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- My name is Dr. Ethel Gordon.

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I'm a member of Salem
State Biology faculty

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and treasurer for the
Darwin Festival committee.

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It is my pleasure to welcome

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the Salem State University community

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and our guests from the
local area and beyond.

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We are happy to welcome some of our area

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high school students and
STEM students of any age.

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Before we begin, I'd like to
remind you that we will have

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questions and answers
at the end of the talk,

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and you will see on the
bottom of your screen

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a box that says Q and A,

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you can write your questions at any time,

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and Dr. Ogbunu will be happy to answer

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as many as possible at
the end of the talk.

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So again, it is my
honor today to introduce

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our afternoon speaker
Dr. C. Brandon Ogbunu.

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Dr. Ogbunu is Assistant Professor

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in the Department of Ecology
and Evolutionary Biology

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at Yale university.

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He is a computational
biologist whose research

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investigates complex problems
in epidemiology, evolutionary

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and population genetics and evolution.

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His work utilizes a range of methods

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from experimental
evolution, to biochemistry,

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mathematics and evolutionary computation.

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He has published on topics as varied

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as the evolution of
anti-microbial resistance,

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virus evolution and also on health policy.

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In addition, he runs a
parallel research program

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at the intersection of
science, society and culture,

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where he writes for publications including

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"Scientific American," the
nonprofit magazine, "Undark"

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and the "Boston Review."

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He gives public lectures and creates media

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in this program as well.

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Dr. Ogbunu's talk is entitled

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Environment times everything interactions,

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from protein evolution
to the stories of us.

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It is supported by an honorarium

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from Bruce Perry and the campus center.

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Please give a warm Salem State welcome

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to Dr.C.Brandon Ogbunu.

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- Great, thank you for
that kind introduction.

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I am so excited to be here.

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From what I understand,

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Professor Jablonski spoke this morning.

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And that is to say that, that's
a challenging act to follow,

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it's an understatement.

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I'm a tremendous fan
of Professor Jablonski.

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So, I'm gonna do the
best I can after talking

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after a legend but I
think we're gonna be able

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to have hopefully some fun today

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and talk about some interesting ideas.

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The title of my talk,

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E by everything interactions
from protein evolution

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to the stories of us.

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And I promise you that
will make sense as we talk.

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There's gonna be a theme
that we're gonna use,

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we're gonna introduce it early,

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and we're gonna kind
of run this throughout

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my research program and one of the themes

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is going to be kind of the way
that I think about my actual

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work in the laboratory and
the work that I'm publishing

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and the relationship between that

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and the work that I'm
doing kind of in life.

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And these things are not kind of

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as separate as one often thinks.

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That scientists are part
of a functional world

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and that the ideas that we
have can interact with the way

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we live and the way we
treat each other, et cetera.

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I wanna thank a whole host
of collaborators and mentors

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and friends and people who have supported

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various parts of this work.

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Mentees are now mentors,
that's the way this profession

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works and I wanna make sure
they are all acknowledged.

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Some of these characters you
might meet along the way,

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but I wanted to make sure people
were probably acknowledged.

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Similarly, I wanna make
sure all of the institutions

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who supported this work in its
various iterations and forms

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through the years are
properly acknowledged

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and want to thank them
for their administrative

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and financial support through the years.

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Absolutely, I wanna thank
Professors Fisher, Gordon,

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for your support and the rest
of the Department of Biology

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at Salem State for the kind invitation

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and for facilitating my participation

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which I'm honored by.

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Again, all of the sponsors and organizers

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of this great festival.

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Really, really a unique
and important event,

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one of the most interesting of its kind.

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So congratulations to
all of you for putting

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on another successful version of this

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and I'm really, really
thrilled to participate.

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And perhaps last but not
least, perhaps last but most,

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all of you for taking
time out of your afternoon

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to listen to what I have to say.

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Talking about what my lab does.

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And I think, am like
I'm one of these people

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who every five years, I like
to rebrand myself, right?

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Not because I'm an actual
company, but because I think

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every few years I get different
things to think about.

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And I think one of the
things that I've been trained

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in a lot of different things,
formally in population,

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genetics and epidemiology

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and mathematical modeling,
experiment evolution.

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But I like to brand myself differently.

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It's kind of I like to brand myself

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by the ways I like to think about nature.

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That's actually how I would
prefer to brand myself.

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And really the thing
that I'm interested in

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is how interactions between
actors and the environments

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shape disease dynamics, right?

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That's really, really kind...

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I call myself a non-linearity nerd,

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as in, you put one thing
and another thing together

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and something surprising happens,

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in biology and the way
that creates disease.

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That's how I think about
what my interests are.

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And that really does run
through my research program.

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And I think really,
really one of the things

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in the title of the talk, right,

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Environment by everything, is
kind of considering carefully

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what this notion of an
environment is, right?

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When we think about what an environment is

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in biology and in society, right?

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So, when a biologist is
talking about the environment,

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they're usually thinking
about several things,

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several types of definitions

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and different types of biologists, right?

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Think about it and apply it
in their work a different way.

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So for the individuals who are familiar

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with the field called
quantitative genetics

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or population genetics, right?

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Or if you're really a
super nerd and you've heard

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of this concept called plasticity, right?

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You might be thinking about
an environment this way.

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Don't let the equations or
anything like that throw you off.

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All it means is this.

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Let's just say you have
person and one person two

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or tree type one and tree
type two and basically

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you have hot and cold temperature
and that different people,

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some of you individuals in the audience

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are warm-weather people,

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some of you are kind
of cold-weather people.

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Some of you are morning
people, some of you are not.

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Some of you like to thrive
at night, some of you don't.

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The point is that
organisms differ in the way

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that we perform based on
some kind of environment.

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And that environment again,
could be time of day,

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it could be temperature, it
could be a type of nutrient,

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it could be a type of infection
with something, right?

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So the idea here is that,

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that's one way we
consider this environment,

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it's thinking about the
way different organisms

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of different kinds or different mutations

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of an organism, function
across different environments.

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For those of you that
might be more informed,

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we call this graph something

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called a norm of reaction, right?

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And for the high school students
you can write that down.

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Sound very smart in class,
talk about a Norm of Reaction.

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It basically demonstrates how an organism

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performs across different environments.

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On the right here is just
an equation that people use,

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where the information
from this graph is used.

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So that's one way a biologist
think about the environment.

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What's another way that the biologists

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think about an environment, right?

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Maybe something called micro-environment.

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Now that is a protein,
it's enzyme there, okay.

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And that's not so much like,
environment like time of day,

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you're talking about proteins

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at a very, very small scale here.

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We're talking about
things like temperature,

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so not unlike we experienced temperature.

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Do you think the enzymes in your body

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that break down food
might function differently

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at different temperatures?

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If you go to extreme cold,
you think that might affect

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their ability to break down
food or absolutely, right?

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I think the body kind of has
this very, very sophisticated

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machinery to make sure that,

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you might have here homeostasis,

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we have these things in
place to make sure things

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are functioning at a
level and our temperature

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stays at a level.

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Because getting too
cold and getting too hot

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is bad for the way that we function.

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So, even within ourselves, the
way that proteins are folding

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is gonna be very, very, very specific

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to a certain environment.

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But that's one way we often
think about an environment,

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is at a very micro scale, small,
small, small, small scale,

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and things that that does to us.

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Another way we might
consider environment, right?

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Is this one.

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I can have you guess about
what this is a mapping of,

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what we could probably guess,

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it's kind of like either
temperature you just heard

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this from Professor Jablonski, right?

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In terms of UV radiation, right?

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Because as you see
here, it's gonna be red,

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it's gonna correspond
to a temperature here.

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And of course, this
correlates in some ways

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with UV radiation.

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But the point is, we could
mean something global.

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We could mean kind of
like the global sense.

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And we're talking about climate change

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and these big processes that apply broadly

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across hundreds and thousands of miles.

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That's another way that a lot of us

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think about environments.

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And I think environmental
sciences, for example,

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looking at how climate change influences,

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kind of the population
of a species of bear

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is often times thinking about it this way,

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the environment has kind
of a global process, okay.

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That's another way that biologists think

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about the environment.

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But another way we think
about an environment, is this.

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And what's that?

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So that's Queensbridge housing projects.

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That's the largest, I
think still the largest

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public housing projects in the country.

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I'm not from there, but
I am from New York city,

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and I did spend time growing
up in public housing.

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And so the idea here
is, this is different.

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This is kind of where you live,

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and who you live with and
who you're connected to

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and who you talk to and where you work

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and where you go to school.

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This is another important aspect
of what our environment is.

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And it turns out that, right?

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This one, is connected to this one.

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And what am I looking at here?

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This is in the 1930s, okay.

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This is the 1930s, what
are we noticing here?

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Right?

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This is segregation.

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This individual is drinking
out of a water fountain

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that is relegated to individuals
of a certain color, okay.

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This is the United States, all right.

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So the idea here is, this is another way

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that people think about environments.

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This is kind of a historical thing.

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That's not so historical, right?

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'Cause it obviously has a role

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for the way the world is lived today.

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But the idea here is we can think

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about environments, many, many ways.

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We can think about it with
regards to quantitative genetics.

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We can think about it with
regards to physiology,

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as in the things going on within the body

256
00:10:56,280 --> 00:10:58,520
and the way the body is kind
of maintaining temperature

257
00:10:58,520 --> 00:10:59,610
and things of that nature.

258
00:10:59,610 --> 00:11:02,236
We can think about it in terms of climate,

259
00:11:02,236 --> 00:11:04,290
that's the way we
consider the environment.

260
00:11:04,290 --> 00:11:05,490
We can think about our environment

261
00:11:05,490 --> 00:11:06,650
as like who you actually live with

262
00:11:06,650 --> 00:11:08,530
and where you live and
what neighborhood that is.

263
00:11:08,530 --> 00:11:09,400
And then we can think about it

264
00:11:09,400 --> 00:11:13,000
as what are the forces that
influence where we live

265
00:11:13,000 --> 00:11:15,090
and why do we live on
this side of the town

266
00:11:15,090 --> 00:11:15,923
and not that one?

267
00:11:15,923 --> 00:11:17,710
And why do these people live
on this side of the town

268
00:11:17,710 --> 00:11:18,690
and not that one?

269
00:11:18,690 --> 00:11:21,110
All of these things are important actors,

270
00:11:21,110 --> 00:11:23,690
when we kinda of thinking
about a biological phenomenon,

271
00:11:23,690 --> 00:11:25,840
especially one like disease.

272
00:11:25,840 --> 00:11:27,870
They're all important actors, right?

273
00:11:27,870 --> 00:11:31,110
And I think my work really kind of invokes

274
00:11:31,110 --> 00:11:33,940
all of these different
factors in different levels,

275
00:11:33,940 --> 00:11:36,340
in different parts of my
research program, right?

276
00:11:36,340 --> 00:11:38,340
Now I can't ask everything
and some of these things

277
00:11:38,340 --> 00:11:41,520
I'm obviously better trained
to aggressively pursue.

278
00:11:41,520 --> 00:11:42,470
So the talk you gonna get today

279
00:11:42,470 --> 00:11:44,560
is mostly in these realms, right?

280
00:11:44,560 --> 00:11:45,960
In terms of the basic science

281
00:11:45,960 --> 00:11:49,350
that I do in my laboratory
and a little bit of this.

282
00:11:49,350 --> 00:11:51,560
But I think these other
realms like this local,

283
00:11:51,560 --> 00:11:53,520
lived and corporeal, this historical,

284
00:11:53,520 --> 00:11:55,580
structural and psychological.

285
00:11:55,580 --> 00:11:56,957
People in high school, corporeal,

286
00:11:56,957 --> 00:11:59,350
it's a good word, write it down.

287
00:12:01,206 --> 00:12:04,110
These are other aspects
that my research program

288
00:12:04,110 --> 00:12:06,350
is currently interrogating in the context

289
00:12:06,350 --> 00:12:08,270
of looking at disease, right?

290
00:12:08,270 --> 00:12:10,080
And in looking at life.

291
00:12:10,080 --> 00:12:12,390
And so that's what I'm gonna
talk to you about today, right?

292
00:12:12,390 --> 00:12:14,630
When I say environment by
everything interactions,

293
00:12:14,630 --> 00:12:18,680
my point is, there's not a
problem in biology, none.

294
00:12:18,680 --> 00:12:21,380
With one of these factors
isn't actually influencing

295
00:12:21,380 --> 00:12:23,400
the biological phenomenon of interest.

296
00:12:23,400 --> 00:12:25,550
I don't care if you're interested in trees

297
00:12:25,550 --> 00:12:29,530
or plants or human disease
or rabbit reproduction

298
00:12:29,530 --> 00:12:34,480
or photosynthesis, something
in here is influencing that

299
00:12:34,480 --> 00:12:37,130
and I think this influences
biological phenomenon

300
00:12:37,130 --> 00:12:40,170
more than we think and in
ways that may surprise us.

301
00:12:40,170 --> 00:12:42,060
Okay, right.

302
00:12:42,060 --> 00:12:43,990
And so we'll talk about today, right?

303
00:12:43,990 --> 00:12:45,960
Mostly is the evolution
infectious diseases,

304
00:12:45,960 --> 00:12:46,930
like I told you.

305
00:12:46,930 --> 00:12:48,810
Evolution at the level
of genes and proteins,

306
00:12:48,810 --> 00:12:52,640
and mostly I'll be talking
about how micro-environments

307
00:12:52,640 --> 00:12:55,290
influence adaptive evolution, okay?

308
00:12:55,290 --> 00:12:58,440
So what we'll get then, right,
we'll do something today,

309
00:12:58,440 --> 00:13:01,700
the way we'll kind of proceed
is we'll start with a problem

310
00:13:01,700 --> 00:13:04,040
like anti-microbial resistance in malaria.

311
00:13:04,040 --> 00:13:06,050
Malaria is a disease that
isn't so much a problem

312
00:13:06,050 --> 00:13:08,630
in the United States but remains
a very, very large problem

313
00:13:08,630 --> 00:13:10,920
throughout the world, in Asia

314
00:13:10,920 --> 00:13:13,690
and certainly in Sub-Saharan Africa.

315
00:13:13,690 --> 00:13:15,210
And of course it is kind of transmitted

316
00:13:15,210 --> 00:13:17,460
by the anopheles mosquito.

317
00:13:17,460 --> 00:13:20,600
And of course there's a lot
of resistance to the drugs

318
00:13:20,600 --> 00:13:22,280
that we use that were once effective

319
00:13:22,280 --> 00:13:24,160
and still are in some places, right?

320
00:13:24,160 --> 00:13:27,080
So we'll take this problem,
we'll kind of think about

321
00:13:27,080 --> 00:13:29,320
how to think about antibiotic
resistance in general,

322
00:13:29,320 --> 00:13:32,070
so not just malaria, but we'll
take that to thinking about

323
00:13:32,070 --> 00:13:34,670
kind of problems and how proteins evolve

324
00:13:34,670 --> 00:13:37,640
and we'll do that to think about
big questions in evolution.

325
00:13:37,640 --> 00:13:40,530
So, we'll start with an individual problem

326
00:13:40,530 --> 00:13:42,140
and how that's influenced
by the environment.

327
00:13:42,140 --> 00:13:44,060
And then we'll walk that to hack,

328
00:13:44,060 --> 00:13:46,683
ask some bigger questions
about who we are, right?

329
00:13:48,110 --> 00:13:50,730
Alright and so I use proteins.

330
00:13:50,730 --> 00:13:52,790
Now, most of you when
we think about proteins

331
00:13:52,790 --> 00:13:54,840
that our daily life and our dietary life,

332
00:13:54,840 --> 00:13:55,887
we're thinking about,

333
00:13:55,887 --> 00:13:58,090
how much protein is in my protein shake?

334
00:13:58,090 --> 00:14:00,060
Or am I getting my 20 pounds of protein?

335
00:14:00,060 --> 00:14:01,420
Again, I know when I was in high school

336
00:14:01,420 --> 00:14:03,640
and I heard there were some
high school students here,

337
00:14:03,640 --> 00:14:05,100
I was a little bit of a jock

338
00:14:05,100 --> 00:14:06,810
and I played high school football.

339
00:14:06,810 --> 00:14:08,990
And I was just like obsessed with just...

340
00:14:09,930 --> 00:14:12,700
I thought, mostly incorrectly,

341
00:14:12,700 --> 00:14:14,797
that if you just like
feed your body protein,

342
00:14:14,797 --> 00:14:17,610
you'll grow big muscles
and you'll be in the NFL.

343
00:14:17,610 --> 00:14:18,710
Doesn't work that way.

344
00:14:20,990 --> 00:14:22,740
But the point is protein
consumption is very, very important

345
00:14:22,740 --> 00:14:24,710
for kind of maintaining all this function

346
00:14:24,710 --> 00:14:25,940
that the body is, right?

347
00:14:25,940 --> 00:14:29,080
So the proteins are a class of biomolecule

348
00:14:29,080 --> 00:14:30,360
and they're really responsible

349
00:14:30,360 --> 00:14:33,950
for lot of the kind of
acting within ourselves.

350
00:14:33,950 --> 00:14:35,470
A lot of the switches, a lot of the things

351
00:14:35,470 --> 00:14:37,250
that break down enzymes,
a lot of the things

352
00:14:37,250 --> 00:14:39,700
that perform functions, are protein.

353
00:14:39,700 --> 00:14:42,380
And so we'll talk about kind
of individual proteins today.

354
00:14:42,380 --> 00:14:44,450
Right and we'll talk about these things,

355
00:14:44,450 --> 00:14:47,510
kind of, proteins can be
considered two things.

356
00:14:47,510 --> 00:14:49,740
Proteins can be considered, right?

357
00:14:49,740 --> 00:14:51,230
They're real physical things,

358
00:14:51,230 --> 00:14:53,140
I mean, these are made of
kind of atoms and the things

359
00:14:53,140 --> 00:14:55,940
you learn in chemistry with
rows electrons and all of that.

360
00:14:55,940 --> 00:14:58,400
That's real, that happens
here and we also study that.

361
00:14:58,400 --> 00:15:01,170
But today we're gonna use
a simpler way of thinking

362
00:15:01,170 --> 00:15:03,770
about proteins, we won't
go into the electron level.

363
00:15:05,600 --> 00:15:08,000
I'm not gonna make you invoke
your high school chemistry,

364
00:15:08,000 --> 00:15:10,320
your college chemistry knowledge really.

365
00:15:10,320 --> 00:15:13,550
Thinking about a protein
kind of as a collection

366
00:15:13,550 --> 00:15:16,800
of information and when we
think about it that way,

367
00:15:16,800 --> 00:15:19,810
I think a lot of different questions arise

368
00:15:19,810 --> 00:15:22,990
that make it easier to understand, right?

369
00:15:22,990 --> 00:15:26,570
So the way it'll go, we'll
talk about protein evolution,

370
00:15:26,570 --> 00:15:28,330
we'll talk about reverse evolution,

371
00:15:28,330 --> 00:15:30,330
and then we'll talk about this last part.

372
00:15:30,330 --> 00:15:31,860
So, on protein space.

373
00:15:31,860 --> 00:15:35,082
Now, junior people in particular, okay,

374
00:15:35,082 --> 00:15:36,930
I don't mean young, but junior,

375
00:15:36,930 --> 00:15:39,410
it doesn't matter how old or
young you are chronologically,

376
00:15:39,410 --> 00:15:41,980
but wherever you are in
your scientific training,

377
00:15:41,980 --> 00:15:45,130
I'm gonna tell you a really,
really useful piece of advice.

378
00:15:45,130 --> 00:15:48,100
You should be a historian
of your own field.

379
00:15:48,100 --> 00:15:50,420
If you're interested in marine biology,

380
00:15:50,420 --> 00:15:52,630
read the history of marine biology.

381
00:15:52,630 --> 00:15:54,450
How did the ideas come up?

382
00:15:54,450 --> 00:15:56,070
How did we arrive at those ideas?

383
00:15:56,070 --> 00:15:57,140
Who did them first?

384
00:15:57,140 --> 00:15:58,903
Why do we measure things
this way or that way?

385
00:15:58,903 --> 00:16:00,710
It's a very important thing.

386
00:16:00,710 --> 00:16:02,570
If you're interested in a botany,

387
00:16:02,570 --> 00:16:04,077
if you're interested in medicine, right?

388
00:16:04,077 --> 00:16:07,180
And the reason why I say that,
it's not just because I want

389
00:16:07,180 --> 00:16:09,460
you to spend kind of
needless time reading,

390
00:16:09,460 --> 00:16:12,570
it's because when you study
the history of a field,

391
00:16:12,570 --> 00:16:16,130
you identify some ideas that may be wrong

392
00:16:16,130 --> 00:16:19,700
or need improvement or
on the positive side,

393
00:16:19,700 --> 00:16:22,390
things that are like
inspiring that other people

394
00:16:22,390 --> 00:16:25,650
have forgotten or aren't
being kinda used properly

395
00:16:25,650 --> 00:16:28,300
or maybe can give you a new framework

396
00:16:28,300 --> 00:16:30,900
for thinking about your work today.

397
00:16:30,900 --> 00:16:33,350
You think just because work
happened a long time ago

398
00:16:33,350 --> 00:16:35,660
that somehow they were less
intelligent than you are.

399
00:16:35,660 --> 00:16:37,380
That ain't true, okay?

400
00:16:37,380 --> 00:16:39,830
They thought just as clearly about nature

401
00:16:39,830 --> 00:16:41,760
as you did if not more,

402
00:16:41,760 --> 00:16:43,100
the difference is they just didn't have

403
00:16:43,100 --> 00:16:45,063
the technology that you have, right?

404
00:16:46,950 --> 00:16:49,270
This story here is about how
I started reading historically

405
00:16:49,270 --> 00:16:52,640
about my field and it ended up

406
00:16:52,640 --> 00:16:54,920
changing my career forever, right?

407
00:16:54,920 --> 00:16:57,270
Another cool thing about
reading about history is,

408
00:16:57,270 --> 00:16:59,160
this is not just true for science.

409
00:16:59,160 --> 00:17:01,550
You ever read a history
book and you identify people

410
00:17:01,550 --> 00:17:03,810
in history that you're kind of like?

411
00:17:03,810 --> 00:17:05,350
You're like, I'm kind of like this person,

412
00:17:05,350 --> 00:17:06,820
I mean or you wish you
were like this person

413
00:17:06,820 --> 00:17:08,780
but there's strands of their personality.

414
00:17:08,780 --> 00:17:11,500
I just read a great biography
of Frederick Douglas

415
00:17:11,500 --> 00:17:13,400
and of course, I'm not
saying I'm Frederick Douglas,

416
00:17:13,400 --> 00:17:17,403
my goodness, one of the
greatest Americans ever.

417
00:17:18,670 --> 00:17:20,950
But what I'm saying is there's
threads of the way he thought

418
00:17:20,950 --> 00:17:23,060
about society and himself and I was like,

419
00:17:23,060 --> 00:17:24,370
I really do feel that way.

420
00:17:24,370 --> 00:17:26,460
And so that it's also
important to do for science.

421
00:17:26,460 --> 00:17:30,800
And for me, the figure that I
discovered in reading science

422
00:17:30,800 --> 00:17:33,360
was somebody named John Maynard Smith.

423
00:17:33,360 --> 00:17:37,283
Now John Maynard Smith, was
an Englishman, brilliant.

424
00:17:38,590 --> 00:17:41,010
And as somebody who thinks
a lot about the world

425
00:17:41,010 --> 00:17:42,800
the way that I do and I've kind of adopted

426
00:17:42,800 --> 00:17:46,533
John Maynard Smith as somebody
that I really admire, okay?

427
00:17:47,730 --> 00:17:49,210
And what did John Maynard Smith do?

428
00:17:49,210 --> 00:17:54,210
When I was in graduate school,
I read this important paper.

429
00:17:54,750 --> 00:17:58,040
Now, don't worry about
reading about it now,

430
00:17:58,040 --> 00:17:59,300
it's called Natural selection

431
00:17:59,300 --> 00:18:00,940
and the concept of a protein space.

432
00:18:00,940 --> 00:18:02,643
Now mind you, at this time

433
00:18:06,419 --> 00:18:10,363
I was not studying proteins, at all.

434
00:18:17,871 --> 00:18:19,410
I was not studying proteins at this time.

435
00:18:19,410 --> 00:18:21,360
I was actually studying microbes,

436
00:18:21,360 --> 00:18:22,800
but I read this paper anyway

437
00:18:22,800 --> 00:18:25,310
and it had a very big impact in my life.

438
00:18:25,310 --> 00:18:27,920
Note the date 1970, okay.

439
00:18:27,920 --> 00:18:32,170
It's 2021 now, but 2020
was his 50th anniversary.

440
00:18:32,170 --> 00:18:34,780
So I ended up writing a
reflection on this, right?

441
00:18:34,780 --> 00:18:35,613
In genetics, right?

442
00:18:35,613 --> 00:18:36,880
A very kind of nice journal.

443
00:18:36,880 --> 00:18:40,650
I wrote a reflection on the
importance of this paper.

444
00:18:40,650 --> 00:18:43,780
And like I told you, again,
I am not a historian.

445
00:18:43,780 --> 00:18:46,400
I'm a population geneticist
who studies proteins.

446
00:18:46,400 --> 00:18:49,200
So why am I doing a
historical take on this?

447
00:18:49,200 --> 00:18:52,070
Well, doing the history of
this paper gave me real ideas.

448
00:18:52,070 --> 00:18:54,930
And so what I discovered
a couple of things, okay?

449
00:18:54,930 --> 00:18:59,770
About kind of the origin of this paper.

450
00:18:59,770 --> 00:19:03,940
It turns out that this
paper was a rebuttal.

451
00:19:03,940 --> 00:19:06,170
So the paper here was a rebuttal,

452
00:19:06,170 --> 00:19:08,890
to a paper that was published in 1969

453
00:19:08,890 --> 00:19:10,713
by somebody named Frank.B.Salisbury.

454
00:19:11,890 --> 00:19:14,740
Now, Frank B. Salisbury
was a well-regarded plant

455
00:19:14,740 --> 00:19:17,300
physiologist, very well-regarded.

456
00:19:17,300 --> 00:19:20,303
And what Frank. B Salisbury
said is the following,

457
00:19:21,867 --> 00:19:25,207
"if life really depends on
each gene being as unique

458
00:19:25,207 --> 00:19:28,147
"as it appears to be, then
it is too unique to come

459
00:19:28,147 --> 00:19:29,987
"into being by chance to mutations."

460
00:19:32,100 --> 00:19:34,630
This is also published in
"Nature" one of the most

461
00:19:34,630 --> 00:19:36,874
prestigious journals in the world, right?

462
00:19:36,874 --> 00:19:39,340
And basically what
Frank. B.Salisbury said.

463
00:19:39,340 --> 00:19:42,580
is that natural selection
can't really solve

464
00:19:42,580 --> 00:19:45,820
a lot of complex problems, right?

465
00:19:45,820 --> 00:19:47,930
That there's gotta be some other force.

466
00:19:47,930 --> 00:19:51,040
If that sounds fishy to you stay with me.

467
00:19:51,040 --> 00:19:54,560
John Maynard Smith, read this
paper and was like, no, no.

468
00:19:54,560 --> 00:19:55,690
I mean, I don't know if John Maynard Smith

469
00:19:55,690 --> 00:19:57,130
said that, because I
haven't met him or asked him

470
00:19:57,130 --> 00:19:59,370
but I'm imagining to myself
that John Maynard Smith

471
00:19:59,370 --> 00:20:00,970
was like, no, no, no, no, no, no.

472
00:20:00,970 --> 00:20:03,550
And John Maynard Smith
said the following, right?

473
00:20:03,550 --> 00:20:06,257
If you notice the first
line, "Salisbury has argued

474
00:20:06,257 --> 00:20:07,780
"that there is an apparent contradiction."

475
00:20:07,780 --> 00:20:09,450
He basically says, you're wrong

476
00:20:09,450 --> 00:20:11,390
and I'm gonna demonstrate
why you're wrong.

477
00:20:11,390 --> 00:20:13,710
And the model of protein
evolution I want to discuss

478
00:20:13,710 --> 00:20:17,490
is best understood by analogy
with a popular word game.

479
00:20:17,490 --> 00:20:19,770
I don't know how many
of you all are gamers.

480
00:20:19,770 --> 00:20:21,710
I'm a huge gamer, we'll get to that later.

481
00:20:21,710 --> 00:20:24,030
I love board games, I love video games

482
00:20:24,030 --> 00:20:26,170
but one of the most important papers ever

483
00:20:26,170 --> 00:20:29,470
in evolutionary biology, didn't
really have any equations

484
00:20:29,470 --> 00:20:30,420
in it even though it was written

485
00:20:30,420 --> 00:20:32,210
by a brilliant mathematician.

486
00:20:32,210 --> 00:20:36,960
And it used a child's game to
explain in a central concept.

487
00:20:36,960 --> 00:20:39,700
And I don't know how many of
you have played word ladder

488
00:20:39,700 --> 00:20:42,020
but this is what word
ladder is like, right?

489
00:20:42,020 --> 00:20:43,530
In word ladder, what do you do?

490
00:20:43,530 --> 00:20:46,320
In word ladder, you're
basically changing one letter

491
00:20:46,320 --> 00:20:49,407
at a time, you wanna go from
word and you wanna to gene.

492
00:20:49,407 --> 00:20:51,833
and you wanna change one letter at a time.

493
00:20:53,640 --> 00:20:56,070
Now, what is the winning pathway?

494
00:20:56,070 --> 00:20:59,850
The winning pathway is the
one where all the words

495
00:20:59,850 --> 00:21:02,362
make sense along the way,
which one would win here.

496
00:21:02,362 --> 00:21:04,740
If I was in person, I'd like call on you

497
00:21:04,740 --> 00:21:06,160
and maybe embarrass
people, I'm just kidding.

498
00:21:06,160 --> 00:21:08,060
But the point is, I'm
not gonna do that here.

499
00:21:08,060 --> 00:21:10,963
You all are so smart, you
probably already landed here.

500
00:21:12,340 --> 00:21:13,600
That's the winner.

501
00:21:13,600 --> 00:21:15,090
Why is that the winner?

502
00:21:15,090 --> 00:21:17,010
That's the winner because all the words

503
00:21:17,010 --> 00:21:18,500
are meaningful words.

504
00:21:18,500 --> 00:21:19,333
Look at this.

505
00:21:19,333 --> 00:21:20,600
That's a word that's meaningful.

506
00:21:20,600 --> 00:21:22,320
Werd, not really.

507
00:21:22,320 --> 00:21:24,410
Wene, not really, right?

508
00:21:24,410 --> 00:21:26,940
Gord, hmm, not really, right?

509
00:21:26,940 --> 00:21:29,126
Wond, sure, wone, nope.

510
00:21:29,126 --> 00:21:34,058
All these other ones have
a word in the pathway

511
00:21:34,058 --> 00:21:36,003
that does not make sense or you know

512
00:21:36,003 --> 00:21:37,670
it doesn't really make sense.

513
00:21:37,670 --> 00:21:40,970
Whereas this one, all
of the words make sense.

514
00:21:40,970 --> 00:21:44,620
Now, what was John Maynard
Smith trying to say, okay,

515
00:21:44,620 --> 00:21:47,270
John Maynard Smith was making
a very important point.

516
00:21:47,270 --> 00:21:51,200
He said that adaptive
evolution works this way.

517
00:21:51,200 --> 00:21:55,400
It does not need to solve
word to gene in one step.

518
00:21:55,400 --> 00:22:00,400
It is solving it bit by bit by bit by bit.

519
00:22:00,730 --> 00:22:05,230
Evolution's working slightly
and it's searching a space

520
00:22:05,230 --> 00:22:09,540
and that's how it identifies,
long-term solutions.

521
00:22:09,540 --> 00:22:13,330
The only thing that has to
happen to solve a big problem

522
00:22:13,330 --> 00:22:16,180
in evolution, one thing has
to happen in order to solve

523
00:22:16,180 --> 00:22:19,440
a big problem in evolution
that the things along the way

524
00:22:19,440 --> 00:22:21,270
have to make sense.

525
00:22:21,270 --> 00:22:23,480
So as long as the thing, it makes sense,

526
00:22:23,480 --> 00:22:25,530
meaning what that the organism functions.

527
00:22:26,400 --> 00:22:28,680
So as long as the thing
that maybe we're evolving

528
00:22:28,680 --> 00:22:31,240
a new protein, maybe we're
evolving a new enzyme,

529
00:22:31,240 --> 00:22:33,240
maybe we're evolving some kind of pigment.

530
00:22:33,240 --> 00:22:36,340
We talk about professor
Jablonski's work earlier.

531
00:22:36,340 --> 00:22:38,120
Maybe we're talking about
the genes having to do

532
00:22:38,120 --> 00:22:40,900
with skin pigmentation or
eye color or what have you.

533
00:22:40,900 --> 00:22:42,410
The only thing that has to happen in order

534
00:22:42,410 --> 00:22:45,180
for you to get from one color
to a much different color

535
00:22:45,180 --> 00:22:48,360
is that the things along
the way have to make sense.

536
00:22:48,360 --> 00:22:50,500
And this is the way adaptive evolution

537
00:22:50,500 --> 00:22:52,250
actually works, right?

538
00:22:52,250 --> 00:22:54,750
And we'll see that, okay?

539
00:22:54,750 --> 00:22:56,250
What happened to this debate?

540
00:22:56,250 --> 00:22:57,930
So John Maynard Smith
goes on to one of the most

541
00:22:57,930 --> 00:22:59,960
illustrious careers he was
already famous at that time.

542
00:22:59,960 --> 00:23:01,340
Goes on one of the most illustrious

543
00:23:01,340 --> 00:23:04,514
careers ever, ever, okay?

544
00:23:04,514 --> 00:23:06,660
You ask different people
in different fields,

545
00:23:06,660 --> 00:23:08,570
they'll tell you a different
John Maynard Smith.

546
00:23:08,570 --> 00:23:10,720
Some of you all are really, really...

547
00:23:10,720 --> 00:23:12,960
Some of you are game theory nerds, right?

548
00:23:12,960 --> 00:23:14,140
People who might've heard a game theory,

549
00:23:14,140 --> 00:23:16,160
might've seen a beautiful mind, right?

550
00:23:16,160 --> 00:23:17,660
Where with John Nash, where he came up

551
00:23:17,660 --> 00:23:19,951
with kind of some of the
basic tenants of kind of,

552
00:23:19,951 --> 00:23:23,080
how game theory is
applied and economics...

553
00:23:23,080 --> 00:23:24,710
The mathematical
foundations of game theory,

554
00:23:24,710 --> 00:23:26,060
which were applied to economics

555
00:23:26,060 --> 00:23:27,840
ended up winning a Nobel
prize in economics.

556
00:23:27,840 --> 00:23:29,430
John Maynard Smith was one of the people

557
00:23:29,430 --> 00:23:32,430
who brought it to biology, okay?

558
00:23:32,430 --> 00:23:34,450
So my point is, John Maynard
Smith was a brilliant person

559
00:23:34,450 --> 00:23:37,030
who thought really broadly
across disciplines.

560
00:23:37,030 --> 00:23:38,950
What happened to Salisbury?

561
00:23:38,950 --> 00:23:41,980
Who was well-respected,
not long after that paper

562
00:23:41,980 --> 00:23:44,190
got published, he didn't
rebut, didn't really fire back

563
00:23:44,190 --> 00:23:46,000
at John Maynard Smith.

564
00:23:46,000 --> 00:23:50,143
He ended up starting to
write books like this, right?

565
00:23:51,630 --> 00:23:53,713
And then of course his Magnum Opus.

566
00:23:55,530 --> 00:23:57,530
So you can see the beginnings

567
00:23:57,530 --> 00:24:01,770
of creationist logic that
he was trying to hide

568
00:24:01,770 --> 00:24:05,490
in that scientific paper that
John Maynard Smith rebutted

569
00:24:05,490 --> 00:24:08,900
I must say the one book
that Salisbury did publish

570
00:24:08,900 --> 00:24:12,000
that I am fond of, or
rather, not so much fond of

571
00:24:12,000 --> 00:24:14,480
but I am kinda intrigued
by and I'll empathize

572
00:24:14,480 --> 00:24:17,410
with, is the one on UFO's,
that one I'll give him, okay?

573
00:24:17,410 --> 00:24:18,620
That one I'll give it, right?

574
00:24:18,620 --> 00:24:21,180
But the rest of the stuff,
again, was trying to kind

575
00:24:21,180 --> 00:24:24,940
of hide some creationists
ideas and evolution.

576
00:24:24,940 --> 00:24:28,200
So really it's this protein space concept

577
00:24:28,200 --> 00:24:30,170
that John Maynard Smith mentioned

578
00:24:30,170 --> 00:24:33,010
is a very, very, very rich concept.

579
00:24:33,010 --> 00:24:36,960
A lot of ideas in modern
biology, sophisticated

580
00:24:36,960 --> 00:24:41,763
and hyper nerdy ones, live
within that basic word game.

581
00:24:43,060 --> 00:24:46,510
That basic word game of
going from word to gene

582
00:24:46,510 --> 00:24:48,320
you can get a lot of concepts.

583
00:24:48,320 --> 00:24:51,690
There's this thing called the
fitness or adaptive landscape

584
00:24:51,690 --> 00:24:53,630
that some of you might've
heard of that falls

585
00:24:53,630 --> 00:24:55,760
directly out of that.

586
00:24:55,760 --> 00:24:57,300
That predated protein space.

587
00:24:57,300 --> 00:25:00,080
But my point is from protein space

588
00:25:00,080 --> 00:25:01,580
you can get a lot of cool things.

589
00:25:01,580 --> 00:25:03,270
And that's really what
my research program did.

590
00:25:03,270 --> 00:25:05,460
I think I learned protein space first.

591
00:25:05,460 --> 00:25:08,073
And then I learned all of
these sophisticated ideas,

592
00:25:09,040 --> 00:25:10,980
but one of the cool things
is pay most attention

593
00:25:10,980 --> 00:25:11,930
to this last thing.

594
00:25:13,760 --> 00:25:16,220
The thing about protein space going

595
00:25:16,220 --> 00:25:19,510
from word to gene is that also implies

596
00:25:19,510 --> 00:25:22,940
that evolution might be
able to be predictable.

597
00:25:22,940 --> 00:25:26,290
If we know what all the words
are and we know which words

598
00:25:26,290 --> 00:25:28,903
make sense, what if
that's true for proteins?

599
00:25:30,210 --> 00:25:32,370
We can maybe predict the steps

600
00:25:32,370 --> 00:25:34,860
that evolution will take, right?

601
00:25:34,860 --> 00:25:37,110
So you can conceivably turn evolution

602
00:25:37,110 --> 00:25:39,340
into a predictive process, right?

603
00:25:39,340 --> 00:25:40,630
So let's think about that for a second.

604
00:25:40,630 --> 00:25:41,463
Say, for example, we talked about this.

605
00:25:41,463 --> 00:25:43,960
And this is basically the same word game

606
00:25:43,960 --> 00:25:46,060
but just kind of reorganize it
to a different graph, right?

607
00:25:46,060 --> 00:25:48,150
Word to wore to gore to gone to gene,

608
00:25:48,150 --> 00:25:50,030
remember all those paths,
all that is in here.

609
00:25:50,030 --> 00:25:52,590
So this is basically the
same thing I just showed you.

610
00:25:52,590 --> 00:25:54,910
This is the exact same
John Maynard Smith example

611
00:25:54,910 --> 00:25:56,323
but reorganized, right?

612
00:25:58,210 --> 00:26:02,090
What if I said, we can
apply some of these to real

613
00:26:02,090 --> 00:26:04,520
biological data sets, right?

614
00:26:04,520 --> 00:26:07,792
So for example, instead
of a word with letters,

615
00:26:07,792 --> 00:26:11,820
instead of this being W, O,
R, D, what if I was talking

616
00:26:11,820 --> 00:26:12,833
about an enzyme?

617
00:26:13,950 --> 00:26:18,570
And instead of four letters,
these were four mutations

618
00:26:18,570 --> 00:26:22,077
that were associated to
resistance to a drug, right?

619
00:26:23,530 --> 00:26:26,820
And what if I told you there's
a problem that looks exactly

620
00:26:26,820 --> 00:26:31,820
the same and there is
one, resistance to drugs

621
00:26:32,110 --> 00:26:34,313
in malaria parasites.

622
00:26:35,916 --> 00:26:40,916
This rises from four mutations, okay?

623
00:26:41,210 --> 00:26:45,140
So you can take this exact
same game, word to gene

624
00:26:45,140 --> 00:26:48,180
and make it into this different framework,

625
00:26:48,180 --> 00:26:50,460
these are four amino acids, substitutions.

626
00:26:50,460 --> 00:26:51,330
And you know, you'll look up

627
00:26:51,330 --> 00:26:52,390
what they are but that's not the point.

628
00:26:52,390 --> 00:26:55,030
The point is, you know,
these are four amino acids.

629
00:26:55,030 --> 00:26:59,180
This is kind of their
natural existing form,

630
00:26:59,180 --> 00:27:00,800
this is the resistant form.

631
00:27:00,800 --> 00:27:01,990
We can actually look that way.

632
00:27:01,990 --> 00:27:04,070
So for example, we can take this exact map

633
00:27:04,070 --> 00:27:06,360
that I told you is a basic word game.

634
00:27:06,360 --> 00:27:07,810
And we can basically do this.

635
00:27:08,720 --> 00:27:12,550
You have the problem of
anti-malaria resistance.

636
00:27:12,550 --> 00:27:15,080
It's amazing, sometimes biology
just works in your favor.

637
00:27:15,080 --> 00:27:17,720
I never would have
stumbled on this framework,

638
00:27:17,720 --> 00:27:20,980
if I was not reading
broadly early in my career.

639
00:27:20,980 --> 00:27:22,400
And I saw this and I said, wait a minute,

640
00:27:22,400 --> 00:27:24,360
this is the exact same problem.

641
00:27:24,360 --> 00:27:26,210
So once you have a problem like this

642
00:27:26,210 --> 00:27:28,820
you can begin to think about
things in a different way.

643
00:27:28,820 --> 00:27:31,130
You can think some questions
about antibiotic resistance.

644
00:27:31,130 --> 00:27:33,460
You can say, all right,
well, is it predictable?

645
00:27:33,460 --> 00:27:35,530
If I know that I can go
from word, to wore, to gore,

646
00:27:35,530 --> 00:27:38,180
to gone, to gene, maybe
there's something similar

647
00:27:38,180 --> 00:27:40,000
for resistance and if we
can predict resistance

648
00:27:40,000 --> 00:27:44,666
then maybe we can treat it better, right?

649
00:27:44,666 --> 00:27:48,480
And really what are the forces
that are shaping right now?

650
00:27:48,480 --> 00:27:50,310
Don't get caught up in the
big word, trajectories,

651
00:27:50,310 --> 00:27:52,340
the paths, right?

652
00:27:52,340 --> 00:27:54,160
I already told you that
it's meaningful words

653
00:27:54,160 --> 00:27:57,200
but what are the extra
forces that are shaping it?

654
00:27:57,200 --> 00:27:58,033
So what are the forces?

655
00:27:58,033 --> 00:28:00,280
We said, meaningful words,
word, that makes sense.

656
00:28:00,280 --> 00:28:04,150
Wore, that makes it, gone,
gene, that makes sense.

657
00:28:04,150 --> 00:28:05,700
But what if I kinda gave you a little bit

658
00:28:05,700 --> 00:28:07,620
of a wrinkle right now?

659
00:28:07,620 --> 00:28:11,640
There's still a little bit
of a wrinkle in here, right?

660
00:28:11,640 --> 00:28:16,460
What if I said that John
Maynard Smith was not being

661
00:28:16,460 --> 00:28:21,110
culturally sensitive to himself,
because in British English

662
00:28:21,110 --> 00:28:23,513
this is not the only way to win this game.

663
00:28:24,590 --> 00:28:27,490
This is American English,
in British English.

664
00:28:27,490 --> 00:28:31,610
G, O, R, D, is an acronym
because you know they throw

665
00:28:31,610 --> 00:28:33,262
that little, oh, on in British English.

666
00:28:33,262 --> 00:28:34,095
I don't know why they do that.

667
00:28:34,095 --> 00:28:36,980
But you know, they throw
this little, oh, on,

668
00:28:36,980 --> 00:28:39,240
which means that G, O, R, D, is a word

669
00:28:39,240 --> 00:28:42,693
in which case that's
an accessible pathway.

670
00:28:44,780 --> 00:28:48,920
So there's two different pathways meaning,

671
00:28:48,920 --> 00:28:52,340
what is meaningful, what evolution can do

672
00:28:52,340 --> 00:28:54,900
is going to be dependent on the context

673
00:28:54,900 --> 00:28:57,240
and the environment that you're in.

674
00:28:57,240 --> 00:28:59,140
You can't simply say that, there's one

675
00:28:59,140 --> 00:29:00,330
route through evolution.

676
00:29:00,330 --> 00:29:03,173
What's gonna happen depends
on your environment.

677
00:29:04,410 --> 00:29:07,520
It's going to depend on
your environment, okay?

678
00:29:07,520 --> 00:29:08,970
Now what are those environments?

679
00:29:08,970 --> 00:29:12,040
We use this British English
example to say that, okay, well,

680
00:29:12,040 --> 00:29:15,633
what's meaningful in British
English is different, right?

681
00:29:19,545 --> 00:29:21,060
But what would be the environment

682
00:29:21,060 --> 00:29:23,210
be in the context of
antibiotic resistance?

683
00:29:24,630 --> 00:29:25,640
What would it mean?

684
00:29:25,640 --> 00:29:29,030
Well, we're talking about
an antibiotic resistance.

685
00:29:29,030 --> 00:29:30,490
We're talking about several things.

686
00:29:30,490 --> 00:29:32,790
We're talking about the
type of drugs you use,

687
00:29:33,870 --> 00:29:37,030
how much of that drug
is in your bloodstream.

688
00:29:37,030 --> 00:29:38,977
So all the people, there's
like a million people

689
00:29:38,977 --> 00:29:42,120
in this seminar, which is
amazing and very intimidating.

690
00:29:42,120 --> 00:29:42,953
How many people in here?

691
00:29:42,953 --> 00:29:44,070
Let's just see, oh, whatever.

692
00:29:44,070 --> 00:29:46,320
I used to have the number, but
whatever, it doesn't matter.

693
00:29:46,320 --> 00:29:49,910
I think it was 130 something
or something crazy like that.

694
00:29:49,910 --> 00:29:51,900
The point is, if you take all the people

695
00:29:51,900 --> 00:29:54,830
in this talk and I give
you all the same drug

696
00:29:54,830 --> 00:29:56,530
maybe even let it be aspirin.

697
00:29:56,530 --> 00:29:59,000
Do you think you're all
going to have the same amount

698
00:29:59,000 --> 00:30:02,670
of aspirin in your bloodstream
in two hours or two days?

699
00:30:02,670 --> 00:30:03,503
Of course not.

700
00:30:03,503 --> 00:30:04,651
That's why I have these people here,

701
00:30:04,651 --> 00:30:05,484
you know I'm from New York city

702
00:30:05,484 --> 00:30:07,130
and it's kind of crazy
seeing this now, right?

703
00:30:07,130 --> 00:30:08,340
Given where we are.

704
00:30:08,340 --> 00:30:10,419
But the point is, humans are kind

705
00:30:10,419 --> 00:30:12,980
of genetically diverse in a lot of ways.

706
00:30:12,980 --> 00:30:15,633
And so the point is we
metabolize drugs differently.

707
00:30:16,850 --> 00:30:19,940
So my point is that
there's a malaria parasite

708
00:30:19,940 --> 00:30:21,740
in this person and they take a drug

709
00:30:21,740 --> 00:30:24,740
and this person they take
a drug and this person

710
00:30:24,740 --> 00:30:27,400
they take a drug, that malaria parasite

711
00:30:27,400 --> 00:30:30,223
will experience different
things in different people.

712
00:30:32,390 --> 00:30:34,500
And if the environments are different

713
00:30:34,500 --> 00:30:36,650
then maybe we need to be asking questions

714
00:30:36,650 --> 00:30:39,140
about how that influences how we move

715
00:30:39,140 --> 00:30:42,880
through the game, right?

716
00:30:42,880 --> 00:30:44,520
So how do we do this?

717
00:30:44,520 --> 00:30:46,220
Don't get confused here,
it's just a combination

718
00:30:46,220 --> 00:30:48,047
of experimental stuff
and computational stuff.

719
00:30:48,047 --> 00:30:49,270
And my work's here.

720
00:30:49,270 --> 00:30:53,270
So basically what I do is
I engineer actual proteins

721
00:30:53,270 --> 00:30:55,950
in the lab with all the words,
think about this as the word

722
00:30:55,950 --> 00:30:58,890
to gene, you engineer all the mutations

723
00:30:58,890 --> 00:31:01,455
into the protein of interest, right?

724
00:31:01,455 --> 00:31:05,210
There'll be the one that we
use to treat with drug, okay?

725
00:31:05,210 --> 00:31:09,050
You engineer all those, then
you grow all of those different

726
00:31:09,050 --> 00:31:13,503
variants, all these words in a
range of drug concentrations,

727
00:31:14,720 --> 00:31:17,030
different environments,
you grow them in a bunch

728
00:31:17,030 --> 00:31:19,240
of different environments
and then you use simulation,

729
00:31:19,240 --> 00:31:21,350
this is where computer science comes in.

730
00:31:21,350 --> 00:31:23,993
Your computer science can tell you, right?

731
00:31:25,380 --> 00:31:26,930
The organs can tell you kind

732
00:31:26,930 --> 00:31:30,060
of what's gonna be likely to happen, okay?

733
00:31:30,060 --> 00:31:31,175
So we can do it this way.

734
00:31:31,175 --> 00:31:34,063
We can ask questions like this.

735
00:31:35,470 --> 00:31:39,230
We can say, all right, well
maybe in drug one or drug A,

736
00:31:39,230 --> 00:31:40,870
you get this path, maybe in drug B

737
00:31:40,870 --> 00:31:42,583
you get this one and maybe
in drug C, you get this,

738
00:31:42,583 --> 00:31:45,000
this is a perfectly reasonable question.

739
00:31:45,000 --> 00:31:46,480
And so we can ask these questions

740
00:31:46,480 --> 00:31:51,200
about how drug context influences
the way evolution works.

741
00:31:51,200 --> 00:31:53,350
The same way I just told you

742
00:31:53,350 --> 00:31:55,928
that American English looks this way

743
00:31:55,928 --> 00:31:58,470
and British English gives you another way.

744
00:31:58,470 --> 00:32:00,900
Conceptually, it's the exact same thing

745
00:32:00,900 --> 00:32:02,920
as John Maynard Smith said in 1970

746
00:32:02,920 --> 00:32:06,470
that again applies to a word game, okay?

747
00:32:06,470 --> 00:32:08,770
We can ask that question and we did.

748
00:32:08,770 --> 00:32:11,550
So we run simulations
and I think if you're...

749
00:32:11,550 --> 00:32:13,340
Let's focus on the left, I
just put the right there,

750
00:32:13,340 --> 00:32:15,290
just because some of you wanna see the,

751
00:32:17,590 --> 00:32:19,610
computer nerds wanna see
the actual simulation.

752
00:32:19,610 --> 00:32:21,860
This is an example of what
a simulation looks like.

753
00:32:21,860 --> 00:32:23,267
So basically when you treat with no drug,

754
00:32:23,267 --> 00:32:25,360
you have all, you have a
simulation where you have

755
00:32:25,360 --> 00:32:29,450
like a million of these and
they replicating and mutating.

756
00:32:29,450 --> 00:32:31,600
So it's like almost like
a computer game, right?

757
00:32:31,600 --> 00:32:33,360
And this way we're gonna
treat with no drug.

758
00:32:33,360 --> 00:32:37,490
And in that situation, this one wins.

759
00:32:37,490 --> 00:32:38,950
That's the one you're
seeing at a hundred percent.

760
00:32:38,950 --> 00:32:40,850
One point is a hundred percent, right?

761
00:32:41,830 --> 00:32:45,270
But if you treat with
a low amount of a drug

762
00:32:45,270 --> 00:32:50,270
called Pyrimethamine, look what happens,

763
00:32:51,040 --> 00:32:52,090
evolution moves here.

764
00:32:53,780 --> 00:32:55,830
If you treat with an intermediate amount,

765
00:32:57,120 --> 00:32:58,120
evolution also moves there.

766
00:32:58,120 --> 00:33:01,146
So good, okay, we treat with a low amount,

767
00:33:01,146 --> 00:33:02,260
we treat with an intermediate amount.

768
00:33:02,260 --> 00:33:03,960
we get the same solution,

769
00:33:03,960 --> 00:33:06,523
But when we treat with a
high amount, what do we get?

770
00:33:08,005 --> 00:33:09,440
A different solution.

771
00:33:09,440 --> 00:33:12,040
Now that's for a drug
called pyrimethamine.

772
00:33:12,040 --> 00:33:13,330
I don't if I have
anti-meds in the audience

773
00:33:13,330 --> 00:33:16,390
but pyrimethamine is very
similar to another drug

774
00:33:16,390 --> 00:33:21,173
we use to treat malaria
called cycloguanil, okay?

775
00:33:22,080 --> 00:33:25,805
We do the same thing
for cycloguanil, okay?

776
00:33:25,805 --> 00:33:29,700
But in cycloguanil, low
amounts of cycloguanil,

777
00:33:29,700 --> 00:33:30,613
get you here.

778
00:33:32,710 --> 00:33:35,480
Intermediate cycloguanil gets you there

779
00:33:35,480 --> 00:33:38,160
and high amounts of
cycloguanil gets you here.

780
00:33:38,160 --> 00:33:40,860
So putting everything
together what do we have?

781
00:33:40,860 --> 00:33:42,710
This is kind of pyrimethamine one drug,

782
00:33:42,710 --> 00:33:45,060
this is a very similar
drug called cycloguanil.

783
00:33:46,080 --> 00:33:48,283
The colors mean different solutions.

784
00:33:50,020 --> 00:33:53,110
So basically at low drug, you
get one solution, at high drug

785
00:33:53,110 --> 00:33:55,910
you get another and low
concentration of cycloguanil,

786
00:33:55,910 --> 00:33:59,830
you get one solution and
intermediate high, you get another.

787
00:33:59,830 --> 00:34:02,290
And so what did we just discover?

788
00:34:02,290 --> 00:34:05,360
The direction of evolution
depends on the type of drug

789
00:34:05,360 --> 00:34:07,370
you're using and the concentration of drug

790
00:34:07,370 --> 00:34:08,300
that you're using.

791
00:34:08,300 --> 00:34:10,190
And this is knowledge that
we don't really appreciate

792
00:34:10,190 --> 00:34:13,450
right now, right now you come
in with a certain infection,

793
00:34:13,450 --> 00:34:14,830
they give you drugs.

794
00:34:14,830 --> 00:34:16,440
That drug isn't tailored to you,

795
00:34:16,440 --> 00:34:18,033
that drug isn't tailored
to what's inside your body.

796
00:34:18,033 --> 00:34:20,850
And I'm not saying that's
wrong, based on the knowledge

797
00:34:20,850 --> 00:34:24,070
that we've had but the
future of treatment of things

798
00:34:24,070 --> 00:34:27,410
is going to be a lot better, right?

799
00:34:27,410 --> 00:34:31,550
Like the personalized paradigm,
as in, Professor Gordon,

800
00:34:31,550 --> 00:34:36,550
Professor Fischer and me,
the drugs should be tailored

801
00:34:37,350 --> 00:34:40,530
to very specific things
about the way our bodies work

802
00:34:40,530 --> 00:34:43,800
but kind of what is the, you
know, if say there's a good

803
00:34:43,800 --> 00:34:46,720
drug to treat some kind
of infection we have,

804
00:34:46,720 --> 00:34:48,010
the drugs should be tailored

805
00:34:48,010 --> 00:34:50,400
to the particulars of us in the drug.

806
00:34:50,400 --> 00:34:52,600
And we need to understand
this information.

807
00:34:52,600 --> 00:34:55,030
The other thing is how resistance happens

808
00:34:55,030 --> 00:34:56,460
is gonna kind of be dependent

809
00:34:56,460 --> 00:34:58,293
on these things about us, right?

810
00:34:59,280 --> 00:35:02,890
But more broadly if you
wanna make any predictions

811
00:35:02,890 --> 00:35:05,730
in evolution and we tried
do this climate change,

812
00:35:05,730 --> 00:35:08,820
this has come up in
COVID, which is an area

813
00:35:08,820 --> 00:35:10,130
of my research, right?

814
00:35:10,130 --> 00:35:11,738
In terms of kind of being able to predict

815
00:35:11,738 --> 00:35:14,070
what's gonna happen and
when and how, you know,

816
00:35:14,070 --> 00:35:16,000
why it's been so difficult to understand?

817
00:35:16,000 --> 00:35:18,310
The reason why it was difficult
to predict the evolution

818
00:35:18,310 --> 00:35:22,800
of the strains, is because
COVID-19 is not one disease,

819
00:35:22,800 --> 00:35:24,690
it's a bunch of micro outbreaks in a bunch

820
00:35:24,690 --> 00:35:27,360
of different places, where
people are connected differently,

821
00:35:27,360 --> 00:35:29,930
live differently, are structured
differently, have access

822
00:35:29,930 --> 00:35:32,480
to different things, have
access to different healthcare.

823
00:35:32,480 --> 00:35:34,660
And that's gonna influence again, right?

824
00:35:34,660 --> 00:35:37,260
The way that it evolves,
which is why of course

825
00:35:37,260 --> 00:35:40,000
it's been challenging
to keep track of, right?

826
00:35:40,000 --> 00:35:41,200
So that's another example,

827
00:35:41,200 --> 00:35:43,803
another unfortunate example, okay?

828
00:35:44,870 --> 00:35:46,070
So I talked to you just now

829
00:35:46,070 --> 00:35:49,750
mostly about evolution of resistance.

830
00:35:49,750 --> 00:35:51,640
Now some of you are very smart, okay?

831
00:35:51,640 --> 00:35:54,140
And you heard that and
were like, all right, cool.

832
00:35:55,720 --> 00:35:57,330
What next?

833
00:35:57,330 --> 00:35:59,290
How do we actually treat
this more intelligently?

834
00:35:59,290 --> 00:36:00,123
What do we do next?

835
00:36:00,123 --> 00:36:01,890
And there's a lot of
ways that we can actually

836
00:36:01,890 --> 00:36:03,220
you know, that have been proposed

837
00:36:03,220 --> 00:36:04,610
and are being used to treat.

838
00:36:04,610 --> 00:36:07,399
One of the things, one of the
strategies we kind of thought,

839
00:36:07,399 --> 00:36:09,180
you know, it's still on the table

840
00:36:09,180 --> 00:36:12,470
but we kind of thought
historically might be a good way

841
00:36:12,470 --> 00:36:16,790
to get rid of antibiotic resistance is,

842
00:36:16,790 --> 00:36:20,190
what if we just took drug
away and let evolution

843
00:36:20,190 --> 00:36:22,660
evolve back to the treatable form?

844
00:36:22,660 --> 00:36:25,360
And then we can treat the treatable form.

845
00:36:25,360 --> 00:36:26,750
That seems like a pretty reasonable

846
00:36:26,750 --> 00:36:28,980
and smart way to go about
doing things, right?

847
00:36:28,980 --> 00:36:31,770
So you kind of were
but here's the question

848
00:36:31,770 --> 00:36:33,573
is evolution really reversible?

849
00:36:35,530 --> 00:36:38,310
Turns out there's a big
literature on this, right?

850
00:36:38,310 --> 00:36:41,130
It turns out, there's a big
literature on this, right?

851
00:36:41,130 --> 00:36:42,540
About reverse evolution.

852
00:36:42,540 --> 00:36:45,000
Do things happen the
way they move forward?

853
00:36:45,000 --> 00:36:47,540
And it's kinda like a
philosophical thing almost, right?

854
00:36:47,540 --> 00:36:49,497
About the ability to undo things right?

855
00:36:49,497 --> 00:36:53,860
And it turns out this
Dollo character has a lot.

856
00:36:53,860 --> 00:36:56,453
Now Dollo was talking
about from paleontology,

857
00:36:57,350 --> 00:36:58,713
I do not study paleontology, okay?

858
00:36:58,713 --> 00:37:00,740
I mean, I love it and it's cool.

859
00:37:00,740 --> 00:37:03,970
And I'm into all that,
I'm into Jurassic park

860
00:37:03,970 --> 00:37:06,490
as much as everyone else
is but I do not study that.

861
00:37:06,490 --> 00:37:08,793
But this idea is a profound one.

862
00:37:10,157 --> 00:37:12,397
"An organism is unable
to return even partially,

863
00:37:12,397 --> 00:37:14,697
"to a previous stage already
realized in the ranks

864
00:37:14,697 --> 00:37:15,950
"of its ancestors."

865
00:37:15,950 --> 00:37:18,970
Meaning you ain't getting back.

866
00:37:18,970 --> 00:37:23,380
If you evolve a function,
it's very, very difficult

867
00:37:23,380 --> 00:37:24,733
to unevolve that function.

868
00:37:28,883 --> 00:37:32,657
And so Richard Dawkins was
right about this, he said,

869
00:37:32,657 --> 00:37:35,247
"It's just a statement about
the improbability of following

870
00:37:35,247 --> 00:37:38,140
"the exact same evolutionary
trajectory twice."

871
00:37:38,140 --> 00:37:42,070
Or basically, if you hard for
you to do one thing one way,

872
00:37:42,070 --> 00:37:44,450
it's gonna be really hard for
you to do the exact opposite

873
00:37:44,450 --> 00:37:46,800
thing in the other direction
but that's Dollo's law.

874
00:37:46,800 --> 00:37:48,538
And so my point there's
law that exists that says,

875
00:37:48,538 --> 00:37:51,610
evolution reversibility is rare.

876
00:37:51,610 --> 00:37:54,480
What I sought to do, was
test this but for the problem

877
00:37:54,480 --> 00:37:55,790
I just showed you.

878
00:37:55,790 --> 00:37:56,900
So here we go, right?

879
00:37:56,900 --> 00:37:58,770
And if you really think about
this, because again, right?

880
00:37:58,770 --> 00:38:00,060
So here we go.

881
00:38:00,060 --> 00:38:02,360
Let's do the same pathways,
it's a very similar enzyme,

882
00:38:02,360 --> 00:38:07,360
but in reverse, that is, we
have the resistant variant here.

883
00:38:11,570 --> 00:38:14,360
Our starting point now
is the most resistant one

884
00:38:14,360 --> 00:38:15,960
and we wanna get back to the word, right?

885
00:38:15,960 --> 00:38:19,557
So now we're at gene and
we wanna go to word, right?

886
00:38:21,260 --> 00:38:24,370
We're at gene and we wanna
go to word, now according

887
00:38:24,370 --> 00:38:27,260
to Maynard Smith's game,
that should be reversible.

888
00:38:27,260 --> 00:38:30,200
You can go back because meaning
in that situation is simple.

889
00:38:30,200 --> 00:38:32,740
But biology we're gonna
find works differently.

890
00:38:32,740 --> 00:38:34,670
So what I did was I did
the same thing I did before

891
00:38:34,670 --> 00:38:38,300
I engineered all of these in
the laboratory, I grew them

892
00:38:38,300 --> 00:38:41,520
up and basically measured
their kind of protein function

893
00:38:41,520 --> 00:38:44,450
across a bunch of different
environments, right?

894
00:38:44,450 --> 00:38:46,289
And I ran the same simulation
that we talked about.

895
00:38:46,289 --> 00:38:47,450
So, I mean, we're all experts now.

896
00:38:47,450 --> 00:38:50,130
So I basically said, all right,
let's start the simulation,

897
00:38:50,130 --> 00:38:51,740
this one is the most resistant one.

898
00:38:51,740 --> 00:38:54,580
I'll start it with treating
with a high amount of drug.

899
00:38:54,580 --> 00:38:59,470
But again remember, my goal is
to reverse from gene to work.

900
00:38:59,470 --> 00:39:02,774
And so at high drug, this one wins.

901
00:39:02,774 --> 00:39:06,400
And this is pyrimethamine,
when I go to lower drug

902
00:39:06,400 --> 00:39:09,963
the population moves
here, one mutation closer.

903
00:39:11,080 --> 00:39:13,883
So it's like gene, then gone, right?

904
00:39:13,883 --> 00:39:15,507
I think that helps, right?

905
00:39:15,507 --> 00:39:17,100
And you can see the simulation here,

906
00:39:17,100 --> 00:39:18,450
if you really wanna see it.

907
00:39:19,320 --> 00:39:21,780
I go even to no drug.

908
00:39:21,780 --> 00:39:24,260
So I start here and I use no drug

909
00:39:25,260 --> 00:39:26,850
and I'm still trapped there.

910
00:39:26,850 --> 00:39:29,390
I would expect that if I'm using no drug,

911
00:39:29,390 --> 00:39:31,820
remember the first time we
talked about this result,

912
00:39:31,820 --> 00:39:34,980
this one is the one that's
favored with no drug.

913
00:39:34,980 --> 00:39:36,180
So why can't I get back?

914
00:39:38,070 --> 00:39:40,040
So I said, okay, well there's
gotta be something wrong

915
00:39:40,040 --> 00:39:41,410
with my work, right?

916
00:39:41,410 --> 00:39:43,440
So I basically sat there,
I was a lot more careful.

917
00:39:43,440 --> 00:39:45,490
So I said, alright, this is what I'll do.

918
00:39:46,330 --> 00:39:48,860
I'll start the population
evenly distributed here,

919
00:39:48,860 --> 00:39:50,577
that means, let's just
say, I got a million here,

920
00:39:50,577 --> 00:39:52,570
a million here, a million
here, a million here

921
00:39:52,570 --> 00:39:53,590
and they're all mutating.

922
00:39:53,590 --> 00:39:55,840
And I said, alright, because
I've given this a push

923
00:39:55,840 --> 00:39:58,080
I've started giving it a
head start, I'll be able

924
00:39:58,080 --> 00:40:00,030
to get here, right?

925
00:40:00,030 --> 00:40:00,863
With no drug.

926
00:40:00,863 --> 00:40:04,903
And what happens, none of them get there.

927
00:40:06,780 --> 00:40:09,951
In no simulations did I ever get back

928
00:40:09,951 --> 00:40:12,830
to this, what we call wild
type, which is the one

929
00:40:12,830 --> 00:40:17,503
that I would expect to grow
the best in no drug, right?

930
00:40:18,430 --> 00:40:19,730
And why is that?

931
00:40:19,730 --> 00:40:22,100
Why is evolution irreversible?

932
00:40:22,100 --> 00:40:24,400
Why can't I go back?

933
00:40:24,400 --> 00:40:26,710
And the reason why I can't go back

934
00:40:26,710 --> 00:40:31,610
is because there are things
called compensatory mutations.

935
00:40:31,610 --> 00:40:32,570
Now, what are those like?

936
00:40:32,570 --> 00:40:35,050
Let's think about some
analogies for those.

937
00:40:35,050 --> 00:40:37,630
What those are is, right?

938
00:40:37,630 --> 00:40:39,880
If I'm on one side, if I have a word here

939
00:40:39,880 --> 00:40:44,850
that is struggling and the
best version is all the way

940
00:40:44,850 --> 00:40:48,630
on the other side, in order
for me to get there, right?

941
00:40:48,630 --> 00:40:53,630
Again, if this one is the best one,

942
00:40:53,760 --> 00:40:56,570
these have to be incrementally
a little bit better

943
00:40:56,570 --> 00:40:57,890
in order for me to get here.

944
00:40:57,890 --> 00:41:01,200
But what if I have a version of a word

945
00:41:02,160 --> 00:41:06,517
that grows pretty well
when there's no drug

946
00:41:06,517 --> 00:41:09,850
and grows pretty well when
there is drugs, right?

947
00:41:09,850 --> 00:41:12,640
And it turns out this one mutation

948
00:41:12,640 --> 00:41:16,480
gives you pretty good
growth with and without drug

949
00:41:16,480 --> 00:41:18,460
and because of that, it prevents

950
00:41:18,460 --> 00:41:21,260
you from getting back across,
it just to kinda go here.

951
00:41:21,260 --> 00:41:25,383
It turns out anytime you get
this kind of end mutation here,

952
00:41:26,650 --> 00:41:29,150
anytime you get it, you have a hard time

953
00:41:29,150 --> 00:41:30,420
crossing back across.

954
00:41:30,420 --> 00:41:35,370
So the part of the key
here is that the results

955
00:41:35,370 --> 00:41:38,090
are not surprising because
reversing resistance

956
00:41:38,090 --> 00:41:40,120
has been very challenging.

957
00:41:40,120 --> 00:41:42,220
Look at this, long-term
persistence of resistant

958
00:41:42,220 --> 00:41:45,500
Enterococcus species after
antibiotics to eradicate.

959
00:41:45,500 --> 00:41:47,070
Persistence of antibiotic resistance...

960
00:41:47,070 --> 00:41:49,750
We are having a very hard time in nature

961
00:41:49,750 --> 00:41:52,060
getting rid of resistance,
that's bad news.

962
00:41:52,060 --> 00:41:55,493
That means once you have a
resistance in high numbers,

963
00:41:56,360 --> 00:41:58,820
there are mutations that
kind of keep it there

964
00:41:58,820 --> 00:42:01,730
even if you take drug away,
so the only wake thing

965
00:42:01,730 --> 00:42:04,386
we can do now and we're doing this right?

966
00:42:04,386 --> 00:42:06,090
We can do two things.

967
00:42:06,090 --> 00:42:08,330
We can either be more
responsible with the drugs

968
00:42:08,330 --> 00:42:12,190
that we're using, which is
one thing we've started to be.

969
00:42:12,190 --> 00:42:16,600
I think that is one that
people have learned, A.

970
00:42:16,600 --> 00:42:19,610
Or B, we can come up with new drugs

971
00:42:19,610 --> 00:42:23,520
that maybe target the resistant forms.

972
00:42:23,520 --> 00:42:25,560
And that's actually a frontier
in medicinal chemistry.

973
00:42:25,560 --> 00:42:27,150
And that's some work that we're doing.

974
00:42:27,150 --> 00:42:28,860
How can we kind of identify ways

975
00:42:28,860 --> 00:42:31,640
to treat the resistant forms, right?

976
00:42:31,640 --> 00:42:35,300
Again, frontiers of the
way we treat things, okay?

977
00:42:35,300 --> 00:42:37,150
So what I told you so far, right?

978
00:42:37,150 --> 00:42:37,983
We've gotten a lot...

979
00:42:37,983 --> 00:42:41,840
I mean, this is some interesting things.

980
00:42:41,840 --> 00:42:44,050
I took you from a historical thing, right?

981
00:42:44,050 --> 00:42:46,660
That John Maynard Smith
came up as a rebuttal.

982
00:42:46,660 --> 00:42:49,140
We took that concept, we
wielded it to understand

983
00:42:49,140 --> 00:42:54,140
how antibiotic resistance, anti
malaria resistance evolves.

984
00:42:54,210 --> 00:42:56,170
And then we added this
dimension of the environment,

985
00:42:56,170 --> 00:42:58,220
if the drug environment
looks this way or this way

986
00:42:58,220 --> 00:43:00,240
you're gonna get different
solutions, right?

987
00:43:00,240 --> 00:43:02,140
Which is a very, very
kind of powerful point,

988
00:43:02,140 --> 00:43:03,580
that kind of influences how we think

989
00:43:03,580 --> 00:43:04,940
about antibiotic resistance.

990
00:43:04,940 --> 00:43:09,050
Then I said, all right,
well reversal, right?

991
00:43:09,050 --> 00:43:10,530
We used the kind of same word analogy

992
00:43:10,530 --> 00:43:12,370
think about going from gene to word

993
00:43:12,370 --> 00:43:13,780
and it turns out it's really, really hard

994
00:43:13,780 --> 00:43:14,820
to do that in evolution.

995
00:43:14,820 --> 00:43:17,170
And we demonstrated why
that is the case, okay?

996
00:43:18,140 --> 00:43:20,840
But in the last part of this
conversation we're gonna have,

997
00:43:20,840 --> 00:43:23,450
we're gonna talk about why it is.

998
00:43:23,450 --> 00:43:25,360
We're gonna talk about the
other kind of dimensions

999
00:43:25,360 --> 00:43:27,190
of the environment, okay?

1000
00:43:27,190 --> 00:43:30,350
The ones that kind of
influenced my ontogeny

1001
00:43:30,350 --> 00:43:32,350
another good word, high school students.

1002
00:43:34,030 --> 00:43:36,321
How I got to where I am, okay?

1003
00:43:36,321 --> 00:43:39,070
And I think this is an important
part of the environment

1004
00:43:39,070 --> 00:43:41,810
because this one does not
function at a molecular level,

1005
00:43:41,810 --> 00:43:45,000
this one is funky at a macro
level but it influences

1006
00:43:45,000 --> 00:43:48,630
the work that I do and why I
do it and how I do it, okay?

1007
00:43:48,630 --> 00:43:50,790
That's what we'll talk
about here, alright?

1008
00:43:50,790 --> 00:43:52,790
The role of the scientist in society,

1009
00:43:52,790 --> 00:43:55,340
my own attempt to reconcile
environment culture

1010
00:43:55,340 --> 00:43:56,173
and the moment.

1011
00:43:56,173 --> 00:43:58,070
And this is why I do
stuff like this, right?

1012
00:43:58,070 --> 00:43:59,630
Because this is part of my job.

1013
00:43:59,630 --> 00:44:03,160
And I take a lot of pride in
this part of my job, okay?

1014
00:44:03,160 --> 00:44:04,620
So I told you about this, right?

1015
00:44:04,620 --> 00:44:08,460
We talked about housing projects here,

1016
00:44:08,460 --> 00:44:12,090
we talked about kind of
the history of this country

1017
00:44:12,090 --> 00:44:14,570
and the way this was part of this country

1018
00:44:17,060 --> 00:44:20,337
and I talked about what do
these have to do with my work?

1019
00:44:20,337 --> 00:44:22,330
Oh, you just told me all this nerdy stuff

1020
00:44:22,330 --> 00:44:23,930
about molecular evolution, oh come on,

1021
00:44:23,930 --> 00:44:24,840
what does that have to do with your work?

1022
00:44:24,840 --> 00:44:27,490
Well, number one, I think
this work on the other side

1023
00:44:27,490 --> 00:44:30,310
of my research, I think about
the way pandemics spread.

1024
00:44:30,310 --> 00:44:32,693
This is very directly related to my work.

1025
00:44:33,889 --> 00:44:35,350
And what we find is that when we actually

1026
00:44:35,350 --> 00:44:37,010
think about kind of the way people live

1027
00:44:37,010 --> 00:44:41,030
and how they live well, that's
connected to this, right?

1028
00:44:41,030 --> 00:44:42,890
How housing was built and who had access

1029
00:44:42,890 --> 00:44:44,750
to where that persists to date.

1030
00:44:44,750 --> 00:44:45,710
And that has history.

1031
00:44:45,710 --> 00:44:47,930
So if I'm interested in
the way COVID is spreading

1032
00:44:47,930 --> 00:44:49,640
in certain communities,
well I have to understand

1033
00:44:49,640 --> 00:44:50,940
all these things.

1034
00:44:50,940 --> 00:44:54,100
So my point is this actually
directly feeds into my work.

1035
00:44:54,100 --> 00:44:56,520
But more than that, it
feeds into how I think

1036
00:44:56,520 --> 00:44:59,880
about what my role is in society, right?

1037
00:44:59,880 --> 00:45:03,260
Because when I go, why am
I doing what I'm doing?

1038
00:45:03,260 --> 00:45:05,440
I'm doing it because of this woman.

1039
00:45:05,440 --> 00:45:06,923
It's my mother, okay?

1040
00:45:08,180 --> 00:45:10,280
Fannie Louise Bennett, right?

1041
00:45:10,280 --> 00:45:13,580
And unquestionably, I mean,
that is an amazing afro.

1042
00:45:13,580 --> 00:45:15,507
Okay, I'm sorry, alright.

1043
00:45:15,507 --> 00:45:18,250
I don't have, I didn't get...

1044
00:45:18,250 --> 00:45:20,290
I'm certainly not as good looking.

1045
00:45:20,290 --> 00:45:23,030
I don't have this great
hair, but most importantly

1046
00:45:23,030 --> 00:45:24,683
I'm not anywhere near as smart.

1047
00:45:27,260 --> 00:45:29,340
If she had my opportunities

1048
00:45:29,340 --> 00:45:31,653
she would be a better
scientist than I was.

1049
00:45:33,590 --> 00:45:37,870
She grew up and she's from
Baltimore as a little girl,

1050
00:45:37,870 --> 00:45:42,743
she experienced the end of
Jim Crow, visiting her family.

1051
00:45:43,840 --> 00:45:45,750
And she said, she didn't
understand why she had to sit

1052
00:45:45,750 --> 00:45:46,583
on the back of the bus.

1053
00:45:46,583 --> 00:45:48,900
This is my mother, right?

1054
00:45:48,900 --> 00:45:51,660
So what I'm saying is
in a different context,

1055
00:45:51,660 --> 00:45:55,940
she would have been way better than I was.

1056
00:45:55,940 --> 00:45:59,710
So my charge in this profession

1057
00:45:59,710 --> 00:46:02,217
is to identify Fannie Louise Bennett's

1058
00:46:02,217 --> 00:46:05,140
and give them an opportunity.

1059
00:46:05,140 --> 00:46:07,560
And the way that I've
chosen to do that, right?

1060
00:46:07,560 --> 00:46:09,500
Is to kind of speak out on things

1061
00:46:09,500 --> 00:46:11,650
and that means talking
about the relationship

1062
00:46:11,650 --> 00:46:16,650
between stuff like this
and stuff like that, okay?

1063
00:46:17,380 --> 00:46:21,030
Because it is these forces
that were responsible

1064
00:46:21,030 --> 00:46:22,970
for her being unable to be in my seat.

1065
00:46:22,970 --> 00:46:25,340
She should have been a
National Academy's member now.

1066
00:46:25,340 --> 00:46:28,310
She was a teacher and
that's about as far as women

1067
00:46:28,310 --> 00:46:31,730
her color went in that day, okay?

1068
00:46:31,730 --> 00:46:35,630
And so it is my job to do
this and it's my job further

1069
00:46:35,630 --> 00:46:38,280
to talk about the relationship
between this and this.

1070
00:46:39,870 --> 00:46:42,320
This is my job as a scientist

1071
00:46:42,320 --> 00:46:43,990
to articulate all these things.

1072
00:46:43,990 --> 00:46:45,810
So how do I do that?

1073
00:46:45,810 --> 00:46:47,830
I tell stories, okay?

1074
00:46:47,830 --> 00:46:49,450
And I talk about the experiences.

1075
00:46:49,450 --> 00:46:52,970
So I tell some stories,
number one and number two,

1076
00:46:52,970 --> 00:46:56,040
I write, I love to write,
another thing my mother

1077
00:46:56,040 --> 00:46:58,007
was good at and better
at than I was, I write.

1078
00:46:58,007 --> 00:46:59,240
And I write for a lot of things,

1079
00:46:59,240 --> 00:47:00,860
I'm fortunate to be a
columnist at "Wired,"

1080
00:47:00,860 --> 00:47:03,863
where I write a lot about
live experiences, alright?

1081
00:47:04,860 --> 00:47:06,183
And you know, just recently
I kind of wrote this.

1082
00:47:06,183 --> 00:47:08,020
This is my most recent
piece where I actually

1083
00:47:08,020 --> 00:47:10,420
took the stuff that you
just heard about disease

1084
00:47:11,620 --> 00:47:14,590
and I actually made an analogy
for a lot of the problems

1085
00:47:14,590 --> 00:47:16,676
we're experiencing, right?

1086
00:47:16,676 --> 00:47:17,840
People like this, I mean, you know,

1087
00:47:17,840 --> 00:47:19,220
regardless how you feel about politically,

1088
00:47:19,220 --> 00:47:23,910
the point is I try to be
smart and careful and creative

1089
00:47:23,910 --> 00:47:27,460
with about the way I kind of
write and talk about society.

1090
00:47:27,460 --> 00:47:31,180
But more than that, I think
about what my mother's influence

1091
00:47:31,180 --> 00:47:35,160
was, I do this kind of work
in part because it is my job

1092
00:47:35,160 --> 00:47:39,360
to identify other people
and inspire other people.

1093
00:47:39,360 --> 00:47:41,910
Because when I was a young person, right?

1094
00:47:41,910 --> 00:47:44,160
I thought about this type of work

1095
00:47:44,160 --> 00:47:46,920
and I would think about,
why should we do this?

1096
00:47:46,920 --> 00:47:48,930
Does it distract from my real work?

1097
00:47:48,930 --> 00:47:50,170
And how does it detract?

1098
00:47:50,170 --> 00:47:52,110
Well, number one, what is real work?

1099
00:47:52,110 --> 00:47:54,510
This work formally helps my research.

1100
00:47:54,510 --> 00:47:57,820
I have built bridges scientifically
because of this work.

1101
00:47:57,820 --> 00:48:01,360
But more importantly, this is
my way of paying it forward

1102
00:48:01,360 --> 00:48:03,220
because I would not be a scientist,

1103
00:48:03,220 --> 00:48:04,780
if it was not for my mother

1104
00:48:04,780 --> 00:48:07,940
and I would not be a scientist
if it was not for somebody,

1105
00:48:07,940 --> 00:48:09,930
you might've heard
named Stephen Jay Gould,

1106
00:48:09,930 --> 00:48:14,460
who wrote and when I was in
college, I had never ever

1107
00:48:14,460 --> 00:48:16,420
I barely taken biology in high school.

1108
00:48:16,420 --> 00:48:17,360
I think I like slept, dude.

1109
00:48:17,360 --> 00:48:20,940
I was not a good high
school student, but I read.

1110
00:48:20,940 --> 00:48:23,070
And when I read Stephen Jay Gould essays

1111
00:48:23,070 --> 00:48:25,580
in natural history, I was
like, this is brilliant.

1112
00:48:25,580 --> 00:48:27,250
Steve Jay Gould wrote,
I'm a sports fanatic.

1113
00:48:27,250 --> 00:48:30,290
He wrote one of the best
essays ever about baseball,

1114
00:48:30,290 --> 00:48:32,490
about Joe DiMaggio's
56 game hitting streak.

1115
00:48:32,490 --> 00:48:34,160
He's also a Yankees fan from New York.

1116
00:48:34,160 --> 00:48:35,796
So you see a little Yankee thing there.

1117
00:48:35,796 --> 00:48:38,170
Maybe that's gonna get me
deleted from this call,

1118
00:48:38,170 --> 00:48:39,690
I know we were in red sock country.

1119
00:48:39,690 --> 00:48:40,780
I'm nice, I promise.

1120
00:48:40,780 --> 00:48:43,120
The point is this is the kind of stuff

1121
00:48:43,120 --> 00:48:46,360
that kind of inspired
me to be a biologist.

1122
00:48:46,360 --> 00:48:48,850
And so all of these
efforts, both to pay homage

1123
00:48:48,850 --> 00:48:51,550
to my mother, identify people with her

1124
00:48:51,550 --> 00:48:54,540
and to kind of pay back
people like Stephen Jay Gould,

1125
00:48:54,540 --> 00:48:56,750
who inspired a generation
of people like me.

1126
00:48:56,750 --> 00:48:59,500
This is as much of a part of my research,

1127
00:48:59,500 --> 00:49:02,100
as everything else that you've heard.

1128
00:49:02,100 --> 00:49:04,630
And with that I'll end and
I'll take some questions.

1129
00:49:04,630 --> 00:49:05,463
Thank you.

1130
00:49:08,618 --> 00:49:10,210
- Thank you very much, Dr. Bruno

1131
00:49:10,210 --> 00:49:14,200
for a wonderfully engaging talk, sharing

1132
00:49:14,200 --> 00:49:19,170
and into splicing biology,
genetics, and culture.

1133
00:49:19,170 --> 00:49:22,990
My first question to you is
from one of my colleagues,

1134
00:49:22,990 --> 00:49:25,960
Dr. Amy Sprinkle, who's a microbiologist.

1135
00:49:25,960 --> 00:49:28,070
What is your reaction to
the recent announcement

1136
00:49:28,070 --> 00:49:31,040
that AI has solved protein folding?

1137
00:49:31,040 --> 00:49:34,483
Do you use AI in your work
studying protein space?

1138
00:49:35,620 --> 00:49:36,470
- Great question.

1139
00:49:38,405 --> 00:49:40,537
So, my reaction, well..

1140
00:49:41,690 --> 00:49:44,650
It's funny because I'm currently writing,

1141
00:49:44,650 --> 00:49:45,483
I'll make this relevant...

1142
00:49:45,483 --> 00:49:47,840
I'm currently writing a 20
year, scientific American is

1143
00:49:47,840 --> 00:49:51,770
running a series of essays
on the 20th anniversary

1144
00:49:51,770 --> 00:49:54,032
of the announcement of
the human genome project

1145
00:49:54,032 --> 00:49:56,900
but then that's the
completion of the draft

1146
00:49:56,900 --> 00:49:59,350
of the humans genome
which was 20 years ago.

1147
00:49:59,350 --> 00:50:04,350
And since then, I've always
leaned towards being skeptical

1148
00:50:05,370 --> 00:50:08,830
of things until they actually
deliver me something, right?

1149
00:50:08,830 --> 00:50:13,540
So I think right now it
a very, very kind of...

1150
00:50:13,540 --> 00:50:15,610
It's cool, the example problems
that they've demonstrated

1151
00:50:15,610 --> 00:50:20,350
are very, very interesting but
yet deliver me a drab first,

1152
00:50:20,350 --> 00:50:22,440
deliver me some kind of
fundamental understanding

1153
00:50:22,440 --> 00:50:23,273
and then we'll talk.

1154
00:50:23,273 --> 00:50:25,470
But right now it's a gadget
and I think it allows

1155
00:50:25,470 --> 00:50:27,010
us to explore certain things.

1156
00:50:27,010 --> 00:50:29,757
But I think I'm very,
very, very skeptical.

1157
00:50:29,757 --> 00:50:31,576
You know, it's not so much skeptical.

1158
00:50:31,576 --> 00:50:35,500
I wanna see it deliver
before you're gonna get me.

1159
00:50:35,500 --> 00:50:36,630
But I think it's a cool technology.

1160
00:50:36,630 --> 00:50:39,260
I do use some AI and machine learning

1161
00:50:39,260 --> 00:50:42,414
in my work in various ways
are kind of like lowercase AI,

1162
00:50:42,414 --> 00:50:45,030
not big time, little stuff.

1163
00:50:45,030 --> 00:50:47,180
But I'm excited about the applications,

1164
00:50:47,180 --> 00:50:48,757
but like the notion of having solved

1165
00:50:48,757 --> 00:50:50,483
to the protein folding, like no,

1166
00:50:52,590 --> 00:50:54,890
I think let's all slow
our roles a little bit.

1167
00:50:59,550 --> 00:51:04,280
- Dr. Ogbunu, I really
appreciate your willingness

1168
00:51:05,200 --> 00:51:10,200
to connect science and the
humanities and that we are all

1169
00:51:10,770 --> 00:51:12,950
in this as a way to try
to understand things.

1170
00:51:12,950 --> 00:51:16,500
And I'm in all of your work, thank you.

1171
00:51:16,500 --> 00:51:17,618
That's wonderful.

1172
00:51:17,618 --> 00:51:18,618
- It's okay.

1173
00:51:20,440 --> 00:51:22,710
- A student has asked
when organisms evolve

1174
00:51:22,710 --> 00:51:25,710
antibiotic resistance, is
it similar to how we develop

1175
00:51:25,710 --> 00:51:27,720
antibodies when a foreign
entity is detected

1176
00:51:27,720 --> 00:51:30,520
in our body or do they evolve proteins

1177
00:51:30,520 --> 00:51:33,220
and enzymes that break down
the antibiotics substance?

1178
00:51:36,170 --> 00:51:40,020
- That is a well-informed,
one of those questions

1179
00:51:40,020 --> 00:51:41,820
that reveals the question asker

1180
00:51:41,820 --> 00:51:44,110
knows what they're talking about.

1181
00:51:44,110 --> 00:51:45,630
So let's get to the first part of that.

1182
00:51:45,630 --> 00:51:49,360
Is it like the generation of antibodies?

1183
00:51:49,360 --> 00:51:52,570
I mean, I've made fun of
immunologists for 20 years, right?

1184
00:51:52,570 --> 00:51:54,310
I'm like, y'all don't know
what you're talking about.

1185
00:51:54,310 --> 00:51:55,910
It's just a mess.

1186
00:51:55,910 --> 00:51:58,810
And I think, they've had their moment here

1187
00:51:58,810 --> 00:52:02,440
and in COVID because ,boy, have
they risen to the occasion.

1188
00:52:02,440 --> 00:52:06,710
And I think in part is,
because I think the process

1189
00:52:06,710 --> 00:52:08,910
of antibody generation
just used to look so messy.

1190
00:52:08,910 --> 00:52:13,480
I mean, I used to look
at an immunology textbook

1191
00:52:13,480 --> 00:52:14,730
and it used to look like,

1192
00:52:15,670 --> 00:52:17,070
I don't even know what the language is.

1193
00:52:17,070 --> 00:52:20,180
I mean, you pick a confusing textbook

1194
00:52:20,180 --> 00:52:21,900
and it'd be like, what is this?

1195
00:52:21,900 --> 00:52:23,380
What are these cytokines?

1196
00:52:23,380 --> 00:52:25,410
There's no theory here at all.

1197
00:52:25,410 --> 00:52:27,350
It's just like a bunch of
kinds of things spinning out.

1198
00:52:27,350 --> 00:52:28,590
I'm like, let me get this out of my face

1199
00:52:28,590 --> 00:52:29,703
because there's no logic here, right?

1200
00:52:29,703 --> 00:52:31,140
I think that's no longer the case.

1201
00:52:31,140 --> 00:52:33,640
And I think the process
of antibody generation

1202
00:52:33,640 --> 00:52:35,880
is very, very evolutionary.

1203
00:52:35,880 --> 00:52:38,970
So the process is the right
'cause there's a selection

1204
00:52:38,970 --> 00:52:42,710
process, so at a fundamental
level there is a lot

1205
00:52:42,710 --> 00:52:46,420
of similarity about the
process with which we generate

1206
00:52:46,420 --> 00:52:49,130
antibodies and antibiotic resistance.

1207
00:52:49,130 --> 00:52:51,580
So number one, I think
at a level the processes

1208
00:52:51,580 --> 00:52:53,720
are very, very similar absolutely.

1209
00:52:53,720 --> 00:52:56,180
And the people who actually
are studying this had a kind

1210
00:52:56,180 --> 00:52:58,840
of careful even mutation by
mutation level, the way I am.

1211
00:52:58,840 --> 00:53:00,810
So that's number one, number two you said

1212
00:53:00,810 --> 00:53:03,290
something else very intelligent, which was

1213
00:53:04,540 --> 00:53:09,540
about microbes that break down
and that's also true, right?

1214
00:53:10,220 --> 00:53:11,053
That's also true.

1215
00:53:11,053 --> 00:53:13,402
So the mechanism of resistance
that I was talking about

1216
00:53:13,402 --> 00:53:15,450
that's only one mechanism of resistance.

1217
00:53:15,450 --> 00:53:16,750
And that is you have an enzyme

1218
00:53:16,750 --> 00:53:21,109
that blood needs to
metabolize and you treat it

1219
00:53:21,109 --> 00:53:22,880
with drug and then
mutations, what do they do?

1220
00:53:22,880 --> 00:53:24,770
They just close off that site

1221
00:53:24,770 --> 00:53:26,890
and the drug can't get in anymore.

1222
00:53:26,890 --> 00:53:28,410
That's the mechanism I'm talking about

1223
00:53:28,410 --> 00:53:30,080
but there are many other mechanisms

1224
00:53:30,080 --> 00:53:32,170
through which a drug have efflux pumps.

1225
00:53:32,170 --> 00:53:33,227
Sometimes they have pumps
where it's like the drug

1226
00:53:33,227 --> 00:53:37,013
comes in and the microbes
just like, bye, peace.

1227
00:53:38,020 --> 00:53:39,190
That's different.

1228
00:53:39,190 --> 00:53:42,810
Now fundamentally I
think as a process level,

1229
00:53:42,810 --> 00:53:43,840
it's very, very similar

1230
00:53:43,840 --> 00:53:45,620
but mechanistically it's different.

1231
00:53:45,620 --> 00:53:47,890
So the things that I
talk about still apply

1232
00:53:49,330 --> 00:53:52,730
but they may be applied
differently in some ways.

1233
00:53:52,730 --> 00:53:55,083
So, these are excellent questions.

1234
00:53:56,757 --> 00:54:00,077
- Dr. Ogbunu hare's another
very topical question

1235
00:54:00,077 --> 00:54:03,680
given the COVID environment we're in.

1236
00:54:03,680 --> 00:54:04,720
And the question reads,

1237
00:54:04,720 --> 00:54:07,030
have you applied your
work on protein space

1238
00:54:07,030 --> 00:54:10,750
on the Sars-Cov-2 protein
and what does that entail?

1239
00:54:10,750 --> 00:54:12,350
- Excellent, excellent question.

1240
00:54:15,172 --> 00:54:17,370
So I think most of my work in Sars-Cov-2,

1241
00:54:17,370 --> 00:54:20,270
until pretty recently was
more a mathematical modeling

1242
00:54:20,270 --> 00:54:23,330
and more of the high scale
side, I actually haven't done

1243
00:54:23,330 --> 00:54:27,260
but I am getting into molecular work now.

1244
00:54:27,260 --> 00:54:29,950
And to give you another example

1245
00:54:29,950 --> 00:54:32,850
of how the public work helps the science,

1246
00:54:32,850 --> 00:54:37,250
I wrote an article about COVID
in the "NBA for ESPN", right?

1247
00:54:38,130 --> 00:54:39,583
I'm being a hundred percent serious.

1248
00:54:39,583 --> 00:54:42,910
And that is how I have
my new collaboration

1249
00:54:44,250 --> 00:54:47,300
because somebody saw me
articulate the issues,

1250
00:54:47,300 --> 00:54:48,876
with controlling COVID and they were like,

1251
00:54:48,876 --> 00:54:50,662
well, these ideas are good.

1252
00:54:50,662 --> 00:54:51,780
(laughs)

1253
00:54:51,780 --> 00:54:52,690
You wanna collaborate?

1254
00:54:52,690 --> 00:54:53,830
I was like, yeah.

1255
00:54:53,830 --> 00:54:55,270
So what I'm saying is, right?

1256
00:54:55,270 --> 00:54:58,860
So the point is what are
the questions to be asked?

1257
00:54:58,860 --> 00:55:01,390
And I think they are
powerful and important

1258
00:55:01,390 --> 00:55:03,347
and they relate to the
problems with COVID right now.

1259
00:55:03,347 --> 00:55:04,283
And what are they?

1260
00:55:05,750 --> 00:55:07,760
This protein, this spike protein,

1261
00:55:07,760 --> 00:55:09,830
everybody's kind of
favorite or least favorite

1262
00:55:09,830 --> 00:55:11,000
depending on how you think about a protein

1263
00:55:11,000 --> 00:55:13,033
in the world right now.

1264
00:55:13,033 --> 00:55:15,330
That is basically the one
that's underground mutations

1265
00:55:15,330 --> 00:55:18,220
in all the strains and will
continue to undergo mutations.

1266
00:55:18,220 --> 00:55:23,220
One of the fundamental
questions is can we construct

1267
00:55:23,300 --> 00:55:26,940
a word to gene map for that spike protein?

1268
00:55:26,940 --> 00:55:29,820
And do we understand what
all the different mutations.

1269
00:55:29,820 --> 00:55:32,740
Now it's gonna be a lot
more than four mutations.

1270
00:55:32,740 --> 00:55:34,580
We're talking about dozens of mutations.

1271
00:55:34,580 --> 00:55:35,670
So there's gonna be a huge map

1272
00:55:35,670 --> 00:55:38,100
but the conceptually it's similar,

1273
00:55:38,100 --> 00:55:39,270
conceptually it's similar.

1274
00:55:39,270 --> 00:55:42,540
Is there a route that this thing

1275
00:55:42,540 --> 00:55:46,307
can take to evade community, right?

1276
00:55:48,070 --> 00:55:51,661
So my point is the
questions are being asked.

1277
00:55:51,661 --> 00:55:52,910
Like the people who are doing it

1278
00:55:52,910 --> 00:55:55,210
aren't phrasing it that way.

1279
00:55:55,210 --> 00:55:56,810
But the questions are
being asked that way.

1280
00:55:56,810 --> 00:55:58,883
And I am similarly interested in that.

1281
00:56:00,420 --> 00:56:03,270
There's a technique called
deep mutational scanning.

1282
00:56:03,270 --> 00:56:05,460
And with deep mutation
scanning is you can basically

1283
00:56:05,460 --> 00:56:06,760
take a protein and like create,

1284
00:56:06,760 --> 00:56:09,050
like tens of thousands
of different variants

1285
00:56:09,970 --> 00:56:12,230
and measure them in
some kind of phenotypes.

1286
00:56:12,230 --> 00:56:14,460
So in my case, it would be
like, how will you grow in drug?

1287
00:56:14,460 --> 00:56:17,213
But the phenotype might be,
how will you bind an antibody?

1288
00:56:18,130 --> 00:56:19,890
And from that, you can create a mapping

1289
00:56:19,890 --> 00:56:21,860
and you can basically
say, oh okay, this one,

1290
00:56:21,860 --> 00:56:22,693
this one, this one, this one, this one

1291
00:56:22,693 --> 00:56:24,523
this one are gonna be a problem.

1292
00:56:25,790 --> 00:56:28,380
Oh, let's sequence and
watch out for those.

1293
00:56:28,380 --> 00:56:30,470
And we can have something
called a watch list,

1294
00:56:30,470 --> 00:56:32,700
an epidemiological watch
list where we're sequencing

1295
00:56:32,700 --> 00:56:36,530
in nature and we're like,
oh, maybe this one here

1296
00:56:36,530 --> 00:56:38,920
isn't a problem yet but if
it gets this one mutation,

1297
00:56:38,920 --> 00:56:40,280
this one's gonna be a problem.

1298
00:56:40,280 --> 00:56:42,350
Let's mobilize resources here.

1299
00:56:42,350 --> 00:56:44,100
So this is where we wanna go with this,

1300
00:56:44,100 --> 00:56:46,980
we wanna make surveillance informed

1301
00:56:46,980 --> 00:56:50,870
by details of that protein space, right?

1302
00:56:50,870 --> 00:56:51,990
And I think this is what I hope,

1303
00:56:51,990 --> 00:56:53,490
this is what I wanna do with my work.

1304
00:56:53,490 --> 00:56:55,087
when we get these samples,
I'm getting samples.

1305
00:56:55,087 --> 00:56:56,100
I can't say which.

1306
00:56:56,100 --> 00:56:58,600
From one of the leagues, from
one of the sports leagues,

1307
00:56:58,600 --> 00:57:00,730
I'm getting samples from
one of the sports leagues.

1308
00:57:00,730 --> 00:57:01,580
It's pretty cool.

1309
00:57:05,310 --> 00:57:07,633
- Nelson, do you have other questions?

1310
00:57:09,638 --> 00:57:13,730
- There's none from the
audience but I just wonder

1311
00:57:16,316 --> 00:57:21,316
about, do you see this
being a continual race

1312
00:57:21,750 --> 00:57:26,070
or do you see that there
being some way to actually

1313
00:57:26,070 --> 00:57:28,890
instead of just recognize
that spike protein

1314
00:57:28,890 --> 00:57:32,730
are there ways to attach
things to it, to disable

1315
00:57:32,730 --> 00:57:34,760
kind of thing, because we
got to get rid of this.

1316
00:57:34,760 --> 00:57:35,593
I mean, this is an awful thing.

1317
00:57:35,593 --> 00:57:36,910
- Excellent, you're totally right.

1318
00:57:36,910 --> 00:57:38,140
So a couple of things, number one, I mean

1319
00:57:38,140 --> 00:57:40,050
the best way to have dealt

1320
00:57:40,050 --> 00:57:42,390
with this would have been
to have this under control

1321
00:57:42,390 --> 00:57:44,740
So you didn't give the
opportunity for this to happen.

1322
00:57:44,740 --> 00:57:45,573
- Yes.

1323
00:57:45,573 --> 00:57:47,700
- If we had control of
this thing, like we just,

1324
00:57:47,700 --> 00:57:49,920
if you give evolution enough chances

1325
00:57:52,510 --> 00:57:53,380
I mean, we got a lot of examples of that

1326
00:57:53,380 --> 00:57:54,360
you know what I'm saying?

1327
00:57:54,360 --> 00:57:55,193
It's just rolling.

1328
00:57:55,193 --> 00:57:56,026
It's had all these chances.

1329
00:57:56,026 --> 00:57:57,700
I mean, it's been partying,
it's just infecting

1330
00:57:57,700 --> 00:57:59,350
all these people were like,
okay, let's roll this dice.

1331
00:57:59,350 --> 00:58:01,390
Let's roll this dice
and this came up ACEs.

1332
00:58:01,390 --> 00:58:03,310
It's still very rare.

1333
00:58:03,310 --> 00:58:04,143
Changes, things happen.

1334
00:58:04,143 --> 00:58:05,990
It took a while, but it still happens

1335
00:58:05,990 --> 00:58:08,230
if you give them all these
opportunities to replicate.

1336
00:58:08,230 --> 00:58:10,220
So that's what's happened, right?

1337
00:58:10,220 --> 00:58:11,560
So that's too late for that one.

1338
00:58:11,560 --> 00:58:13,040
We can't actually prevent
that from happening now

1339
00:58:13,040 --> 00:58:13,873
'cause it's already happening.

1340
00:58:13,873 --> 00:58:15,400
So your question is what to do now.

1341
00:58:15,400 --> 00:58:18,550
And I think we have to get
back into the drug business.

1342
00:58:18,550 --> 00:58:20,590
I think vaccines are
definitely the best way,

1343
00:58:20,590 --> 00:58:24,170
the best way to really,
really kick a viral epidemic

1344
00:58:24,170 --> 00:58:26,270
or pandemic in the
teeth is with a vaccine.

1345
00:58:26,270 --> 00:58:28,150
So I think we got to really, really

1346
00:58:28,150 --> 00:58:30,380
do that so we can prevent
harm in as many people

1347
00:58:30,380 --> 00:58:33,080
as possible in a short
amount of time, that's clear.

1348
00:58:33,080 --> 00:58:36,260
But, we have to kind of get
back into the drug business

1349
00:58:36,260 --> 00:58:37,950
and the drug business
basically say, all right, well

1350
00:58:37,950 --> 00:58:39,950
we know what the spike protein looks like.

1351
00:58:39,950 --> 00:58:43,090
Do we have a molecule
that can get in the way?

1352
00:58:43,090 --> 00:58:44,415
And the answer is we have candidates.

1353
00:58:44,415 --> 00:58:48,790
We have antivirals, there's
the T20 fusion inhibitors

1354
00:58:48,790 --> 00:58:50,835
for HIV, which you know.

1355
00:58:50,835 --> 00:58:52,340
We have very effective drugs for HIV.

1356
00:58:52,340 --> 00:58:53,440
They do a lot of things, but one of them,

1357
00:58:53,440 --> 00:58:56,150
it just blocked HIV from
blinding to T-cells.

1358
00:58:56,150 --> 00:58:58,600
So we have drugs that do
this in other viruses.

1359
00:58:58,600 --> 00:59:01,280
I think we've kind of like,
the attention's gone away

1360
00:59:01,280 --> 00:59:04,140
from drugs because of our
investment in the vaccine

1361
00:59:04,140 --> 00:59:05,490
which is appropriate.

1362
00:59:05,490 --> 00:59:08,060
But I think long-term, we
do wanna get in the business

1363
00:59:08,060 --> 00:59:09,100
of being able to kind of...

1364
00:59:09,100 --> 00:59:11,060
I mean, frankly, the
same problem I outlined,

1365
00:59:11,060 --> 00:59:14,500
we wanna be able to identify
what the proteins look like,

1366
00:59:14,500 --> 00:59:17,070
what their phenotypes are
and be able to like target

1367
00:59:17,070 --> 00:59:18,780
certain things with drugs.

1368
00:59:18,780 --> 00:59:20,110
So I think conceptually it's very similar

1369
00:59:20,110 --> 00:59:21,170
to some things I outlined.

1370
00:59:21,170 --> 00:59:24,060
So yes, I think we definitely
wanna get into that business.

1371
00:59:24,060 --> 00:59:24,893
- [Nelson] Cool.

1372
00:59:26,160 --> 00:59:28,740
- Just a final question to you, Dr. Ogbunu

1373
00:59:28,740 --> 00:59:32,003
before I pass you back to
my colleague, Ethel Gordon,

1374
00:59:32,940 --> 00:59:35,980
you were speaking about
different scales of environment

1375
00:59:35,980 --> 00:59:38,480
the micro scale and then
you went up to describe

1376
00:59:38,480 --> 00:59:41,340
your mother's history.

1377
00:59:41,340 --> 00:59:43,490
If we go up to the planetary scale

1378
00:59:43,490 --> 00:59:47,390
the South African variant,
do you think there's evidence

1379
00:59:47,390 --> 00:59:51,360
and your gut feel that very
different national environments

1380
00:59:51,360 --> 00:59:56,263
will lead to different ways
of these variants mutating?

1381
01:00:00,280 --> 01:00:01,580
- So the answer is yes

1382
01:00:01,580 --> 01:00:05,930
but I think the national
scale might be arbitrary.

1383
01:00:05,930 --> 01:00:07,610
I don't know if that's the meaningful way

1384
01:00:07,610 --> 01:00:10,851
to organize that information,
well in some ways, right?

1385
01:00:10,851 --> 01:00:15,851
It is in some ways 'cause like
places that control the virus

1386
01:00:16,160 --> 01:00:18,900
a certain way certainly
have a different experience.

1387
01:00:18,900 --> 01:00:19,733
So I think that's true.

1388
01:00:19,733 --> 01:00:22,430
So national boundary
is useful in that way.

1389
01:00:22,430 --> 01:00:24,850
Here's what I think is much more important

1390
01:00:24,850 --> 01:00:27,400
it's just how people are living, right?

1391
01:00:27,400 --> 01:00:30,040
If you're living in a lot of
people that could be in Brazil,

1392
01:00:30,040 --> 01:00:31,160
that could be in Brooklyn,

1393
01:00:31,160 --> 01:00:33,440
that could be in Johannesburg, right?

1394
01:00:33,440 --> 01:00:34,870
The virus experience is just seeing

1395
01:00:34,870 --> 01:00:36,540
a lot of homo sapiens living together.

1396
01:00:36,540 --> 01:00:38,830
If you have age structure
in a certain place.

1397
01:00:38,830 --> 01:00:42,790
So my point is there's a lot
of ways that national stuff

1398
01:00:42,790 --> 01:00:46,600
or state stuff influence
the trajectory of evolution

1399
01:00:46,600 --> 01:00:48,780
but it's not like anything
essential to Connecticut

1400
01:00:48,780 --> 01:00:51,730
and it's not like in Massachusetts.

1401
01:00:51,730 --> 01:00:54,430
It's about the way people
are structured in Connecticut

1402
01:00:54,430 --> 01:00:56,490
or the way we chose to
control it in Connecticut

1403
01:00:56,490 --> 01:00:57,740
relative to Massachusetts.

1404
01:00:57,740 --> 01:01:00,380
I don't remember how we're
doing relative to one another.

1405
01:01:00,380 --> 01:01:01,940
I think we're a little
bit better but whatever.

1406
01:01:01,940 --> 01:01:02,830
And I'm just teasing.

1407
01:01:02,830 --> 01:01:04,943
Obviously, I gotta live for Massachusetts,

1408
01:01:06,000 --> 01:01:08,430
Anyway, yeah, yeah but
that's a great question.

1409
01:01:08,430 --> 01:01:10,087
- Thanks very much, I'll
pass it back to Dr. Gordon.

1410
01:01:10,087 --> 01:01:11,990
Thank you for a lovely talk.

1411
01:01:11,990 --> 01:01:13,560
- Thank you.

1412
01:01:13,560 --> 01:01:16,070
- Thank you, Dr. Ogbunu,
it's been a pleasure.

1413
01:01:16,070 --> 01:01:21,070
Thank you for very insightful
and interesting perspectives

1414
01:01:21,730 --> 01:01:23,480
on these important questions.

1415
01:01:23,480 --> 01:01:24,863
Given us a lot to think about.

1416
01:01:24,863 --> 01:01:27,750
And I'd like to thank
all of our participants,

1417
01:01:27,750 --> 01:01:30,090
all of our audience members,

1418
01:01:30,090 --> 01:01:32,600
whether from Salem State Community

1419
01:01:32,600 --> 01:01:36,060
or the larger community and
our high school students

1420
01:01:36,060 --> 01:01:39,530
and STEM students across the area.

1421
01:01:39,530 --> 01:01:42,580
So thank you again for
participating with us

1422
01:01:42,580 --> 01:01:45,840
in this 42nd Annual Darwin festival

1423
01:01:45,840 --> 01:01:50,270
and thank all of our guests as well.

1424
01:01:50,270 --> 01:01:53,220
- Thank you Dr. Gordon, thank
you all for your kindness

1425
01:01:53,220 --> 01:01:55,430
and generosity and for
the audience members.

1426
01:01:55,430 --> 01:01:56,780
I appreciate you attending.

1427
01:01:57,850 --> 01:01:58,815
- Thank you.

1428
01:01:58,815 --> 01:01:59,648
- Okay.