[Music]
Hello everyone and welcome to the first
round of talks for the day one for the
general track. Um I'm Ash. I'll be
session chairing this block and for our
first talk we have Nick Moore talking
about the primordial code. So, everybody
give a big warm welcome to Nick.
Thank you.
There we go. Just to to start off, um uh
content warnings. We mentioned
infectious and genetic diseases
including cancer and COVID, but not like
any gruesome detail or anything like
that. Just content warnings.
All right. My name is Nick Nick Moore.
Um I'm a consultant. I work in software
development and systems architecture for
a number of clients in a number of
industries through my own company
Nimote. Um I've been doing that for a
lot of years now and it's it's awesome
fun. You should try it. Um I've worked
in with micro I've presented here
previously about microython about the
internet of things about visual
programming languages about functional
programming in JavaScript. um not at
this conference at Linux conf and about
how did you do NoSQL in an SQL database
and now I'm having the luck of being
able to try my hand at being a
biioinformatician
um
to start with thanks and apologies to
all my colleagues at Walter and Eliza
Hall Institute of Medical Research and
the University of Washington Genome
Sciences and the Broman Batty Institute.
Um the mistakes and oversimplifications
in this presentation are all mine.
They've been incredibly patient helping
me learn and find my feet in this really
amazing field.
Uh there is or at least there will be
the slides, the notes and the arata on
this presentation up at that URL on that
QR code. Um so it'll be updated soon
enough. Um and that way any really
gratuitous mistakes I make I can at
least apologize for later. Um all right,
let's talk about bioinformatics. What is
bioinformatics? Does anyone other than
Alan and Estelle know what
bioinformatics is? Cool. That was way
more people than I expected. Hopefully,
you don't all work for Weihi. Um,
uh, so bioinformatics is the study of
biological systems kind of numerically.
And because biological systems are a
smidge complicated, that basically
involves computers.
We're really lucky. Computers and and
biology have kind of developed in
parallel over the last couple of
centuries. So it dates back to sort of
the invention of computing in the 19th
century and the idea of of data as this
continuous tape for for telegraphy and
things like that. Um and at roughly the
same time we were discovering the idea
that inheritance of characteristics in
plants and animals was was governed by
sort of discrete inheritable units. They
weren't really labeled genes yet, but
that's what we're talking about. We're
talking about genes. Um, and Darwin was
working on natural selection. Um,
and and how all of this could actually
end up with the diversity of life we
see. Um,
at the same time, we actually did
discover the DNA molecule and discovered
there was this enormously long wiggly
molecule and and it wasn't very
interesting and it just looks like
boogers. But we had no idea what it was
for. I just think it's funny that it at
the start of this story, we have DNA
already lying around and everyone goes,
"Oh, gross." Anyway, at some point or
another though, people worked out that
these inheritable units had a
relationship. Ones that were more likely
to be inherited together, ones that were
less likely to be inherited together.
And with a paper and pen, people worked
out that there was like a linear pattern
to this. These things had there were
closer ones and further ones and the
distances kind of added up in a long
line which is really interesting when
you think about it. Um Colo proposed
that this was a giant hereditary
hereditary molecule a molecule that was
passed down from a parent organism to a
child organism and that must be how
heredity works. I don't think he knew it
was DNA at that point. It's one of those
things where the Soviet world and the
Western world maybe weren't talking that
much. Um unfortunately that ran a foul
of lysenoist lamarist theories of
evolution and so he didn't go too well.
Um at the same time Alan Turing was
working on the fundamentals of computing
and he came up with the idea of a
universal computing machine that went
back and forth along a tape. It's not
really DNA but it's it's an idea of this
linear organization of information that
was being developed at the same time.
Boolean algebra kind of got developed
and the the theories behind that by
people like Claude Shannon and then of
course the war distracted people
somewhat into cryp analysis which ended
up giving us general purpose computing
which was kind of awesome. Um in the 40s
Frederick Sanger I think working on a
theory that proteins were what you
inherited worked out how to sequence
proteins which was pretty awesome and he
won a Nobel Prize for it. Um but it
turned out that that's not actually how
things are inherited. Oh yeah and in the
meantime Allan after the war goes on to
work on morphagenesis which is
bioinformatics. So obviously this is an
interesting field that's developing.
In about 44 DNA turned out to be the
carrier of genetic information. That
horrible booger looking molecule turned
out to be actually where all of our
heredity comes from. um and Watson Frank
and Crick and Rosalyn Franklin
discovered that structure managed to
derive a structure of the double helix
that you're familiar with and you'll see
in a minute Frederick Sanger not to be
put off by the fact that the molecule
he's working on wasn't the source of
herity goes and works out a sequence DNA
wins another Nobel Prize you know just
another Tuesday um and then in the 2000s
we developed DNA editing we worked out
mechanisms that allow people to actually
edit DNA molecules change genes. That's
pretty awesome. Uh, in recent decades,
we've worked out how to actually change
genes in a living cell, how to change
genes in a living person, and this has
actually been used to cure people's
genetic diseases, which is, I think,
just amazingly awesome. Um,
all right. So, to make any sense out of
this presentation, we're going to have
to teach you a bit of biology, about 4
billion years of biology in about 10
minutes. So, hang on. Um,
organisms are made of cells.
A cell is like a little tiny computer.
It's an encapsul an encapsulation. It
has a bunch of code inside it which we
call genes. That code makes proteins.
The little machines that make the cell
work.
The cell is sort of defined by its
membrane, by its its its edge, its
outside container that keeps the
outsides out and the insides in. You can
think of that as a bit like a firewall.
It has various ports through it called
channels that let information in the
form of molecules come in and out of the
cell under the cell's control. So the
cell kind of controls its own gateways
and things like that. And that's really
what a cell is. It's a collection of
stuff in one place. There are different
kinds of cell. That's a um a bacterial
sort of cell, typical bacterial cell,
typical animal cell, typical plant cell.
They have a lot of things in different
but they have a lot of things in common
which is largely this this idea that the
the genetic information is hoarded very
carefully in the center.
All right DNA itself the molecule we're
talking about is indeed a very long
molecule. This is just a little section
here of a very long spiraly molecule
meters in length you know that is
composed of these sort of bases these
subunits and the subunits pair up so
it's like a very long tape instead of
two bits one and zero we have four bits
A C G and T um and they complement each
other and build themselves up like a
ladder so you get that idea of that
symmetry
That's more or less exactly what Colov
described, funnily enough, even though
he had none of the equipment required to
see or work out any of this stuff. He
know if we're going to record all this
stuff as some very long molecule, it'll
have to zip together with some other
very long molecule to stabil. Anyway,
that's pretty amazing.
When I talk about the genomes coming up,
we'll talk about in terms of of base
pairs or mega base pairs or whatever. A
base pair is is effectively two bits
because there's four options for base
pairs. So therefore it's worth two bits
of information. So that's just to give
you an idea of what the currency here
is.
So
in about 77 we started being able to
sequence whole organisms. Uh the first
one sequenced I believe was a bacteria
phase a virus that attacks bacteria. Um
it has only about five kilobase
kilobases. It's single stranded RNA I
think. Um and only 11 genes. Anyone here
who is a programmer of very old
computers will sympathize. Those 11
genes actually kind of overlap each
other and they interfere with each other
and it's very compact compact because it
manages to fit enough
potential into such a tiny number of
instructions. Incredible. A couple of
decades later, we're sequencing much
more complicated organisms like E.coli.
So pretty common human gut bacteria
which have millions of bases and yeast.
ask me about my yeasts. Um, uh, which
has millions of bases and and thousands
of genes. We eventually work up to the
fruitfly, which is a a an organism
beloved of biologists because you can
catch them in a milk bottle and breed
them and mail them to people and things
like that. It's they're very good to
work with. They have hundreds of
millions of base pairs. And eventually,
in about 2003, we worked up to
sequencing a whole human genome, which
is like three billion base pairs. As a
bonus, we all have two copies of it
we're carrying around, but and about
that forms about 20,000 genes. So, a
human genome is quite a large project.
Uh this is just a chart showing kind of
an idea of the different species. And
and part of the point of this chart is
actually the the kind of the opposite of
what I just said. Um mammals are down
here in this little region here, but
here's flowering plants, which actually
turn out to be like way more complicated
at sometimes or not. You know, there's a
huge variation in the size of genomes
between species, I think, is the point
of that one. Um, but the funny thing
about it is we're all using basically
the same genetic code. There's so much
in common between different organisms.
This is, I've been told, a compulsory
slide that must be included in every
single biioinformatics
um, presentation. This is the cost to
sequence a human genome. It falls
precipitously. But wait, those of you
who aren't terrible science nerds may
not have noticed that that is a log
scale. The top point there is $100
million to sequence a single human
genome. And it falls by this end to less
than $1,000 to sequence a single human
genome. That's kind of awesome. Um,
so I mean that kind of under represents
if I plotted it on a linear scale, it
would just fall like a log and go across
there. Um,
this is really exciting for a couple of
reasons. One of which is, of course, it
allows us to do precision medicine to
really analyze one particular patient
and really get to the heart of what the
hell is wrong with them and maybe find a
specific cure for that specific person.
And that's really cool,
but also it enables us to look more
broadly at what a human genome is. The
human genome project has like one
sequence from it's combined from a a a
couple of people I think but it is
basically representing a person right
the pang genome is is like a
representation of what is all of the
possibilities for healthy humans
worldwide and it opens up the
possibility of doing medicine for um
people in developing countries for
example who are not really in the market
for precision medicine but maybe we can
at least understand
conditions that are more applicable to
them more easily which I think is is
really awesome too. And so these pang
genome projects are rolling out to look
at thousands of people, tens of
thousands of people, hundreds of
thousands of people across the world's
population and to build up a picture of
what all of that combined
might be. All right.
Okay. So let's talk about genes. Let's
talk about evolution.
The reason we can evolve is because we
can change and the reason we can change
is because we can make mistakes. Um,
mistakes creep in whenever um a cell
replicates, whenever a cell receives
like radiation damage or something like
that. Viruses can write themselves back
into your DNA. Most of these changes are
very unhelpful and and not good for you.
But in evolutionary time scales, the
good outweighs the bad and you you end
up evolving new abilities, new whatever.
not quite like Xben or whatever, you
know, it doesn't help an individual very
often very much, but over billions of
years, you get somewhere. So, the kind
of changes we're talking about are like
substitution of a single base for
another base, insertion of some bases,
deletion of some bases, or just
duplication. Sometimes the mechanism
gets a bit gummed up and you end up with
little duplicate runs. So, as a
programmer, you can kind of imagine
doing this to your own code, right? It
would very often not be helpful. Every
now and then it might turn out that it
actually improves something. The
duplication is especially interesting.
If you copied a function out of a piece
of code and then now you have two copies
and they both kind of are doing the job,
then that frees you up to have one of
them change a bit more without getting
rid of the job the first one was doing.
So that's part of the mechanism of
evolution. Um, I think it's really
interesting that if if replication and
radiation protection and all that stuff
was perfect, nothing would ever evolve.
We wouldn't be here. The system has to
be imperfect for it to work. I think
that's amazing. Bacteria have an even
better trick up their sleeve. They do
horizontal gene transfer. A bacteria can
basically build a thing called a plasmid
and inject it across into one of its
neighbors using this sort of little
hairy tube things and give away some of
its genes to its neighbors. not even
necessarily the same species. This seems
really remarkable because it means that
bacteria are the first sort of free
software advocates.
What's really interesting about this
one, right, is that it's not the
bacteria's survival that matters in the
long run. It's the gene's survival. If
I'm a bacteria and I have a gene for for
antibiotic resistance and I give that to
a neighbor, that neighbor can develop
that antibiotic resistance, that gene
will now be replicated by that neighbor.
Even if I now get out competed, the gene
lives on
and and we've all seen open source
libraries that do this too, you know.
Um, so
I want to talk about this this concept
at the center of biology. called the
central dogma of of biology. The idea is
that DNA gets transcribed into RNA that
gets translated into proteins, right?
It's very orderly, very neat. DNA turns
into RNA turns into proteins. You know,
you have code, you compile it, you link
it, you run it. Fine. Of course, there's
also retrotranscription. That's when a
virus writes from RNA back into DNA.
There's also replication where DNA gets
copied into more DNA. So, it's not okay.
Um, and also sometimes RNA actually has
a useful job to do itself. It's called
non-coding RNA. So sometimes you're
using like the think of as a data
segment in your code. Gets used directly
rather than being turned into a protein.
But that's that's fine. That's not too
complicated. Um, except for RNA
polymerase, right? RNA polymerase is a
protein that does the transcription,
does the job of transcription. So So I
think that's okay though because we've
got the protein at the end, the thing we
just compile and run, and that's our
compiler. Um hang on. And then um also
RNA needs to be spliced and splicing is
kind of complicated. It's done by
proteins and also when you do the
translation that's done by proteins and
non-coding RNA.
So so we need to run the compiler in
order to run the compiler. Um this
starts to get a bit tricky. The only
reason this works at all is because it's
bootstrapped. The cell that you grew
from, the usite is full of these logical
units enough to keep building itself for
long enough to get itself up and
running. It's kind of a little bit like
a boot disc. There's enough of an
operating system on there that it can
make another boot disc and another boot
disc and copy errors kind of creep in.
Um,
so we talk about this as bootstrapping
from the metaphor of pulling yourself up
by your own bootstraps, which is of
course impossible. But it raises the
question, when did the first cell manage
to do this? And this is a thing we we
just don't know. There's no source
control in biology. We can't check out
the first version and find out. It's
very frustrating. But we can look at the
common features. Oh, that's not the
right slide. We can look at the common
features. This is actually one of the
common features.
of of all these different branches of
life. And so what have they all got in
common? And we can kind of theorize this
existence of a a common ancestor that
must have arisen at some point.
Possibly it had RNA instead of DNA and
sort of started halfway through.
Possibly
possibly a lot of things. Um
and that's where all life kind of
evolved from in its different ways. This
is just a little example. I was talking
before I said proteins are like little
machines. As I said before, I just
wanted to demonstrate to you that this
is not a metaphor. This is this is not
one of those whole endofunctors in the
whatever. This is this is a an actual
real machine. This is a thing called ATP
synthes. It's a protein complex. The
different colored bits are different
proteins. They all lock together. They
build a little machine. It has a
rotating crankshaft
and it literally pumps either either
takes protons, push it around, and it
makes ATP or ATP pushes around and it
pumps protein.
protons
either. Um, it's like a engine in every
single cell of every single organism.
And there slightly different versions of
it around. Again, not great source
control, but it is one of the basic kind
of units of what we call life. You could
also say that life is just ATP syntheses
way of making more ATP synthes. Depends
on which your perspective you look at
this. Okay. Translation. I talked about
translation before. translation happens
inside when you turn RNA into a protein.
There's like a lookup table of what bits
of RNA turn into what amino acids and
then the amino acids get assembled into
a protein into a exciting little
machine. This looks a little bit
computery, doesn't it? It's like we've
got different encodings. We've got start
codeons which say where to start
running. We've got stop codeons that
when say when to stop running. And it
kind of looks
kind of computery a little bit like a
decode table from an early
microprocessor or something like that.
But there's no loops, there's no
branching, there's no anything. So how
the hell is this a program? You can't
really have a program without loops and
branching and all of that sort of stuff,
can you?
The answer is that these the chromosome,
the thing that connects all the genes
up, isn't just the genes. It's also um
control regions like a library file.
There are there are sections that say
how much of this gene you should you
should or how much this protein you
should make using this gene, how much
should this gene be activated, how much
should it be repressed, etc., etc. And
also how should it be spliced up and all
of that can vary based on the action of
either external stimuli or other things
happening inside the cell, other genes
expression and so on and so forth. So
it's actually really complicated. The
program is in the interactions between
genes. Just as a like super simplest
possible example in E.coli, there's a
group of genes that make enzymes that
break lactose down. Lactose is a sugar,
but it's slightly more complicated
sugar. E.lo would really rather just eat
glucose lollies um rather than drink
milk.
But in the presence of lactose and and
the absence of glucose, it can make an
enzyme that breaks the lactose down into
glucose and glactose. But anyway, um but
there's no point it wasting its energy
making those enzymes if they're just
going to lie around the place being
useless. So, it has this little bit of
logic in its in its genetics that says,
"Okay, if there's lactose hanging around
and I don't have enough glucose, then
make some enzymes to convert them." So,
it looks a bit like that, except being
biology, it's all squishy. None of it is
like just logically boolean or anything
like that. It's all about it. It
enhances it a bit, it represses it a
bit. It's all very variable and and
squishy. That's that's the way of these
things. And this is like the simplest
possible gene regulation.
Most gene regulation maps looks more
like something like that where you have
very many genes enhancing or suppressing
other genes, many outside influences
coming in. Many many causes, many
effects. It this is not happy
programming territory. This is real
spaghetti code stuff. And and even
picking apart the simplest one of these
relationships is extremely difficult,
extremely complicated. So I I mean I
think this is this is one of the
frontiers of of biology at this point is
understanding how these things interact
and building these maps and trying to
get an idea of what the hell is going on
inside the cell.
All right, one last little example about
biology sort of existing things. So I
talked before about all these different
forms of life depending from one place.
A really interesting thing happened
about two billion years ago I think. Um
yeah, two billion years ago where some
particularly kind of entrepreneurial um
archa absorbed some uh other aerobic
bacteria and just put them to work. You
know, they set up the first companies.
Um they were the founders. So they they
moved in, the other ones moved in. They
got protection in return for basically
providing a lot of energy to the cell
that contained them. Um and you have
this encapsulation going on. So inside a
ukarote which is I I should just explain
ukarotes includes animals and plants and
um yeasts and um slime molds and all
sorts of things and humans. So that's
nice. Inside the ukariot cell we
actually have a lot of encapsulation
going on. We have a a nucleus which is
like the central kind of most defended
part of the cell. That's where the DNA
lives. That's your kernel code. That's
the most protected part. around that you
have other like user space parts of the
code in the cytoplasm and you also have
these mitochondria which are running
separately
out there doing their jobs a little bit
like a a container image or something
like that that runs separately. Weirdly
they're running an older version of the
operating system. If you think about all
of this translation table stuff and all
that as an operating system for a cell,
the mitochondria are still running the
one they came with or some version of
it. It uses a different slightly
different encoding. It has its own um
its own enzymes, its own everything
inside that little encapsulation. They
even replicate themselves as the cell
replicates. They also replicate and send
some copies this way and some copies
that way. So they're they're in there
and they're replicating along with the
cell. um but within their own thing.
Plants do even better. They did it
twice. They also absorbed a
cyanobacteria and that's how they do
photosynthesis. So plants are actually
way ahead of us. I believe there's a
some algae that's done it three times
and also has things called nitro plasts
or something which are basically another
thing. Oh wow, this is good.
Um
many not all ukarots are multisellular.
uh specialization is done at runtime. We
all have the same genome in every single
cell in your body. Um at runtime, at at
the time you are alive, your cells will
work out what they're meant to be doing.
They will shift into that mode. They
will do that job. They communicate with
each other, exchanging messages through
those cell walls. If a cell is like a
computer, then your body is like a whole
network of computers interacting all the
time. Um, even funnier, things like
slime molds and some bacteria basically
can form big groups when times are tough
and then they can disperse into
individual cells when they'd rather do
that. They're very interesting that
multisellular life turns out to be kind
of yeah optional. All right,
applications. Biology is really cool,
but it's it's awesome that we can
actually do cool things with it. Um, for
example, we can look at genetic
disorders, right? So, there are various
genes. If that gene doesn't work as well
as it should, you may have various
problems with that function in your
body. So for example, G6PD is an
antioxidant. It makes a protein called
G6PD. Very imaginative. Thank you,
biologists. Uh which is an antioxidant.
If it's faulty, you might get a thing
called hemolytic anemia, which is where
your red blood cells explode. Extremely
metal, quite painful, I imagine. Um
not a very pleasant thing to have. You
probably don't know if you have it
unless you eat the wrong thing and wake
up in hospital. I imagine
what we want to do is we want to find
out about those variants before we have
people lying around in the hospital. So
the kind of experiments that I've been
working on with people are taking genes
like G6PD and altering them. Take every
possible base in that thing and make
every single onelet typo that could
possibly happen in G6PD. Right? We run
through those one-letter typos and we
build a library of those one-letter
typos.
Then ask me about my yeasts. We insert
them into yeasts.
This is an awesome thing. A yeast, a
yeast, a single cellular little thing
you make beer with. This is brewer's
yeast. Can run the software that runs a
human just fine. They're on the same
operating system. I think that is just
stunning. The nice thing about yeasts is
they double in population every hour and
a half in ideal conditions and you can
kill a billion of them before lunchtime
and no one cares. Um, this is this is
awesome for science because, you know,
this is what we like to do. This is the
experimental setup. It's really just a
fancy home brewing setup called a
tabidat that keeps the tabidity, the
number of the population the same. The
yeast think they're growing indefinitely
and infinitely. Um, we feed them, then
we torture them with bleach, and we see
which ones survive and which ones don't.
And from that, we can work out which of
those variants we introduced really jam
up the works, and which ones aren't that
relevant, which ones don't really
matter.
We can compare them by looking at their
population over time, and we can see
that that some things really just fade
out, some things thrive,
some things are in the middle. They're
kind of mediocre variants, which is even
more interesting, I think.
And what we can do from that, we can
build up a map of those variants. And
just to zoom in on that bottom graph
there, this is the map showing the the
blue ones and ones we were pretty sure
were going to be duds. The red ones were
ones we were pretty sure were going to
be fine. The gray ones are like all of
the population we tested. So we've been
able to sort the population of neverbe
saw before seen variants into good and
bad and kind of mediocre which is really
interesting and it has you know really
good clinical meaning for people.
Is this a thing I have to worry about?
Is this not a thing I have to worry
about.
Other genes this has been done for are
things like uh tumor suppressor genes
and stuff like that. So a a a good or a
less effective gene might deeply affect
your odds of getting cancer in life,
which is is a thing that is good to
know. All right. And we can build up a
map of what part of the protein looks
like it does what, which is was what
that is. If that looks incomprehensible
to you, welcome to the club. I'm the
numbers guy, not the squiggly drawings
guy. So you know All right, I'm going to
rush through to one more thing. I think
I I'm just going to run through the
question time. Sorry. Ask me later. Um,
we want to be able to debug these
things. A really complicated system, but
we want to know how it works, right?
Who's done hello world debugging? Print
statement debugging. Yeah, it's awesome,
isn't it? Sometimes you just have to. If
you ever worked in embedded systems,
though, you've discovered that bugs
happen so quick that the first H doesn't
even have time to come out of the UART
before the whole thing crashes and
restarts. So, what we do there is we
light up LEDs instead. Right? LEDs light
up one after another. Hell does this
have to do with cellular biology? Well,
jellyfish. Jellyfish
flues. If you take a fluorescent
jellyfish protein and you couple it to
the gene you care about, then when that
gene is expressed, that protein is also
expressed and it glows. And now you can
sort the cells by how much the protein
was expressed, how much the gene was
expressed. Sorry. You can sort them and
and count them that way rather than just
counting their population going up and
down. And this is a real experiment.
It's a technique called Vamps. And you
can use this to to analyze what's going
on in these in these cells.
All right. This is another cool example.
Who's got an email that looks a bit like
this? Not one of those characters is
ASKY. They're all random weird things
from different languages. Um, they get
past your spam filter because none of
the words you were looking for are in
there. You can do the same thing with
DNA or with RNA. in messenger RNA, which
you're all familiar with, I imagine by
now. It's been on the news. It's very
scary. It's not very scary. Um, it's in
all your cells. It's it's the
instructions for how to make a protein.
Um, you can normally your body would
filter that out. It would not accept
that mRNA. But it's been discovered that
if we just replace some of the bases
with bases that look a lot like that but
aren't quite the same, it gets past the
spam filter but the cell will still read
it and it will still go and make you
COVID spike proteins that you you can
then have an immune response to.
This technique was pioneered and
discovered largely by Catalyn Caraco who
in the 80s was working on dual stranded
RNA. Moved to the US under somewhat
dubious visa circumstances went to on to
research mRNA. Went on to work on RNA
pharmaceuticals. I'm hurrying this up
because we're getting close to the end.
Including the pseudouodine technique was
invented in 2005. um
and then went on that went on to be the
the mRNA vaccines we all know and many
of us have experienced and she won a
Nobel Prize in physiology and medicine
um for that. This is my thank you slide
for that. It's made my life a lot more
pleasant and I'm sure a lot of other
people too. mRNA vaccines are now being
looked at for many other purposes. Oh
yeah, I I ruined my own joke. Um many
other purposes including cancer
therapies and things like that. Since
I've been talking about biology in this
software conference, I want to move on
and talk about politics. I mean, not
really, but sort of, right? All of this
stuff has only been possible because of
open collaboration. This something
something should be very dear to our
hearts in the programming world as well.
People send each other samples, people
send each other fruit flies, people send
each other sequencing data, people share
and collaborate. And this stuff is not
possible while hiding in your basement,
not working with other people.
The concept of this amunotherapy for
cancer is incredibly important. A lot of
these therapies are easier and cheaper
as well than radiotherapies and stuff
like that. So there's huge potential to
rid the developing world of diseases
like malaria that are horrible and
terrible and all of that stuff. And
really the applications of this stuff
are bounded only by our imagination and
our willingness to just have a go at
stuff. Spend a couple of decades working
on something that nobody else thinks is
going to work. you might end up curing a
pandemic. You never know. All right.
Thank you very much. I think I've run
through all my question time. Possibly,
precisely, possibly not. So, thank you
very much and please ask me questions
after the the end of this session. Thank
you.