a human being and this might
actually be the best place to be a human
being so anyway you know for for anyone
who loves science this is this is it
this is awesome it's a great time
at the same time just a swift comment
all i meant
is that uh if we look several hundred
years from now and we
end up somehow not uh destroying the uh
ourselves yeah
people will probably look back at this
time in computer science
and uh at your work of minos at mit
i like to joke very often with my
students that you know we've written so
many papers we've published so much
we've been cited so much and every
single time i tell my students you know
the best is ahead of us
what we're working on now is the most
exciting thing
i've ever worked on so in a way i do
have this sense of
yeah even the papers i wrote 10 years
ago they were awesome at the time
but i'm so much more excited about where
we're heading now and i don't mean to
minimize any of the
stuff we've done in the past but you
know there's just
this sense of excitement about what
you're working on now
that as soon as a paper is submitted
it's like ugh
it's old like you know i can't talk
about that anymore at the same time
you're not
you probably are not going to be able to
predict what are the most
uh impactful papers and ideas when
people look back 200 years from now at
your work what
would be the most exciting papers and it
may very well be not the thing that you
expected
or yeah the things you got awards for
or you know this might be true in some
fields i don't know i feel slightly
differently about it in our field i feel
that
i kind of know what what are the
important ones and there's a very big
difference between what the press picks
up on
and what's actually fundamentally
important for the field and i think for
the fundamentally important ones we kind
of have a pretty good idea what they are
and it's hard to sometimes get the press
excited about the fundamental advances
but you know we we take what we get
and celebrate what we get and sometimes
you know one of our papers which was in
a minor journal
made the front page of reddit and
suddenly had like hundreds of thousands
of views
even though it wasn't a minor journal
because you know somebody pitched it the
right way that it suddenly caught
everybody's attention
whereas other papers that are sort of
truly fundamental you know we have a
hard time getting the editors even
excited about them
when so many hundreds of people are
already using the results and building
upon them
so i do i do appreciate that there's a
discrepancy between
the perception and the perceived success
and the awards that you get for various
papers but i think that
fundamentally and know that you know
some people i'm
so so so when you're writing that you're
most proud
you know you just you trapped yourself
no no no no i mean
is there a line of work that you you
have a sense
uh is really powerful that you've done
today you've done so much work in so
many directions which is interesting
um is there something where you you
think is quite special
i i mean it's like asking me to say
which of my three children i love best
i mean
exactly so i mean and it's such a
give me question that it's so so
difficult not to brag about the awesome
work
that my team and my students have done
um
and i'll i'll just mention a few of the
top of my head i mean
basically there's a few landmark papers
that i think have shaped
my scientific path and
you know i like to somehow describe it
as a linear continuation of one thing
led to another led to another led to
another
and you know it kind of all started with
skip skip skip skip skip let me try to
start somewhere in the middle
so my first phd paper was
uh the first comparative analysis of
multiple species
so multiple complete genomes so for the
first time we we basically con
developed the concept of genome-wide
evolutionary signatures the fact that
you could look across the entire genome
and understand how things evolve
and from these signatures of evolution
you could go back
and study any one region and say that's
a
protein coding gene that's an rna gene
that's a regulatory motif
that's a you know binding site and so
forth so
sorry so comparing different different
species
of the same so so i think human mouse
rat and dog
you know they're all animals they're all
mammals they're all performing
similar functions with their heart with
their brain with their lungs etc etc
so there's many functional elements that
make us uniquely
mammalian and those mammalian elements
are actually conserved
99 of our genome does not code for
protein
one percent codes for protein the other
we frankly didn't know what it does
until we started doing these comparative
genomic studies
so basically these series of papers in
in my career
have basically first developed that
concept of evolutionary signatures and
then apply them to yeast
apply them to flies apply them to four
mammals apply them to 17 fungi apply
them to 12 drosophila species
apply them to them 29 mammals and now
200 mammals
so sorry so can we so the evolutionary
signatures this
seems like a such a fascinating idea uh
and we're probably gonna linger in your
early phd work for two hours but uh
what is how can you reveal something
interesting about the genome by looking
at the uh
multiple multiple species
and looking at the evolutionary
signatures yeah like so
so um you basically
uh align the matching regions
so everything evolved from a common
ancestor way way back
and mammals evolved from a common
ancestor about 60 million years back
so after you know the
meteor that killed off the dinosaurs
landed a legend near machu picchu
we know the crater it didn't allegedly
land
that was the aliens okay no just
slightly north of machu picchu
in the gulf of mexico there's a giant
hole that that meteorite by the way
sorry is that uh definitive to people
have people um
um conclusively uh figured out what
killed the dinosaurs i think so so it
was
media well you know for volcanic
activity
all kinds of other stuff is coinciding
but the meteor is pretty unique and we
know how terrifying
i wouldn't if i we still have a lot of
20 20 left so if i
think no no but think about it this way
so the the dinosaurs ruled the earth
for 175 million years
we humans have been around for
what less than one million years if
you're super generous about what you
call humans
and you include gems basically so
so uh we are just getting warmed up
and you know we've ruled the planet much
more ruthlessly than tyrannosaurus rex
[Laughter]
t-rex had much less of an environmental
impact than we did yeah
and um if you if you give us another 154
million years
you know humans will look very different
if we make it that far
so i think dinosaurs basically are
much more of life history on earth than
we are
in all respects but look at the bright
side when they were killed off
another life form emerged mammals and
that's that whole
the evolutionary uh branching that's
happened so you you kind of have
uh when you have these evolutionary
signatures you see is there
basically a map of how the genome
changed yeah exactly exactly so now
you can go back to this early mammal
that was hiding in caves
and you can basically ask what happened
after the dinosaurs were wiped out a ton
of evolutionary niches opened up
and the mammals started populating all
of these niches
and in that diversification
there was room for expansion of new
types of functions
so some of them populated the air
with bats flying a new evolution of
light
some populated the oceans with dolphins
and whales
going off to swim etc but we all are
fundamentally mammals
so you can take the genomes of all these
species and align them on top of each
other
and basically create nucleotide
resolution
correspondences what my phd work showed
is that when you do that when you line
up species on top of each other
you can see that within protein coding
genes there's a particular pattern of
evolution that is dictated
by the level at which evolutionary
selection
acts if i'm coding for a protein
and i change the third codon position of
a triplet
that codes for that amino acid the same
amino acid will be encoded
so that basically means that any kind of
mutation
that preserves that translation that is
invariant
to that ultimate functional assessment
that evolution will give is tolerated so
for any function that you're trying to
achieve there's a set of sequences that
encode it
you can now look at the mapping the
you know graph isomorphism if you wish
between all of the possible dna
encodings of a particular function
and that function and instead of having
just that exact
sequence at the protein level you can
think of the set of protein
sequences that all fulfill the same
function what's evolution doing
evolution has two components one
component is random
blind and stupid mutation the other
component
is super smart ruthless
selection that's my mom calling from
greece
yes i might be a fully grown man
[Laughter]
did you just cancel the call wow i know
i'm in trouble
she's gonna be calling the cops
[Laughter]
so so yeah so there's a lot of encoding
for the same kind of function yeah so
so you now have this mapping between all
of the set of functions that could all
encode the same all of the set of
sequences that can all encode the same
function
what evolutionary signatures does is
that it basically looks at the shape
of that distribution of sequences that
all encode
the same thing and based on that shape
you can basically say ooh
proteins have a very different shape
than rna structures
than regulator motifs etc so just by
scanning a sequence
ignoring the sequence and just looking
at the patterns of change
i'm like wow this thing is evolving like
a protein and that thing is evolving
like a motif and that thing is evolving
so that's exactly what we just did for
covid
so our paper that we posted about our
archive about coronavirus
basically took this concept of
evolutionary signatures and applied it
on the sarsko v2 genome that is
responsible for the carbon-19 pandemic
uh and comparing it to 44 cerbicovirus
species so this is
the beta word did you just use cervical
sarbic virus sars related beta
corona virus it's a port ponto so that
one family of viruses
yeah so it was that family by the way we
have 44 species
that or 24 species in the fam yeah virus
is a clever
no no but but there's just 44 and again
we don't call them species
in in viruses we call them strange but
anyway there's 44 strains
and that's a tiny little subset of you
know maybe another 50 strains that are
just far too distantly related
most of those only infect bats
as the host and a subset of only four or
five
have ever infected humans and we
basically took all of those and we
aligned them
in the same exact way that we've aligned
mammals and then we looked at
what proteins are you know which of the
currently hypothesized genes
for the coronavirus genome are in fact
evolving like proteins and which ones
are not
and what we found is that orf10 the last
little
open reading frame the last little gene
in the genome is bogus
that's not a protein at all what is it
it's an rna structure
that doesn't have a it doesn't get
translated into amino acids
and that's so it's important to narrow
down to basically discover what's useful
and what's not
exactly basically what are what is even
the set of genes the other thing that
these evolutionary signatures showed is
that
within or 3a lies a tiny little
additional gene encoded within the other
gene
so you can translate a dna sequence in
three different reading frames
if you start in the first one it's you
know atg et cetera if you start on the
second it's tgc
etc and with there's a there's a gene
within a gene
so there's a whole other protein that we
didn't know about that might be super
important
so we don't even know the building
blocks of sarsko v2
so if we want to understand coronavirus
biology and eventually find it
successfully
we need to even have the set of genes
and and these evolutionary signatures
that are developed in my phd work we
just
recently used you know what let's uh
let's run with that tangent for a little
bit
if it's okay uh is uh can we talk about
uh the the kovic 19 a little bit more
like how what's your sense about the the
genome
the proteins the functions that we
understand about
covet 19 where do we stand in in your
sense
what are the big open problems and and
also
you know you you kind of said it's
important to understand what are the
like the the important proteins and
like why is that important
so what else does the
comparison of these species tell us what
it tells us is
how fast are things evolving it tells us
about
at what level is the acceleration or
deceleration pedal
set for every one of these proteins so
the genome has you know 30 some genes
some genes evolve super super fast
others evolve
super super slow if you look at the
polymerase gene that basically
replicates the genome that's a super
slow evolving one
if you look at the nuclear capsid
protein that's also super
slow evolving if you look at the spike
one protein
this is the part of the spike protein
that actually touches the h2 receptor
and then enables the virus to attach
to your cells that's the thing that
gives it that
that visual yeah the corona look
basically the coronal look yeah
so basically the spike protein sticks
out of the virus and there's a first
part of the protein
s1 which basically attaches to the h2
receptor
and then s2 is the latch that sort of
pushes and channels the fusion of the
membranes and then the incorporation
of the um viral rna
inside our cells which then gets
translated into all of these 30 proteins
so the s1 protein is evolving
ridiculously fast so if you look at
the stop professor's gas pedal the gas
pedal is all the way down
or 8 is also evolving super fast and or
six is evolving super fast we have no
idea what they do
we have some idea but nowhere near what
s1 is
so what the isn't that terrifying that
s1 is evol that means
that's a really useful function and if
it's evolving fast
doesn't that mean new strains could be
created or it does something that means
that it's searching for
how to match how to best match the host
so basically anything
in in general in evolution if you look
at genomes anything that's contacting
the environment
is evolving much faster than anything
that's internal and the reason is that
the environment changes
so if you look at um the evolution of
these cervical viruses
the s1 protein has evolved very rapidly
because it's attaching to different
hosts each time we think of them as bats
but there's thousands of species of bats
and to go from one species of bat to
another species of bat you have to
adjust
one to the new ace2 receptor that you're
going to be facing in that new species
sorry quick tangent yeah is it
fascinating to you
that viruses are doing this i mean it
feels like they're this intelligent
organism
i mean is it like does that give you
pause how
incredible it is that they're the the
evolutionary dynamics that you're
describing is actually happening
and they're freaking out figuring out
how to jump from bass to humans
all in this distributed fashion and then
most of us don't even say they're alive
or intelligent whatever
so intelligence is in the eye of the
beholder
you know stupid is a stupid dose as
forest gum would say yes and
intelligence is as intelligent does so
basically if the virus is
finding solutions that we think of as
intelligent yeah it's probably
intelligent
but that's again in the eye of the
beholder do you think viruses are
intelligent
of course not really no
because so incredible so remember
remember when i was talking about the
two components of evolution
one is the stupid mutation yeah which is
completely blind
and the other one is the super smart
selection
which is ruthless so
it's not viruses who are smart it's this
component of evolution that's smart
so it's evolution that that sort of
appears smart
and how is that happening by huge
parallel search across thousands of
you know parallel infections throughout
the world right now
yes but so to perfect on that so yes so
then the
the intelligence is in the mechanism but
then uh by that argument uh viruses
would be more intelligent because
there's just more of them
so the search they're basically the the
brute force
search that's happening with viruses
because there's so many more of them
than humans
then they're taken as a whole are more
intelligent
i mean so you don't think it's possible
that
i i mean who runs would we even be here
with if viruses weren't
i mean who runs this thing so
survivors so let me answer yeah let me
answer your your question
um so um
we would not be here if it wasn't for
viruses yes
and part of the reason is that if you
look at mammalian evolution
early on in this mammalian radiation
that basically happened after the death
of the dinosaurs
is that some of the viruses that we had
in our genome
spread throughout our genome and created
binding sites for new classes of
regulatory proteins
and these binding sites that landed all
over our genome
are now control elements that basically
control our genes
and sort of help the complexity of the
circuitry
of mammalian genomes so you know
everything's co-evolution and we're
working together
yeah but and yet you saw
they just don't care they don't care
another thing oh is the virus trying to
kill us no it's not the virus is not
trying to kill you it's true
it's not it's actually actively trying
to not kill you
so when you get infected if you die
palmer i killed him is what the reaction
of the virus will be why because that
virus won't spread
many people have a misconception of oh
viruses are smart or
oh viruses are mean they don't care
it like you have to clean yourself of
any kind of anthropomorphism
out there i don't know oh yes
so there's a there's a sense when taken
as a whole that there's
it's in a eye of the beholder stupid is
a stupid does intelligent injustice
intelligence does
so if you want to call them intelligent
that's fine then because
and the end result is that they're
finding amazing solutions
right i mean i mean but they're all
they're so dumb about it they're just
doing dumb they don't care they're not
dumb and they're not interested
they don't care they care the care word
is really interesting exactly i mean
there could be an argument that they're
conscious
they're just dividing they're not
they're just dividing
they're just a little entity which
happens to be dividing and spreading it
does doesn't want to kill us in fact it
prefers not to kill us
it just wants to spread and when i say
once again i'm anthropomorphizing
but it's just that if you have two
versions of a virus one acquires a
mutation that spreads more
that's going to spread more one acquires
a mutation that's pressed less that's
going to be lost
yes one acquires a mutation that enters
faster that's going to be kept
one requires a mutation that kills you
right away it's going to be lost
so over evolutionary time the viruses
that spread super well
but don't kill the host are the ones
that are going to survive
yeah but so you see you brilliantly
describe the basic mechanisms of how it
all happens but when you zoom out
and you see the uh you know the entirety
of viruses maybe across
different strains of viruses it seems
like a living organism
i am in awe of biology i find biology
amazingly beautiful i find the design
of the current coronavirus however
lethal it is amazingly beautiful
the way that it is encoded the way that
it tricks
your cells into making 30 proteins from
a single rna
human cells don't do that human cells
make
one protein from each rna molecule they
don't make two they make one
we are hardwired to make only one
protein from every rna molecule
and yet this virus goes in throws in a
single messenger rna
just like any messenger rna we have tens
of thousands of messenger rnas in our
cells in any one time
in every one of our cells it throws in
one rna
and that rna is so
i'm going to use your word here not my
word intelligent
yeah that it hijacks the entire
machinery of
your human cell yeah it basically
has at the beginning a giant open
reading frame
that's a giant protein that gets
translated two-thirds of that rna
make a single giant protein that single
protein
is basically what a human cell would
make it's like oh here's a start codon
i'm going to start translating here
human cells are kind of dumb i'm sorry
again this is
not the word that we normally use but
the human cell basically is oh this is
an rna it must be mine let me translate
and it starts translating it and then
you're in trouble why
because that one protein as it's growing
gets cleaved into about 20 different
peptides
the first peptide and the second peptide
start
interacting and the third one and the
fourth one and they
shut off the ribosome of the whole cell
to not translate human
rnas anymore so the virus basically
hijacks
your cells and it cuts it cleaves every
one of your human rnas
to basically say to the ribosome don't
translate this one junk don't look at
this one junk
and it only spares its own rnas
because they have a particular mark that
it spares then all of the ribosomes that
normally make protein
in your human cells are now only able to
translate
viral rnas and make more and more and
more and more of them
that's the first 20 proteins in fact
halfway down about
protein 11 between you know 11 and 12
you basically have a translational
slippage where the ribosome skips
reading frame
and it translates from one reading frame
to another reading frame that means that
about half of them are going to be
translated from 1 to
11 and the other half are going to be
translated from 12 to 16.
wow it's gorgeous and then
then you're done then that mrna will
never translate elastin proteins but
spike is the one right after that one so
how does spike even get translated
this positive strand rna virus has a
reverse transcriptase
which is an rna-based reverse
transcriptase so from the rna on the
positive strand it makes an rna of the
negative strand
and in between every single one of these
genes these open reading frames
there's a little signal aac gca or
something like that
that basically loops over to the
beginning
of the rna and basically instead of sort
of having a single
full negative strand or an a it
basically has a partial negative strand
rna
that ends right before the beginning of
that gene and another one that ends
right before the beginning of that gene
these negative strand rnas now make
positive strand rnas
that then look to the human whole cell
just like any other
human mrna it's like oh great i'm going
to translate that one because it doesn't
have the cleaving that the virus has now
put on all your human genes
and then you've lost the battle that
cell is now only making proteins for the
virus
that will then create the spike protein
the envelope protein the membrane
protein the nucleocapsid protein that
will package up the rna
and then sort of create new viral
envelopes
and these will then be secreted out of
that cell
in new little packages that will then
infect the rest of the cells and repeat
the whole process
beautiful right it's hard not to
anthropomorphize it
oh but it's so gorgeous so there is a
beauty to it
is there is it is it terrifying to you
so this is something that has happened
throughout history humans have been
nearly wiped out over and over and over
again and yet never fully wiped out
so i'm yeah i'm not concerned about the
human race i'm not even concerned about
you know the impact on sort of our
our survival as a species um
this is absolutely something i mean you
know
human life is so invaluable and every
one of us is so invaluable but if you
think of it
as sort of is this the end of our
species
by by no means basically so so let me
explain
the black death killed what 30 of europe
that has left a tremendous imprint
uh you know a huge hole a horrendous
hole
in the genetic makeup of humans
there's been series of wiping out of
huge fractions of entire species or just
entire species all together
and that has a consequence on
the human immune repertoire
if you look at how europe was shaped
and how africa was shaped by malaria for
example
all the individuals that carry a
mutation that protected from malaria
were able to survive much more and if
you look at the frequency
of sickle cell disease and the frequency
of malaria
the maps are actually showing the same
pattern the same imprint on africa
and that basically led people to
hypothesize that the reason why sickle
cell disease is so much more frequent
in americans of african descent is
because there was selection in africa
against malaria leading to sickle cell
because when the cells sickle malaria is
not able to
you know replicate inside your cells as
well and therefore you protect against
that
so if you look at human disease all of
the genetic associations that we do
with human disease you basically see
the imprint of these
waves of selection killing off
gazillions of humans
and there's so many immune
processes that are coming up as
associated with so many different
diseases
the reason for that is similar to what i
was describing earlier where the
outward facing proteins evolve much more
rapidly
because the environment is always
changing but what's really interesting
the human genome is that we have
co-opted many of these immune genes to
carry out non-immune functions
for example in our brain we use immune
cells
to cleave off neuronal connections that
don't get used
this whole user will lose it we know the
mechanism it's microglia the cleave of
neuronal synaptic connections that are
just not utilized
when you utilize them you mark them in a
particular way that basically when the
microglia
come tell it don't kill this one it's
it's used now
and the microwave will go off and kill
once you don't use this is an immune
function which is co-opted to do
non-immune things
if you look at our adipocytes m1 versus
m2 macrophages inside our fat
will basically determine whether you're
obese or not and these are again immune
cells that are resident and living
within
these tissues so many disease
associations
that we co-opt these kinds of things for
incredibly uh complicated functions
exactly evolution works in
so many different ways which are all
beautiful and mysterious
it's not intelligent not intelligent
it's in the eye of the beholder
[Laughter]
but but but the the the point that i'm
trying to make is that
if you look at the imprint that kovit
will have
hopefully it will not be big hopefully
the u.s will get attacked together and
stop the virus from
spreading further but if it doesn't it's
having an imprint
on individuals who have particular
genetic repertoires
so if you look at now the genetic
associations of blood type
and immune function cells etc there's
actually association genetic variation
that basically says how much more likely
am i or you to die
if we contact the virus and it's it's
through these rounds of shaping
the human genome that humans have
basically made it so far
and uh selection
is ruthless and it's brutal and it only
comes with a lot of killing
but this is the way that viruses and
environments have shaped the human
genome basically when you go through
periods of famine
you select for particular genes and
what's left
is not necessarily better it's just
whatever survived
and it may have been the surviving one
back then not because it was better
maybe the ones that ran slower survived
i mean you know again not necessarily
better
but the surviving ones are basically the
ones that then
are shaped for any kind of subsequent
evolutionary
condition and environmental condition
but if you look at
for example obesity obesity was selected
for
basically the genes that now predisposes
to obesity were at two percent frequency
in africa
they rose to 44 frequency in europe wow
that's fascinating
because you basically went through the
ice ages and there was a scarcity of
food
so you know there was a selection to
being able to store every single
calorie you consume eventually
environment changes so the better allele
which was the
fat storing allele became the worst
allele because it's
the fat storing allele it still has the
same function
so if you look at my genome speaking of
mom calling
mom gave me a bad copy of that gene
these fto locus
basically has to do with the obesity or
the obesity
yeah i basically now have a bad copy
from mom that makes me more likely to be
obese and i also
also have a bad copy from dad that makes
me more likely to be obese i'm
homozygous
and that's the allele
it's still the minor allele but it's at
44 frequency
in southeast asia 42 frequency in europe
even though it started at 2
it was an awesome allele to have 100
years ago
right now it's pretty terrible so the
other concept is that diversity matters
if we had a hundred million nuclear
physicists living the earth right now
we'd be in trouble you need diversity
you need artists and you need musicians
and you need mathematicians and you need
you know politicians yes even those and
you need like
it's not it's not get crazy enough but
so because then if uh virus comes along
or whatever
exactly exactly so no there's two
reasons number one
you want diversity and immune repertoire
and we have built in diversity
so basically they're they are the most
diverse basically if you look at our
immune system there's layers and layers
of diversity
like the way that you create your cells
generates diversity because of the
selection for the
vdj recombination that basically
eventually leads to a huge number of
repertoires
but that's only one small component of
diversity the blood type is another one
the major histogram
histocompatibility complex the hla
alleles are
you know another source of diversity so
the immune system of humans
is by nature incredibly diverse
and that basically leads to resilience
so basically what i'm saying that
i don't worry for the human species
because we are so diverse
immunologically
we are likely to be very resilient
against
so many different attacks like this
current virus so you're saying
natural pandemics may not be something
that you're really afraid of
because of the diversity in our
genetic makeup what about engineered
pandemics
do you have fears of us messing
with the makeup of viruses or well yeah
let's say with the makeup of viruses to
create something that we can't control
and we'd be much more destructive
than it would come about naturally
remember how we were talking about how
smart evolution is
humans are much dumber so you mean like
human scientists yeah
humans humans just humans overall yeah
but i mean even you know the sort of
synthetic biologists
um you know basically
if you were to create a you know
virus like sars that will kill other
people you would probably stars
start with stars so whoever
you know would like to design such a
thing would basically start with stars
tree or at least some relative of stars
the source genome for the current virus
was something completely different it
was something that has never infected
humans
no one in their right mind would have
started there oh but when you say source
is like the nearest
the nearest relative relative he's in a
whole other branch
no species of which has ever infected
humans in that branch
so you know let's put this to rest this
was not designed
by someone to kill off the human race so
you don't you don't believe it was
engineered
the likely yeah the the path to
engineering a deadly virus would not
come
from this strain that got it that was
used
uh moreover there's been various
um claims of haha this was mixed and
matched in lab
because the s1 protein has three
different components each of which has a
different evolutionary tree
so you know a lot of popular press
basically said aha
this came from pangolin and this came
from you know all kinds of other species
this is what has been happening
throughout the coronavirus tree
so basically the s1 protein has been
recombining across species all the time
remember when i was talking about the
positive strand the negative strands sub
genomic rnas
these can actually recombine and if you
have two different viruses infecting the
same cell
they can actually mix and match between
the positive strand and the negative
strand and basically create
a new hybrid virus with recombination
that now has the s1 from one
and the rest of the genome from another
and this is something that happens a lot
in s1 you know
fade etc and that's something that's
true of the whole training
for the whole family exactly viruses so
it's not like someone has been
messing with this for millions of years
and you know changing
this happens naturally that's again
beautiful that that somehow happens that
they recombine in the
so two different strands can affect the
body and recombine
so all of this actually magic happens
inside uh
hosts like all like yeah yeah that way
that's why classification wise virus is
not thought to be alive
because it doesn't self-replicate it's
not autonomous it's something that
enters
a living cell and then co-opts it to
basically make it its own
but by itself people ask me how do we
kill this bastard i'm like you stop it
from replicating
it's not like a bacterium that will just
live in a you know puddle or something
it's a virus viruses don't live without
their host
and they only live in their house for
very little time so if you stop it from
replicating
it'll stop from spreading i mean it's
not like hiv which can stay dormant for
a long time basically coronaviruses just
don't do that they're not integrating
genomes there are any genomes
so if it's not expressed it degrades rna
degrades
it doesn't just stick around well let me
ask also um
about the immune system you mentioned a
lot of people kind of ask
you know um how can we strengthen the
immune system
to respond to this particular virus but
the viruses in general
do you have from a biological
perspective thoughts on what we can do
as humans
uh too if you look at our traits across
different countries
people with less vaccination have been
dying more
if you look at north italy the
vaccination rates are
abysmal there and a lot of people have
been dying if you look at greece
very good vaccination rates almost no
one has been dying
so yes there's a policy component
so italy reacted very slowly greece
reacted very fast
so yeah many fewer people died in greece
but there might actually be
a component of genetic immune repertoire
basically how did people die off you
know in the history
of the greek population versus the
italian population there's a
that's interesting to think about uh and
then there's a component
of what vaccinations did you have as a
kid and what are the off-target effects
of those vaccinations
so basically a vaccination can have two
components one is
training your your immune system against
that specific insult
the second one is boosting up your
immune system for all kinds of other
things
if you look at allergies northern europe
super clean environments tons of
allergies
southern europe my kids grew up eating
dirt
no allergies so growing
up i never had even heard of what
allergies are like really allergies
and the reason is that i was playing in
the garden i was putting all kinds of
stuff in my mouth from you know all
kinds of dirt and stuff
tons of viruses there tons of bacteria
there you know my immune system was
built up
so the more you protect your immune
system from exposure
the less opportunity it has to learn
about non-self
repertoire in a way that prepares it for
the next insult
so it's a horizontal thing too like the
says throughout your lifetime in the
lifetime of the
of the people that uh your ancestors
yeah that kind of thing yeah
what about the so again it returns
against free will
on the free will side of things is there
something we could do
to strengthen our immune system in 2020
is there like uh you know exercise
diet all that kind of stuff so it's kind
of funny um
there's a cartoon that basically shows
uh two windows
with a teller in each window one has a
humongous line
and the other one has no one the one
that has no one above says
health no it says exercise and diet
and the other one says pill yeah
and there's a huge line for pill so
we're looking basically for magic
bullets for sort of
ways that we can you know beat cancer
and beat coronavirus and beat this and
beat that and it turns out that the
window with
like just diet and exercise is the best
way to boost
every aspect of your health if you look
at alzheimer's
exercise and nutrition
i mean you're like really for my brain
neurodegeneration
absolutely if you look at cancer
exercise and nutrition if you look at
coronavirus
exercise and nutrition every single
aspect of human health
gets improved and one of the studies
we're doing now is basically looking at
what
are the effects of diet and exercise
how similar are they to each other we're
basically taking
diet intervention and exercise
intervention in human and in mice
and we're basically doing single cell
profiling of a bunch of different
tissues to basically understand
how are the cells both the stromal cells
and the immune cells of each of these
tissues responding to
the effect of exercise what are the
communication networks between different
cells
where with the muscle that exercises
sends signals through the bloodstream
through the lymphatic system through all
kinds of other systems
that give signals to other cells that i
have exercised and you should change in
this particular way
which basically reconfigure those
receptor cells
with the effect of exercise how well
understood is
the those reconfigurations very little
we're just starting now basically is
there is the hope there uh
to understand the effect on
uh so like the effect on the immune
system on the immune system the effect
on brain
the effect on your liver on your
digestive system on your adipocytes
adipose you know the most misunderstood
organ everybody thinks oh
fat terrible no fat is awesome your fat
cells is what's keeping you alive
because if you didn't have your fat
cells all those lipids and all those
calories would be floating around in
your blood and you'd be dead by now
your adipocytes are your best friend
they're basically storing
all these excess calories so that they
don't hurt
all of the rest of the body and they're
also
fat burning in many ways so you know
again
when you don't have the homozygous
version that i have your cells are able
to burn calories much more easily
by sort of flipping a master metabolic
switch
that involves this fto locus that i
mentioned earlier and its target genes
irx3 and rx5
that basically switch your adipocytes
during their
three first days of differentiation as
they're becoming mature dipocytes to
basically become either fat burning
or fat storing fat cells and the fat
burning fat cells are your best friends
they're much closer to muscle
than they are to white egg boss eyes is
there a lot of difference between people
like
that you could give science could
eventually give advice
that is very generalizable or
is our differences in our genetic makeup
like you mentioned
is that going to be basically something
we have to
be very specialized individuals any
advice we give in terms of diet
like we were just talking about believe
it or not the most personalized advice
that you give for nutrition
don't have to do with your genome they
have to do with your
gut microbiome with the bacteria that
live inside you so most of your
digestion is actually happening by
species that are not human inside you
you have more non-human cells and you
have human cells
you're basically a giant bag of bacteria
with a few human cells along
and those do not necessarily have to do
with your genetic makeup
they interact with your genetic makeup
they interact with your ruby genome they
interact with your nutrition they
interact with your environment
they're you know basically
an additional source of variation so
when you're thinking about sort of
personalized nutritional advice
part of that is actually how do you
match your microbiome
and part of that is how do we match your
genetics
but again you know this is a very
diverse set of um
you know contributors and the effect
sizes are not
enormous so i think the science for that
is not fully developed yet
speaking of dyes because i've wrestled
in combat sports with sports my whole
life or
weight matters so you have to cut and
all that stuff
one thing i've learned a lot about my
body and
which seems to be i think true about
other people's bodies is that
you can adjust to a lot of things that's
the miraculous thing about this
biological system
is um like i fast
often i used to eat like five six times
a day
and thought that was absolutely
necessary how could you not eat that
often
and then when i started fasting your
body adjusts to that
and you learn how to not eat you know
and it's it was
uh if you just give it a chance for a
few weeks actually
over a period of a few weeks your body
can adjust to anything yeah and that's a
miraculous that's such a beautiful thing
so i'm a computer scientist
and i've basically gone through periods
of 24 hours without eating or stopping
and you know then i'm like oh must eat
and i eat a ton i used to order two
pizzas just with my brother and
you know like so i i've gone through
these extremes as well and i've gone the
whole
intermittent fasting thing so i can
sympathize with you both on the seven
meals a day
to the zero meals a day um so i think
when i say everything in moderation i i
actually
think your body responds interestingly
to these different changes in diet
i think part of the reason why we lose
weight with pretty much every kind of
change in behavior is because
our epigenome and the set of proteins
and enzymes that are expressed
and our microbiome are not well suited
to that nutritional source
and therefore they will not be able to
sort of catch everything that you
give them and then you know a lot of
that will go undigested
and that basically means that your body
can then you know lose weight in the
short term but very
quickly will adjust to that new normal
and then we'll be able to sort of
perhaps gain a lot of weight yeah from
the diet so
anyway i mean there's also studies in um
factories where basically
people you know dim the lights and then
suddenly everybody started working
better it was like wow that's amazing
three weeks later they made the lights a
little brighter everybody started
working better
so any kind of intervention has a
placebo effect of wow now i'm healthier
and i'm going to be running more often
etc so it's very hard to uncouple
the placebo effect of wow i'm doing
something to intervene on my diet
from the wow this is actually the right
thing for me
so you know yeah from the perspective
from nutrition science psychology
both things i'm interested in especially
psychology it seems that
it's extremely difficult to do good
science
because uh there's so many variables
involved it's so difficult to control
the variables
so difficult to do sufficiently
large-scale experiments
uh both sort of in terms of number of
subjects and
temporal like how long you do the study
for
that uh it just seems like it's not even
a real science
for now like nutrition science i want to
jump into the whole placebo effect for a
little bit here
and basically talk about the
implications of that
if i give you a sugar pill and tell you
it's a sugar pill
you won't get any better but if i tell
you sugar appeal and tell you and
i tell you wow this is an amazing drug
it actually will stop your cancer
your cash will actually stop with much
higher probability
what does that mean that's so amazing
that means that if i can trick your
brain into thinking that i'm healing you
your brain will basically figure out a
way to heal itself to heal the body
and that tells us that there's so much
that we don't understand
in the interplay between our cognition
and our biology that if we were able to
better harvest
the power of our brain to sort of you
know
impact the body through the placebo
effect we would be
so much better in so many different
things just by tricking yourself into
thinking that you're doing better you're
actually doing better
so there's something to be said about
sort of positive thinking about optimism
about sort of
you know just getting your
brain and your mind into the right
mindset
that helps your body and helps your
entire biology
yeah from a science perspective that's
just fascinating i
obviously most things about the brain is
a total mystery for now
but that's a fascinating interplay that
the brain
yeah that the brain can reduce
uh the brain can help cure cancer as a
i don't even know what to do with that i
mean the way to think about that is the
following
the converse of the equation is
something that we are much more
comfortable with
like oh if you're stressed then
your heart right might rise and all
kinds of sort of toxins might be
released and
that can have a detrimental effect in
your body etc
so maybe it's easier to understand your
body
healing from your mind by your mind is
not killing your body
or at least it's killing it less so i
think the you know
that aspect of the stress equation is a
little easier
for most of us to conceptualize but then
the healing part
is you know perhaps the same pathways
perhaps different pathways but again
something that is totally untapped
scientifically i think we try to bring
this question up a couple of times
but let's return to it again is what do
you think is the difference between the
way a computer
represents information the human genome
represents and stores information
like what and maybe broadly what is the
difference between
how you think about computers and how
you think about biological systems
so i made a very provocative claim
earlier that we are a digital computer
like that at the core lies a digital
code and that's true in many ways but
surrounding that digital core there's a
huge amount of analog
if you look at our brain it's not really
digital if you look at our sort of
rna and all of that stuff inside our
cell it's not really digital it's really
analog in many ways but let's start with
the code and then we'll expand
to the rest so the code itself is
digital
so there's genes you can think of the
genes as i don't know the
procedures the functions inside your
language and then
somehow you have to turn these functions
on how do you call a gene how do you
call that function the way that you
would do it in old
programming languages is go to address
whatever in your memory and then you
start running from there
and you know modern programming
languages have encapsulated this into
functions and objects and all of that
and it's nice and cute but in the end
deep down
there's still an assembly code that says
go to that instruction and it runs that
instruction
if you look at the human genome and you
know the genome of pretty much
most species out there it's
there's no go-to function you just don't
start
in you know transcribing in position
thirteen
hundred five you know thirteen thousand
five hundred twenty
seven in chromosome 12. you instead have
content based indexing so at every
location
in the genome in front of the genes that
need to be turned on
i don't know when you drink coffee
there's a little coffee marker
in front of all of them and whenever
your
cells that metabolize coffee need to
metabolize coffee they basically see
coffee and they're like oh
let's go turn on all the coffee marked
jeans so
there's basically these small motifs
these small sequences
that we call regulatory motifs they're
like patterns of dna they're only eight
characters long or so like gat gca
et cetera and these motifs work in
combinations
and every one of them has some
recruitment
affinity for a different protein that
will then come and bind it
and together collections of these motifs
create regions
that we call regulatory regions that can
be either
promoters near the beginning of the gene
and that basically tells you where the
function actually starts where you call
it
and then enhancers that are looping
around of the dna
that basically bring the machinery that
binds those enhancers
and then bring it onto the promoter
which then
recruits the right sort of the ribosome
and the polymerase and all of that thing
which will first transcribe and then
export and then eventually translate in
the cytoplasm
you know whatever rna molecule so
the beauty
of the way that the digital computer
that's the genome works
is that it's extremely fault tolerant
if i took your hard drive and i messed
with twenty percent of the letters
in it of those zeros and ones and i
flipped them
you'd be in trouble if i take the genome
and i flip 20
of the letters you probably won't even
notice
and that resilience that's fascinating
again is a
key design principle and again i'm
triple morphizing here
but it's a key driving principle of how
biological systems work
they're first resilient and then
anything else
and when you look at this incredible
beauty of life
from the most i don't know beautiful
i don't know human genome maybe of
humanity and all of the ideals that
should come with it
to the most terrifying genome like i
don't know kovit-19
sarsko v2 and the current pandemic
you basically see this elegance as
the epitome of clean design
but it's dirty it's a mess it's
you know the the way to get there is
hugely messy and that's something that
we as computer scientists
don't embrace we like to have clean code
you know as like in engineering they
teach you about compartmentalization
about sort of separating functions about
modularity about hierarchical design
none of that applies in bio testing
[Laughter]
testing sure yeah biology does plenty of
that but
i mean through evolutionary exploration
but um
if you look at biological systems first
they are robust and then they specialize
to become anything else
and if you look at viruses the reason
why they're so
elegant when you look at the design of
this you know
genome it seems so elegant and
the reason for that is that it's been
stripped down
from something much larger because of
the pressure to keep it compact
so many compact genomes out there have
ancestors that were much larger
you don't start small and become big you
go through a loop of
add a bunch of stuff increase complexity
and then
you know slim it down and one of my
early papers was in fact
on genome duplication one of the things
we found is that baker's yeast which is
the
you know yeast that you use to make
bread but also the yeast that you use to
make wine
which is basically the dominant species
when you go in the fields of tuscany and
you say you know what's out there
it's basically saccharomyces cerevisiae
or the way my italian friends say
saccharomyces
so um uh which means what
oh sakura okay i'm sorry i'm i'm greek
so yeah zacharo
mickeys zacharo is sugar minky's is
fungus
yes cerevisiae cerveza beer
so so it means the sugar fungus of beer
yeah
you know less less sounding to the
still poetic yeah so anyway uh
saccharomyces cerevisiae basically the
major baker's yeast out there
is the descendant of a whole genome
duplication
why would a whole genuine duplication
even happen when it happened
is coinciding with about 100 million
years ago
and the emergence of
fruit-bearing plants
why fruit-bearing plants because animals
would eat
the fruit and would walk around and poop
huge amounts of nutrients along with a
seed
for the plants to spread before that
plants were not spreading through
animals they were spreading through wind
and all kinds of other ways but
basically the moment you have
fruit-bearing plants
the the the these plants are basically
creating this abundance of sugar in the
environment so there's an evolutionary
niche that gets created
and in that evolutionary niche you
basically have enough sugar
that a whole genome duplication which
initially is a very messy event
allows you to then you know relieve some
of that complexity
so to pause what does genome duplication
mean
that basically means that instead of
having eight chromosomes
you can now have 16 chromosomes
so but with the duplication at first
when you have
six when you go to 16 you're not using
that
oh yeah you are yeah so basically from
one day to the next
you went from having eight chromosomes
to having 16 chromosomes
probably a non-disjunction event during
a duplication during a division
so you basically divide the cell instead
of half the genome going this way and
half the genome going the other way
after
duplication of the genome you basically
have all of it going to one cell
and then there's a sufficient messiness
there that you end up with
slight differences that make most of
these chromosomes be actually preserved
it's a long story short but it's a big
upgrade right so that's
not necessarily because what happens
immediately thereafter is that you start
massively losing
tons of those duplicated genes so ninety
percent of those genes were actually
lost
very rapidly after holding duplication
and the reason for that is that
biology is not intelligent it's just
ruthless selection random mutation
so the ruthless selection basically
means that as soon as one of the random
mutations hit one gene
ruthless selection just kills off that
gene it's just you know
you you know if you have a pressure to
maintain a small compact genome
you will very rapidly lose the second
copy of most of your genes
and a small number 10 were kept in two
copies and those had to do a lot with
environment adaptation with the speed of
replication
with the speed of translation and with
sugar processing
so i'm making a long story short to
basically say that evolution is
messy the only way like so so
you know the example that i was giving
of messing with 20
of your bits in your computer totally
bogus
duplicating all your functions and just
throwing them out there in the same
you know function just totally bogus
like this would never work in an
engineer system
but biological systems because of this
content-based indexing and because of
this
modularity that comes from the fact that
the gene is controlled by a series of
tags and now if you need this gene in
another
setting you just add some more tags that
will basically turn it on
also in those settings so this gene is
now pressured
to to do two different functions and it
builds up complexity
i see a whole term duplication and gene
duplication in general as a way to
relieve that complexity
so you have this gradual buildup of
complexity as functions get
past get sort of added on to the
existing genes
and then boom you duplicate your your
workforce
and you now have two copies of this gene
one will probably specialize to do one
and the other one will specialize to do
the other or one will maintain the
ancestral function the other one will
sort of
be free to evolve and specialize while
losing the ancestral functions
and so forth so that's how genomes
evolve they're they're just
messy things but they're extremely fault
tolerant
and they're extremely able to deal with
mutations because that's
the very way that you generate new
functions so new functionalization comes
from
the very thing that breaks it so even in
the current pandemic
many people are asking me which
mutations matter the most
and what i tell them is well we can
study the evolutionary dynamics of the
current genome
to then understand which mutations have
previously happened or not and
which mutations happen in genes that
evolve rapidly or not
and one of the things we found for
example is that
the genes that evolved rapidly in the
past are still evolving rapidly now in
the current pandemic
the genes have evolved slowly in the
past are still evolving slowly
which means that they're useful which
means that they're
under the same evolutionary pressures
but then the question
is what happens in specific mutations
so if you look at the d614 gene mutation
that's been all over the news so
in position 614 in the amino acids and
harvest 14
of the s protein there's a
d to g mutation that that sort of has
creeped over
the population mutation
we found out through my work disrupts a
perfectly conserved nucleotide position
that has never been changed in the
history of
millions of years of equivalent
mammalian evolution
of these viruses that basically means
that it's a completely new adaptation
to human and that mutation has now gone
from
one percent frequency to 90 frequency in
almost all outbreaks
so this mutation i like how you say in
the mu the
416 what was it okay yes 6 on 14 sorry
614 right that
d614g dc so so literally
so what you're saying is it's like a
chess move yeah so
it's just mutated one letter to another
exactly and that hasn't happened before
yeah and and this somehow this mutation
is really useful
uh it's really useful in the current
environment of the genome
which is moving from human to human when
it was moving from bad to bad
it couldn't care less for that mutation
but it's environment specific so now
that it's moving from human to human
whoo-hoo it's moving way better like by
orders of magnetism what do you okay so
so you're like tracking this
evolutionary dynamics
which is fascinating but what do you do
with that
so what does that mean what does this
mean what do you make
what do you make of this mutation in uh
trying to anticipate i guess
is is the is one of the things you're
trying to do is anticipate
where how this unrolls into the future
this this
evolutionary dynamics such a great
question so so there's there's two
things
remember when i was saying earlier
mutation is the path
to new things but also the path to break
old things
so what we know is that this position
was extremely preserved
through gazillions of mutations that
mutation was never tolerated when it was
moving from best to bats
so that basically means that that
contain that position is extremely
important
in the function of that protein that's
the first thing it tells the second one
is that
that position was very well suited to
bat transmission but now
is not well suited to human transmission
so it got rid of it and it now has a new
version
of that amino acid that basically makes
it much easier to transmit from human to
human
so in terms of the
evolutionary history teaching us about
the future
it basically tells us here's the regions
that are
currently mutating here's the regions
that are most likely to imitate going
forward
as you're building a vaccine here's what
you should be focusing on
in terms of the most stable regions that
are the least likely to mutate
or here's the newly evolved functions
that are most likely to be important
because they've overcome this local
maximum that it had reached in the in
the
bat transmission so anyway it's a
tangent to basically say that
evolution works in messy ways and the
thing that
you would break is the
thing that actually allows you to first
go through a lull
and then reaching new local maximum
and i often like to say that if
engineers
had basically designed evolution we
would still be
perfectly replicating bacteria
because it's by making the bacterium
worse that you allow evolution to reach
a new
optimum that's just a pause on that
that's so profound
the the that's so profound for the
entirety of um
this scientific and engineering
disciplines
exactly we as engineers need to embrace
breaking things
we as engineers need to embrace
robustness as the first principle
beyond perfection because nothing is
going to ever be perfect
and when you're sending a satellite to
mars when something goes wrong
it'll break down as opposed to building
systems that tolerate failure
and are resilient to that
and in fact get better through that so
the spacex approach versus nasa
for the for example
is there something we can learn about
the incredible
uh take lessons from the incredible
biological systems in their resilience
in their in the mushiness the messiness
to uh
to our computing systems to uh to our
computers
it would basically be starting from
scratch in many ways
it would basically be building new
paradigms
that don't try to get the right answer
all the time
but try to get the right answer most of
the time or a lot of the time
do you see deep learning systems in the
whole world of machine learning is kind
of taking a step in that direction
absolutely absolutely basically by
allowing
this much more natural evolution of
these parameters you basically and then
if you look at sort of deep learning
systems again
they're not inspired by the genome
aspect of biology they're inspired by
the brain aspect of biology
and again i want you to pause for a
second and
realize the complexity of the entire
human brain
with trillions of connections within our
you know neurons
with millions of cells talking to each
other
is still encoded within that same genome
that same genome encodes every single
freaking cell type
of the entire body every single cell is
encoded by the same code
and yet specialization
allows you to have this single
viral-like genome that self-replicates
the single module modular automaton
work with other copies of itself it's
mind-boggling
create complex organs through which
blood flows
and what is that blood the same freaking
genome
create organs that communicate with each
other
and what are these organs the exact same
genome
create a brain that is innervated
by massive amounts of blood pumping
energy to it
20 of our energetic needs
to the brain from the same genome and
all of the neuronal connections
all of the auxiliary cells all of the
immune cells
the astrocytes the ligand size the
neurons the excitatory the inhibitory
neurons all of the different classes of
parasites
the blood-brain barrier all of that same
genome
one way to see that in a sad
so this one is beautiful the sad thing
is thinking about the trillions
of organisms that died to create that
you mean on the evolutionary path and
the evolutionary path to humans
that's crazy there's two descendants of
apes just
talking on the podcast okay this is
so mind-boggling just just to boggle our
minds a little bit more
yeah us talking to each other
we are basically generating a series of
vocal utterances
through our pulsating of vocal chords
received through this the people who
listen to this are taking a completely
different
path to that information transfer yet
through language
but imagine if we could connect these
brains
directly to each other the
amount of information that i'm
condensing into a small number of words
is a huge funnel which then you receive
and you expand into a huge number of
thoughts
from that small funnel
in many ways engineers would love to
have the whole information transfer
just take the whole set of neurons and
throw them away i mean throw them to the
other person
this might actually not be better
because in
your misinterpretation of every word
that i'm saying
you are creating new interpretation that
might actually be way better than what i
meant to the first place
the ambiguity of language perhaps
might be the secret to creativity
every single time you work on a project
by yourself
you only bounce ideas with one person
and
your neurons are basically fully
cognizant of what these ideas are
but the moment you interact with another
person the
misinterpretations that happen might be
the most creative part of the process
with my students every time we have a
research meeting i very often pause and
say
let me repeat what you just said in a
different way and i sort of go on and
brainstorm
with what they were saying but by the
third time
it's not what they were saying at all
and when they pick up what i'm saying
you're like oh
well now they they've sort of learned
something very different from what i was
saying and that is the same kind of
messiness that i'm describing in the
genome itself
it's sort of embracing the messiness and
that's a feature not a book
exactly and in the same way when you're
thinking about sort of these deep
learning systems
that will allow us to sort of be more
creative perhaps or
learn better approximations of these
complex functions
again tuned to the universe that we
inhabit
you have to embrace the breaking you
have to embrace the
you know how do we get out of these
local optima and a lot of the
design paradigms that have made deep
learning so successful
are ways to get away from that ways to
get
better training by sort of sending long
range messages
these lstm models and the
you know sort of feed forward loops that
you know sort of jump through layers of
a convolutional neural network
all of these things are basically ways
to push you out of this local
maxima and that's sort of what evolution
does that's what language does that's
what
conversation and brainstorming does
that's what our brain does
so you know this design paradigm is
something that's pervasive
and yet not taught in schools not taught
in engineering schools
where everything is minutely modularized
to make sure that we never deviate from
you know whatever signal we're trying to
emit
as opposed to let all hell breaks loose
because that's the way that's the path
of paradise
the path to paradise yeah i mean it's
difficult to know how to teach that and
uh what to do with it i mean it's um
it's difficult to know how to build up a
sign the scientific method
around messiness you i mean
it's not all messiness we need we need
some cleanness and going back to the
example with mars
that's probably the place where i want
to sort of moderate
error as much as possible and sort of
control the environment as much as
possible but if you're trying to
repopulate mars
well maybe messiness is a good thing
then
on that uh you quick you quickly
mentioned this in terms of
us using our vocal cords to speak on a
podcast
um so elon musk and neurolink are
working on
trying to plug
as per discussion with computers and
biological systems
to connect the two he's trying to con
connect our brain
to a computer to create a brain computer
interface where they can
communicate back and forth on this
line of thinking do you think this is uh
possible to bridge the gap between our
engineered computing systems
and the messy biological systems
my answer would be absolutely we
you know there's no doubt that we can
understand more and more about what goes
on in the brain
and we can sort of train the brain
i mean i don't know if you remember the
palm pilot
yeah palm pilot yeah remember this whole
sort of alphabet that they had created
am i thinking of the same thing um it's
basically you had you had a little pen
and for every character you had a little
scribble
that was unique that the machine could
understand and that
instead of trying the machine trying to
teach the machine to recognize human
characters
you had basically they figured out that
it's better and easier
to train humans to create human-like
characters that the machine is better at
recognizing so
in the same way i think what will happen
is that humans will be trained to be
able to create
the mind pattern that the machine will
respond to
before the machine truly comprehends our
thoughts so the
first human brain interfaces will be
tricking humans to speak the machine
language
where with the right set of electrodes i
can sort of trick my brain into doing
this
and this is the same way that many
people teach like learn to control
artificial limbs
you basically try a bunch of stuff and
eventually you figure out how your limbs
work
that might not be very different from
how humans learn to use their
natural limbs when they first grow up
basically you have these
you know neoteny period of
you know this puddle of soup inside your
brain
trying to figure out how to even make
your own connections before you're born
and then learning sounds in utero of
you know all kinds of echoes and
you know eventually getting out in the
real world and
i don't know if you've seen newborns but
they just stare around a lot
you know one way to think about this as
a machine learning person is oh they're
just training their edge detectors
and eventually they figure out how to
train their edge detectors they work
through the second layer of the visual
cortex and the third layer and so forth
and you basically have this
um learning how to control your limbs
that probably comes at the same time
you're sort of you know throwing random
things there and you realize that oh wow
when i do this thing
my limb moves let's do the following
experiment take a breath
what muscles did you flex now take
another breath and think what muscles do
i flex
the first thing that you're thinking
when you're taking a breath
is the impact that he has on your lungs
you're like oh i'm now going to increase
my lungs or i'm not going to bring air
in
but what you're actually doing is just
changing your diaphragm yeah
that's not conscious of course you never
think of the diaphragm as a thing
yeah and why is that that's probably the
same reason why i think of moving my
finger when i actually move my finger
i think of the effect instead of
actually thinking of whatever muscle is
twitching
that actually causes my finger to move
so we basically in our first years of
life
build up this massive lookup table
between whatever neuronal firing we do
and whatever action happens
in our body that we control
if you have a kid grow up with a third
limb
i'm sure they'll figure out how to
control them probably at the same rate
as their natural limbs
and uh a lot of the work would be done
by the
so if the third limb is the computer you
kind of have a
uh not a faith but a thought that
um the brain might be able to figure out
like if the plasticity would come from
the brain yeah like the brain would be
cleverer than the machine at first when
i talk about a third limb that's exactly
what i'm saying
an artificial limb that basically just
controls your mouse while you're typing
you know perfectly natural thing i mean
again you know in a few hundred years
maybe sooner than that but but basically
there's
as long as the machine is consistent in
the way that it will respond
to your brain impulses you'll figure out
how to control that
and you could play tennis with your
third limb and
let me go back to consistency people who
have
dramatic accidents that basically take
out a whole chunk of their brain
can be taught to co-opt other parts of
the brain to then control that part
you can basically build up that tissue
again and eventually train your body
how to walk again and how to read again
and how to play again and how to think
again how to speak a language again etc
so there's a massive amount of
malleability
that happens you know naturally in
our way of controlling our body our
brain or
thoughts or vocal cords or limbs etc and
human machine interfaces are inevitable
if we sort of figure out how to read
these electric impulses
but the resolution at which we can
understand human thought
right now is nil is ridiculous
so how are human thoughts encoded it's
basically combinations
of neurons that co-fire and these create
these things called
engrams that eventually form memories
and so so forth
we know nothing of all that stuff
so before we can actually read into your
brain that you want to build a program
that does this anytime it's on that
we need a lot of neuroscience well so
uh to push back on that do you think
it's possible that
without understanding the functionally
about the brain or the from the
neuroscience or the cognitive science or
psychology whichever level of the brain
will look at do you think if we just
connect
connect them just like per your previous
point
if we just have a high enough resolution
between connection between
uh wikipedia and your brain the brain
will just figure it out
with us understanding um because that's
one of the
innovations of neural link is they're
increasing the number of
connections to the brain to like several
thousand which before was
you know in the dozens or whatever
you're still off by a few orders of
magnets
right but the the thing is the hope is
if you increase that number more and
more and more
maybe you don't need to understand
anything about the actual
how human thought is represented in the
brain you could just
let it let it figure it out by itself
well uh cannery is waking up and saying
i know
yeah exactly exactly so
yeah sure you don't have faith in the
plasticity of the brain to that degree
it's not about brain plasticity it's
about the input aspect
basically i think on the output aspect
being able to control a machine
is something that you can probably train
your neural impulses
that you're sending out to sort of match
whatever
response you see in the environment if
this thing moved every single time i
thought a particular thought
then i could figure out i could hack my
way into moving this thing with just a
series of thoughts i could think
guitar piano tennis ball
and then this thing would be moving and
then you know i would just have the
series of
thoughts that would sort of result in
the impulses that will move this thing
the way that i want it and then
eventually it'll become natural because
i won't even think about it
um i mean the same way that we control
our limbs in a very natural way
but babies don't do that babies have to
figure it out
and you know some of it is hard-coded
but some of that is actually learned
based on the whatever soup of neurons
you ended up with whatever connections
you pruned them to and eventually you
were born with
you know a lot of that is coded in the
genome but a huge chunk of that is
stochastic
instead of the way that you sort of
create all these neurons they migrate
they form connections they sort of
you know spread out they have particular
branching patterns but then the
connectivity itself
unique in every single new person all
this to say that on the output side
absolutely i'm very very you know um
hopeful that we can have machines that
read
thousands of these neuronal connections
on the output side but on the input side
oh boy
i don't expect any time in the near
future we'll be able to sort of send a
series of impulses that will tell me
oh earth to sun distance 7.5 million
et cetera like nowhere i mean i think
language will still the
be the input way rather than sort of any
kind of more complex
it's a really interesting notion that
the ambiguity of language is a feature
yeah
and we evolved for millions of years
to uh to take advantage of that
ambiguity exactly
and yet no one teaches us the subtle
differences between
words that are near cognates and yet
evoke so much more
than you know one from the other and yet
you know when you're choosing words from
a list of 20 synonyms
you know exactly the connotation of
every single one of them and that's
something that
you know is there so so yes there's
ambiguity
but there's all kinds of connotations
and in the way that we select our words
we have so much baggage that we're
sending along the way that we're
emoting the way that we're moving our
hands every single time we speak
the you know the pauses the eye contact
etc so much higher baud rate than just a
vocal
you know string of characters well let
me
just take a small tangent on that oh
tangent we haven't done that yet and i
haven't done an idea
uh we'll return to the origin of life
so i mean you're greek but i'm i'm going
on this
personal journey uh i'm going to paris
for the explicit purpose of
talking to one of the most famous uh a
couple who's a famous translators of
russian literature
dostoyevsky tolstoy yeah and they go
that's their art is the translation and
um everything i've learned about the
translation art
it makes me feel um
it's so profound in a way that's
so much more profound than the natural
language processing papers i read in the
machine learning community
that there's such depth to language
that um i don't know what to do with i
don't know if you've experienced that in
your own life
with knowing multiple languages um
i don't know what to i don't know how to
make sense of it but there's so much
loss in translation between russian and
english
and getting a sense of that like for
example
there's like just taking a single
sentence from dostoyevsky
and like there's a lot of them you could
you could talk for
hours about how to translate that
sentence properly
uh that captures the meaning the the the
period
the culture the humor the wit the
suffering that
was in the context of the time all of
that it could be a single sentence uh
you could you could talk forever about
what it takes to translate that
correctly i don't know what to do with
that
so being greek it's very hard for me to
think of a
sentence or even a word without going
into the full etymology of that word
breaking up every single atom of
that that sentence and every single atom
of these words
and rebuilding it back up i have three
kids
and the way that i teach them greek is
the same way that you know the
documentary i was mentioning earlier
about sort of understanding the deep
roots
of all of these you know words um
and it's very
it's very interesting that every single
time i hear a new word that i've never
heard before
i go and figure out the etymology of
that word because i will never
appreciate that word
without understanding how it was
initially formed
interesting but how does that help
because that's that's not the full
picture
no no of course of course but what i'm
trying to say is that knowing the
components
teaches you about the context of the
formation of that word
and sort of the original usage of that
word and then of course the word takes
new meaning
as you create it you know from its parts
and that meaning then gets augmented
and two synonyms that that sort of have
different roots
will actually have implications that
carry a lot of that baggage
of the historical provenance of these
words so before working on genome
evolution
my passion was evolution of language
and sort of tracing cognates across
different languages
through their etymologies and that's
fascinating that there's parallels
between i mean
of course the idea that there's
evolutionary dynamics to our language
yeah every single word that you
utter parallels parallels what does
parallels mean
para means side by side alleles from
alleles
which means identical twins parallels
i mean name any word and there's so much
baggage
so much beauty in how that word came to
be and how this word took a new meaning
than the sum of its parts
yeah and that and those and they're just
they're just words they don't have any
physical
exactly and now you take your words and
you weave them
into a sentence the emotional
invocations of that weaving are
fathomless and they're all all of those
emotions
all live in our in the brains of humans
in the eye of the beholder
no seriously you have to embrace this
concept of the eye of the beholder
it's it's the the conceptualization that
nothing takes meaning with one person
creating it everything takes meaning
in the receiving end and the emergent
properties
of these communication networks where
every single you know if you look at the
network of our cells and how they're
communicating with each other
every cell has its own code this code is
modulated by the epigenome
this creates a bunch of different cell
types each cell type now has its own
identity
yet they all have the common root of the
stem cells that sort of
led to them each of these identities is
now communicating with each other
they take meaning in their interaction
there's an emergent property that comes
from a bunch of cells being together
that is not in any one of the parts if
you look at neurons communicating again
these
engrams don't exist in any one neuron
they exist in the connection in the
combination of neurons
and the meaning of the words that i'm
telling you
is empty until it reaches you and it
affects you in a very different way
then it affects whoever's listening to
this conversation now
because of the emotional baggage that
i've grown up with that you've grown up
with and that they've grown up with yeah
and that's i think the magic of
translation
if you start thinking of translation as
just simply capturing that emotional
set of reactions that you have that you
evoke
you need a different set of words to
evoke that same set of reactions to a
french person
than to a russian person because of the
baggage of the culture that we grew up
in
yeah i mean there's so so basically you
shouldn't find
the best word sometimes it's a
completely different sentence structure
that you will need
matched to the cultural context
of the target audience that you have
yeah the it's i mean
you're just i usually don't think about
this but right now
there's this feeling as a reminder that
it's just you and i talking
but there's several hundred thousand
people will listen to this
there's some guy in russia right now
running
uh like in moscow listening to us
and there's somebody in india i
guarantee you there's somebody in china
and south america there's somebody
in texas and and they all have different
emotional baggage they probably got
angry earlier on about the whole
discussion about coronavirus and
uh about some aspect of it uh yeah it's
and there's that network effect yeah
yeah
that's uh it's a beautiful thing and and
this lateral transfer of information
that's what makes the collective
quote-unquote genome
of humanity so unique from any other
species
so you somehow miraculously wrapped it
back to the very beginning
of when we were talking about the human
the beauty of the human genome
so i think this is the right time unless
we want to go for
a six to eight hour conversation we're
gonna have to talk again but
i think for now to wrap it up um this is
the right time to talk about
the uh the biggest most ridiculous
question of all
meaning of life off mike you mentioned
to me that you um
you had your 42nd birthday 40
a second being a very special absurdly
special number
uh and you had to kind of um
get together with friends to discuss the
meaning of life so let me ask you
in your as a biologist as a computer
scientist
and as a human
what is the meaning of life i've been
asking this question
for a long time ever since my 42nd
birthday
but well before that and even planning
the meaning of life symposium
and symposium
means together posey actually means to
drink together so symposium is actually
a drinking party
[Laughter]
so can you actually elaborate about this
meaning of life that you put together
it's like the most genius idea i've ever
heard so
42 is obviously the answer to life the
universe and everything from the
hitchhiker's guide to the galaxy
and as i was turning 42 i've had the
theme for every one of my birthdays when
i was turning 32
it's one zero zero zero zero zero in
binary
so i celebrated my 100 000th binary
binary birthday and i had a theme of
going back 100 000 years
you know let's dress something
in the last hundred thousand years
anyway it was we've i've always had
these
that's such an interesting human being
okay that's awesome i've always had
these sort of
uh sort of numerology
[Music]
related announcements for my for my
birthday party
so what came out of that
meaning of life symposium is that i
basically asked 42 of my colleagues 42
my friends 42 of my
you know collaborators to basically give
seven minute species
on the meaning of life each from their
perspective and
i really encourage you to go there
because it's mind-boggling that every
single person
said a different answer every single
person started with
i don't know what the meaning of life is
but and then give this beautifully
eloquently answer eloquent answer
and they were all different but they all
were consistent with each other and
mutually synergistic
and together forming a beautiful view of
what it means to be human in many ways
some people talked about the loss of
their loved one
their life partner for many many years
and how their life changed through that
some people talked about the origin of
life some people talked about
the difference between purpose and
meaning
i'll you know maybe quote one of the
answers
which is this linguistics uh professor
friend of mine at harvard
who basically said
that she was gonna she's greek as well
and she said i will give a very pythian
answer so pithia was the oracle of
delphi
who would basically give these very
cryptic answers very short but
interpretable in many different ways
there was this whole
set of priests who were tasked with
interpreting what pethia had said
and very often you would not get a clean
interpretation but she said
i will be like pethi and give you a very
short
and multiple interpretable answer but
unlike her i will actually also give you
three interpretations
and she said the answer to the meaning
of life is become one
and the first interpretation is like a
child
become one year old with the excitement
of discovering everything about the
world
second interpretation in whatever you
take on
become one the first the best excel
drive yourself to perfection for every
one of your tasks
and become one when people
are separate become one come together
learn to understand each other
damn that's an answer and one way to
summarize this whole meaning of life
symposium
is that the very symposium was
illustrating
the quest for meaning which might itself
be
the meaning of life this constant quest
for something sublime something human
something intangible some you know
aspect of what defines us as a species
and as an individual both the quest of
me as a person through my own life
but the meaning of life could also be
the meaning of all of life
what is the whole point of life why life
why life itself
because we've been talking about the
history and evolution of life
but we haven't talked about why life in
the first place is life inevitable
is life part of physics
does life transcend physics but fighting
by fighting against entropy
by compartmentalizing and increasing
concentrations rather than diluting away
is life um a distinct entity in the
universe
beyond the traditional very simple
physical rules that govern gravity and
you know electromagnetism and all of
these forces
is life another force is there a life
force is there a unique
kind of set of principles that emerge of
course built on top of the hardware
of physics but is it sort of a new layer
of software
or a new layer of a computer system so
that's at the level
of you know big questions there's
another aspect of
gratitude of basically
what i you know what i like to say is
during this pandemic i've basically
worked from 6 a.m until 7 00 pm
every single day non-stop including
saturday and sunday
i've basically broken all boundaries of
where life
personal life begins and work life you
know ends
and uh that has been exhilarating for me
just just the intellectual pleasure
that i get from a day of exhaustion
where at the end of the day my brain is
hurting i'm telling my wife
wow i was useful today
and there's a certain
pleasure that comes from feeling useful
and there's a certain pleasure that
comes from feeling grateful
so i've written this little sort of
prayer
for my kids to say at bedtime every
night
where they basically say thank you god
for
all you have given me and give me the
strength
to give unto others with the same love
that you have given unto me
we as a species are so special
the only ones who worry about the
meaning of life
and maybe that's what makes us human
and what i like to say to my wife and to
my students during this pandemic
work extravaganza is
every now and then they ask me but how
do you do this and i'm like i'm a
workaholic
i love this this is me
in the most unfiltered way the ability
to do something
useful to feel that my brain is being
used
to interact with the smartest people on
the planet
day in day out and to help them discover
aspects of the human genome
of the human brain of human disease and
the human condition that no one has seen
before
with data that we're capturing that
has never been observed and there's
another aspect
which is on the personal life many
people say oh i'm not going to have kids
why bother
i can tell you as a father
they're missing half the picture if not
the whole picture
teaching my kids about my view of the
world
and watching through their eyes the
naivete with which they start
and the sophistication with which they
end up
they understanding that they have of not
just the natural world around them but
of me too
the unfiltered criticism
that you get from your own children
that knows no bounds
of honesty and i've grown
components of my heart that i didn't
know i had
until you sense that fragility
that vulnerability of the children
that immense love and passion
the unfiltered egoism that we as adults
learn how to hide so much better it's
just
this back of emotions that
tell me about the raw materials that
make a human being
and how these raw materials can be
arranged with more sophistication that
we learn through life
to become truly human adults
but there's something so beautiful about
seeing that progression between them
the complexity of the language growing
as more neural connections are formed
to to realize that the hardware is
getting rearranged
as their software is getting implemented
on that hardware
that their frontal cortex continues to
grow for another 10 years
these neuronal connections are
continuing to form new neurons that
actually get
replicated and formed and it's it's just
incredible that we have this not just
you grow the hardware for 30 years and
then you feed it all of the knowledge no
no the knowledge is fed throughout and
is shaping these neural connections as
they're forming
so seeing that transformation from
either your
own blood or from an adopted child is
the most beautiful thing you can do as a
human being
and it completes you it completes that
path that journey
the create life oh sure that's at
conception that's easy
but create human life to add the human
part
that takes decades of compassion
of sharing of love and of anger
and of impatience and patience
and as a parent
i think i've become a very different
kind of teacher
because again i'm a professor my first
role is to bring
adult human beings into a you know more
mature level of adulthood
where they learn not just to do science
but they learn
the process of discovery and the process
of collaboration the process of sharing
the process of
conveying the knowledge of encapsulating
something incredibly complex and and
sort of giving it up
in sort of bite-sized chunks that the
rest of humanity can appreciate
i tell my students all the time if you
you know
like when an apple fall when when when a
tree falls in the forest and no one's
there to listen has it really fallen
the same way you do this awesome
research if you write an impenetrable
paper that no one will understand
it's as if you never did the awesome
research so conveying of knowledge
conveying
this lateral transfer that i was talking
about at the very beginning
of sort of human humanity and sort of
the sharing of information
all of that has gotten so much
more rich by seeing human beings
grow in my own home because that
that makes me a better parent and that
makes me a better teacher
and a better mentor to the nurturing
of my adult children which are my
research group first of all
beautifully put connects
beautifully to the vertical and the
horizontal
inheritance of ideas that we've talked
about at the very beginning
i don't think there's a better way to
end it uh on this poetic
and powerful note uh manolas thank you
so much for talking there's a huge honor
we have to talk again
about the origin of life about
epigenetics epigenomics and uh
some of the incredible research you're
doing truly an honor thanks so much for
talking
thank you such a pleasure it's such a
pleasure i mean your questions are
outstanding i've had such a blast here
i can't wait to be back awesome thanks
for listening to this conversation with
manolas kellis and thank you
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and now let me leave you with some words
from charles darwin
that i think manolis represents quite
beautifully
if i had my life to live over again i
would have made a rule to read some
poetry
and listen to some music at least once
every week
thank you for listening and hope to see
you next time
you