Okay.
I’d like to tell you something about choice
and variability in the immune system this
afternoon and to do that; first of all, I
need to tell you a little bit about how the
immune system actually works.
So, when you get an infection with some viral
sol bacteria that’s sent to the body instead
of a beach head, the immune system responds
by rushing a lot of different cells to that
area to try and attack it and contain that
infection.
So, all of those cells, they’re very diverse.
There’s also different types of cells, very
specialized for particular functions.
So some cells might make some antibodies.
Other cells might directly kill infected cells.
Other cells might eat bacteria.
So that degree of specialization means you’ve
got to have a lot of choices about what type
of cell that you deploy to fight that infection
and during the response there also some changes
that go on where one cell type will change
into another more effective type and one of
the ones that we’ve been very interested
in is the “T” cells of the immune response.
They’re one of the major arms and during
a response; those initial starting “T”
cells don’t have a lot of functions.
They can’t really attack the pathogen themselves
very much but they can then change into cells
with much more effective functions and they
can change, for example, into “TH 1” cells
and those are very good for attacking bacteria
and viruses.
They can also change into “TH 2” cells
and those cells are very good if you’re
trying to combat a parasitic worm but they’re
not so good for the virus and bacteria.
So, the immune system needs to make the right
choice about what type of response to induce
and that’s particularly important because
these cells also have a downside so these
“TH 2” cells that are so good for parasitic
worms, they can also induce allergy and asthma
so you really need to get that choice right.
So we need a very precise mechanism for choosing
but the same time we know that cells in the
immune response in various of these populations
I’ve been describing even within those populations,
they can be very diverse.
So you have this enormous amount of variability
within a population and in these scatter plots
at the bottom here, you can see that the expression,
each one dot is each cell.
The expression of different proteins can vary
over a level of tenfold or more.
So that is a lot of variability of the population
and yet you want a very precise response,
very precise choice as the infection proceeds.
So I’m going to go through a few thoughts
on what might, what happens when you combine
that choice and that variability.
So what I’m going to use is what’s called
a landscape model to illustrate some principles
and the idea of doing this is not that our
cells are really rolling around a landscape
but as people we can visual things quite effectively
on this particular kind of image.
We’re all use to the idea of what happens
when balls roll the landscape so it’s an
idiom that we can use to try and talk about
cell behavior.
So what I am going to do is say that the landscape
represents a whole bunch of different states
of a cell.
So if a “T” cell is occupying one part
of the landscape that means it’s a particular
kind of cell and it can change.
It can move across the landscape into something
else and it will obey the laws of gravity
so it will roll down and so a basin will tend
to be a state where that cell is very stable.
It’s going to stay there for quite a long
time where’s a hillside is not going to
be very stable.
It’s going to roll down and what I want
to do is combine the idea of the balls rolling
down a landscape with the variability that
I was speaking about and we set up a computer
simulation that allow cells roll down one
of these complicated landscapes but at the
same time have some random movement built
in so that the motion isn’t completely controlled
by gravity.
There’s a lot of shaking around as the cell
has this random movement applied over the
top.
So when we do that, I’ll start off with
a very simple model where we simply got a
tilted landscape and over on the top there’s
a basin with a whole bunch of black cells
in it and when we started rolling then those
cells would go down the hill and they’ll
generally go towards these two basins at the
bottom and some go into the green basin, some
go into the red basin and you can see that
we’ve got this random motion programmed
into it and about half the cells go into each
pot.
We can run the simulation over and over again
and about half of them go each way because
we’ve made it symmetric.
But what happens when we look at, rather than
the whole population, if we look at just two
cells within that population and so I’ve
colored two of them so you can see them and
as they start to run down, then, you can see
that they’re also shaking around like all
the black ones and their movement doesn’t
really give you much of a hint about where
they’re going to end up.
If you try to guess where they’re going
to go, you know, as later on you can, but
for quite a while it really wasn’t clear
what that preference would be.
So if we run it again just with slight different
cell positions the same sort of thing happens.
Those cells are dancing around so the yellow
one is pretty easy to predict where it’s
going to go.
The blue one I think is going to confuse you
a lot more and I think he finally settles
down about now coming down.
He drops into the red one as well.
So the point I want to take away from this
is that the behavior of populations of cells,
even with this random variability, we can
predict the population behavior pretty well
but it’s very hard to predict the behavior
of the individual cell.
So if we take that a step further and think
about the effect that just variability might
have on a response and on the change of state
of these cells from one state to another.
Here I have a basin in the middle that represents
relatively stable state of cells.
So, cells in there are rattling around.
There’s a fair amount of moment.
They’re not all just sitting at the bottom
but none of them are escaping that state.
So we’d say that be a very stable state.
We could push the cells one way or another
to try to get them to change but instead I’m
going to do something different.
I’ve now turned on increased variability
in the cells so now they’re simply moving
more, not in any particular direction but
that has the effect that a few of them start
to spill over the edges and now you state
to states that I’ve shown as pink and the
blue states off to the side that might be
very different and there’s a very important
feature of this diagram and that’s the idea
that if you want to know what’s going to
happen in this model, it’s no good looking
at the middle of the well.
So, the great bulk of cells in the middle
aren’t telling you what’s going to happen.
It’s those very few out wires on the fringe
of the population that may be the crucial
difference between whether you get some of
these new cells or you don’t.
So I think that’s something that we can
certainly talk about in human populations
as well.
In some ways you want to pay a lot of attention
to what the great bulk of people are doing
and thinking but they’re certain situations
where it’s the out wires that really make
a difference that will make some change that
the great majority of the people will not
get to immediately.
So we can take this analogy a step further
by looking at what happens if cells have multiple
paths to get to the same place and so in this
animation I’ve again got some cells sitting
in a well at the top and we have a target
well at the bottom and they’re all going
to end up down there eventually but we’ve
given them some intermediate states that are
sort of semi-stable that they can sample on
their way down and when you start that running
these cells flow down.
We’ve colored them if they go through one
well, they go blue, another one green and
third one red and as they flow down you can
see the colors being added tell you what path
the cell has taken down the mountainside.
By the time you get to the bottom, it’s
obvious that you have cells of pretty much
any color that you can imagine from those
combinations.
These cells are taking all sorts of different
ways to get from one state to another state.
So that could be very interesting if you have
a situation where you want to modify some
of these pathways and we can look now at the
same animation except on the left hand side
I’m going to emphasize the green pathway.
So the cells are going to go mostly off to
the right and that’s a dominant pathway
that seems to be the main way the cells get
from the top to the bottom state but on the
left, on the right hand side I’ve changed
one of those bowls, one of the attractors,
I’ve changed it into a mountain so the cells
will not be going in there.
They’ll be deflected off of that.
So when you run that, you can see that in
fact happens.
We get a majority of the cells going in the
green direction in once case.
When we block the green direction, the cells
simply find other ways down the mountainside
and we still end up with similar numbers of
cells in the final state.
It’s just that they’ve got there by a
majority of a different way.
So, that may be very important, for example,
if we have an infection many pathogens interfere
very strongly with the immune response and
they’ll block different pieces of the immune
system.
So it’s very good to have a lot of flexibility
so that you can work around roadblocks that
can be put up by the pathogens.
The other way around it maybe a little more
awkward if you’re trying to identify a dominant
pathway and produce a therapeutic drug to
block that you may be surprised at the immune
response works around that as well.
So I just like to summarize the mainstream
points I tried to make today.
First one that populations can often be, their
behavior can be often predicted quite well
but it’s very difficult to predict the behavior
of these individual cells that make up the
populations and so if we really want to understand
how the system works in detail, we have to
make more and more measurements at the single
cell level which is a lot more difficult then
measuring population behaviors.
The second point was the variability in of
itself can initiate changes in the population
and you can go from what appears to be a very
stable situation to a movement into a different
state just by increasing the variability without
inducing any other change and it’s an interesting
point, I think, that for some purposes we
may wish to look at diagnostic methods and
say “When should we be looking at bulk results
and when should be actually looking at variability
in the population as well” and I think the
point about multiple paths to the same goal
may be very important as I mentioned for the
flexibility theme response in different directions
and I wanted to close off though by just pointing
out that everything I’ve told you so far
is fantasy.
So I’ve used this landscape model to try
and make some conceptual points about what
happens when we consider the interplay between
choice and variability and I think now this
might give us some interesting ideas to follow-up
in more experimental approaches.
Thank you.
