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So this lecture is called the future
of biology.  And I want to associate

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somewhat freely.
I'm not going to write on the board.

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I am going to post this
presentation so you can pull

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down stuff from it.
And I think there are two basic

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threads where we're going in biology.
One of them is a basic

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understanding,
what don't we know about biology?

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And the second is how are we going
to use that information to go

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somewhere profound in the future?
So the first one is how life works.

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And the obvious place to start is
the Human Genome Project.

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The Human Genome Project was a very
bold initiative that was first

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spoken about in the late 1980s.
And I remember being at some of the

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first conversations.
I was at the time finishing off my

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graduate work.
I remember being at some of the

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first conversations about the Human
Genome Project where the goal was to

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sequence the human genome,
identify all the genes and DNA,

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and then use them to do a bunch of
things that I'll talk about in a

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moment.  And at the time,
I have to say, it seemed like a

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really stupid idea.
It was very difficult to determine

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the sequence of DNA.
It took hours and days and days to

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read even a thousand base pairs.
And, as you know, we have more than

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ten to the ninth base pairs.
So the notion of sequencing the

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entire human genome seemed
incredibly expensive and incredibly

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stupid.  But that was a reflection,
I think, of my naivety.  And, in

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fact, it's been a very useful
exercise.  Sequencing has gotten

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better, largely because it had to in
order for this project to succeed.

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And I think there's a real lesson
there.  If something has to happen,

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if you have to get a project done,
there are people,

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you guys, who can make techniques
better and get things done.

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So DNA sequencing is much, much
orders of magnitude faster than it

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was ten years ago.
And, in fact, this project was

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initiated in 1990.
The sequencing, per se,

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was completed a couple of years ago.
But, still, there are many people

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who are looking at the sequence and
trying to figure out what it means.

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Because, as you remember from
everything we've talked about,

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DNA sequence is a code.  And even if
you get three times ten to the ninth

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base pairs of the human genome,
all you've gotten is a code.  And

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now you have to crack the code.
And we know how to crack the code

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kind of, that's what we've been
talking about over and over.

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What does a promoter look like?
What does an RNA look like?  What

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does a coding region look like?
What are the signals in a coding

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region that tells a protein
synthesis to begin and to end?

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But when you've just given the,
when you're given this huge mass of

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information that contains maybe 5%
genes and 95% other stuff,

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to actually find the genes in the
human genome from all the sequence

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data is not trivial.
And so there is still analysis

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going on trying to figure out the
identity of a bunch of genes and

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indeed the gene number.
And you may notice every now and

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then revised estimates for gene
numbers in the human genome,

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and it hovers somewhere around 20,
00 to 30,000.  Before the sequence

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was obtained, it was thought that
there were at least 100,

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00 distinct genes in the genome.
The number came down and down and

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down.  And now we think they're
somewhere around 25,

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00 genes.  That's the latest
estimate.  It doesn't really matter.

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Give or take a few thousand.
But still the Human Genome Project

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is not complete.
But it's complete enough that many

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people, including Professor Lander
who is here at MIT who is over at

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the Broad Institute that's being
built across Main Street.

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If you guys walk up towards the
Stata Center and look across Main

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Street, there's a new building going
up.  That is the Broad Institute

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that is being organized by Professor
Lander who was instrumental,

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one of the pivotal people in
sequencing the human genome.

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And he and others are now doing the
takeoff from the Human Genome

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Project, and it goes like this.
Basically describe everything else

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about molecular biology.
And it's a daunting list of what

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people want to do.
Find all the RNAs,

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all the proteins in every cell type
at every time during a cell's life.

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Figure out all the DNA-protein
interactions, so all the

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transcription factors that bind to
DNA.  Figure out all the proteins

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that bind to RNA and might regulate
their translation or might regulate

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their stability.
Figure out all the protein-protein

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interactions, all those enzyme
complexes, all those proteins that

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interact in all those signal
transduction cascades you've been

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talking about.
We have no idea what all the

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protein-protein interactions that go
on in every cell at every point in a

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cell's life are.
All the signal transduction events.

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All the regulatory circuits.  I'll
talk more about that in a moment.

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All gene function.  All diseased
genes.  This is an enormous list.

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It's going to take decades of many
people to get through this list and

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get all this information.
And, of course, in the end this is

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just information.
And you have to do something with it

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and put it together so that you do
land up with understanding gene

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function and being able to build
circuits of the kind that

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bioengineers like to do.
One thing I want to point out for

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those of you who are interested in
computer science.

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One of the things that has come out
of the Human Genome Project is a lot

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of data, but it's actually not that
much data.  It's a few terabytes.

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OK?  So 80,000 CDs will store all
the information coming from the

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Human Genome Project.
But that's just DNA sequence.

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OK?  If you're starting to look at
protein-protein interaction,

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all the RNAs, everything that I just
went through on that list,

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we're talking about billions and
billions and probably trillions of

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terabytes.  Where is that
information going to go?

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Is there a good way to store the
information now?

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There probably ought to be some
real reevaluation of data storage.

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And there is.  There is some
interesting work being done to try

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to figure out how to store and how
to access the information that's

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going to come out of the follow-up
of the Human Genome Project.

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How do you find proteins that are
present in all cells at different

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times in a cell life?
So here's a piece of real data.

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In the study of proteomics the
notion is to look for proteins that

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are present in one cell type and not
in another cell type.

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This is a technique that you know,
gel electrophoresis.  It's called

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2-dimensional gel electrophoresis.
In the first dimension you separate

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proteins by charge.
And then you actually turn your gel

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around, rerun it and separate
proteins according to their size.

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And what you get are a
constellation of spots,

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each of which represents a protein.
And you can look at the spectrum of

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proteins from one cell type and from
another cell type and ask what's

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different and what's similar between
the two cell types.

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So, for example,
this arrow up here.

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Actually, let's look at this one.
In this cell type one there's one,

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two, three spots that are in the
circle and an arrow pointing to

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nothing.  If you look at cell type
two, here are one,

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two, three, the same spots.
And here one, two, three.  And here

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the arrow is pointing to another
spot which is a protein that's

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present in cell type two and not
cell type one.

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And this kind of method is the way
that people are figuring out which

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proteins are present
in which cell type.

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What you can do now is to actually
cut this little spot out of the gel

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of cell type two,
put it through the mass spec and

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figure out the identify of that
protein.  So you can do this stuff.

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It's just a lot of work.  And there
are more sophisticated methods than

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this to go about finding all the
proteins, but basically you have to

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look and you have to identify the
protein.  And then you have to store

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that data and use it somehow.
Here's something else that's being

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done by Professor Young at MIT.
Professor Young is trying to figure

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out all the regulatory networks
between all the genes in yeast.

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So yeast is a small organism.  It
has just a few thousand genes.

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And it has actually just a few
hundred transcription factors.

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And their names are arrayed around
the outside of the circle.

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And what he's done,
using various techniques,

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is to figure out which transcription
factor activates the expression or

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changes the activity of which other
transcription factor.

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And so every arrow indicates that
there is some kind of interaction

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between these different
transcription factors.

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And this gives a kind of regulatory
network of the circuitry involved in

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controlling yeast transcription.
Now, yeast is a single cell with

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very few genes.
We are, as you know,

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multicellular organisms with many
genes.  And so the regulatory maps

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for humans are going to look many,
many orders of magnitude more

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complex than this one.
That's where we're going.

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And part of going there is using
computational biology.

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One of the things that there is
focus on in a number of departments

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at MIT, including Course 7.
, is the question of computational

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biology or that include
systems biology.

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And how can you use computational
methods to work together with real

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data to predict these circuits,
to describe these very, very complex

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circuits, to describe the circuit of
life?  And I can tell you something.

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It sounds as though it's a doable
task, and in theory it is,

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but actually we are not able to
describe the circuit of life for

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even the simple viruses.
So there is a virus called phage

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Lambda that's been mentioned to you.
It has been studied for many, many,

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many decades.  And we know about its
lifecycle in great detail.

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It doesn't have that many genes.
We know which genes turn on and off.

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And yet we still don't have a
completely reliable computer model

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of how this phage responds to
various environmental inputs.

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We don't quite know when it's going
to lyse the cell or when it's going

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to incorporate into the bacterial
cell chromosome.

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We don't even have a complete
computational description for a

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simple virus.  So to get it for a
cell is a daunting task.

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And this is where computational
biology is going to have to work

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with the real data and where you
guys come in to try to bring things

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together so we can actually get
reasonable equations of life.

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Here's an equation that I took from
one of my colleagues,

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Professor Eric Davidson who is at
Caltech, who has been working with

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someone else to look at one of the
regulatory circuits in drosophila.

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And again this is just the tip of
the iceberg of gene interactions.

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You can look at this on the
PowerPoint later.

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These are gene interactions,
and this is just a little bit of the

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circuitry that sets up a little bit
of the body plan in the fruit fly

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drosophila.  Here's another frontier
of biology.  Imaging.

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Imaging in biology is fantastic
right now.  So we can do stuff like

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look at fish that has got its red
blood cells fluorescently labeled.

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And we can actually see in
real-time the movement of the red

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blood cells through the different
parts of the body.

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We can put various drugs on the
fish.  We can use various fish

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mutants that are defective in
components of the extracellular

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matrix, for example,
or some other aspect of the animal

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that might control red blood cell
movement or function.

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And we can look in real-time at what
happens to the animal.

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This works great in fish,
but it's only the beginning because

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there are things we still cannot see
clearly enough,

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even in fish which are transparent.
So these methods work well.  The

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challenges become very great in
mammals where development occurs

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inside the mother and where the
animal is opaque.

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And how do you actually follow
single cells through the animal as

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they're doing whatever
they're doing?

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So, for example,
if one wants to know what happens to

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a cancer cell when it's introduced
into an animal,

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does it go directly to the place
where it's going to make a tumor or

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does it wander around the body until
it actually finds where it's going?

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You have to be able to image single
cells in a very profound way.

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And this is one of the current
frontiers of biology.

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But then you can go deeper down
into the cell.

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You can expand that by a few orders
of magnitude and say,

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well, it's not just looking at the
outside of the cell.

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You really want to be looking inside
the cell in real-time to see

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proteins interacting,
to see transcription happening in

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real-time in the cell.
It's not quite clear how to do that

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right now.  It's a combination of
physics.  So if you're thinking of a

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Course 8.0 major,
this is something you might think

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about.  It's a combination of
physics and biology.

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How do you get imaging on a
resolution high enough that you can

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look in real-time at these events
that are occurring in a cell?

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Fascinating problem.
Neurobiology we touched on.

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Where is neurobiology going?
Well, lots of places.  How do you

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make the brain?
We have no idea how you construct

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the 3-dimensional brain.
And we don't understand the

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significance of the 3-dimensional
structure of the brain.

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In neurobiology we talked about the
circuitry in the brain and about the

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billions and billions of circuits
that there are in the brain,

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probably ten to the fifteenth
circuits within the brain itself.

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How on earth are we going to
actually figure out that circuitry?

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I have no idea.  I have no idea
what we can do in the mammalian

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brain.  We cannot even do it
properly in something fairly simple

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like the fruit fly,
so how are we going to do it in the

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human brain?  This is a real
frontier of biology that,

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again, brings together multiple
disciplines; physics,

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biology, brain and cognitive science.
What's the molecular basis for

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thought and how can we think about
ourselves thinking about ourselves

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thinking about ourselves?
What does that mean?  OK?

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You've had one test on action
potentials.  You know about synapses.

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It's got something to do with
channels and synapses,

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right?  But what goes beyond there?
How does it come back to something

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that allows us to think in such
complex ways?  Here's one.

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Why do we sleep?
Simple question.

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We sleep.  You have to sleep.
If you don't sleep, this is

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something for you guys to bear in
mind.  If you don't sleep,

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after two weeks you will drop dead.
That is true of rats.  If you

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prevent a rat sleeping for two weeks
it drops dead.

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There is something that happens
during sleep, and it's not clear

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what, it's really not clear what.
It's thought that it might be some

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kind of metabolic restoration of the
brain that maybe you run out of some

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essential components that you need
in order to get normal circuitry.

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But you literally have to sleep or
you will die.  But we don't know why.

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OK?  So that's a frontier that is
particularly interesting.

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OK.  This is work I wanted to show
you from my own laboratory.

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We're interested in the
3-dimensional structure of the brain

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and why you have a 3-dimensional
structure and how you make the

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3-dimensional structure.
We're looking in the zebra fish.

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This is a normal zebra fish brain.
And you can see it's got these

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three red regions,
which are actually cavities in the

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brain.
And then this is a whole series of

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mutant fish we've isolated that have
got really messed up brains.

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They turn out to be really messed
up animals.  They've got abnormal

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behavior, their neurons grow in the
wrong place, and there's something

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profoundly wrong with both the
architecture of the brain and the

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function of the brain.
This is one kind of approach we can

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take but, in fact this is asking a
rather simple question.

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It's not asking the question of how
the zebra fish thinks about

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itself, if it does.
OK.  Oh, so what I'd like to do in

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the last lecture also is to point
you in the direction of relevant

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movies, some of which you'll have
heard of and some of which you won't.

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This is one you've probably heard
of because it's a new one.

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So I particularly liked this movie
with respect to neurobiology because,

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actually, I didn't like this movie.
I thought it was a really

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depressing movie,
but the part that I thought was

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really relevant to this class is
that there is a company called

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Lacuna Incorporated that will go in
and selectively erase memories from

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your memory banks.
And they actually can plug in,

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you know, they put a thing on your
head with electrodes coming out.

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And then they have a TV monitor.
And they can actually see the

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circuits that correspond to a
particular memory.

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And then they hit the erase button
or the delete button and that memory

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goes.  And this movie is about this
guy, you look in so

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pain, Dr. Gardel.
This movie is about this guy,

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Jim Carey, who is trying not to be
erased.  Anyway,

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it's very interesting because I
thought, gee, will there ever come a

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time when we actually can have a TV
screen and we actually can see the

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circuits that correspond to a
particular memory?

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So see it if for no other reason.
OK, here we go, basic understanding.

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Something we have not talked much
about in this course but is really

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very important with regard to the
future of biology is evolution.

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What does evolution mean,
especially in molecular terms?

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And I actually wanted to throw this
out at you because this is in the

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news presently,
this term "intelligent design" and

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the contrast to evolution.
I think that you guys, even now but

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certainly as you develop and go
through MIT, really become

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spokespeople for science and become
commentators on current issues in

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science.  And I think you really
should be aware of some current

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issues, so I'm throwing this out at
your to increase your awareness.

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There is a term floating around
called "intelligent design" which is

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sort of, I would say,
an extension of creationism where

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the sense is that things are just so
complex and so interesting and seem

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to be so well designed that how
could this have happened by the

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process of evolution?
And so if you look in the news,

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00:19:50,000 --> 00:19:54,000
if you do a Google news or if you
just look in the newspapers,

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you'll see there are raging
controversies about the notion of

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00:19:58,000 --> 00:20:02,000
intelligent design versus evolution
around the country.

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And indeed evolution is complex,
and we cannot explain how everything

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occurs.  This is a picture of
Darwin's finches,

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the thing that got him thinking
about evolution.

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These finches that live in the
Galapagos Islands and are believed

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to have arisen from a single pair of
finches that the wind blew astray

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00:20:23,000 --> 00:20:27,000
about 100,000 years ago.
And that turned into a bunch of

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different species that can be picked
out by their head shape

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00:20:31,000 --> 00:20:36,000
and their beak size.
And it's really not clear how you

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actually got this set of different
beak shapes and head size.

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The sense of selection for
particular beaks that allowed the

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00:20:46,000 --> 00:20:52,000
birds to eat particular foods and so
on is a very compelling one.

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00:20:52,000 --> 00:20:57,000
And there certainly is no doubt in
my mind, or I would say in most

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people's mind who work in biology,
that natural selection and evolution

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is the way to go.
But I want to raise with you an

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interesting question and then I want
to tell you about a new paper that I

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00:21:11,000 --> 00:21:16,000
read concerning natural selection.
So natural selection leading to

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00:21:16,000 --> 00:21:20,000
evolution is thought to act on three
different kinds of changes in DNA.

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00:21:20,000 --> 00:21:25,000
Single based mutations, you
remember those,

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00:21:25,000 --> 00:21:30,000
frame shifts, missense,
nonsense mutations and so on.

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00:21:30,000 --> 00:21:34,000
Cis-regulatory mutations,
those refer to mutations in the

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promoter regions of genes.
So those would change the

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transcription of a gene.
A single base mutation would change

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00:21:42,000 --> 00:21:47,000
whether a protein is made and what
the actual sequence of the protein

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00:21:47,000 --> 00:21:51,000
is and therefore its potential
function.  The cis-regulatory

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00:21:51,000 --> 00:21:55,000
mutations would change how much of a
message was made, how much

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00:21:55,000 --> 00:22:00,000
of a protein was made.
And here's one that you've touched

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00:22:00,000 --> 00:22:04,000
on a bit, but I want to touch on a
bit more, which is the repeat number

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00:22:04,000 --> 00:22:08,000
of motifs within one coding sequence.
So what am I talking about?

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00:22:08,000 --> 00:22:12,000
Well, I'll tell you what I'm
talking about.

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And I'll use it to describe the
example of dog evolution.

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So dogs are really different from
one another.  They're

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extraordinarily different from one
another.  If you look at their size

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and their actual faces and the bones
of their face,

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00:22:28,000 --> 00:22:32,000
the shapes of the bones,
the size of the snout and so on are

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00:22:32,000 --> 00:22:36,000
really different.
You know, not only are they cute,

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00:22:36,000 --> 00:22:40,000
but they're really different from
one another.  OK.

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00:22:40,000 --> 00:22:45,000
And if you actually look,
over the past 150 years, there has

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00:22:45,000 --> 00:22:49,000
been a huge increase in the number
of breeds and a huge increase in the

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00:22:49,000 --> 00:22:54,000
changes that you see in dog facial
skeleton.  Now,

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00:22:54,000 --> 00:22:59,000
this bothers people who think about
evolution.

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00:22:59,000 --> 00:23:04,000
Because if you look at the number of
single based mutations that are

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00:23:04,000 --> 00:23:09,000
around in coding sequences,
it would not seem to be enough to

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00:23:09,000 --> 00:23:14,000
accomplish these rapid changes in
dog morphology.

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And so a very interesting paper
came out last year that will lead to

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00:23:19,000 --> 00:23:24,000
a conclusion I'll tell you a moment.
The conclusion has to do with

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00:23:24,000 --> 00:23:30,000
variations in the number of motif
repeats within a protein.

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00:23:30,000 --> 00:23:34,000
So what am I talking about?
So forget this.  This is actually

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00:23:34,000 --> 00:23:38,000
on your handout.
So if you look at,

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00:23:38,000 --> 00:23:43,000
I did give you a handout and I
haven't been referring to it,

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00:23:43,000 --> 00:23:47,000
but this in fact is number seven on
your handout.  So if you look at

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protein A and allele A of protein A
or allele B of protein A,

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00:23:52,000 --> 00:23:56,000
they may differ in the following way.
In protein A there may be a small

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00:23:56,000 --> 00:24:01,000
amino acid stretch that is repeated
a couple of times.

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00:24:01,000 --> 00:24:04,000
It could be directly contiguous or
it could be a little far apart from

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00:24:04,000 --> 00:24:08,000
each other.  And then if you look at
allele B of protein A,

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00:24:08,000 --> 00:24:12,000
you might have five copies of that
repeat sequence.

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00:24:12,000 --> 00:24:16,000
And, in fact, that change in number
of copies of a particular part of a

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protein can profoundly change the
function of the protein.

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00:24:20,000 --> 00:24:24,000
It can change confirmation.
It can change enzymatic activity.

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00:24:24,000 --> 00:24:28,000
It can change localization in the
cell.  It can change stability of

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00:24:28,000 --> 00:24:32,000
the protein and so on.
These repeats and variation in the

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00:24:32,000 --> 00:24:37,000
number of repeats are actually very
easy to get.  They are about 100,

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00:24:37,000 --> 00:24:42,000
00 times more frequent than point
mutations if you look in genomes.

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00:24:42,000 --> 00:24:46,000
And they occur during recombination
where the DNA sequences might

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00:24:46,000 --> 00:24:51,000
misalign with one another.
And I'm not going to get into this

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00:24:51,000 --> 00:24:56,000
now, but if you want to come ask me
later I'll email you.  We

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00:24:56,000 --> 00:25:01,000
can go into this more.
But they occur because the DNA

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00:25:01,000 --> 00:25:05,000
sequences don't quite align properly
during recombination,

358
00:25:05,000 --> 00:25:10,000
and you get the protein changing a
bit with respect to these repeat

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00:25:10,000 --> 00:25:14,000
sequences.  And so Fondon and Garner
looked at 92 breeds of dogs,

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00:25:14,000 --> 00:25:19,000
and they looked in 17 genes that
they thought might important in

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00:25:19,000 --> 00:25:23,000
shaping the facial skeleton because
we know what those genes are.

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00:25:23,000 --> 00:25:28,000
And they found, very interestingly,
that these 17 genes had 37 repeat

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00:25:28,000 --> 00:25:32,000
regions amongst them which is
actually much higher than you find

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00:25:32,000 --> 00:25:37,000
in just your general
spread of genes.

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00:25:37,000 --> 00:25:41,000
And when they looked from breed to
breed they found there was a huge

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00:25:41,000 --> 00:25:45,000
variation in the numbers of repeats
in different genes from one breed of

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dog to another breed of dog.
Now, I don't know really what this

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means.  It's very interesting
potentially for looking at how

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00:25:54,000 --> 00:25:59,000
breeds of dogs have evolved or how
we have forced their evolution.

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Does this have something to do
profoundly with evolution and

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00:26:03,000 --> 00:26:07,000
changes in form in general?
Don't know that.  But it's

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00:26:07,000 --> 00:26:11,000
something that you should be aware
of as you go on because it's a

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00:26:11,000 --> 00:26:16,000
slightly different way to think
about how rapid evolution can occur.

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00:26:16,000 --> 00:26:20,000
OK.  Clinical understanding.
Disease taxonomy,

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00:26:20,000 --> 00:26:24,000
you've talked a bit about this in
cancer.  Over the last few decades

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00:26:24,000 --> 00:26:28,000
there has been an enormous increase
in the number of genes that can be

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00:26:28,000 --> 00:26:33,000
assigned to be associated with a
particular disease.

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And my chart here only goes up to
2002.  It would probably be

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00:26:36,000 --> 00:26:40,000
somewhere out here on the roof for
2005.  How do you do this?

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00:26:40,000 --> 00:26:44,000
Well, this is where the Humane
Genome Project comes in.

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00:26:44,000 --> 00:26:47,000
One can look.  And each of these
squares represents a gene and its

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00:26:47,000 --> 00:26:51,000
expression, and the level of
expression is proportional to the

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00:26:51,000 --> 00:26:55,000
color or is associated with the
color.  It doesn't matter which.

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00:26:55,000 --> 00:26:59,000
But you can look in different
cancers.

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00:26:59,000 --> 00:27:02,000
And each of these lines is a cancer.
And you can look at different genes.

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00:27:02,000 --> 00:27:06,000
And you can see that different
tumors have got different patterns

387
00:27:06,000 --> 00:27:10,000
of gene expression.
And you can use those patterns of

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00:27:10,000 --> 00:27:14,000
gene expression to classify the
tumors.  And this has been done by

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00:27:14,000 --> 00:27:18,000
Professor Lander and Dr.
Golub over at the Genome Center.

390
00:27:18,000 --> 00:27:22,000
So, for example, in acute
lymphocytic leukemia,

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00:27:22,000 --> 00:27:26,000
you can see one pattern of gene
expression.  Again,

392
00:27:26,000 --> 00:27:30,000
each of these squares represents a
gene and the color represents the

393
00:27:30,000 --> 00:27:33,000
level of expression of the gene.
And you can see in acute myelogenous

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00:27:33,000 --> 00:27:37,000
leukemia there's a completely
different pattern of expression.

395
00:27:37,000 --> 00:27:41,000
This is fantastic because it starts
to allow you to classify a disease

396
00:27:41,000 --> 00:27:44,000
in molecular detail.
The old way of pathologists looking

397
00:27:44,000 --> 00:27:48,000
at diseases, looking at cells and
trying to classify both cancers and

398
00:27:48,000 --> 00:27:52,000
other disorders on the basis of
morphology of cells and of staining

399
00:27:52,000 --> 00:27:56,000
of cells is actually not that
precise.  It's much better

400
00:27:56,000 --> 00:28:00,000
than nothing.
But being able to do it at a

401
00:28:00,000 --> 00:28:05,000
molecular level really lets you know
what disease you're dealing with and

402
00:28:05,000 --> 00:28:10,000
what spectrum of drugs might be
appropriate to treat that disorder.

403
00:28:10,000 --> 00:28:14,000
And so that segues nicely into the
future of prediction.

404
00:28:14,000 --> 00:28:19,000
What can we predict in biology?
So here are a couple.  Will your

405
00:28:19,000 --> 00:28:24,000
specific disorder respond to
particular drugs?

406
00:28:24,000 --> 00:28:29,000
If you have acute Lymphocytic
leukemia, will it respond to a

407
00:28:29,000 --> 00:28:34,000
particular spectrum of drugs?
And if you have a particular variant

408
00:28:34,000 --> 00:28:38,000
of acute Lymphocytic leukemia,
will it respond to particular

409
00:28:38,000 --> 00:28:43,000
variants of drugs?
We're already on the cusp of

410
00:28:43,000 --> 00:28:47,000
classifying cancers in a way that
you can give a particular spectrum

411
00:28:47,000 --> 00:28:51,000
of drugs for a particular kind of
cancer.  And this is really going to

412
00:28:51,000 --> 00:28:56,000
escalate to everything.
There is almost no disorder that is

413
00:28:56,000 --> 00:29:00,000
treatable by medication that where
different people are not sensitive

414
00:29:00,000 --> 00:29:05,000
at different levels to a
particular medication.

415
00:29:05,000 --> 00:29:08,000
So some people might respond very
well and some people might respond

416
00:29:08,000 --> 00:29:12,000
very poorly, not just in cancer but
in almost all disorders.

417
00:29:12,000 --> 00:29:15,000
In the future, and I think it's
going to be in the near future,

418
00:29:15,000 --> 00:29:19,000
really in the next few years, I
think it's going to be possible to

419
00:29:19,000 --> 00:29:22,000
say what specific disorder do you
have and should you be taking this

420
00:29:22,000 --> 00:29:26,000
particular combination of drugs?
And here's another one.  Are you

421
00:29:26,000 --> 00:29:30,000
genetically predetermined to get a
specific disease?

422
00:29:30,000 --> 00:29:34,000
That's a really tough one.
Maybe you want to know.  Maybe you

423
00:29:34,000 --> 00:29:38,000
don't want to know.
We'll come to that to in a moment.

424
00:29:38,000 --> 00:29:42,000
Here's a movie that I particularly
liked that has to do with predicting

425
00:29:42,000 --> 00:29:46,000
who you're going to be and what
you're going to get or not get.

426
00:29:46,000 --> 00:29:50,000
And it's called Gattaca.  You guys
may or may not have seen it,

427
00:29:50,000 --> 00:29:54,000
or used to see it long ago.  Have
you guys seen Gattaca?

428
00:29:54,000 --> 00:29:58,000
Yes.  OK.  Good.  Fine.
I'm not that far out.  I was trying

429
00:29:58,000 --> 00:30:02,000
to gauge your level here.
I liked that.  I particularly liked

430
00:30:02,000 --> 00:30:07,000
ìthere is no gene for the human
spiritî.  So Gattaca falls into the

431
00:30:07,000 --> 00:30:12,000
prediction aegis quite well.
I next few years, and I would say

432
00:30:12,000 --> 00:30:18,000
within ten years easy,
you are going to be about to get

433
00:30:18,000 --> 00:30:23,000
your personal DNA profile,
including information about the

434
00:30:23,000 --> 00:30:28,000
approximately 1,
00 bad alleles of genes that we all

435
00:30:28,000 --> 00:30:32,000
carry.  So that's good, I guess.
And that's bad.

436
00:30:32,000 --> 00:30:35,000
Do you want this information?
Do you want to know what you're

437
00:30:35,000 --> 00:30:38,000
going to get?  Do you want to know
that you're going to get a

438
00:30:38,000 --> 00:30:41,000
neurological disease as you get
older?  Do you want to know that

439
00:30:41,000 --> 00:30:44,000
you're likely to have a heart attack
before you're 50?

440
00:30:44,000 --> 00:30:47,000
Do you want others to have this
information?  Would you like your

441
00:30:47,000 --> 00:30:50,000
perspective partner to know that
you're likely to get some horrible

442
00:30:50,000 --> 00:30:53,000
neurological disease?
Would you like your insurance

443
00:30:53,000 --> 00:30:56,000
company to know this?
Would you like your children to

444
00:30:56,000 --> 00:31:00,000
know it?  I don't know.
I don't know what the answer is.

445
00:31:00,000 --> 00:31:04,000
Myself, I prefer actually not to
know and to go through life

446
00:31:04,000 --> 00:31:08,000
day-to-day having as good a time as
I can and letting whatever,

447
00:31:08,000 --> 00:31:12,000
God or chance take care of the rest.
But there is certainly merit in

448
00:31:12,000 --> 00:31:17,000
trying to prevent some things.
And this is really going to be a

449
00:31:17,000 --> 00:31:21,000
reality very soon.
Already, actually,

450
00:31:21,000 --> 00:31:25,000
you can get for your dog a DNA
profile that's actually

451
00:31:25,000 --> 00:31:30,000
fairly detailed.
It is RFLP mapping that we talked

452
00:31:30,000 --> 00:31:34,000
about in class where you can make
sure that your dog is who you think

453
00:31:34,000 --> 00:31:38,000
it is and who its parents are and
who it thinks it is.

454
00:31:38,000 --> 00:31:42,000
So you can get your own in the
future.  I don't know what they're

455
00:31:42,000 --> 00:31:46,000
going to call the American Kennel
Club equivalent for humans,

456
00:31:46,000 --> 00:31:50,000
but you'll be able to get your
certificate of DNA analysis.

457
00:31:50,000 --> 00:31:55,000
OK.  Design.  We talked long ago
about rational drug design.

458
00:31:55,000 --> 00:31:58,000
And Gleevec is really one of the
shinning examples of being able to

459
00:31:58,000 --> 00:32:02,000
look at the structure of a protein
and saying, hey,

460
00:32:02,000 --> 00:32:06,000
this protein is a bad protein,
it's an abnormal protein, and it's

461
00:32:06,000 --> 00:32:10,000
causative of leukemia.
And wouldn't it be great to inhibit

462
00:32:10,000 --> 00:32:13,000
its function?  And so let's design
the screen molecule which looks as

463
00:32:13,000 --> 00:32:17,000
though it will inhibit ATP binding
and prevent this kinase from acting.

464
00:32:17,000 --> 00:32:21,000
And, in fact, Gleevec works really
well that way.

465
00:32:21,000 --> 00:32:25,000
And this is something that is being,
this approach is being very,

466
00:32:25,000 --> 00:32:29,000
very actively pursued, and will only
be more actively pursued.

467
00:32:29,000 --> 00:32:33,000
And if you're thinking of a Course 5.
major or if you're thinking of many

468
00:32:33,000 --> 00:32:37,000
of the engineering majors,
you may well get into rational drug

469
00:32:37,000 --> 00:32:42,000
design and figuring out how to get
it to work.  Here's another one,

470
00:32:42,000 --> 00:32:46,000
prediction for the future and
Jurassic Park.

471
00:32:46,000 --> 00:32:51,000
Xenotransplantation,
using pigs that have immune systems

472
00:32:51,000 --> 00:32:55,000
engineered to look like the human
immune system for organ transplants.

473
00:32:55,000 --> 00:33:00,000
There are companies trying to do
this.

474
00:33:00,000 --> 00:33:04,000
Bionics.  Here is something for all
of you interested in mechanical

475
00:33:04,000 --> 00:33:08,000
engineering and other,
bionics.  Robocop.  Here's a movie

476
00:33:08,000 --> 00:33:12,000
to see if you haven't.
Artificial hearts.  Artificial

477
00:33:12,000 --> 00:33:17,000
hearts are a disaster presently.
AbioCor has a heart now that is

478
00:33:17,000 --> 00:33:21,000
fully implantable and that will take
over ventricular function,

479
00:33:21,000 --> 00:33:25,000
but it is a lousy heart.  And if one
of you could go and make a new heart

480
00:33:25,000 --> 00:33:29,000
that actually would work properly
that would be a real service

481
00:33:29,000 --> 00:33:34,000
to humankind.
Blood substitutes.

482
00:33:34,000 --> 00:33:38,000
We still do not have a blood
substitute that really works.

483
00:33:38,000 --> 00:33:43,000
There is one patented.  It's called
Oxygent.  It's a perfluorocarbon

484
00:33:43,000 --> 00:33:47,000
that will carry oxygen around the
blood in the body for a while,

485
00:33:47,000 --> 00:33:52,000
but it's not very good.  And finally
aging.  Here's a movie you probably

486
00:33:52,000 --> 00:33:56,000
haven't heard of,
Zardoz.  Zardoz, a great movie about

487
00:33:56,000 --> 00:34:01,000
a community where nobody aged and
they were really, really unhappy.

488
00:34:01,000 --> 00:34:05,000
And then Sean Connery comes along
and introduces this community that

489
00:34:05,000 --> 00:34:09,000
doesn't age and doesn't have sex and
is just generally miserable to the

490
00:34:09,000 --> 00:34:13,000
joys of procreation.
And they go for it.  And then they

491
00:34:13,000 --> 00:34:18,000
age and they die happily ever after.
So that's OK.  And if you're

492
00:34:18,000 --> 00:34:22,000
looking for a summer book to read,
Professor Guarente at MIT has

493
00:34:22,000 --> 00:34:26,000
written a great book about aging,
which is what his research focuses

494
00:34:26,000 --> 00:34:31,000
on.  And you might want
to look at that.

495
00:34:31,000 --> 00:34:35,000
So the challenge I give to you is
where are you going to come in?

496
00:34:35,000 --> 00:34:40,000
I wish you all the best of luck.
And it's been a pleasure to teach

497
00:34:40,000 --> 00:34:43,000
you.  [APPLAUSE]