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PROFESSOR: So, welcome
to systems biology.

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There are many different numbers
that you might have signed up

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00:00:31,580 --> 00:00:32,870
for this class through.

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My name is Jeff Gore.

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I'm an assistant professor
in the physics department.

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And I think that
this is really--

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it's a fun class to teach.

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I hope that those of
you that stick around

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00:00:42,269 --> 00:00:44,560
for the rest of the semester
find that it's a fun class

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to take as well.

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I just want to give a little
bit of an introduction

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of the teaching
staff, and then we'll

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00:00:51,770 --> 00:00:53,490
go over some
administrative details

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00:00:53,490 --> 00:00:56,120
before-- most of
today, what we'll do

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00:00:56,120 --> 00:00:58,110
is we'll basically
just spend an hour.

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00:00:58,110 --> 00:01:02,160
I'll give you a flash
summary of the course,

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00:01:02,160 --> 00:01:04,420
and then you'll have a
sense of the kinds of ideas

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00:01:04,420 --> 00:01:06,000
we'll be exploring.

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So, what is this
thing, systems biology?

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So it's, I would
say, ill-defined.

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But in general, we have this
idea that, in many cases,

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the really exciting functions
that we see in biology

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are arising from
the interactions,

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at a lower level, of
relatively simple components.

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So, the kinds of behavior
that we'd like to understand

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are nicely encapsulated in this
video of a neutrophil chasing

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a bacterium.

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And if I can-- if my computer
is actually doing something,

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then-- there's something.

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00:01:40,380 --> 00:01:42,980
So, this is a classic
video that many of you

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guys might have
seen over the years.

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So, this was taken in the 1950s.

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00:01:53,580 --> 00:01:55,080
It's, as I said, a
neutrophil, which

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is part of your
innate immune system.

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So, sort of the first
line of defense.

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00:01:59,530 --> 00:02:02,110
When you get a bacterial
infection, for example,

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00:02:02,110 --> 00:02:06,920
this white blood cell is
going to chase the bacterium

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and eat it up before it
can divide and harm you.

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So, I'm going to
play that over again,

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because it's pretty cool.

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So, the features that you
want to try to pay attention

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00:02:19,270 --> 00:02:22,330
to-- so, this is
a single cell that

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somehow is able to track
this bacterial invader.

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It's using chemical
cues in order

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to figure out where it is
that bacterial cell is going.

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It's able to disregard
these red blood cells that

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are in its way, push them
aside, change direction,

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before it eventually
captures this bacterial cell.

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So, all of these are striking,
amazing information-processing

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capabilities, where
that information has

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to be coupled not only to
some sort of decision-making

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within that cell,
but also, it has

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to be transduced into
these mechanical forces

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and motions that allow that cell
to capture the bacterial cell.

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So, let's try it again.

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So, here it is.

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You can see the
bacterial cell is here.

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So, it's ignoring
this other cell,

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keeping focus on this one,
pushing aside the red blood

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cells, so it can
follow it along.

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Every now and then-- now
the cell changed direction.

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But eventually, you
can see it catches up

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to the bacterial
cell and eats it.

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Now you're not going to get
sick from that infection.

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So, that's an example of the
kinds of remarkable behavior

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that can be implemented
even just by a single cell.

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So, we know that, as humans,
we have brains with 10

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to the 12 neurons or so.

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So, maybe you'd say,
it's not surprising

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that we can do fancy things.

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What's remarkable is
that even at the level

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of an individual cell,
it's possible to implement

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rather sophisticated information
processing capabilities.

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So, this is the kind
of thing that we'd

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like to be able to say something
about by the end of the class.

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Many of you, I think, probably
read the course description,

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and this gives you a sense
of the kinds of topics

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that we're going to be
covering over the course

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of the semester.

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00:04:06,696 --> 00:04:08,170
I'm not going to read it to you.

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But one thing I
want to stress is

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I brought up this general
idea of systems biology.

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How is it that function
arises from interactions

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of smaller, simpler parts?

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00:04:18,860 --> 00:04:22,000
I think it's very important,
right at the beginning,

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to be clear that
there are really,

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00:04:23,480 --> 00:04:25,510
I'd say, two
distinct communities

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that self-identify as
studying systems biology.

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And they're-- to simplify it a
bit, what I would say is that,

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basically, there's the physics,
or physics-inspired community,

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00:04:37,070 --> 00:04:39,390
where-- and I'm in the
physics department,

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00:04:39,390 --> 00:04:41,700
so that's where I fall, and
where this class is going

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00:04:41,700 --> 00:04:42,450
to be.

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So, it's really trying
to use some simple models

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00:04:46,230 --> 00:04:49,770
from nonlinear dynamics
or stochastic processes

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00:04:49,770 --> 00:04:51,990
combined with
quantitative experiments,

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00:04:51,990 --> 00:04:55,180
often on single
cells, in order to try

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to illuminate how this cellular
decision-making process works.

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00:05:00,070 --> 00:05:01,720
And on over the
next hour, you'll

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00:05:01,720 --> 00:05:03,634
see-- get a flavor of
what I mean by this.

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Now, there's another
branch of systems biology

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that is also very exciting,
and that many of you

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may want to learn more
about in the future.

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00:05:10,920 --> 00:05:12,862
And maybe some of
even were thinking

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that this was what the
class is going to be in.

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But let me explain what is.

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00:05:16,174 --> 00:05:18,090
This other branch, I'd
say, is more influenced

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by computer scientists
and engineers,

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where what they're
really trying to do

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is use complex models,
machine learning techniques,

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and so forth, in order to
extract signal from large data

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sets.

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And this is also,
again, systems biology

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because it is
trying to understand

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how the global
properties of the cell

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result from all
these interactions,

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but it's a rather different
aesthetic take on the subject.

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And, indeed, much
of the activity,

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that one would be doing here is
different from the more physics

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branch of systems biology.

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00:05:49,910 --> 00:05:55,990
So, if what you were looking
for was more of this large data

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set, high throughput,
branch of systems biology,

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then you may not be
at the right place.

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And there's an
interesting fact, which

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is that if you
decide that you want

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this other branch of
systems biology, then, very

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conveniently, you actually
have space in your schedule,

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because Manolis
Kellis is teaching

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a class in computational
biology-- really,

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this other branch of systems
biology-- at the same time.

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So, if you think you're
at the wrong place,

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you're welcome to
just sneak out now.

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Go to over 32-141, and I'm
sure that he will welcome you,

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and no hard feelings.

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Similarly, in the
spring, there's

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another computational biology
class that some of you

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may be thinking about.

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And this is taught by
Chris Burge and company,

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and also, I'd say, maybe
more of this other branch

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of systems biology.

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And, finally, once again, in
the spring there's a class,

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quantitative biology
for graduate students,

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that I'd say probably
assumes somewhat less

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mathematical background.

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So, if after looking at the
syllabus, or maybe even getting

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started looking at
the first problem set,

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if you think that maybe this
class is expecting too much,

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then you may want to consider
taking quantitative biology

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in the spring, and maybe
taking systems biology--

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this class-- next fall.

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So, on that note, I
want to say something

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about the prerequisites.

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The major challenge
with this class--

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certainly, teaching
it, from my standpoint,

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and I think for
many of you taking

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it-- is that there's a wide
range of different backgrounds.

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So, we can maybe get
a sense of that now.

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Just a show of hands, how many
of you are undergraduates?

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So, we've got a
solid third or so.

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How many are-- mixing together
undergraduate and graduate--

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but how many of you are in
the physics department at one

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00:07:42,850 --> 00:07:44,350
level or another?

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So, we have maybe,
again, a third.

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Biology department?

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So, we have a quarter, a fifth.

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Engineers?

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So, a substantial
fraction of engineers.

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And chemists?

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We've got a few of them.

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Mathematicians?

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All right, we've got one.

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So, if you did not raise
your hand, where you based,

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physically,
intellectually, something?

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Did we get everybody?

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OK.

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So, you can see that
there's a really broad range

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of different backgrounds.

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And what that means in
a concrete way for us

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is that I will very
much try to avoid

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using unnecessary jargon
or unnecessary mathematics.

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I think that mathematics
is a wonderful thing,

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but in some cases
it, I think, obscures

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as much as it illuminates.

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So, for me, I very
much try to focus

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on conceptual understanding.

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And on top of that, I try to
build-- you like math, too.

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00:08:46,060 --> 00:08:47,920
But I think that
it's very important

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to be able to, for example,
plot your solution.

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00:08:50,580 --> 00:08:55,140
So, after you derive some fancy
equation describing something,

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you should know
whether that thing

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goes up or down as a function
of something or another.

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00:08:59,120 --> 00:09:01,070
And I think it's
very easy to lose

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00:09:01,070 --> 00:09:03,050
sight of these
basic aspects when

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00:09:03,050 --> 00:09:08,550
we get too deep into the
mathematical equations.

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But, that being said, we do,
I'd say, expect something--

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not necessarily
full-- you don't have

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to have taken the full class,
701 or 702, but at least

206
00:09:16,700 --> 00:09:19,730
a solid high school
class of biology.

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00:09:19,730 --> 00:09:22,530
If it's been more than 10 years
since you took a biology class,

208
00:09:22,530 --> 00:09:25,550
you might want to take
one before coming here.

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00:09:25,550 --> 00:09:29,530
You could, in principle,
catch up, like all things.

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We also assume some comfort
with differential equations

211
00:09:32,600 --> 00:09:33,960
and probability.

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So, we've actually added
those as prerequisites,

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00:09:36,930 --> 00:09:39,530
particularly from the
standpoint of an undergraduate

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00:09:39,530 --> 00:09:42,660
to give you a sense of
the sort of material

215
00:09:42,660 --> 00:09:44,800
that we expect you to
be comfortable with.

216
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So, we will not be defining
probably distributions so much.

217
00:09:48,510 --> 00:09:51,930
We will assume that you can
calculate means and standard

218
00:09:51,930 --> 00:09:56,080
deviations of discrete,
continuous distributions,

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and so forth.

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00:09:57,889 --> 00:09:59,930
And the other thing that
is going to be important

221
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is that a major
goal of the class

222
00:10:02,160 --> 00:10:04,610
is to increase
your comfort level

223
00:10:04,610 --> 00:10:07,282
with using computational
techniques to analyze

224
00:10:07,282 --> 00:10:08,240
some of these problems.

225
00:10:08,240 --> 00:10:10,323
So, every week, we're going
to have a problem set.

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00:10:10,323 --> 00:10:13,100
And on every problem set, there
will be at least one problem

227
00:10:13,100 --> 00:10:16,260
where you have to use some
computational package in order

228
00:10:16,260 --> 00:10:17,400
to calculate something.

229
00:10:17,400 --> 00:10:18,900
So, are you going
to do a simulation

230
00:10:18,900 --> 00:10:21,394
to understand the stochastic
dynamics of this or that?

231
00:10:21,394 --> 00:10:23,560
Or maybe you're going to
integrate some differential

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equations.

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00:10:24,670 --> 00:10:28,769
And in this case, you can use
whatever package you like.

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So if you are a MATLAB
person, that's fine.

235
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Mathematica is fine.

236
00:10:31,950 --> 00:10:35,009
The officially
supported language

237
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is going to be
Python, because that's

238
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what Sarab-- if he's going to
be spending hours helping you

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00:10:40,220 --> 00:10:42,080
with your code, he
wants it to be something

240
00:10:42,080 --> 00:10:44,140
that he's comfortable with.

241
00:10:44,140 --> 00:10:45,640
So, that's going
to be what we might

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00:10:45,640 --> 00:10:48,280
call the official language, in
the sense that he will perhaps

243
00:10:48,280 --> 00:10:51,200
provide some sample code and
so forth to get you started.

244
00:10:51,200 --> 00:10:54,490
But you're welcome to use
anything that you want.

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00:10:54,490 --> 00:10:57,910
And that being said, we
will have a Python tutorial

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00:10:57,910 --> 00:10:59,170
almost certainly on Monday.

247
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We're waiting to
get-- to find out what

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00:11:00,600 --> 00:11:01,808
the classroom is going to be.

249
00:11:01,808 --> 00:11:07,577
But we will send out a notice
to the class about that,

250
00:11:07,577 --> 00:11:10,160
as well as instructions on how
to get Python on your computer.

251
00:11:12,880 --> 00:11:15,890
Are there any questions
about where we are so far?

252
00:11:15,890 --> 00:11:17,150
What we've said?

253
00:11:17,150 --> 00:11:18,806
Expectations, prerequisites?

254
00:11:25,340 --> 00:11:26,300
All right.

255
00:11:26,300 --> 00:11:27,510
So, grading.

256
00:11:27,510 --> 00:11:30,090
One of the things that we have
to do is we have to grade.

257
00:11:30,090 --> 00:11:34,340
I'd say that our goal is very
much to help you learn material

258
00:11:34,340 --> 00:11:35,760
that we're excited about.

259
00:11:35,760 --> 00:11:40,900
So, I am not in any way trying
to grade in any mean way.

260
00:11:40,900 --> 00:11:43,696
And what that means
is that-- we also just

261
00:11:43,696 --> 00:11:45,320
don't want you guys
to feel like you're

262
00:11:45,320 --> 00:11:46,528
competing against each other.

263
00:11:46,528 --> 00:11:48,440
So, what that
means is that-- so,

264
00:11:48,440 --> 00:11:50,190
these are the grade cutoffs.

265
00:11:50,190 --> 00:11:53,250
So, they will not-- numbers
will not go up from here.

266
00:11:53,250 --> 00:11:55,290
If I screw up and I make
some really hard exam,

267
00:11:55,290 --> 00:11:58,430
then I reserve the right
to lower these numbers.

268
00:11:58,430 --> 00:12:00,720
But basically, this
is what's worked

269
00:12:00,720 --> 00:12:03,000
for the last several years.

270
00:12:03,000 --> 00:12:08,410
So, you should feel comfortable
collaborating with your friends

271
00:12:08,410 --> 00:12:10,420
to study, to try to
figure out the material,

272
00:12:10,420 --> 00:12:12,003
because your grade
is just going to be

273
00:12:12,003 --> 00:12:15,340
determined by where things end
up on this chart, basically.

274
00:12:15,340 --> 00:12:19,650
And the course grade is going
to be split, as you can see.

275
00:12:19,650 --> 00:12:22,320
There's a fair component
on problem sets,

276
00:12:22,320 --> 00:12:24,121
and that's because
the problem sets are--

277
00:12:24,121 --> 00:12:25,370
they're going to be hard work.

278
00:12:25,370 --> 00:12:27,080
We're going to have
problem sets every week,

279
00:12:27,080 --> 00:12:29,090
and you can expect to spend
a significant amount of time

280
00:12:29,090 --> 00:12:29,900
on them.

281
00:12:29,900 --> 00:12:32,694
And the thing that
you learn in doing

282
00:12:32,694 --> 00:12:34,360
these computational
problems is somewhat

283
00:12:34,360 --> 00:12:36,150
different from what
you learn and what

284
00:12:36,150 --> 00:12:37,800
you demonstrate on an exam.

285
00:12:37,800 --> 00:12:41,649
So, that's why it's not
all just an exam grade.

286
00:12:41,649 --> 00:12:44,190
I'm going to say something more
about these pre-class reading

287
00:12:44,190 --> 00:12:44,690
questions.

288
00:12:44,690 --> 00:12:47,350
There are going to be
two midterms and an exam.

289
00:12:47,350 --> 00:12:51,782
The dates are on your syllabus,
so please mark these evenings

290
00:12:51,782 --> 00:12:52,490
on your calendar.

291
00:12:56,525 --> 00:12:57,400
So, the problem sets.

292
00:12:57,400 --> 00:12:58,399
You can read about this.

293
00:12:58,399 --> 00:13:02,320
But basically, every
Friday at 7 o'clock,

294
00:13:02,320 --> 00:13:03,980
they're going to be due.

295
00:13:03,980 --> 00:13:08,610
A box out between the
third floor of building six

296
00:13:08,610 --> 00:13:10,310
and the fourth floor
of building 16,

297
00:13:10,310 --> 00:13:13,980
I suppose-- these are the
physics homework boxes.

298
00:13:13,980 --> 00:13:16,250
So, the idea is we'd like
you to have a weekend

299
00:13:16,250 --> 00:13:18,490
to catch up and start
reading for the next week.

300
00:13:18,490 --> 00:13:21,570
So, that's why they're dude
just before dinner on Friday.

301
00:13:21,570 --> 00:13:24,190
That being said, we understand
that sometimes there

302
00:13:24,190 --> 00:13:26,880
are a lot of problem sets, or
sometimes you're overwhelmed

303
00:13:26,880 --> 00:13:27,280
with something else.

304
00:13:27,280 --> 00:13:27,966
So, that's fine.

305
00:13:27,966 --> 00:13:29,340
You can turn it
in for 80% credit

306
00:13:29,340 --> 00:13:31,131
till Monday morning at
10:00 AM, when we're

307
00:13:31,131 --> 00:13:32,840
going to post the solutions.

308
00:13:32,840 --> 00:13:36,950
So, we won't be accepting
problem sets after that,

309
00:13:36,950 --> 00:13:39,740
unless you get agreement
from Sarab in advance.

310
00:13:43,290 --> 00:13:44,790
So, the pre-class
reading questions.

311
00:13:44,790 --> 00:13:47,115
I'd say this is a key
part of the class.

312
00:13:47,115 --> 00:13:48,990
It's only 5% of
the grade, and it's

313
00:13:48,990 --> 00:13:51,110
graded really only
on participation--

314
00:13:51,110 --> 00:13:52,350
that you've done it.

315
00:13:52,350 --> 00:13:55,790
But this is an essential
element of what we like

316
00:13:55,790 --> 00:13:57,245
to call a flipped classroom.

317
00:13:57,245 --> 00:13:58,870
So, today's class is
going to be rather

318
00:13:58,870 --> 00:14:02,090
different from the rest of the
semester in that today is more

319
00:14:02,090 --> 00:14:03,540
like a lecture, I would say.

320
00:14:03,540 --> 00:14:06,030
Whereas the rest
of the semester,

321
00:14:06,030 --> 00:14:10,820
there will not be any PowerPoint
slides and it'll be very much,

322
00:14:10,820 --> 00:14:12,880
I hope, very interactive.

323
00:14:12,880 --> 00:14:14,312
In order to
facilitate that, there

324
00:14:14,312 --> 00:14:15,770
are a number of
different elements.

325
00:14:15,770 --> 00:14:19,340
One is that we do require
reading before class.

326
00:14:19,340 --> 00:14:22,020
And the way that we encourage
you to do the reading

327
00:14:22,020 --> 00:14:26,510
is that we ask you to answer
questions the night before.

328
00:14:26,510 --> 00:14:29,160
So, what you're going
to do is you'll,

329
00:14:29,160 --> 00:14:32,730
by 10:00 PM, the night before,
just three questions-- just

330
00:14:32,730 --> 00:14:34,230
a couple of sentences
each question.

331
00:14:34,230 --> 00:14:36,530
It's not that you're supposed
to have to do a lot of work.

332
00:14:36,530 --> 00:14:37,970
It's just that if
you did the reading,

333
00:14:37,970 --> 00:14:39,440
you should be able to
give your take on it,

334
00:14:39,440 --> 00:14:41,000
and you think about
it a little bit.

335
00:14:41,000 --> 00:14:45,250
And then Andrew will go
over the submitted answers,

336
00:14:45,250 --> 00:14:48,590
and we'll send out his favorite
answers among the group.

337
00:14:48,590 --> 00:14:51,300
So, your answer
will occasionally

338
00:14:51,300 --> 00:14:55,680
be represented there, if you
say something that's reasonable.

339
00:14:55,680 --> 00:14:58,539
Now, it's really important
to have done this reading

340
00:14:58,539 --> 00:15:00,330
and thought about the
material some before,

341
00:15:00,330 --> 00:15:02,440
because the idea is that,
in class, we'd really

342
00:15:02,440 --> 00:15:05,571
like to engage in what you
might call some higher level

343
00:15:05,571 --> 00:15:06,070
learning.

344
00:15:06,070 --> 00:15:10,160
So, it's not just this idea
that-- the traditional lecture

345
00:15:10,160 --> 00:15:12,960
arose when books
were very expensive.

346
00:15:12,960 --> 00:15:16,080
So, if you're at a university
in the 13th century,

347
00:15:16,080 --> 00:15:17,210
you don't have a textbook.

348
00:15:17,210 --> 00:15:21,180
So, what you need is for me to
stand up front and read to you.

349
00:15:21,180 --> 00:15:24,570
And that's fine, except that
it's better for you to read it.

350
00:15:24,570 --> 00:15:27,590
And you can read it outside of
class, think about it a bit.

351
00:15:27,590 --> 00:15:29,381
And then that means
when you come to class,

352
00:15:29,381 --> 00:15:31,505
we can actually discuss it.

353
00:15:31,505 --> 00:15:33,520
In particular, I'll
give you my take

354
00:15:33,520 --> 00:15:36,680
on the material, the
research that I'm

355
00:15:36,680 --> 00:15:40,090
excited about in the area
that's been published recently.

356
00:15:40,090 --> 00:15:44,030
And we will also try to get
you involved via these concept

357
00:15:44,030 --> 00:15:44,530
questions.

358
00:15:44,530 --> 00:15:48,930
So, in future sessions we'll
have these flash cards,

359
00:15:48,930 --> 00:15:51,480
or these colored
cards, so we can

360
00:15:51,480 --> 00:15:54,930
ask these conceptual questions.

361
00:15:54,930 --> 00:15:58,640
A, B, C, D-- if you drop an
apple, does it go up, down,

362
00:15:58,640 --> 00:15:59,550
left, right?

363
00:15:59,550 --> 00:16:01,380
And then, you guys get to vote.

364
00:16:01,380 --> 00:16:05,535
And then, after the vote, we
will often have you pair up.

365
00:16:05,535 --> 00:16:06,910
And the goal there
is that you're

366
00:16:06,910 --> 00:16:09,980
trying to convince your
neighbor that you're right.

367
00:16:09,980 --> 00:16:12,500
And after that, you might
expand it to fours or so.

368
00:16:12,500 --> 00:16:15,530
But the idea there is that
it's very important for you

369
00:16:15,530 --> 00:16:19,740
to try to confront the
material, make your best guess,

370
00:16:19,740 --> 00:16:21,240
and then discuss
it with a neighbor.

371
00:16:21,240 --> 00:16:23,015
And I think that
this is actually

372
00:16:23,015 --> 00:16:24,936
one of the fun
aspects of the course.

373
00:16:24,936 --> 00:16:27,990
At least, I think so.

374
00:16:27,990 --> 00:16:32,490
And I'll say also that
this basic technique

375
00:16:32,490 --> 00:16:35,550
is the result of-- there's
a whole field of education

376
00:16:35,550 --> 00:16:36,370
research.

377
00:16:36,370 --> 00:16:39,290
And there are very,
very consistent

378
00:16:39,290 --> 00:16:41,100
and strong signals
in this, suggesting

379
00:16:41,100 --> 00:16:44,240
that this sort of flipped
classroom, active learning

380
00:16:44,240 --> 00:16:48,060
style, actually is
good for learning.

381
00:16:48,060 --> 00:16:50,312
So I'm not just doing this
because-- it is more fun,

382
00:16:50,312 --> 00:16:52,020
but that's not actually
why I'm doing it.

383
00:16:52,020 --> 00:16:54,330
I'm doing it because
the people who

384
00:16:54,330 --> 00:16:56,427
have spent their lives
studying this topic

385
00:16:56,427 --> 00:16:58,260
have included this is
the best way to teach.

386
00:17:02,420 --> 00:17:06,060
Any questions about
the pre-class questions

387
00:17:06,060 --> 00:17:08,190
or my notion of active learning?

388
00:17:12,444 --> 00:17:12,950
All right.

389
00:17:17,280 --> 00:17:19,510
You can mark your
calendars in advance.

390
00:17:19,510 --> 00:17:20,609
We do have a final.

391
00:17:20,609 --> 00:17:22,184
It has not been scheduled yet.

392
00:17:22,184 --> 00:17:24,990
It'll be sometime the
week of December 15-19.

393
00:17:24,990 --> 00:17:28,780
So, for those of
you who are looking

394
00:17:28,780 --> 00:17:34,472
online for plane tickets
back home, after December 19.

395
00:17:34,472 --> 00:17:36,430
Or, if you'd like, you
can wait a couple weeks,

396
00:17:36,430 --> 00:17:37,971
and then the final
will be scheduled.

397
00:17:41,950 --> 00:17:44,250
We have two required
textbooks for the class.

398
00:17:44,250 --> 00:17:48,100
The first is An Introduction
to Systems Biology by Uri Alon.

399
00:17:48,100 --> 00:17:51,740
I think it's a wonderfully
clear, exciting introduction

400
00:17:51,740 --> 00:17:53,420
to the topic.

401
00:17:53,420 --> 00:17:55,140
The flip side of being
wonderfully clear

402
00:17:55,140 --> 00:17:57,270
is that it's a
little-- you could

403
00:17:57,270 --> 00:17:59,180
complain that it's too simple.

404
00:17:59,180 --> 00:18:02,490
And what that means is that we
will be supplementing the book

405
00:18:02,490 --> 00:18:05,860
in a variety of ways,
both with separate notes,

406
00:18:05,860 --> 00:18:08,980
and also by extensive
reading of papers

407
00:18:08,980 --> 00:18:12,190
from the primary literature.

408
00:18:12,190 --> 00:18:14,590
The second half or
third of the class,

409
00:18:14,590 --> 00:18:17,460
we'll be reading some chapters
from Evolutionary Dynamics,

410
00:18:17,460 --> 00:18:18,990
a book by Martin Nowak.

411
00:18:18,990 --> 00:18:22,330
Again, a very nice, I think,
clear, exciting introduction

412
00:18:22,330 --> 00:18:23,750
to that field.

413
00:18:23,750 --> 00:18:25,360
So, I think that
these are both books

414
00:18:25,360 --> 00:18:28,490
that, if you're at all
interested in this area,

415
00:18:28,490 --> 00:18:31,104
you should own anyways.

416
00:18:31,104 --> 00:18:32,520
There are two other
books that you

417
00:18:32,520 --> 00:18:34,160
might want to recommend buying.

418
00:18:34,160 --> 00:18:36,340
So, first there's
Essential Cell Biology,

419
00:18:36,340 --> 00:18:40,890
which is kind of like the
easy version of the cell, also

420
00:18:40,890 --> 00:18:41,660
by Alberts.

421
00:18:41,660 --> 00:18:43,890
So, be careful of just
buying a book by Alberts.

422
00:18:43,890 --> 00:18:48,084
So, I'd say The Cell
is everything you ever

423
00:18:48,084 --> 00:18:49,750
wanted to know about
the cell-- and more

424
00:18:49,750 --> 00:18:50,880
than you want to
know about the cell--

425
00:18:50,880 --> 00:18:52,880
whereas Essential
Cell Biology is really

426
00:18:52,880 --> 00:18:55,610
just a wonderful book.

427
00:18:55,610 --> 00:18:59,540
We read this in my lab as
kind of a summer book reading

428
00:18:59,540 --> 00:19:02,707
project, where each
week, we read a chapter,

429
00:19:02,707 --> 00:19:03,790
and we got get over lunch.

430
00:19:03,790 --> 00:19:05,597
And we just went
around the table,

431
00:19:05,597 --> 00:19:08,055
and we went through all the
questions in the book-- really.

432
00:19:08,055 --> 00:19:11,350
And we just alternated, and we
discussed, and it was-- really,

433
00:19:11,350 --> 00:19:12,150
it's wonderful.

434
00:19:12,150 --> 00:19:15,510
It focuses on the ideas.

435
00:19:15,510 --> 00:19:17,310
You have to memorize
a few things.

436
00:19:17,310 --> 00:19:19,831
But every now and then, you
need to memorize something

437
00:19:19,831 --> 00:19:21,580
in order to keep track
of what's going on.

438
00:19:21,580 --> 00:19:24,970
But I would say that if you're
really interested in biology

439
00:19:24,970 --> 00:19:29,159
in any serious way, then I would
recommend you buy this book.

440
00:19:29,159 --> 00:19:31,450
And then, finally, there's
this book Nonlinear Dynamics

441
00:19:31,450 --> 00:19:33,770
and Chaos by Steven
Strogatz, which

442
00:19:33,770 --> 00:19:37,326
is a beautiful introduction
to denominator dynamics.

443
00:19:37,326 --> 00:19:41,310
If you have not seen the book,
I encourage you to check it out.

444
00:19:41,310 --> 00:19:45,620
And in particular, some of the
topics on stability analysis,

445
00:19:45,620 --> 00:19:48,820
and oscillations, bifurcations,
and so forth-- this

446
00:19:48,820 --> 00:19:50,570
is a really great way
to learn about them.

447
00:19:54,250 --> 00:19:57,550
I want to just give a
brief plug for-- there's

448
00:19:57,550 --> 00:20:00,294
another class that some of
you, especially the first year

449
00:20:00,294 --> 00:20:01,710
students interested
in biophysics,

450
00:20:01,710 --> 00:20:02,790
might be interested in.

451
00:20:02,790 --> 00:20:08,260
This is 8.590J slash 20.416J
slash seven something.

452
00:20:08,260 --> 00:20:12,420
So it's a class targeted for
first year graduate students

453
00:20:12,420 --> 00:20:13,520
interested in biophysics.

454
00:20:13,520 --> 00:20:15,950
Basically, each week,
we read a paper.

455
00:20:15,950 --> 00:20:17,500
We have a different
guest lecturer

456
00:20:17,500 --> 00:20:20,500
come from across campus, either
physics, chemistry, biology,

457
00:20:20,500 --> 00:20:23,020
biological engineering, civil
or environmental engineering.

458
00:20:23,020 --> 00:20:25,300
So, a great way to meet
different faculty who

459
00:20:25,300 --> 00:20:27,520
are working in the interface
of physics and biology

460
00:20:27,520 --> 00:20:29,560
in one manifestation or another.

461
00:20:29,560 --> 00:20:32,333
The class-- it's going to be
this Friday from 3:00 to 5:00

462
00:20:32,333 --> 00:20:32,466
PM.

463
00:20:32,466 --> 00:20:33,965
But then, in later
weeks, it's going

464
00:20:33,965 --> 00:20:36,925
to be 4:00 to 6:00 PM because
we-- because it conflicted

465
00:20:36,925 --> 00:20:37,550
with something.

466
00:20:42,820 --> 00:20:45,640
So, I'm going to tell
you-- I'm going to give you

467
00:20:45,640 --> 00:20:49,380
the overview of the rest
of the semester in terms

468
00:20:49,380 --> 00:20:50,460
of the science.

469
00:20:50,460 --> 00:20:53,440
But I just want to
first remind all of you

470
00:20:53,440 --> 00:20:56,764
that, starting on Tuesday, it's
going to be the real class.

471
00:20:56,764 --> 00:20:58,430
What that means in
particular is that we

472
00:20:58,430 --> 00:21:01,260
expect you to have
done some reading,

473
00:21:01,260 --> 00:21:05,000
and we expect you
to have submitted

474
00:21:05,000 --> 00:21:10,072
your pre-class reading questions
by Monday night at 10:00 PM.

475
00:21:10,072 --> 00:21:12,030
We used to have it at
midnight, but then Andrew

476
00:21:12,030 --> 00:21:15,030
has to stay up really late
to go over all your responses

477
00:21:15,030 --> 00:21:17,600
and send out them--
so, 10 o'clock.

478
00:21:20,390 --> 00:21:25,750
And then we'll get going
on simple interactions

479
00:21:25,750 --> 00:21:29,100
between doing enzyme and
substrate, simple gene

480
00:21:29,100 --> 00:21:30,510
expression ideas, and so forth.

481
00:21:33,640 --> 00:21:37,070
So, I'd say that the
course has three parts.

482
00:21:37,070 --> 00:21:39,370
There's like-- the
first half is part one,

483
00:21:39,370 --> 00:21:45,000
and then part two is
the half to 3/4 mark,

484
00:21:45,000 --> 00:21:49,010
and then the last part is
maybe four or five lectures.

485
00:21:49,010 --> 00:21:51,700
And the structure
of this is really--

486
00:21:51,700 --> 00:21:55,100
it is going from the
microscopic scale,

487
00:21:55,100 --> 00:21:57,840
and then-- in terms of
just the basic ideas of,

488
00:21:57,840 --> 00:22:01,130
what happens if molecule A
binds with molecule B. What

489
00:22:01,130 --> 00:22:03,320
are the features that
we should be aware of?

490
00:22:03,320 --> 00:22:05,810
So, pretty basic there.

491
00:22:05,810 --> 00:22:08,230
All the way up to
questions in ecology.

492
00:22:08,230 --> 00:22:11,580
The last lecture is going to
be questions about the origin

493
00:22:11,580 --> 00:22:15,430
of diversity in ecosystems.

494
00:22:15,430 --> 00:22:17,920
So, we'll basically march
from the molecular scale

495
00:22:17,920 --> 00:22:21,040
up to the population scale
throughout the semester.

496
00:22:21,040 --> 00:22:23,470
For those of you who are
interested in thinking

497
00:22:23,470 --> 00:22:27,746
about these questions of
how to organize a class,

498
00:22:27,746 --> 00:22:29,370
there's quite an
interesting discussion

499
00:22:29,370 --> 00:22:32,400
at the beginning of Bill
Bialek's Biological Physics

500
00:22:32,400 --> 00:22:34,770
book, where he very
explicitly says

501
00:22:34,770 --> 00:22:38,100
that he tried to resist
the temptation to do what

502
00:22:38,100 --> 00:22:40,330
it is that we do in our class.

503
00:22:40,330 --> 00:22:43,710
He resisted the temptation
to start from the small scale

504
00:22:43,710 --> 00:22:46,150
and then build up to
these larger scales.

505
00:22:46,150 --> 00:22:47,930
And the reason he
says he wants to avoid

506
00:22:47,930 --> 00:22:50,890
that is because he does not want
to give students the impression

507
00:22:50,890 --> 00:22:54,880
we actually understand how
you go from the lower scales

508
00:22:54,880 --> 00:22:56,600
up to the higher scales.

509
00:22:56,600 --> 00:23:00,980
And I think that's a totally
reasonable viewpoint.

510
00:23:00,980 --> 00:23:05,610
But that being said, the whole
point of this endeavour is

511
00:23:05,610 --> 00:23:07,710
to try to say
something about it.

512
00:23:07,710 --> 00:23:12,420
We may not really understand
it all, but we have to try.

513
00:23:12,420 --> 00:23:17,830
And it's certainly true that is
how function arises, that there

514
00:23:17,830 --> 00:23:21,600
are lower level interactions
that lead to higher scale

515
00:23:21,600 --> 00:23:23,220
functions, dynamics, behaviors.

516
00:23:23,220 --> 00:23:25,720
We may not be able to predict
exactly what's going on there,

517
00:23:25,720 --> 00:23:28,170
but that is the way
that nature does it.

518
00:23:28,170 --> 00:23:32,510
So, I don't want a second
guess nature, certainly.

519
00:23:32,510 --> 00:23:34,960
So, that's going
to be our approach.

520
00:23:34,960 --> 00:23:37,600
But if at the end of the class,
you prefer a different order,

521
00:23:37,600 --> 00:23:39,554
you can always just
turn yourself around,

522
00:23:39,554 --> 00:23:41,220
and then it all jumble
up in your brain.

523
00:23:41,220 --> 00:23:43,160
And then, it can be
whatever order you like.

524
00:23:45,556 --> 00:23:47,180
On Tuesday, we're
really going to start

525
00:23:47,180 --> 00:23:48,560
with the most basic ideas.

526
00:23:48,560 --> 00:23:50,820
What happens if you
have, for example,

527
00:23:50,820 --> 00:23:53,202
one gene that is going
to turn on another gene?

528
00:23:53,202 --> 00:23:54,910
So, you might have a
transcription factor

529
00:23:54,910 --> 00:23:57,975
X that's going to
activate so gene Y, that

530
00:23:57,975 --> 00:24:01,790
says it's going to cause
gene Y to be expressed.

531
00:24:01,790 --> 00:24:04,690
Now, it's as simple
as you can get.

532
00:24:04,690 --> 00:24:06,380
But what are the
general features

533
00:24:06,380 --> 00:24:09,200
that you can say about
this sort of process?

534
00:24:09,200 --> 00:24:10,940
Well, you can say,
there's one thing.

535
00:24:10,940 --> 00:24:14,560
It's that X could either
be an activator of Y,

536
00:24:14,560 --> 00:24:16,462
or maybe it's a
repressor of Y. So,

537
00:24:16,462 --> 00:24:18,420
these are the two symbols
that we'll often use.

538
00:24:18,420 --> 00:24:21,680
An arrow will
either be an-- well,

539
00:24:21,680 --> 00:24:23,870
this symbol will
always be a repressor.

540
00:24:23,870 --> 00:24:27,310
A plain arrow may be
ambiguous, so beware.

541
00:24:27,310 --> 00:24:28,990
Now, the question is,
what happens here?

542
00:24:28,990 --> 00:24:32,380
For example, you might have
this transcription factor--

543
00:24:32,380 --> 00:24:34,730
let's say 10R-- that's
repressing expression

544
00:24:34,730 --> 00:24:39,315
of this gene that is encoding
GFP, green fluorescent protein.

545
00:24:39,315 --> 00:24:41,690
You're going to see this many,
many times over the course

546
00:24:41,690 --> 00:24:42,860
of the semester.

547
00:24:42,860 --> 00:24:44,800
In some ways, one of
the things that we're

548
00:24:44,800 --> 00:24:48,310
going to see in the class
is that new ideas often

549
00:24:48,310 --> 00:24:52,570
arise from new techniques
or new capabilities.

550
00:24:52,570 --> 00:24:56,560
Now, it was really, I think,
the Y-- the spread of GFP

551
00:24:56,560 --> 00:25:00,210
and related proteins that
allowed us to visualize gene

552
00:25:00,210 --> 00:25:02,050
expression in individual cells.

553
00:25:02,050 --> 00:25:06,190
And it led to this real
flowering of new ideas,

554
00:25:06,190 --> 00:25:08,620
of how, for example,
stochasticity may be relevant,

555
00:25:08,620 --> 00:25:10,702
cell to cell heterogeneity.

556
00:25:10,702 --> 00:25:12,660
These are all, I think,
very interesting ideas.

557
00:25:12,660 --> 00:25:16,280
But in order for them to
be concrete, you need data.

558
00:25:16,280 --> 00:25:18,270
And this was a
powerful way for us

559
00:25:18,270 --> 00:25:21,020
to get data that was
relevant for these sorts

560
00:25:21,020 --> 00:25:23,570
of big questions.

561
00:25:23,570 --> 00:25:27,280
So, the idea here is that
if this protein is made,

562
00:25:27,280 --> 00:25:28,276
it's expressed.

563
00:25:28,276 --> 00:25:29,900
Then that cell will
become fluorescent.

564
00:25:29,900 --> 00:25:31,570
In particular,
it'll become green

565
00:25:31,570 --> 00:25:34,456
if you shine the
proper light on it.

566
00:25:34,456 --> 00:25:35,580
And then, we can do things.

567
00:25:35,580 --> 00:25:37,310
We can ask questions
about, for example,

568
00:25:37,310 --> 00:25:38,835
the dynamics of this process.

569
00:25:43,170 --> 00:25:47,010
So, here we have a
case where, now, this

570
00:25:47,010 --> 00:25:51,280
is a repressor that is--
if you have this repressor,

571
00:25:51,280 --> 00:25:55,100
then it stops expression of
that fluorescent protein.

572
00:25:55,100 --> 00:25:58,240
Now, you can ask, what happens
if you start in a situation

573
00:25:58,240 --> 00:26:03,600
where the cell is repressing
expression of that gene.

574
00:26:03,600 --> 00:26:06,650
So, in this case, the
protein concentration is 0,

575
00:26:06,650 --> 00:26:08,000
so the cell is not fluorescent.

576
00:26:08,000 --> 00:26:11,240
But then, you add
something so that now you

577
00:26:11,240 --> 00:26:16,007
cause that repressor to fall off
and stop repressing that gene.

578
00:26:16,007 --> 00:26:17,590
Now, the question
is, how long does it

579
00:26:17,590 --> 00:26:20,570
take for the protein
concentration

580
00:26:20,570 --> 00:26:23,010
to grow to some equilibrium?

581
00:26:23,010 --> 00:26:24,440
It starts out at 0.

582
00:26:24,440 --> 00:26:27,240
Eventually, it's going to
reach some steady state.

583
00:26:27,240 --> 00:26:31,270
So, what is it that
sets this time scale?

584
00:26:31,270 --> 00:26:35,410
What's the characteristic time
that it takes for the cell

585
00:26:35,410 --> 00:26:36,774
to respond to this signal?

586
00:26:36,774 --> 00:26:38,440
What we're going to
find is that there's

587
00:26:38,440 --> 00:26:41,070
a very general sense in which
that characteristic time

588
00:26:41,070 --> 00:26:45,890
scale is really the
cell generation time.

589
00:26:45,890 --> 00:26:48,220
So, cells divide at some rate.

590
00:26:48,220 --> 00:26:52,190
It depends on the kind of cell,
the environment, and so forth.

591
00:26:52,190 --> 00:26:54,470
Does anybody have a sense
for a bacterial cell

592
00:26:54,470 --> 00:26:57,620
in nice, rich media, good
temperature-- how long it

593
00:26:57,620 --> 00:26:59,272
takes for it to divide?

594
00:26:59,272 --> 00:27:00,890
Yeah, 20 minutes.

595
00:27:00,890 --> 00:27:03,140
So, E. coli, for example,
can divide every 20 minutes,

596
00:27:03,140 --> 00:27:04,764
if you put it in the
right environment.

597
00:27:04,764 --> 00:27:06,440
Which is really
an amazing thing,

598
00:27:06,440 --> 00:27:08,660
if you think about the
number of different proteins

599
00:27:08,660 --> 00:27:11,280
that have to be made, and
the complicated mechanics

600
00:27:11,280 --> 00:27:13,400
of growing, and
separating, and so forth.

601
00:27:13,400 --> 00:27:15,490
But every 20 minutes
such a cell can divide.

602
00:27:15,490 --> 00:27:18,360
That's saying that a
bacterial cell, when

603
00:27:18,360 --> 00:27:20,290
it sees a new signal,
it's going to take,

604
00:27:20,290 --> 00:27:23,210
of order, that amount of time
in order for it to do anything.

605
00:27:23,210 --> 00:27:29,050
And that's just because of this
natural process of dilution.

606
00:27:29,050 --> 00:27:33,449
So, as the cell grows, there's
a dilution of the contents.

607
00:27:33,449 --> 00:27:34,240
So, it makes sense.

608
00:27:34,240 --> 00:27:36,740
If you start out with a
protein and you stop making it,

609
00:27:36,740 --> 00:27:38,886
then maybe you'll get
an exponential decay

610
00:27:38,886 --> 00:27:41,510
of that concentration with this
time scale, the cell generation

611
00:27:41,510 --> 00:27:42,050
time.

612
00:27:42,050 --> 00:27:42,990
What's interesting is
that, in some ways,

613
00:27:42,990 --> 00:27:44,530
that resolve is more general.

614
00:27:44,530 --> 00:27:46,860
That, even if you're trying
to turn something on,

615
00:27:46,860 --> 00:27:49,290
there's the same limit,
this cell generation time,

616
00:27:49,290 --> 00:27:52,830
that is placing some limit to
how fast the cell can respond

617
00:27:52,830 --> 00:27:58,080
to new information, if it
uses this mode of information

618
00:27:58,080 --> 00:28:02,824
transmission where you
express a new gene.

619
00:28:02,824 --> 00:28:04,240
So, if you want
to go faster, than

620
00:28:04,240 --> 00:28:05,490
you have to do something else.

621
00:28:07,750 --> 00:28:09,630
So, in some cases,
you can actually

622
00:28:09,630 --> 00:28:15,000
have a situation where a protein
is actually regulating itself.

623
00:28:15,000 --> 00:28:17,050
So, this is an example
of what you might

624
00:28:17,050 --> 00:28:20,370
call negative autoregulation.

625
00:28:20,370 --> 00:28:24,890
So, in this case, that
protein actually comes back,

626
00:28:24,890 --> 00:28:28,564
and it represses
its own expression.

627
00:28:28,564 --> 00:28:30,980
It's found that this is actually
rather common in biology.

628
00:28:30,980 --> 00:28:32,750
And so, of course,
if you see something

629
00:28:32,750 --> 00:28:36,000
that is common in biology,
then it's reasonable that-- so,

630
00:28:36,000 --> 00:28:38,190
maybe there's an
evolutionary explanation.

631
00:28:38,190 --> 00:28:40,580
Not always, but it
gives you a hint

632
00:28:40,580 --> 00:28:42,649
that maybe it's worth looking.

633
00:28:42,649 --> 00:28:44,440
Now, in this case, what
we're going to find

634
00:28:44,440 --> 00:28:46,400
is that such negative
autoregulation does

635
00:28:46,400 --> 00:28:47,982
some very interesting things.

636
00:28:47,982 --> 00:28:50,190
So, for example, one thing
that it's been shown to do

637
00:28:50,190 --> 00:28:55,890
is to increase the rate
of response of that gene.

638
00:28:55,890 --> 00:29:00,320
So, in some ways, you can speed
up a response to some signal

639
00:29:00,320 --> 00:29:03,200
by having that negative
autoregulation.

640
00:29:03,200 --> 00:29:05,790
In a similar way, this
negative autoregulation

641
00:29:05,790 --> 00:29:08,500
increases what you
might call robustness,

642
00:29:08,500 --> 00:29:11,990
the ability of the
function-- in this case,

643
00:29:11,990 --> 00:29:13,970
maybe, the concentration--
of the protein

644
00:29:13,970 --> 00:29:20,300
to be robust to variations in
things like the temperature,

645
00:29:20,300 --> 00:29:21,240
or this or that.

646
00:29:21,240 --> 00:29:23,630
So, environmental
perturbations, or maybe

647
00:29:23,630 --> 00:29:25,300
just stochastic fluctuations.

648
00:29:29,470 --> 00:29:35,740
Now, in this field, I'd say
one of the key advances that

649
00:29:35,740 --> 00:29:39,310
led to the birth of both this
branch of systems biology,

650
00:29:39,310 --> 00:29:42,790
but also the field of what you
might call synthetic biology--

651
00:29:42,790 --> 00:29:45,340
really using engineering
principles to try and design

652
00:29:45,340 --> 00:29:49,595
new gene circuits-- was a
pair of important papers

653
00:29:49,595 --> 00:29:51,720
that we're going to be
talking about in this class.

654
00:29:51,720 --> 00:29:56,120
So, the first of these was a
paper from Jim Collins's group.

655
00:29:56,120 --> 00:29:59,535
He was at BU, although you
may not have heard yet,

656
00:29:59,535 --> 00:30:02,530
but he's actually just agreed
to move over here at MIT.

657
00:30:02,530 --> 00:30:06,550
So, this is very exciting for
us, and hopefully for you.

658
00:30:06,550 --> 00:30:09,610
So, Jim Collins--
in 2000 he showed

659
00:30:09,610 --> 00:30:12,920
that he could engineer
a switch, something

660
00:30:12,920 --> 00:30:14,380
called a toggle switch.

661
00:30:14,380 --> 00:30:16,990
So, if you have two genes
that are mutually repressing

662
00:30:16,990 --> 00:30:19,410
each other, then
this is a system

663
00:30:19,410 --> 00:30:23,690
that the most basic
memory module.

664
00:30:23,690 --> 00:30:25,885
Because if you have
one gene that's high,

665
00:30:25,885 --> 00:30:28,530
it can repress the other one,
and that's a stable state.

666
00:30:28,530 --> 00:30:29,932
But if this other
gene goes high,

667
00:30:29,932 --> 00:30:31,640
then it's going to
repress this one here,

668
00:30:31,640 --> 00:30:33,660
and that's another stable state.

669
00:30:33,660 --> 00:30:35,520
And that state,
since it's stable,

670
00:30:35,520 --> 00:30:39,010
can maintain memory of
the past environment.

671
00:30:39,010 --> 00:30:41,560
And he was able to
demonstrate to his group

672
00:30:41,560 --> 00:30:45,560
that he could construct such
a switch using components

673
00:30:45,560 --> 00:30:49,590
that, in the past, were never
interacting with each other.

674
00:30:49,590 --> 00:30:52,295
So, this is taking advantage
of this fabulous modularity

675
00:30:52,295 --> 00:30:54,250
of the components
of biology in order

676
00:30:54,250 --> 00:30:57,300
to do something that,
is in, principle useful.

677
00:30:57,300 --> 00:31:02,030
And by doing this, it's possible
that you could go and engineer

678
00:31:02,030 --> 00:31:02,530
new things.

679
00:31:02,530 --> 00:31:05,880
But it's also a test bed
for you to take this dictum

680
00:31:05,880 --> 00:31:07,710
from Feynman that if
you can't build it,

681
00:31:07,710 --> 00:31:08,950
then you don't understand it.

682
00:31:08,950 --> 00:31:12,550
And this is a nice way
to go into the cell

683
00:31:12,550 --> 00:31:15,414
and say, if it's really
true, if all these models

684
00:31:15,414 --> 00:31:17,580
that we talk about in systems
biology, for example--

685
00:31:17,580 --> 00:31:18,930
if they're really true,
then we should actually

686
00:31:18,930 --> 00:31:20,970
be able to go into the cell,
put these components together,

687
00:31:20,970 --> 00:31:22,761
and demonstrate that
there is, for example,

688
00:31:22,761 --> 00:31:24,670
this switch-like behavior.

689
00:31:24,670 --> 00:31:27,380
And this was a very
important paper

690
00:31:27,380 --> 00:31:30,058
that demonstrated that
it's possible to do this.

691
00:31:32,620 --> 00:31:33,870
The other paper that I think--

692
00:31:33,870 --> 00:31:35,703
AUDIENCE: Did they
actually make the switch?

693
00:31:35,703 --> 00:31:38,060
PROFESSOR: Yes They
actually constructed it.

694
00:31:38,060 --> 00:31:41,860
They put it on a round, circular
piece like this plasmid,

695
00:31:41,860 --> 00:31:45,070
put it into E. coli, and showed
that they could do this here.

696
00:31:45,070 --> 00:31:47,700
And indeed, this particular
issue of nature, I think,

697
00:31:47,700 --> 00:31:49,660
was hugely influential
for our field,

698
00:31:49,660 --> 00:31:53,040
because that toggle switch
paper and this other paper--

699
00:31:53,040 --> 00:31:55,600
"The Repressilator," by Michael
Elowitz and colleagues--

700
00:31:55,600 --> 00:31:58,620
they were kind of back to
back in that issue of nature.

701
00:31:58,620 --> 00:32:02,020
And I'd say, in some ways, they
were the beginning of systems

702
00:32:02,020 --> 00:32:03,070
and synthetic biology.

703
00:32:03,070 --> 00:32:05,170
Of course, you can
argue about this.

704
00:32:05,170 --> 00:32:07,730
But certainly, I
think they influenced

705
00:32:07,730 --> 00:32:10,430
many, many people in getting
excited about the field.

706
00:32:10,430 --> 00:32:13,160
So, the repressilator--
this is the idea

707
00:32:13,160 --> 00:32:16,700
that you can generate
a gene circuit

708
00:32:16,700 --> 00:32:19,312
like this that will oscillate.

709
00:32:19,312 --> 00:32:22,640
And in this case, instead of
having just two genes that

710
00:32:22,640 --> 00:32:24,380
are repressing each
other, if instead you

711
00:32:24,380 --> 00:32:26,380
have three genes that are
repressing each other,

712
00:32:26,380 --> 00:32:29,670
but in a circular
fashion, then there's

713
00:32:29,670 --> 00:32:33,060
no stable state akin to what we
have with this toggle switch.

714
00:32:33,060 --> 00:32:35,260
But instead, what
happens is that you

715
00:32:35,260 --> 00:32:39,960
get successive waves of each
of these components going

716
00:32:39,960 --> 00:32:40,650
up and down.

717
00:32:40,650 --> 00:32:43,002
So, they oscillate as they
mutually repress each other.

718
00:32:43,002 --> 00:32:44,960
And I just want to be
clear about what this is.

719
00:32:44,960 --> 00:32:48,132
So, here, these
are E. coli cells,

720
00:32:48,132 --> 00:32:50,265
where Elowitz put
in this plasmid--

721
00:32:50,265 --> 00:32:52,890
this circular piece of
DNA-- encoding those three

722
00:32:52,890 --> 00:32:54,680
genes that mutually
repress each other.

723
00:32:54,680 --> 00:32:56,820
And basically, associated
with one of those genes,

724
00:32:56,820 --> 00:32:59,820
he's again attached one of
these fluorescent proteins.

725
00:32:59,820 --> 00:33:02,010
So, the level of
fluorescence in the cell

726
00:33:02,010 --> 00:33:05,760
tells you about the state
of that gene circuit.

727
00:33:05,760 --> 00:33:06,880
Let's see if we can--

728
00:33:11,220 --> 00:33:12,320
So, it starts out.

729
00:33:12,320 --> 00:33:14,111
There's a single cell
you can't really see.

730
00:33:14,111 --> 00:33:15,100
It starts dividing.

731
00:33:15,100 --> 00:33:18,290
Then you see it oscillates--
gets bright, dim, bright, dim.

732
00:33:18,290 --> 00:33:22,730
But you can see that there
are a number of features you

733
00:33:22,730 --> 00:33:24,170
might notice about this movie.

734
00:33:24,170 --> 00:33:30,034
So, first, it does oscillate,
which was huge in the sense

735
00:33:30,034 --> 00:33:31,950
that it wasn't obvious
that you could actually

736
00:33:31,950 --> 00:33:35,210
just put these genes together
and generate something

737
00:33:35,210 --> 00:33:37,404
that oscillates at all.

738
00:33:37,404 --> 00:33:38,820
On the other hand,
you'd say, well

739
00:33:38,820 --> 00:33:42,690
it's not such a good oscillator.

740
00:33:42,690 --> 00:33:46,070
In particular, for example, this
started out as a single cell.

741
00:33:46,070 --> 00:33:48,880
Now it's dividing under
the microscope on agger.

742
00:33:48,880 --> 00:33:51,080
So, it's getting
some nutrients there.

743
00:33:51,080 --> 00:33:52,930
But what you see is
that-- are these cells

744
00:33:52,930 --> 00:33:55,310
all in phase with each other?

745
00:33:55,310 --> 00:33:56,100
No.

746
00:33:56,100 --> 00:33:59,580
So, there's patches--
bright, dark.

747
00:33:59,580 --> 00:34:02,120
So, the question is,
what's going on here?

748
00:34:02,120 --> 00:34:04,435
And it turns out that this
design of an oscillator

749
00:34:04,435 --> 00:34:05,727
is perhaps not a very good one.

750
00:34:05,727 --> 00:34:07,768
And, indeed, one of things
we'll be talking about

751
00:34:07,768 --> 00:34:10,170
is how you can maybe use
some engineering principles

752
00:34:10,170 --> 00:34:11,780
to design better oscillators.

753
00:34:11,780 --> 00:34:13,530
So, for example, Jeff
Hasty at San Diego

754
00:34:13,530 --> 00:34:15,795
has done really
beautiful work showing

755
00:34:15,795 --> 00:34:18,590
that you can make robust,
tunable oscillators in cells

756
00:34:18,590 --> 00:34:19,940
like this.

757
00:34:19,940 --> 00:34:24,110
Now, these oscillations
they were maybe not as good

758
00:34:24,110 --> 00:34:25,300
as you would like.

759
00:34:25,300 --> 00:34:27,290
But this, actually,
is an example

760
00:34:27,290 --> 00:34:32,306
of how a partial failure--
in the sense that they're not

761
00:34:32,306 --> 00:34:33,889
great oscillations,
that maybe somehow

762
00:34:33,889 --> 00:34:37,100
there's noise that's entering
in here that you would not like.

763
00:34:37,100 --> 00:34:40,439
This led to the realization
that maybe noise

764
00:34:40,439 --> 00:34:43,670
is relevant in decision
making within cells.

765
00:34:43,670 --> 00:34:47,070
And this led-- I'll show
you in a few slides--

766
00:34:47,070 --> 00:34:50,671
to another major advance
that Elowitz had.

767
00:34:50,671 --> 00:34:52,170
So, this is, I
think, a good example

768
00:34:52,170 --> 00:34:57,340
of how one-- we might
call it a partial failure.

769
00:34:57,340 --> 00:35:00,640
Some reservations about the
quality of this oscillator

770
00:35:00,640 --> 00:35:03,810
led him to another really
big scientific discovery

771
00:35:03,810 --> 00:35:09,070
on the importance of noise in
decision making within cells.

772
00:35:09,070 --> 00:35:10,810
But before we get
to this noise, we're

773
00:35:10,810 --> 00:35:14,940
going to say something about
the more global structure

774
00:35:14,940 --> 00:35:17,160
of these gene networks.

775
00:35:17,160 --> 00:35:19,950
And, in particular,
we're going to analyze,

776
00:35:19,950 --> 00:35:23,670
and we're going to read
this paper by Barabasi which

777
00:35:23,670 --> 00:35:26,230
represents a simple
mechanism for how you might

778
00:35:26,230 --> 00:35:29,400
what are called these power-law
distributions in networks.

779
00:35:29,400 --> 00:35:31,350
So, if you have these
genes that are mutually

780
00:35:31,350 --> 00:35:32,940
activating-repressing,
what can you

781
00:35:32,940 --> 00:35:35,990
say about the structure of this
gene network within the cell?

782
00:35:39,020 --> 00:35:41,701
Now, you can analyze
such global structure

783
00:35:41,701 --> 00:35:42,950
in a couple of different ways.

784
00:35:42,950 --> 00:35:45,480
One is just to ask, how
many different genes are

785
00:35:45,480 --> 00:35:47,210
different genes connected to?

786
00:35:47,210 --> 00:35:49,480
And that's maybe the
more Barabasi approach.

787
00:35:49,480 --> 00:35:51,860
But then there was
another major discovery

788
00:35:51,860 --> 00:35:54,350
that Uri Alon, the author
of our textbook, made,

789
00:35:54,350 --> 00:35:58,640
which is that you can
ask-- in this crazy network

790
00:35:58,640 --> 00:36:01,020
that you have that describes
the decision making

791
00:36:01,020 --> 00:36:03,290
within the cell-- are
there common patterns

792
00:36:03,290 --> 00:36:06,910
or motifs that appear
over and over again?

793
00:36:06,910 --> 00:36:09,330
So, just like this
idea of autoregulation,

794
00:36:09,330 --> 00:36:11,982
when a gene represses
or activates itself,

795
00:36:11,982 --> 00:36:13,690
that's something that
appears frequently.

796
00:36:13,690 --> 00:36:15,148
So, you can ask,
why might that be?

797
00:36:15,148 --> 00:36:17,050
Similarly, if there
are other patterns that

798
00:36:17,050 --> 00:36:18,790
appear in these networks,
then maybe they arose,

799
00:36:18,790 --> 00:36:20,956
or they were selected for
by evolution, because they

800
00:36:20,956 --> 00:36:22,150
perform some other function.

801
00:36:22,150 --> 00:36:25,240
In particular, we're going to
analyze this feed-forward loop

802
00:36:25,240 --> 00:36:29,600
motif, where you have some gene
that activates, for example,

803
00:36:29,600 --> 00:36:32,370
another gene Y. Y
activates-- I'm sorry,

804
00:36:32,370 --> 00:36:37,020
this is supposed to be Z. Now,
if X, again, directly activates

805
00:36:37,020 --> 00:36:40,607
or represses Z-- this
bottom gene-- then

806
00:36:40,607 --> 00:36:41,690
what does that do for you?

807
00:36:41,690 --> 00:36:43,760
Because this is something
you see more frequently

808
00:36:43,760 --> 00:36:46,310
than you expect, based
on some notion of chance,

809
00:36:46,310 --> 00:36:47,990
or some null model.

810
00:36:47,990 --> 00:36:50,310
So, the question is, why
would these feed-forward loops

811
00:36:50,310 --> 00:36:51,670
appear over and over again?

812
00:36:51,670 --> 00:36:55,350
And it turns out that they can
provide some nice functions

813
00:36:55,350 --> 00:36:56,870
in the sense that,
for example, you

814
00:36:56,870 --> 00:36:59,010
can provide some
asymmetrical response

815
00:36:59,010 --> 00:37:02,160
to temporary fluctuations of
inputs, et cetera, et cetera.

816
00:37:02,160 --> 00:37:04,300
So, we'll try to get some
sense of these ideas.

817
00:37:07,430 --> 00:37:12,650
So, as I alluded to before in
this idea of the repressilator

818
00:37:12,650 --> 00:37:14,560
that Michael
Elowitz made, he saw

819
00:37:14,560 --> 00:37:16,400
that it was surprisingly noisy.

820
00:37:16,400 --> 00:37:18,160
And this got him
thinking about the role

821
00:37:18,160 --> 00:37:20,990
of stochastic
fluctuations within cells.

822
00:37:20,990 --> 00:37:24,160
And I think that this is a
common theme throughout much

823
00:37:24,160 --> 00:37:25,380
of systems biology.

824
00:37:25,380 --> 00:37:28,510
It's the role of
noise in biology.

825
00:37:28,510 --> 00:37:31,806
And this could be within a cell
for individual decision making.

826
00:37:31,806 --> 00:37:33,430
It could be in context
of development--

827
00:37:33,430 --> 00:37:38,720
how is that you robustly
make a body, given noise?

828
00:37:38,720 --> 00:37:43,150
It could be at the level
of evolutionary ecosystems,

829
00:37:43,150 --> 00:37:45,640
that maybe noise actually
plays a dominant role in,

830
00:37:45,640 --> 00:37:47,640
for example, determining
the abundance

831
00:37:47,640 --> 00:37:49,360
or diversity of ecosystems.

832
00:37:49,360 --> 00:37:52,740
So, we'll see these themes
pop up on multiple scales

833
00:37:52,740 --> 00:37:54,210
throughout the semester.

834
00:37:54,210 --> 00:37:56,220
But in this case,
what Elowitz did--

835
00:37:56,220 --> 00:37:59,020
this is just two years after
his repressilator paper--

836
00:37:59,020 --> 00:38:03,280
he showed that if you just
take the-- in a single cell,

837
00:38:03,280 --> 00:38:05,550
you give it the exact
same instructions.

838
00:38:05,550 --> 00:38:09,160
So, you say, make a red
fluorescent protein,

839
00:38:09,160 --> 00:38:11,820
and make a green
fluorescent protein,

840
00:38:11,820 --> 00:38:13,770
with the exact same
instructions to the cell.

841
00:38:13,770 --> 00:38:16,144
And you can say, well, if you
have the same instructions,

842
00:38:16,144 --> 00:38:18,450
then the level of
the red and the green

843
00:38:18,450 --> 00:38:20,250
should do the same thing.

844
00:38:20,250 --> 00:38:21,810
But what he found
was that, actually,

845
00:38:21,810 --> 00:38:25,130
there was surprising
heterogeneity

846
00:38:25,130 --> 00:38:30,050
of the level of those two
proteins, even in single cells.

847
00:38:30,050 --> 00:38:33,500
So, the idea is that even-- this
represents a fundamental limit

848
00:38:33,500 --> 00:38:35,210
to what a cell can
do, because this

849
00:38:35,210 --> 00:38:39,640
is saying that we take-- we try
to do the exact same thing two

850
00:38:39,640 --> 00:38:40,440
different times.

851
00:38:40,440 --> 00:38:41,815
If you don't get
the same output,

852
00:38:41,815 --> 00:38:44,706
then that's a real limit
to what you can do, right?

853
00:38:44,706 --> 00:38:46,330
Because you've done
everything you can.

854
00:38:46,330 --> 00:38:49,770
You said, here's the
sequence of that DNA that has

855
00:38:49,770 --> 00:38:51,830
the instructions, this
promoter sequence.

856
00:38:51,830 --> 00:38:54,770
It's exactly the same, yet you
still get different outputs.

857
00:38:54,770 --> 00:38:56,700
So, the question is,
what's causing that?

858
00:38:56,700 --> 00:38:58,610
And also, how is it
that life can actually

859
00:38:58,610 --> 00:39:03,346
function given this intrinsic
noise that's in the cell?

860
00:39:03,346 --> 00:39:04,720
These are things
that we're going

861
00:39:04,720 --> 00:39:08,960
to look at over the
course of the semester.

862
00:39:08,960 --> 00:39:10,380
And these are
actually some images

863
00:39:10,380 --> 00:39:13,310
that he took in this paper.

864
00:39:13,310 --> 00:39:15,640
And so, we can see
that some of the cells

865
00:39:15,640 --> 00:39:16,690
are really rather red.

866
00:39:16,690 --> 00:39:19,630
Some are rather yellowish green.

867
00:39:19,630 --> 00:39:22,330
And so, this is telling us about
the level of those proteins

868
00:39:22,330 --> 00:39:23,190
in individual cells.

869
00:39:27,310 --> 00:39:29,130
So now, we have some notion.

870
00:39:29,130 --> 00:39:34,660
So, somehow, noise is important
in these molecular scale gene

871
00:39:34,660 --> 00:39:36,400
expression patterns.

872
00:39:36,400 --> 00:39:39,110
Now, there is what I think is
really quite a beautiful paper

873
00:39:39,110 --> 00:39:42,270
by Sunney Xie [INAUDIBLE]
at Harvard in 2006,

874
00:39:42,270 --> 00:39:45,090
where he combined
a single molecule

875
00:39:45,090 --> 00:39:49,470
fluorescence with live
cell imaging in E. coli.

876
00:39:49,470 --> 00:39:55,370
And this allowed him to observe
individual expression events

877
00:39:55,370 --> 00:39:57,760
within individual
cells, where every time

878
00:39:57,760 --> 00:39:59,260
one of these proteins
was expressed,

879
00:39:59,260 --> 00:40:02,040
he got a little yellow
spot, corresponding

880
00:40:02,040 --> 00:40:06,190
to this equivalent of a
yellow fluorescent protein.

881
00:40:06,190 --> 00:40:10,390
And so, he was able to watch
as real, live cells made

882
00:40:10,390 --> 00:40:11,729
individual proteins.

883
00:40:11,729 --> 00:40:13,520
And from that, he was
able to say, I think,

884
00:40:13,520 --> 00:40:18,130
some very nice things about
what it is that's causing noise,

885
00:40:18,130 --> 00:40:20,740
such as what we talked
about in that repressilator,

886
00:40:20,740 --> 00:40:22,420
or in the other Elowitz paper.

887
00:40:22,420 --> 00:40:24,180
And a lot of it just
has to with this idea

888
00:40:24,180 --> 00:40:28,310
that if you're talking about
low number events or low numbers

889
00:40:28,310 --> 00:40:31,600
of molecules-- DNA
typically present, only

890
00:40:31,600 --> 00:40:33,730
one or a few copies
per cell-- then

891
00:40:33,730 --> 00:40:36,290
that means there's some
inherent stochasticity.

892
00:40:36,290 --> 00:40:39,060
Because that piece
of DNA-- it's either

893
00:40:39,060 --> 00:40:42,140
bound by one of these motors,
this RNA polymerase that

894
00:40:42,140 --> 00:40:44,550
can make the RNA, or not.

895
00:40:44,550 --> 00:40:49,110
And that is intrinsically going
to be a stochastic process.

896
00:40:49,110 --> 00:40:51,370
And that kind of
dynamic can lead

897
00:40:51,370 --> 00:40:54,540
to substantial heterogeneity,
or fluctuations,

898
00:40:54,540 --> 00:40:56,010
in expression of
individual genes.

899
00:40:59,340 --> 00:41:01,940
So, it's kind of at
this stage of the course

900
00:41:01,940 --> 00:41:03,940
that we start to think
maybe a little bit more

901
00:41:03,940 --> 00:41:07,420
about some of the global
aspects of what it

902
00:41:07,420 --> 00:41:09,750
is that a cell is trying to do.

903
00:41:09,750 --> 00:41:13,660
And in particular, if
a cell is trying to,

904
00:41:13,660 --> 00:41:17,495
for example, swim to get to
higher concentrations of food,

905
00:41:17,495 --> 00:41:20,640
what are the fundamental
limitations that cell faces?

906
00:41:20,640 --> 00:41:23,206
How does it know what is
uphill, what's downhill?

907
00:41:23,206 --> 00:41:25,580
So, these are cases in which
we have to really understand

908
00:41:25,580 --> 00:41:28,370
something about the
role of diffusion

909
00:41:28,370 --> 00:41:33,192
in the ability of
these small cells

910
00:41:33,192 --> 00:41:34,730
to move in their environment.

911
00:41:34,730 --> 00:41:36,560
And for example, here
is an illustration

912
00:41:36,560 --> 00:41:38,850
of the Reynolds number, which
is telling you something

913
00:41:38,850 --> 00:41:43,670
about the relative
importance of viscous forces

914
00:41:43,670 --> 00:41:46,730
versus inertial forces on
these different organisms.

915
00:41:46,730 --> 00:41:49,070
And some of the
way-- for example,

916
00:41:49,070 --> 00:41:51,750
how an organism
such as us can swim

917
00:41:51,750 --> 00:41:54,290
is just qualitatively
different from how

918
00:41:54,290 --> 00:41:57,690
a microscopic organism,
such as E. coli, can swim.

919
00:41:57,690 --> 00:42:00,390
So we'll try to understand
how that plays out

920
00:42:00,390 --> 00:42:04,310
and, in particular, how it
is that E. coli can move

921
00:42:04,310 --> 00:42:07,000
towards higher food sources.

922
00:42:07,000 --> 00:42:11,540
And there's a very
clever way that bacteria

923
00:42:11,540 --> 00:42:14,020
have that allows
them to have really

924
00:42:14,020 --> 00:42:18,780
robust functioning of
this chemotaxis process

925
00:42:18,780 --> 00:42:20,200
within the cell.

926
00:42:20,200 --> 00:42:23,840
And I think this is a neat
example of the gene networks

927
00:42:23,840 --> 00:42:26,510
coupling into a higher
level behavior that

928
00:42:26,510 --> 00:42:28,524
allows cells to
survive in really

929
00:42:28,524 --> 00:42:29,565
challenging environments.

930
00:42:32,580 --> 00:42:34,730
Another manifestation,
actually, of fluctuations

931
00:42:34,730 --> 00:42:37,730
is this idea of
pattern formation.

932
00:42:37,730 --> 00:42:43,400
And this is actually
experimental data of in vitro--

933
00:42:43,400 --> 00:42:45,510
so, if you take proteins
outside of the cell

934
00:42:45,510 --> 00:42:49,510
and you put them on a
two dimensional membrane.

935
00:42:49,510 --> 00:42:51,160
Now, these are
actually the proteins

936
00:42:51,160 --> 00:42:55,195
that are responsible for
finding the center of the cell.

937
00:42:55,195 --> 00:42:58,589
So, I told that E. coli, for
example-- it grows in length.

938
00:42:58,589 --> 00:43:00,880
And then, once it gets long
enough, it wants to divide,

939
00:43:00,880 --> 00:43:02,770
so it separates in the middle.

940
00:43:02,770 --> 00:43:06,196
And the question is, how does
it know where the middle is?

941
00:43:06,196 --> 00:43:09,040
You know, if you can just stand
outside a cell and look at it,

942
00:43:09,040 --> 00:43:11,437
then you say, I know where
it is, and you just cut.

943
00:43:11,437 --> 00:43:12,520
But imagine you're a cell.

944
00:43:12,520 --> 00:43:14,264
How do you know
where this-- once you

945
00:43:14,264 --> 00:43:16,680
start thinking about all these
challenges that cells face,

946
00:43:16,680 --> 00:43:18,721
it's really remarkable
that they can do anything.

947
00:43:18,721 --> 00:43:21,830
And what it turns out,
is that they implement--

948
00:43:21,830 --> 00:43:27,200
they use what are called these
Min proteins that display what

949
00:43:27,200 --> 00:43:31,486
seem to be the equivalent
of what you might know

950
00:43:31,486 --> 00:43:33,930
of as Turing patterns
in order to cause

951
00:43:33,930 --> 00:43:36,450
these oscillations in the cell
that allows it to find where

952
00:43:36,450 --> 00:43:37,780
the center of the cell is.

953
00:43:37,780 --> 00:43:41,580
So, we'll talk about this
and how these authors were

954
00:43:41,580 --> 00:43:44,620
able to visualize these
beautiful traveling

955
00:43:44,620 --> 00:43:48,190
waves of proteins, where
they successfully bind

956
00:43:48,190 --> 00:43:50,980
to the membrane, and then
are ejected off of it.

957
00:43:50,980 --> 00:43:52,889
And this results in
beautiful patterns

958
00:43:52,889 --> 00:43:54,180
that are traveling, as you saw.

959
00:43:57,760 --> 00:44:01,960
So, this was-- I'd say that
these topics are what you might

960
00:44:01,960 --> 00:44:03,730
call traditional
systems biology,

961
00:44:03,730 --> 00:44:07,500
in the sense that these
are all things that

962
00:44:07,500 --> 00:44:09,010
of physics branch
of systems biology

963
00:44:09,010 --> 00:44:13,790
were all thinking about
for the first 10 years.

964
00:44:13,790 --> 00:44:16,400
And over the last five
years, maybe, there

965
00:44:16,400 --> 00:44:18,820
has been a greater
interest in trying

966
00:44:18,820 --> 00:44:21,130
to understand how these
sorts of ideas and principles

967
00:44:21,130 --> 00:44:23,740
may be relevant
for larger scale.

968
00:44:23,740 --> 00:44:28,080
And larger the sense of,
instead of thinking maybe

969
00:44:28,080 --> 00:44:29,850
about genes as this
fundamental unit--

970
00:44:29,850 --> 00:44:32,960
they try to understand
how genes interact to form

971
00:44:32,960 --> 00:44:34,610
this decision making process.

972
00:44:34,610 --> 00:44:36,440
Maybe instead, if
you think about cells

973
00:44:36,440 --> 00:44:38,065
as somehow being that
fundamental unit,

974
00:44:38,065 --> 00:44:40,250
how is it that
cells come together

975
00:44:40,250 --> 00:44:45,110
to lead to interesting
population level phenomena?

976
00:44:45,110 --> 00:44:47,670
And so, we talk about
both what you might call

977
00:44:47,670 --> 00:44:49,870
evolutionary systems biology.

978
00:44:49,870 --> 00:44:52,937
So, how is it that evolution
within a population behaves?

979
00:44:52,937 --> 00:44:54,520
As well as ecological
systems biology.

980
00:44:54,520 --> 00:44:56,478
What happens if you have
more than one species,

981
00:44:56,478 --> 00:44:58,582
and how is that
the kinds of ideas

982
00:44:58,582 --> 00:45:00,540
we talk about in the
first half of the semester

983
00:45:00,540 --> 00:45:03,095
are relevant in these
population level phenomena?

984
00:45:05,890 --> 00:45:08,960
So, in the first example
that we're going to give

985
00:45:08,960 --> 00:45:11,630
is actually another paper
from Uri Alon's group,

986
00:45:11,630 --> 00:45:16,510
where he showed that there's a
very fundamental sense in which

987
00:45:16,510 --> 00:45:19,080
cells, through the
evolutionary process,

988
00:45:19,080 --> 00:45:21,012
are implementing a
cost-benefit analysis.

989
00:45:21,012 --> 00:45:22,470
And the question
that he asked here

990
00:45:22,470 --> 00:45:24,920
is that, if you
take an E. coli cell

991
00:45:24,920 --> 00:45:27,670
and you put it in different
concentrations of the sugar

992
00:45:27,670 --> 00:45:30,110
lactose.

993
00:45:30,110 --> 00:45:33,040
Now, the question is,
how much of the enzyme

994
00:45:33,040 --> 00:45:35,762
responsible for digesting
lactose-- how much of that

995
00:45:35,762 --> 00:45:36,720
enzyme should you make?

996
00:45:39,321 --> 00:45:41,570
You might say, well, you
should just make a lot of it.

997
00:45:41,570 --> 00:45:42,970
But he said, well,
at some point,

998
00:45:42,970 --> 00:45:44,080
there's going to be a problem.

999
00:45:44,080 --> 00:45:45,480
Because if you make
too much of it,

1000
00:45:45,480 --> 00:45:47,688
then you're going to be
spending all of your energies

1001
00:45:47,688 --> 00:45:48,480
making this enzyme.

1002
00:45:48,480 --> 00:45:50,688
Whereas, on the other hand,
if you don't make enough,

1003
00:45:50,688 --> 00:45:53,300
then you're not going to be able
to get enough of this sugar.

1004
00:45:53,300 --> 00:45:55,696
So, like always, there's
this Goldilocks principle.

1005
00:45:55,696 --> 00:45:57,820
You don't want too little;
you don't want too much.

1006
00:45:57,820 --> 00:46:00,660
And what he showed is that
if he evolved these E. coli

1007
00:46:00,660 --> 00:46:03,380
populations over hundreds of
generations in the laboratory,

1008
00:46:03,380 --> 00:46:05,920
but at different concentrations
of this sugar lactose, what

1009
00:46:05,920 --> 00:46:09,780
he saw is that the
concentration, or the level

1010
00:46:09,780 --> 00:46:12,940
of expression of the enzymes
required to make that,

1011
00:46:12,940 --> 00:46:16,500
to break down that sugar--
it changes over time.

1012
00:46:16,500 --> 00:46:18,551
So, if you have a
lot of the sugar,

1013
00:46:18,551 --> 00:46:20,300
then you want to make
a lot of the enzyme.

1014
00:46:20,300 --> 00:46:21,800
If you have a small
amount of that sugar,

1015
00:46:21,800 --> 00:46:23,508
then you want to make
less of the enzyme.

1016
00:46:23,508 --> 00:46:24,870
So, that all makes sense.

1017
00:46:24,870 --> 00:46:27,370
But this is a case where he
could really demonstrate it

1018
00:46:27,370 --> 00:46:30,560
in the laboratory using
these microbial populations.

1019
00:46:30,560 --> 00:46:33,130
It's a very beautiful
example of how

1020
00:46:33,130 --> 00:46:35,890
simple ideas of
cost-benefit really

1021
00:46:35,890 --> 00:46:40,030
give you insight into
the evolutionary process.

1022
00:46:40,030 --> 00:46:45,350
Now, I told you before
that part of the reason

1023
00:46:45,350 --> 00:46:49,600
that we have to consider the
role of fluctuations, or noise,

1024
00:46:49,600 --> 00:46:51,730
in, for example,
cellular decision making,

1025
00:46:51,730 --> 00:46:53,720
is because of the low
numbers of molecules

1026
00:46:53,720 --> 00:46:55,299
that are often involved.

1027
00:46:55,299 --> 00:46:57,090
So, if you have a small
number of proteins,

1028
00:46:57,090 --> 00:46:59,580
or small numbers of
DNA, then the process

1029
00:46:59,580 --> 00:47:01,460
is intrinsically stochastic.

1030
00:47:01,460 --> 00:47:03,970
Now, the question
naturally arises,

1031
00:47:03,970 --> 00:47:07,390
why is it that we might need
to consider stochastic dynamics

1032
00:47:07,390 --> 00:47:09,060
in the context of evolution?

1033
00:47:09,060 --> 00:47:11,840
Because if you think
about, for example,

1034
00:47:11,840 --> 00:47:15,220
an E. Coli population,
even in a small test tube,

1035
00:47:15,220 --> 00:47:18,070
you might have 1
billion cells there.

1036
00:47:18,070 --> 00:47:20,140
So, 1 billion is a
big number, right?

1037
00:47:20,140 --> 00:47:21,490
Much larger than 1.

1038
00:47:21,490 --> 00:47:24,160
So, it's tempting to conclude
from that, that actually, all

1039
00:47:24,160 --> 00:47:27,400
of this stochastic dynamics,
fluctuations-- maybe it's just

1040
00:47:27,400 --> 00:47:29,910
not relevant for evolution.

1041
00:47:29,910 --> 00:47:33,380
However, if you think about
the evolutionary process,

1042
00:47:33,380 --> 00:47:36,110
fundamentally, any time that
you have a new mutant appear

1043
00:47:36,110 --> 00:47:39,140
in the population that may be
more fit, may be less fit--

1044
00:47:39,140 --> 00:47:41,020
but every new mutant
in the population

1045
00:47:41,020 --> 00:47:43,650
starts out as a
single individual.

1046
00:47:43,650 --> 00:47:46,120
It's a trivial statement,
but it has deep implications,

1047
00:47:46,120 --> 00:47:50,410
because it means that every
evolutionary process goes

1048
00:47:50,410 --> 00:47:52,365
through this regime
where you have

1049
00:47:52,365 --> 00:47:54,880
a small number of fluctuations.

1050
00:47:54,880 --> 00:47:56,780
So, this has very
clear locations

1051
00:47:56,780 --> 00:47:58,750
in many different
contexts, and we'll

1052
00:47:58,750 --> 00:48:00,541
explore it over the
course of the semester.

1053
00:48:03,210 --> 00:48:06,550
And despite the fact that that
evolution is intrinsically,

1054
00:48:06,550 --> 00:48:09,350
you might say, random,
what's interesting

1055
00:48:09,350 --> 00:48:14,189
are cases where that
randomness somehow washes out.

1056
00:48:14,189 --> 00:48:15,980
For example, we're
going to talk about what

1057
00:48:15,980 --> 00:48:18,760
I think is a beautiful paper
by Roy Kishony's group,

1058
00:48:18,760 --> 00:48:22,490
at Harvard Medical
School, who showed

1059
00:48:22,490 --> 00:48:25,232
that if you take a
population and you put

1060
00:48:25,232 --> 00:48:26,690
in some new
environments, there are

1061
00:48:26,690 --> 00:48:28,300
going to different mutations.

1062
00:48:28,300 --> 00:48:30,550
Some of them are going to
be really good; some of them

1063
00:48:30,550 --> 00:48:33,170
are going to be not so good.

1064
00:48:33,170 --> 00:48:36,360
You can imagine that of all
of these possible beneficial

1065
00:48:36,360 --> 00:48:40,290
mutations, they describe
some distribution.

1066
00:48:40,290 --> 00:48:44,580
Whereas, if you asked-- this is
the frequency, or the number,

1067
00:48:44,580 --> 00:48:48,580
of mutations as a function
of how good that mutation is,

1068
00:48:48,580 --> 00:48:50,960
you might say, it should
be some falling function.

1069
00:48:50,960 --> 00:48:53,292
Because you just not are
going to get as many mutations

1070
00:48:53,292 --> 00:48:55,000
that are really just
amazing as they are.

1071
00:48:55,000 --> 00:48:56,040
They're kind of good.

1072
00:48:56,040 --> 00:48:58,754
But it's not obvious whether
the curve should be exponential,

1073
00:48:58,754 --> 00:48:59,920
or maybe it looks like this.

1074
00:48:59,920 --> 00:49:03,340
It could look like
many different things.

1075
00:49:03,340 --> 00:49:06,120
What Roy's group showed
here in this paper is that,

1076
00:49:06,120 --> 00:49:10,242
actually, in some very
reasonable situations,

1077
00:49:10,242 --> 00:49:12,700
it doesn't actually matter what
the underlying distribution

1078
00:49:12,700 --> 00:49:13,440
might be.

1079
00:49:13,440 --> 00:49:16,290
Because if you look at the
distribution of mutations that

1080
00:49:16,290 --> 00:49:20,930
actually fix or spread
in the population,

1081
00:49:20,930 --> 00:49:22,660
those actually all
look kind of the same,

1082
00:49:22,660 --> 00:49:25,750
in the sense that they're
peaked around some value.

1083
00:49:25,750 --> 00:49:31,640
So, there's some sense in which
the random process of evolution

1084
00:49:31,640 --> 00:49:36,137
leads to some patterns that
are probably not so obvious.

1085
00:49:36,137 --> 00:49:37,595
And on the flip
side, what it means

1086
00:49:37,595 --> 00:49:40,590
is that if you go and measure
how good are the mutations that

1087
00:49:40,590 --> 00:49:42,160
actually appear
on the population,

1088
00:49:42,160 --> 00:49:45,030
that actually tells you
surprisingly little about what

1089
00:49:45,030 --> 00:49:48,060
that underlying distribution
is, in terms of the effects

1090
00:49:48,060 --> 00:49:49,950
of the beneficial mutations.

1091
00:49:49,950 --> 00:49:54,360
So, there's some way in which
the details kind of wash out.

1092
00:49:54,360 --> 00:49:57,990
And I think this is fascinating
because a major theme

1093
00:49:57,990 --> 00:50:00,190
or major challenge
in systems biology

1094
00:50:00,190 --> 00:50:03,470
is we want understand how
these underlying parts lead

1095
00:50:03,470 --> 00:50:06,800
to some higher level
function, but we don't always

1096
00:50:06,800 --> 00:50:09,830
know which details
of the interactions

1097
00:50:09,830 --> 00:50:11,590
are important for
leading to that higher

1098
00:50:11,590 --> 00:50:13,180
level of organization.

1099
00:50:13,180 --> 00:50:15,660
In some cases, they're very
important, but in some cases,

1100
00:50:15,660 --> 00:50:16,160
not.

1101
00:50:16,160 --> 00:50:18,720
So, a challenge that we're going
to face over and over again

1102
00:50:18,720 --> 00:50:21,012
is trying to understand, what
are the key features that

1103
00:50:21,012 --> 00:50:23,344
are going to influence the
dynamics of this higher level

1104
00:50:23,344 --> 00:50:23,937
system?

1105
00:50:23,937 --> 00:50:25,770
And this is, I think,
an interesting example

1106
00:50:25,770 --> 00:50:28,287
of how some features of
that underlying distribution

1107
00:50:28,287 --> 00:50:29,620
are important, and some are not.

1108
00:50:33,220 --> 00:50:40,180
So, another interesting
analogy between evolution

1109
00:50:40,180 --> 00:50:42,100
and some ideas from
physics is this idea

1110
00:50:42,100 --> 00:50:44,620
of a fitness landscape.

1111
00:50:44,620 --> 00:50:46,420
So, just like an
energy landscape

1112
00:50:46,420 --> 00:50:50,830
tells you something
about the dynamics

1113
00:50:50,830 --> 00:50:53,210
of a system-- for
example, you can say,

1114
00:50:53,210 --> 00:50:56,820
a ball should roll down a hill.

1115
00:50:56,820 --> 00:50:58,570
Similarly, if you
think about evolution

1116
00:50:58,570 --> 00:51:02,690
in the context of how
fit an organism is

1117
00:51:02,690 --> 00:51:05,810
as a function of some
different parameters,

1118
00:51:05,810 --> 00:51:08,330
you can get what might
be nontrivial structure.

1119
00:51:08,330 --> 00:51:11,780
So, this is some illustration
of what's perhaps

1120
00:51:11,780 --> 00:51:13,170
a nontrivial fitness landscape.

1121
00:51:13,170 --> 00:51:16,360
Now, the height here is
some notion of fitness.

1122
00:51:16,360 --> 00:51:18,250
So, we could imagine
this could be

1123
00:51:18,250 --> 00:51:20,150
the ability of a bird to fly.

1124
00:51:20,150 --> 00:51:22,290
In that case, maybe
these two axes

1125
00:51:22,290 --> 00:51:25,830
could be the length and
the width of the wing.

1126
00:51:25,830 --> 00:51:29,130
Now, the shape of this
landscape tells you

1127
00:51:29,130 --> 00:51:32,040
something about how
evolution is constrained.

1128
00:51:32,040 --> 00:51:34,174
Because if the landscape
looks like this,

1129
00:51:34,174 --> 00:51:36,340
then what that's saying is
that you have to actually

1130
00:51:36,340 --> 00:51:38,600
evolve, in this case,
maybe a wider wing

1131
00:51:38,600 --> 00:51:40,920
before you can
evolve a longer wing.

1132
00:51:40,920 --> 00:51:43,530
So, this is-- if there's
structure to the fitness

1133
00:51:43,530 --> 00:51:45,680
landscape like this, then
it tells you something

1134
00:51:45,680 --> 00:51:48,765
about the path of evolution.

1135
00:51:48,765 --> 00:51:50,140
Now, in this case,
we're thinking

1136
00:51:50,140 --> 00:51:54,595
about this in the context
of phenotypes-- things

1137
00:51:54,595 --> 00:51:57,340
that you can just look at
the organism and measure.

1138
00:51:57,340 --> 00:51:59,690
But instead of think about
this in terms of phenotypes,

1139
00:51:59,690 --> 00:52:03,490
we could instead think of
it in terms of genotype.

1140
00:52:03,490 --> 00:52:06,640
For example, there
is a beautiful paper

1141
00:52:06,640 --> 00:52:09,340
that we're going to read
from Daniel Weinreich

1142
00:52:09,340 --> 00:52:13,260
where he, in the context
of a gene that encodes

1143
00:52:13,260 --> 00:52:17,110
an enzyme that breaks down
antibiotics such as penicillin,

1144
00:52:17,110 --> 00:52:20,120
what he did is he made
all possible combinations

1145
00:52:20,120 --> 00:52:24,850
of five point mutations--
single limitations in the gene.

1146
00:52:24,850 --> 00:52:27,330
So, he made all 32
combinations of this gene

1147
00:52:27,330 --> 00:52:30,840
and then measured the shape of
the resulting fitness landscape

1148
00:52:30,840 --> 00:52:35,060
from those 32 different
versions of the gene.

1149
00:52:35,060 --> 00:52:37,200
And from it, what he
found is that there's

1150
00:52:37,200 --> 00:52:39,620
a very interesting sense
in which evolution,

1151
00:52:39,620 --> 00:52:41,885
at the molecular scale,
is somehow constrained.

1152
00:52:44,664 --> 00:52:46,330
So, the idea there
is that there somehow

1153
00:52:46,330 --> 00:52:48,413
is a rugged fitness landscape
that is constraining

1154
00:52:48,413 --> 00:52:50,775
the path of evolution.

1155
00:52:50,775 --> 00:52:53,150
And so, in all these cases
that we've been talking about,

1156
00:52:53,150 --> 00:52:55,330
there's some notion
that you can say,

1157
00:52:55,330 --> 00:52:58,000
this organism has
this fitness so long

1158
00:52:58,000 --> 00:53:01,050
as it has a wing shape
that looks like this.

1159
00:53:01,050 --> 00:53:03,840
Now, that is perhaps
find in many cases.

1160
00:53:03,840 --> 00:53:06,735
But in some cases,
there are what

1161
00:53:06,735 --> 00:53:09,404
you might call game interactions
between different organisms

1162
00:53:09,404 --> 00:53:10,070
in a population.

1163
00:53:10,070 --> 00:53:11,660
And what I mean by
game interactions

1164
00:53:11,660 --> 00:53:14,070
is that the fitness of
a particular organism

1165
00:53:14,070 --> 00:53:18,330
may depend upon what other
organisms are out there.

1166
00:53:18,330 --> 00:53:20,570
And in that case,
you can't just say

1167
00:53:20,570 --> 00:53:23,725
that one organism is fit or
not, because it just depends

1168
00:53:23,725 --> 00:53:25,880
on what everyone else in
the population is doing,

1169
00:53:25,880 --> 00:53:28,340
or the genotype of the other
individuals in the population.

1170
00:53:28,340 --> 00:53:30,140
So, in that case,
you really have

1171
00:53:30,140 --> 00:53:32,824
to apply some ideas
from game theory

1172
00:53:32,824 --> 00:53:34,990
to try to get insight into
the evolutionary process.

1173
00:53:34,990 --> 00:53:38,050
And we're going to talk
about some really nice cases

1174
00:53:38,050 --> 00:53:40,390
where researchers have
constructed, for example,

1175
00:53:40,390 --> 00:53:44,040
a rock, paper, scissors
game using different E. coli

1176
00:53:44,040 --> 00:53:45,840
strains.

1177
00:53:45,840 --> 00:53:48,010
And if you stick
out to that long,

1178
00:53:48,010 --> 00:53:50,066
I'll tell you about
a case in lizards

1179
00:53:50,066 --> 00:53:51,940
where people have
demonstrated a rock, paper,

1180
00:53:51,940 --> 00:53:54,510
scissors interaction in the
context of different mating

1181
00:53:54,510 --> 00:53:56,850
strategies of the male lizards.

1182
00:53:56,850 --> 00:53:58,380
So, if that's not
an advertisement

1183
00:53:58,380 --> 00:54:02,170
to stick around for a couple
months, I don't know what is.

1184
00:54:02,170 --> 00:54:05,850
There are other cases where
people have demonstrated

1185
00:54:05,850 --> 00:54:09,510
that microbes interact via
cooperative interactions,

1186
00:54:09,510 --> 00:54:11,890
in which it's possible
for cheater strategies

1187
00:54:11,890 --> 00:54:13,980
to arise, spread
throughout the population,

1188
00:54:13,980 --> 00:54:16,350
and cause some harm
to the population--

1189
00:54:16,350 --> 00:54:18,710
maybe even collapse
of the population.

1190
00:54:18,710 --> 00:54:20,520
So, this is a case
where there's tension

1191
00:54:20,520 --> 00:54:22,250
between what's good
for the individual

1192
00:54:22,250 --> 00:54:23,500
and what's good for the group.

1193
00:54:26,560 --> 00:54:30,850
Now, organisms are able to do
a remarkable set of things.

1194
00:54:30,850 --> 00:54:33,850
So, we saw cases where, for
example, that neutrophil

1195
00:54:33,850 --> 00:54:38,100
was able to chase the staph
aureus, that bacterial cell.

1196
00:54:38,100 --> 00:54:39,670
So, that's amazing.

1197
00:54:39,670 --> 00:54:41,460
But that's responding
to something

1198
00:54:41,460 --> 00:54:45,760
that is an immediate
part of the environment.

1199
00:54:45,760 --> 00:54:48,860
So, it's chasing
a bacterial cell.

1200
00:54:48,860 --> 00:54:52,625
But you might ask, is it
possible for cells to learn?

1201
00:54:52,625 --> 00:54:54,250
And, of course, then
you have to define

1202
00:54:54,250 --> 00:54:55,540
what you mean by learning.

1203
00:54:55,540 --> 00:54:58,510
And there's been some really
interesting demonstrations

1204
00:54:58,510 --> 00:55:02,030
of how it's possible
for organisms

1205
00:55:02,030 --> 00:55:05,180
to learn not at the
individual level, necessarily,

1206
00:55:05,180 --> 00:55:07,690
but at the population
level, via evolution.

1207
00:55:07,690 --> 00:55:10,654
And in particular, in this
very well written paper,

1208
00:55:10,654 --> 00:55:12,070
what they were
able to demonstrate

1209
00:55:12,070 --> 00:55:16,520
is that both yeast that have
evolved in the context of wine

1210
00:55:16,520 --> 00:55:22,390
fermentation and E. coli that
have evolved traveling through,

1211
00:55:22,390 --> 00:55:24,120
for example, our
digestive tracts,

1212
00:55:24,120 --> 00:55:27,000
there are characteristics,
sequences of events,

1213
00:55:27,000 --> 00:55:29,290
in which things happen.

1214
00:55:29,290 --> 00:55:32,200
So, the idea is that if a
bacterial cell is ingested

1215
00:55:32,200 --> 00:55:35,300
by a mammal, they will typically
see one carbon source, and then

1216
00:55:35,300 --> 00:55:35,800
another one.

1217
00:55:35,800 --> 00:55:38,650
So, they might see carbon source
A, and then carbon source B,

1218
00:55:38,650 --> 00:55:40,620
as they travel through
the digestive tract.

1219
00:55:40,620 --> 00:55:43,020
But if that is typically
what happens, then

1220
00:55:43,020 --> 00:55:46,000
what it means is that
an organism might have

1221
00:55:46,000 --> 00:55:49,005
an advantage if, when
it sees carbon source A,

1222
00:55:49,005 --> 00:55:52,550
it starts preparing to
digest carbon source B. So,

1223
00:55:52,550 --> 00:55:55,610
we can actually learn something
about typical environmental

1224
00:55:55,610 --> 00:55:56,546
orderings.

1225
00:55:56,546 --> 00:55:58,670
But it's not learning at
the individual cell level.

1226
00:55:58,670 --> 00:56:02,620
It's learning over the course
of evolutionary time scales,

1227
00:56:02,620 --> 00:56:04,300
the typical sequence of events.

1228
00:56:04,300 --> 00:56:06,660
And in this paper,
they show that this

1229
00:56:06,660 --> 00:56:10,050
seems to be the case
for both E. coli

1230
00:56:10,050 --> 00:56:13,230
and for yeast in the
context of fermentation.

1231
00:56:13,230 --> 00:56:15,220
So, I think it's a
really beautiful example

1232
00:56:15,220 --> 00:56:18,240
of different notions of what
you might mean by learning.

1233
00:56:21,290 --> 00:56:26,260
So, another classic
debate within the field

1234
00:56:26,260 --> 00:56:31,590
of evolutionary is this
question of, why sex?

1235
00:56:31,590 --> 00:56:34,680
And in particular, there's
this classic paradox

1236
00:56:34,680 --> 00:56:38,750
which is saying, sex is costly.

1237
00:56:38,750 --> 00:56:41,300
In particular, if you
take a bacterial cell,

1238
00:56:41,300 --> 00:56:43,450
just one cell turns
into two cells.

1239
00:56:43,450 --> 00:56:45,280
And then two can turn
into four, and you

1240
00:56:45,280 --> 00:56:48,197
get very rapid exponential
growth of the population.

1241
00:56:48,197 --> 00:56:50,030
Whereas, if you have
both males and females,

1242
00:56:50,030 --> 00:56:52,740
then there's what you might
call this twofold cost of sex.

1243
00:56:52,740 --> 00:56:54,220
Because males are,
in some sense,

1244
00:56:54,220 --> 00:56:56,820
not contributing to that
exponential growth rate.

1245
00:56:56,820 --> 00:57:00,610
If you start with a male,
female, and they have two kids,

1246
00:57:00,610 --> 00:57:02,580
and you have another
male, female, and then

1247
00:57:02,580 --> 00:57:04,030
you don't get
exponential growth.

1248
00:57:04,030 --> 00:57:07,810
And this is a factor of 2 in
the rate of exponential growth.

1249
00:57:07,810 --> 00:57:09,570
So this is what's
in that exponent.

1250
00:57:09,570 --> 00:57:11,150
So, this is a big, big effect.

1251
00:57:11,150 --> 00:57:13,330
And so, I think it's a
major, major challenge

1252
00:57:13,330 --> 00:57:17,277
to ask, why is it that sex is
so common among what you might

1253
00:57:17,277 --> 00:57:18,360
call the higher organisms.

1254
00:57:20,910 --> 00:57:22,200
And there are many hypotheses.

1255
00:57:22,200 --> 00:57:23,450
We'll talk about some of them.

1256
00:57:23,450 --> 00:57:26,180
One of the leading ones is known
as the Red Queen hypothesis

1257
00:57:26,180 --> 00:57:30,410
from this Lewis Carroll story,
where there's this line.

1258
00:57:30,410 --> 00:57:32,680
The Red Queen has to run
faster and faster in order

1259
00:57:32,680 --> 00:57:34,250
to keep still where she is.

1260
00:57:34,250 --> 00:57:36,290
That is exactly what
you all are doing.

1261
00:57:36,290 --> 00:57:38,623
And the reason that it's
called the Red Queen hypothesis

1262
00:57:38,623 --> 00:57:41,950
is because it's arguing that
perhaps the reason that we

1263
00:57:41,950 --> 00:57:45,550
and other animals have
obligate sexual reproduction

1264
00:57:45,550 --> 00:57:48,962
is because of some arms
race with parasites--

1265
00:57:48,962 --> 00:57:50,420
that the sexual
reproduction allows

1266
00:57:50,420 --> 00:57:54,390
us to evolve more rapidly
against the always

1267
00:57:54,390 --> 00:57:57,020
adapting parasite populations.

1268
00:57:57,020 --> 00:57:58,430
And of course,
we'll have to talk

1269
00:57:58,430 --> 00:57:59,680
about exactly what this means.

1270
00:57:59,680 --> 00:58:02,530
But there have been some
interesting experiments

1271
00:58:02,530 --> 00:58:06,090
in worms, in which they
had different reproductive,

1272
00:58:06,090 --> 00:58:08,110
different sexual
strategies, in the presence

1273
00:58:08,110 --> 00:58:09,480
or absence of parasites.

1274
00:58:09,480 --> 00:58:13,140
And this showed that there are
some interesting cases where

1275
00:58:13,140 --> 00:58:16,008
this may be at least
part of what's going on.

1276
00:58:19,570 --> 00:58:22,280
So, at this stage,
we've been talking first

1277
00:58:22,280 --> 00:58:24,160
about decision
making within cells,

1278
00:58:24,160 --> 00:58:28,710
and then how evolution may
allow cells to anticipate

1279
00:58:28,710 --> 00:58:30,870
different environmental
changes, may

1280
00:58:30,870 --> 00:58:34,094
be able to play games
against other strategies.

1281
00:58:34,094 --> 00:58:35,760
But at the end, we're
just going to talk

1282
00:58:35,760 --> 00:58:39,330
some about inter-species
interactions,

1283
00:58:39,330 --> 00:58:41,220
and what these sorts
of ideas may be

1284
00:58:41,220 --> 00:58:43,500
able to say about that process.

1285
00:58:43,500 --> 00:58:45,920
So, for example, a classic
inter-species interaction

1286
00:58:45,920 --> 00:58:49,560
are predator-prey interactions.

1287
00:58:49,560 --> 00:58:51,200
And this has been
used to explain,

1288
00:58:51,200 --> 00:58:54,260
for example, why it is that many
natural populations oscillate

1289
00:58:54,260 --> 00:58:56,214
over time.

1290
00:58:56,214 --> 00:58:57,755
There are simple
models of a predator

1291
00:58:57,755 --> 00:59:00,267
and prey that lead
to such oscillations.

1292
00:59:00,267 --> 00:59:01,850
And just over the
last 10 years, there

1293
00:59:01,850 --> 00:59:04,240
have been some really
fascinating experiments where,

1294
00:59:04,240 --> 00:59:06,819
in the laboratory, they were
able to take predator-prey,

1295
00:59:06,819 --> 00:59:07,860
show that they oscillate.

1296
00:59:07,860 --> 00:59:10,120
But then, in this case,
they saw some features

1297
00:59:10,120 --> 00:59:11,760
that they weren't expecting.

1298
00:59:11,760 --> 00:59:13,660
The oscillations were
maybe much longer

1299
00:59:13,660 --> 00:59:15,190
than they were anticipating.

1300
00:59:15,190 --> 00:59:16,899
And instead of
oscillating 90 degrees out

1301
00:59:16,899 --> 00:59:18,356
of phase-- which
is what you expect

1302
00:59:18,356 --> 00:59:20,260
from standard predator-prey
models-- instead

1303
00:59:20,260 --> 00:59:23,080
they were oscillating
180 degrees out of phase.

1304
00:59:23,080 --> 00:59:25,420
And I think that this
is a good example of how

1305
00:59:25,420 --> 00:59:27,290
quantitative experiments
in the laboratory

1306
00:59:27,290 --> 00:59:30,100
can actually say something
about the classic models

1307
00:59:30,100 --> 00:59:31,350
of predator-prey oscillations.

1308
00:59:31,350 --> 00:59:33,192
They are over 100 years old.

1309
00:59:33,192 --> 00:59:35,400
But if you go, and you make
quantitative measurements

1310
00:59:35,400 --> 00:59:37,780
in the lab, you see,
actually, in many cases,

1311
00:59:37,780 --> 00:59:38,710
things are different.

1312
00:59:38,710 --> 00:59:40,040
And then, you can ask, why?

1313
00:59:40,040 --> 00:59:41,990
In this case, they
went-- they did modeling,

1314
00:59:41,990 --> 00:59:44,190
and they said, maybe
it's because of evolution

1315
00:59:44,190 --> 00:59:46,130
within the prey population.

1316
00:59:46,130 --> 00:59:48,080
And once they were
able to-- they

1317
00:59:48,080 --> 00:59:49,707
had hypothesized
that from modeling.

1318
00:59:49,707 --> 00:59:51,540
And then they went, and
they did experiments

1319
00:59:51,540 --> 00:59:54,180
where they prevented
that evolution,

1320
00:59:54,180 --> 00:59:56,820
or they prevented-- they reduced
the heterogeneity in the prey

1321
00:59:56,820 --> 00:59:57,460
population.

1322
00:59:57,460 --> 00:59:58,670
And then they were able
to show that those two

1323
00:59:58,670 --> 00:59:59,569
features disappeared.

1324
00:59:59,569 --> 01:00:01,360
So, I think it's really
a beautiful example

1325
01:00:01,360 --> 01:00:03,800
of the interplay that
we always hope for,

1326
01:00:03,800 --> 01:00:05,849
which is that you do
theoretically motivated

1327
01:00:05,849 --> 01:00:08,390
experiments, and experimentally
motivated theory computation.

1328
01:00:08,390 --> 01:00:09,680
Ideally, you go back and forth.

1329
01:00:09,680 --> 01:00:10,660
And together, you
can really learn

1330
01:00:10,660 --> 01:00:12,250
more than you would
ever be able to do just

1331
01:00:12,250 --> 01:00:13,333
by doing one or the other.

1332
01:00:16,330 --> 01:00:18,100
We're also going to
try to say something

1333
01:00:18,100 --> 01:00:21,490
about the dynamics of
populations in space.

1334
01:00:21,490 --> 01:00:24,260
So, just like these
spatial patterns

1335
01:00:24,260 --> 01:00:26,960
that we talked about before,
in the context of maybe gene

1336
01:00:26,960 --> 01:00:28,470
networks, there
are also dynamics

1337
01:00:28,470 --> 01:00:29,960
of populations in space.

1338
01:00:29,960 --> 01:00:32,320
For example, when populations
expand into new territory,

1339
01:00:32,320 --> 01:00:34,444
what does that mean about
the evolutionary process?

1340
01:00:34,444 --> 01:00:36,760
Once again, some
very nice experiments

1341
01:00:36,760 --> 01:00:38,600
have been done over
the last decade

1342
01:00:38,600 --> 01:00:40,645
to try to eliminate
this process.

1343
01:00:40,645 --> 01:00:44,090
And in particular, one of
the things that was found

1344
01:00:44,090 --> 01:00:47,080
is that this process
of genetic drift,

1345
01:00:47,080 --> 01:00:50,340
the role of randomness in
the evolutionary process,

1346
01:00:50,340 --> 01:00:54,150
is somehow strongly enhanced
in many of these experimenting

1347
01:00:54,150 --> 01:00:55,380
populations.

1348
01:00:55,380 --> 01:00:58,520
Because somehow the
effective population size

1349
01:00:58,520 --> 01:01:00,670
that quantifies maybe
the strength of noise

1350
01:01:00,670 --> 01:01:01,660
is somehow enhanced.

1351
01:01:01,660 --> 01:01:03,810
Because it's not the
entire population matters.

1352
01:01:03,810 --> 01:01:05,580
It's just the
population at the front

1353
01:01:05,580 --> 01:01:08,050
of this expanding population
that is somehow relevant.

1354
01:01:08,050 --> 01:01:13,080
So, we'll explore how
these ideas play out.

1355
01:01:13,080 --> 01:01:14,700
And so, towards the
end of the class,

1356
01:01:14,700 --> 01:01:18,200
we'll try to think about some
real ecological phenomena.

1357
01:01:18,200 --> 01:01:20,667
In particular,
we're going to have

1358
01:01:20,667 --> 01:01:23,250
one lecture where we talk about
tipping points in populations.

1359
01:01:23,250 --> 01:01:25,250
It's a theme that my
group, for example,

1360
01:01:25,250 --> 01:01:27,200
has been very excited
about recently.

1361
01:01:27,200 --> 01:01:30,510
So, this here is data from
the Newfoundland cod fishery.

1362
01:01:30,510 --> 01:01:33,800
And it's an example of how
many natural populations can

1363
01:01:33,800 --> 01:01:36,700
actually collapse suddenly
and catastrophically

1364
01:01:36,700 --> 01:01:40,180
in response to deteriorating
environmental conditions.

1365
01:01:40,180 --> 01:01:42,170
Now, what's plotted
here-- this is essentially

1366
01:01:42,170 --> 01:01:45,332
the number of fish that are
caught as a function of time.

1367
01:01:45,332 --> 01:01:47,790
And you may not be able to read
this, but over on the left,

1368
01:01:47,790 --> 01:01:51,430
this is 1850, and
this the modern day.

1369
01:01:51,430 --> 01:01:54,330
So, this was a very productive
fishery for hundreds of years

1370
01:01:54,330 --> 01:01:57,280
and, actually, even for
hundreds of years before this.

1371
01:01:57,280 --> 01:02:00,291
However, in the '60s and '70s,
improved fishing technology

1372
01:02:00,291 --> 01:02:02,290
led to a dramatic increase
in the number of fish

1373
01:02:02,290 --> 01:02:03,490
that were caught here.

1374
01:02:03,490 --> 01:02:05,950
And that increase in fishing
led, in the early '90s,

1375
01:02:05,950 --> 01:02:09,910
to a catastrophic collapse
of that population.

1376
01:02:09,910 --> 01:02:11,860
Similar things have
occurred, for example,

1377
01:02:11,860 --> 01:02:14,110
in the sardine
fishery off the coast

1378
01:02:14,110 --> 01:02:17,820
of Monterey, and many
other populations.

1379
01:02:17,820 --> 01:02:24,200
So, the question here is, how
can we understand these tipping

1380
01:02:24,200 --> 01:02:26,220
points in populations?

1381
01:02:26,220 --> 01:02:28,120
And this is a case
where some of the ideas

1382
01:02:28,120 --> 01:02:30,020
we studied early in
the semester-- so,

1383
01:02:30,020 --> 01:02:34,270
these cases of interactions--
can lead to sudden transitions.

1384
01:02:34,270 --> 01:02:37,550
And for example, this
early example of a toggle

1385
01:02:37,550 --> 01:02:40,150
switch that we talked
about at the beginning--

1386
01:02:40,150 --> 01:02:42,350
so, this case where, if
you have interactions

1387
01:02:42,350 --> 01:02:46,710
within the population, it can
lead to alternative states.

1388
01:02:46,710 --> 01:02:48,820
And it's the same
basic dynamic here.

1389
01:02:48,820 --> 01:02:50,790
If you have interactions
within the population,

1390
01:02:50,790 --> 01:02:52,331
then you can get
alternative states--

1391
01:02:52,331 --> 01:02:55,860
maybe healthy and dead,
or extinct, locally.

1392
01:02:55,860 --> 01:02:58,320
So, you can imagine if you
have these alternative states

1393
01:02:58,320 --> 01:03:00,490
due to interactions
within the population,

1394
01:03:00,490 --> 01:03:02,895
and if you start out
in this healthy state,

1395
01:03:02,895 --> 01:03:06,740
and you start pushing it, then
it's going to-- the feedback

1396
01:03:06,740 --> 01:03:08,700
loops there will
maintain a state

1397
01:03:08,700 --> 01:03:10,400
where it's alive, healthy.

1398
01:03:10,400 --> 01:03:12,150
And then all of a
sudden, when it's not

1399
01:03:12,150 --> 01:03:14,540
able to counteract this
deteriorating environment, all

1400
01:03:14,540 --> 01:03:16,289
of a sudden it's going
to switch and maybe

1401
01:03:16,289 --> 01:03:19,430
collapse in this fishery.

1402
01:03:19,430 --> 01:03:21,490
So, these sorts
of ideas have been

1403
01:03:21,490 --> 01:03:24,320
used to both try to understand
why it is that populations

1404
01:03:24,320 --> 01:03:28,290
might experience tipping points,
but also to get some guidance

1405
01:03:28,290 --> 01:03:30,970
about ways that we can
anticipate that these tipping

1406
01:03:30,970 --> 01:03:32,180
points are approaching?

1407
01:03:32,180 --> 01:03:35,212
For example, in my group, we've
been excited about this idea

1408
01:03:35,212 --> 01:03:37,499
that there are predictions
that the fluctuations

1409
01:03:37,499 --> 01:03:39,040
of the population
should be different

1410
01:03:39,040 --> 01:03:41,410
when a population is approaching
one of these tipping points.

1411
01:03:41,410 --> 01:03:42,868
And, at least in
the laboratory, we

1412
01:03:42,868 --> 01:03:45,400
were actually able to measure
a change in the fluctuations

1413
01:03:45,400 --> 01:03:46,300
before a collapse.

1414
01:03:46,300 --> 01:03:49,940
And this is saying that, in
principle, there are maybe

1415
01:03:49,940 --> 01:03:51,860
universal signatures
of populations

1416
01:03:51,860 --> 01:03:56,660
and other complex systems before
one of these tipping points.

1417
01:03:56,660 --> 01:03:59,320
Now, in the last
lecture, we're going

1418
01:03:59,320 --> 01:04:02,630
to go maybe to the
largest scale to think

1419
01:04:02,630 --> 01:04:04,940
about whole ecosystems.

1420
01:04:04,940 --> 01:04:08,740
And this is, by its nature,
I'd say, less experimental

1421
01:04:08,740 --> 01:04:11,792
than the rest of the
semester in that,

1422
01:04:11,792 --> 01:04:14,450
in this case, we're trying
to understand questions like,

1423
01:04:14,450 --> 01:04:17,490
what is it that determines the
abundance of different tree

1424
01:04:17,490 --> 01:04:20,490
species on Barro
Colorado Island?

1425
01:04:20,490 --> 01:04:22,270
So, it's an island in
Panama where they go

1426
01:04:22,270 --> 01:04:25,519
and they just count every
tree within some region.

1427
01:04:25,519 --> 01:04:27,810
They'd say, this is this
country, this is that country.

1428
01:04:27,810 --> 01:04:29,890
They count thousands
and thousands of trees.

1429
01:04:29,890 --> 01:04:32,040
The question there
is-- some species

1430
01:04:32,040 --> 01:04:35,790
are more common than others.

1431
01:04:35,790 --> 01:04:37,517
And we want to know why.

1432
01:04:37,517 --> 01:04:38,850
It seems like a simple question.

1433
01:04:38,850 --> 01:04:40,724
And the way that we
normally think about this

1434
01:04:40,724 --> 01:04:43,140
is if it's more
common, then maybe it's

1435
01:04:43,140 --> 01:04:45,920
because it's better adapted
to that environment.

1436
01:04:45,920 --> 01:04:48,679
And I think that, often,
that's the right answer.

1437
01:04:48,679 --> 01:04:50,970
But there's been an interesting
movement within ecology

1438
01:04:50,970 --> 01:04:54,700
recently where it's been pointed
out that many of the patterns

1439
01:04:54,700 --> 01:04:57,602
that people have observed in
terms of this relative species

1440
01:04:57,602 --> 01:04:59,810
abundance-- how abundant
some species are as compared

1441
01:04:59,810 --> 01:05:01,620
to others-- that many
of those patterns

1442
01:05:01,620 --> 01:05:04,740
can actually be explained
by a purely neutral model.

1443
01:05:04,740 --> 01:05:05,370
I.e.

1444
01:05:05,370 --> 01:05:08,410
This is a model in which you
assume that all of the species

1445
01:05:08,410 --> 01:05:09,990
are the same.

1446
01:05:09,990 --> 01:05:11,934
So, this tree is just
the same as that tree

1447
01:05:11,934 --> 01:05:14,100
in terms of-- no tree is
better than any other tree.

1448
01:05:14,100 --> 01:05:16,270
But just because of the
stochastic dynamics,

1449
01:05:16,270 --> 01:05:18,640
random birth death
processes, you

1450
01:05:18,640 --> 01:05:22,510
can recover patterns that look
an awful lot like the patterns

1451
01:05:22,510 --> 01:05:24,260
are observed in nature.

1452
01:05:24,260 --> 01:05:26,865
So, you can interpret
this is in multiple ways.

1453
01:05:26,865 --> 01:05:28,740
But, of course, one way
that we should always

1454
01:05:28,740 --> 01:05:30,114
be thinking about
these things is

1455
01:05:30,114 --> 01:05:33,420
that if you observe-- we want
to collect quantitative data.

1456
01:05:33,420 --> 01:05:34,355
We should do that.

1457
01:05:34,355 --> 01:05:36,480
But there's always a
temptation that if you collect

1458
01:05:36,480 --> 01:05:38,229
quantitative data, and
then you write down

1459
01:05:38,229 --> 01:05:40,920
a model that is
consistent with that data,

1460
01:05:40,920 --> 01:05:43,570
we often take that
as strong evidence

1461
01:05:43,570 --> 01:05:45,970
that the assumptions of
our model are correct.

1462
01:05:45,970 --> 01:05:48,460
Even though we know
that's not the way we're

1463
01:05:48,460 --> 01:05:50,770
supposed to do science,
somehow it's just really easy

1464
01:05:50,770 --> 01:05:51,810
to fall into this trap.

1465
01:05:51,810 --> 01:05:55,060
And I think that this particular
example of this neutral theory

1466
01:05:55,060 --> 01:05:57,830
of ecology is a very
concrete example of how

1467
01:05:57,830 --> 01:06:01,320
different models that make
wildly different assumptions

1468
01:06:01,320 --> 01:06:03,600
about the underlying
dynamics-- they

1469
01:06:03,600 --> 01:06:06,100
can all look the
same once you look

1470
01:06:06,100 --> 01:06:07,620
at a particular kind of pattern.

1471
01:06:07,620 --> 01:06:12,270
And so, it's a nice
cautionary tale saying,

1472
01:06:12,270 --> 01:06:15,670
what is it that you can learn
about the dynamics of a system

1473
01:06:15,670 --> 01:06:20,240
or of a process based on a
particular kind of data set.

1474
01:06:20,240 --> 01:06:25,480
And then, after this, we will
just have that final exam.

1475
01:06:25,480 --> 01:06:30,600
That's going to be that
week of 15 to 19, I believe.

1476
01:06:30,600 --> 01:06:35,150
So, once again, do not book
your tickets before then.