21. Synthetic Biology: From Parts to Modules to Therapeutic Systems

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visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: OK. Welcome back to
computational systems biology We have the honor
today of having Professor Ron Weiss visit us. As I told you on
Tuesday, he’s going to talk about synthetic biology. And Ron is probably
from both the department of biological engineering and
the Computer Science department and also a founding member
of the Synthetic Biology Center at MIT. And now, thank you, Ron. PROF. RON WEISS: Thank you, Dave. Thanks for inviting me here. Did you mention our background? Dave was actually–
advised me when I first came to our graduate
school at MIT. And at the time, I was
working on digital video and information retrieval. And Dave started getting
into the business of biology. This is back in the early ’90s. And I was like, this is cool
stuff but it’s really messy. How can you engineer
with these molecules? PROFESSOR: And we’ve
got the answer to that. PROF. RON WEISS: Yeah. So we’ll see. So let’s see if the answer
is– it does actually work. So yeah, so I– after being
a non-believer– I don’t know if non-believer, but just–
I didn’t feel I quite had the engineering
capabilities. Towards around ’96
or so is when I decided to actually
make the switch. At the time, I was
working– around ’96, I was working at this
notion of how can we use what we know in biology
to understand how we program computers and especially
situations where you have lots and lots of computing
elements like– things like smartDOS. I don’t know if people have
heard, but amorphous computing. So back in the mid
’90s or so, this notion that we would be able to
embed computation everywhere was kind of an exciting notion. And I thought to myself, where–
how could I get inspired? And I thought, well,
biology obviously could serve as
great inspiration. Because that’s a situation where
you have millions or billions of little computing
elements that don’t have too much power,
kind of interact locally. But they still perform
very robust operations. And so I performed a
variety of simulations, for example, of embryogenesis
and other processes to try to understand what
happens in biology and can I again use that to
program computers or little tiny computers. And I remember one
day, I just decided to flip the arrow and
basically rather than trying to use biology to understand or
program computers, I decided, let me use what I know
in computing to actually try to program biology, OK? And so now that
field is basically called synthetic biology. So I’ve been in that field–
it’s hard to count now, but I guess maybe
18 years or so. And it’s been fun and
has not been easy. But I think at
least we’re starting to make some– we’re
making some progress. So I’ll try to tell you about
some of our efforts there. And I certainly encourage
you to ask me questions. So please interrupt me
at any point in time. Any kind of question
is fair game. Dave promised me that you
guys are a tough crowd. So let’s see. And you can always stump. Let’s try to have that happen. So when I look at this, I get
excited as an engineer, OK? And I think to myself, wow. This could be really cool to
be able to program something like this, again,
in the same way that we may program computers. And so this notion of genetic
engineering in a direct way, in a way where we
can create new DNA, certainly has been around
since the ’70s or so. And so this notion
has allowed us as a community to create
various mechanisms that control what the cells do. So for example, transcriptional
regulation, translational– so being able to regulate
things in a cell, be able to create
genetically encoded sensors, cell-cell communication
mechanisms, synthesis of various interesting
molecules– biofuels, pharmaceuticals,
and control physical aspects. And so those capabilities
have been around before synthetic biology. But if you were
to ask me what is different about
synthetic biology, I would say it’s really the
emphasis on systems level engineering. OK, so this notion
that we are not just trying to engineer
over expression of this gene or that gene
or a couple of genes, but really trying
to understand how to create systems
of interactions. So in the same way that
systems biology has come to the forefront
with this notion that you can’t understand a
cell by understanding what is the exact purpose of this
particular gene– we always have to think about it within
the context of a pathway within the context of the entire
organism– in the same way, when we want to be able to get
cells to do interesting things, we have to think about
the system as a whole. And to get the
sophistication that we need, we need to understand how to
connect these various elements, regulatory elements, all
these kinds of elements, in reliable, predictable
ways, efficient ways and so on to be able
to get the cells to be as programmable as computers. So that if you were to ask
me what synthetic biology is, that would be my answer. And so now you know it and
you can tell all your friends that it’s a completely
defined notion and so on. Maybe if yo ask
other people, they’ll give you slightly
different answers. But there you go. So how do we develop an
engineering discipline out of that? OK, so that’s really how
can we get undergrads to come in and take Synbio
101 where it’s really a well defined mechanism and set
of methodologies and practices that allow us to
do this reliably. OK, and so we often
try to get inspired by how other
disciplines approach the engineering of
complex systems. And so kind of an
obvious one would be of computing
or robotics where there’s this notion of, for
example, bottom up assembly. And so you start
with basic devices and think about how
to create modules that have specific behaviors in
them and then put those modules and integrate those modules
to create these autonomous entities such as robots. And we often think about
how to create communities of interactions, communities of
robots in this case, and so on. And so that’s worked
quite well in a variety of different other
engineering disciplines. And so we often
ask the question, can we import these mechanisms
into the world of biology? So can we take basic
mechanisms of regulation– it could be
transcriptional, it could be other modes of regulation–
and then wind these things up to create customizable pathways
that we then embed into cells? And then we can create
programmable communities of bacteria. We can create programmable
tissues of mammalian cells and so on. And so the question is, is
this a useful and efficient way to approach things? So for example, how are these
different approaches similar? What can we borrow
from here that make sense to push
on over there? And I will say when
I started working in synthetic biology,
most of my efforts were really focused on
adapting and implementing these things– so adapting
them from other disciplines and trying to understand how to
implement them into the world the biology. But as time has
gone by and as we’ve started to understand and
appreciate the cell more and more, we are also
quite interested in how these things are
different as well. So what makes engineering
biological systems a truly unique, new
engineering discipline? What would you do in
the world of biology that might be different
than what you would do with computers or robots
or building bridges and cars and planes and so on? And so that that’s become more
and more of an important focus in my lab and I think in
the community as a whole although not yet everywhere. And you often see
situations where people come in from other
disciplines and just think, oh, we’ll just program
it, engineer it, just like we do in
computing and so on. And it doesn’t just
work like that. So when we approach these
tasks of programming the cells, we usually divide things up into
modules of sensors, processing, and actuation. So for example, we would
want to develop sensors that can detect in live cells
levels of microRNA messenger and then proteins
and then connect them to synthetic regulatory circuits
that we embed in the cells, OK? So it’s important that these
sensors not just, for example, give us fluorescent
readouts, but it’s important that these sensors then connect
to the regulatory networks that we have in mind and so that
these regulatory networks can then integrate multiple
pieces of information and make decisions
about actuation. So how do we turn on specific
proteins that will then influence that
particular cell or even the environment in a
programmable fashion as dictated by the levels
of particular sensors as well as by other
mechanisms or, for example, from looking at
historical information that the cell itself
has processed as well? And so this is, I
would say, represents the paradigm for most
of the things that do take place in
synthetic biology. And so why do we
want to do this? It’s not the program the next
version of the iOS or iPhone or something like that. even though that
initially that was one of the things
that was discussed, it’s not just for the
sake of computation, but really for the sake
of specific applications. So for example, if we have
really slow logic gates that work on the order of
hours or even days, that might be fine if the
application, for example, is a tissue engineering
application, OK? And so in synthetic
biology, initial emphasis is really been on what
can we do with microbial, let’s say, communities or
individuals, for example, for synthesis of
high value compounds? I mentioned bioenergy,
environmental applications as well. So that, I would say, was
most of the emphasis there. But over the last
few years, there’s been a growing interest in
health-related applications. And so my lab in particular
looks at mostly health related applications. And so I’ll give you
examples of those today, OK? And those include things
involved with cancer, diabetes, in tissues by design. And in order to
do this, in order to have this programmability,
you want to think about scales. So you want to think about how
much DNA does it take to do X? And to a large extent, that
controls the sophistication. It really is an important
defining element what we do is the
scale of the DNA that we can actually
engineer reliably quickly, efficiently, predictably,
in high throughput fashion, and in inexpensive ways. So we would start with
things on the order of genes where I would say that that’s
really the basic elements. I would say that a single
gene that you overexposes or a few genes than
you inducibly express, I wouldn’t count that
as synthetic biology. But when it gets
kind of interesting for synthetic biology is
when we have this circuitry, where we now embed interactions
that didn’t previously exist in that
particular cell context. And so most of synthetic biology
has been really at this level right here– actually,
mostly from here to here in terms of
the scale of the DNA and now trying to go beyond
that– more along the lines can we create something
that’s 20,000 bases, 50,000 bases of DNA? OK, is this something that
a graduate student can come into the lab
and say, I want to design something that will
take 20,000 to 50,000 bases? Is this a reasonable
thing to consider? OK, and then the question
would be, what kind of power does that provide to you? What things can
you do with that? Beyond that, people have
explored this notion of minimal life and even
full genome rewrites. I would say at this
point, this is– what some people clump that in
with synthetic biology, which is fine. We don’t have, at the
moment, really good ways of being able to engineer
minimal life from scratch or even in a really
fundamentally different way. So most of the efforts
on minimal life would be take an
organism and try to figure out what to knock out,
right, as opposed to saying, I’m going to engineer
this new minimal organism. And I’m going to
define what reactions to put in there from scratch. And I’m going to create
a whole bunch of new ones that didn’t exist before. OK, in the future, will
we be able to do this? Hopefully, OK? Not quite yet– this is really
where the action of right now. And again, driving
force for this is how inexpensive
is DNA synthesis. And so we’re following
some kind of Moore’s law with respect to dropping costs
in terms of DNA synthesis. And this is one
of the enabling– I don’t know if it’s– it’s not
the only enabling technology. But is one of the most
important enabling technologies is the fact that
it is less and less expensive to be
able to order longer and longer sequences of DNA. And so this notion
that, for example, you’ll be able to design
something that’s, again, that’s 20,000 to
50,000 bases of DNA and just go online
and order to that and have your advisor
willingly pay for that– not at the level of 20,000
to 50,000 bases yet. But that’s going to change. And that’s going
to get to the point where those really become
available to everyone. And I think that’s going
to fundamentally change how we do business in
biological engineering and how we do, I would
say, almost everything in biology as a whole. So if you have– even if you
don’t care about engineering new biological
functions but you want to understand biological systems
and your adviser told you, well, just design a whole bunch
of circuits that will allow you to regulate things
in arbitrary ways to learn something about
the underlying networks that control a natural
systems, again, I think that that fundamentally
changes what kinds of questions you will ask. OK, so I’ll talk
about basic design. I’ll talk about scalability. So how do we go from
these basic elements to bigger and bigger things? And then I’ll talk
about some recent things that we’re doing where we’re
building this foundation. But we think that this
foundation then can matter. I think this foundation can
change how you approach things that really don’t have
the greatest of solutions. Now, they really change
the paradigm, for example, for cancer, for this
notion of building tissues by design on chips and
for diabetes and so on. OK, so we start with parts. So just about
everything that we do, we define what are
the basic parts that are available in our toolbox. And so these will be
transcriptional regulatory parts. We do things at the
translational level. We do things also at the
protein-protein level. One of the things we
often do, not always, is engineer cell-cell
interactions. Could be by means of
cell-cell communication. We often want to find out
what’s going on in the cell. So just like when
we program– where we create a new software,
new computer program, we have debugging
outputs that tell us what the program is doing. Usually, the way we do this
is with fluorescent protein. It could be with dyes too. So they tell us,
you know, here’s how your circuit is behaving. Here’s how the cell
might be behaving. And another set
of parts would be ones where we want to be
able to create sensors and actuators inside the cell. What are specific
biomarker levels? How can we affect what
the cell is doing? For example, one
that gets used a lot is kill the cell is one
of the favorite actuators that people are using. Another one would be, let’s
say, tell this stem cell to differentiate into
a different cell type. That would be another
kind of actuator. A different one might make the
cell– make this high value compound that would be
relevant for some application. And so right now, if
you’re looking for parts, they actually used to be
stored in Stata up until– or big libraries of
synthetic biology parts were stored in Stata that up
until about two years or so. So I don’t know how many
people know about iGEM. Any folks know about iGEM? So iGEM was started at
MIT, was headquartered, as I mentioned again
here, in Stata. There are these couple of
big freezer that were on the, I think, the fourth floor here. And they stored
5,000 to 10,000 parts that word commonly used by
synthetic biology folks. OK, so now they
moved over closer to Cambridge brewing company. And they’re not affiliated
directly with MIT anymore and they have 15,000
parts or so available. So if you want to get
started in synthetic biology, this is one quick
way to do that. You can contact iGEM
headquarters and say, please send me 1,000 parts, OK? And as long as you’re
credible and not from one of those
blacklisted countries, then they typically
will send it to you. So that’s a good way
to get started, OK? So what are these parts? So this is actually going
back to my Ph.D. here. This is one of the parts
that I characterized. So this notion of an inverter–
so digital logic convert. So I assume people
here– everybody is familiar with logic gates. Is that true? OK, raise your hand if
you are familiar with it. I just want to see- oh. Just trying to calibrate–
and again, ask me questions. So this notion that you have
a single input, single output device that works on binary
values that has– basically inverts the signal. So you have zero on the input. You have one on the output. One in the input, you
have zero in the output. And so one of the
ways in which you can implement this in
a biological system is just use
transcriptional repression. OK, so if you have
no repressor present, then you have a high
level of output protein. If you have a
repressor present, it represses the production
of the alpha protein. And so in theory, you
should be able to use this as a digital logic gate. OK, and so that– sounds–
looks pretty simple here. But for my Ph.D., it
took me about three years to do something like this just
to give you an indication. Now it’s a lot faster. Now you can do this in– you
can do many of those in a day. So there has been progress. Here’s another one of those
gates that I used for my Ph.D. And so this is now not just a
repressor, but a repressor that can be inactivated by
a small molecule, OK? And so the way it
works is you have this repressor that
works as before. And then when a small
molecule comes in, it prevents a repressor
from binding the promoter. And as a result of that, even
if the repressor is present, you can have activation
of the output protein, OK? So this is what’s called a
not x or y or it implements the implies logic function. How many people use the implies
logic function to do anything? OK. So it’s not a commonly
used logic function. And you won’t find it–
there’s no logic gate that does the implies logic
function in a typical computer. But this is a useful
logic function that we can implement in cells. And it allows external control
of gene expression, OK? And so this is a
simple way– and so once you can do that as
a user, essentially you can interact with
the cells and modify what’s going on inside the
cell, OK, using a pretty simple looking mechanism that predates
synthetic biology, if you will. But I don’t know if it was
called the implies logic function before. So anyway, so then logic gates–
can we build logic circuits? This is where I would
say synthetic biology starts kicking in. And so this is one of the first
logic circuits that we built. And the question was, OK– looks
nice to have this logic gate representation. In biology, does this
make any sense at all? Can you really do digital
logic inside cells? Can you take noisy
biological components and actually implement reliable
digital computation in cells? And it wasn’t an obvious
thing, I would say. Is it 100% obvious now? In some situations,
I think we can claim that we can build
digital logic that’s reasonably reliable. So in this particular case, I’m
showing you this implies logic function that allows us
to have small molecule induction of a
cascade of not logic gates or transcriptional
repressors. And so the nice thing
about this in particular is the fact that this is the
input output steady state is that as the circuit so
goes from blue to black to this yellow color here is
as the circuit gets longer, as a cascade gets longer, it
actually becomes more digital. It actually becomes
more step-like, OK? More on off. So we’re going from this
blue input output function to this yellow. So now we have over
1,000 fold change in the output in
response to two to four fold change in the input. OK, and then we have
good noise margins, good signal restorations,
all these good things that we need to have for
the creation of larger and larger reliable
digital circuits. So the basis of digital
computation is that you have– and the reason why you can
actually create computers is that you can have logic gates
that do signal restoration– that the output is a
better representation of the digital meaning
then the input. So as the signal , propagates
this analog signal could be voltage. But it could be
protein concentrations. As it traverses through
the logic gates, it needs to actually become
cleaner in order for us to be able to have reliable
digital computation. So people have figured
out how to do this with electronics
a long time ago. We figured out how to do it
with synthetic biology, let’s say, 10 to 15 years ago. And nature has figured out how
to do this billions of years ago, OK? So things like
cooperativity– so I assume you’ve looked a
little bit on cooperatively in, let’s say, gene regulation. OK, so that is a situation
where you get a nonlinear response in a system that
biology has figured out is a useful mechanism so that
signals that come in actually result in some kind of
actual digital behavior. So you get non-linear
signal processing in these regulatory elements. And at the end of, let’s say,
a signal transduction cascade, the output is
either high or low. There’s no– for the most
part, there’s no in between. The transition between
high and low is super fast. OK, so in a sense,
that’s creating digital or discrete outputs. And that’s really critical
for many situations– certainly in synthetic biology,
but many situations in biology as well. So one example would be, let’s
say, stem cell differentiation. You want the cells to be able
to make a discrete decision. Should I make– should I
become a kidney cell or a liver cell or a muscle cell and so on. So those are discrete
decisions that have to be made by the cells. And so the cells have
come up– or nature’s come up with mechanisms
to guarantee that. And so we’ve now figured
out how to do that ourselves in a synthetic fashion as well. It’s important to note that
when we engineer these systems, we don’t just think
about digital behavior. So we spent an equal amount
of time perhaps thinking about how to implement things
that have transient properties or things that have more kind
of analog behavior to them. And that’s absolutely critical
to be able to program cells to do whatever we want. So this is an example
where we have engineered cell-cell communication
where sender cells make a small diffusible
molecule which then goes to receiver cells. OK, so now the receiver cells
don’t just have an on response, but rather they
have a pulse, OK? So a signal travels from the
sender to the receiver cells. And the cells, what
we engineer them to do is have a pulse response. And the idea is to have GFP
go up– a Green Fluorescent Protein go up–
and then go down. And so to be able to
do that, we engineered a feed forward
motif where we have binding of the small
molecule to this activator which activates two
things simultaneously– a green fluorescent
protein and a repressor which then represses the
green fluorescent protein. And then– so the idea is that
the green fluorescent protein goes up. And then eventually,
the repressor builds up to sufficient
levels to repress the green fluorescent protein. So again, one of those
simple looking motifs. This is about three
years to actually make that happen around
the 2004 frame. Looks simple. If you study a naturally
occurring system that has this motif, you say,
oh yeah– no problem. Yeah, we have this
feed forward motif. And obviously, you can do this
kind of information processing function. Let’s move on to another motif. You actually try to build this
in a lab in a new organism, I’m not sure if it
can drive you insane. But it is not trivial to
actually make it work. It’s much easier now
than it was 10 years ago. But you still have
to pay attention to a lot of things– rate
constants, threshold matching, and so on to actually
make it happen. But eventually, after
looking– creating– so this is our first
attempt at this was this blue line right here. So a completely flat line, OK? Input comes in, nothing happens. So I would say that pretty
much typifies synthetic biology maybe up until today. You build something. You think it’s going to work. It doesn’t work. And then you stop crying
after a little while. But then you have to think
about how do I fix this. And so this iterative
design debug cycle is absolutely critical. So what you normally do is you
create computational models that tell you how different
rate constants in the system affect the behavior
of your circuit, OK? For example, you could
do sensitivity analysis. Which rate constants
have the most influence on the performance
of your system? And so we did some sensitivity
analysis here and learned that, for example, the
degradation of this repressor makes– one of the
things rate constants makes the biggest difference
is on the performance of the system or its
affinity to the binding site on its respective promoter. Yes. AUDIENCE: Just knowing the sheer
[INAUDIBLE] entire circuit, it started off with the
[INAUDIBLE] constant? PROF. RON WEISS: No. I wish it was. Because that would
make life a lot easier. And we are trying to
get better at that. So we’re trying
to– so here’s maybe two ways of thinking about that. One challenge would
be somebody comes in, gives you DNA
sequence, and you have to predict the rate constants. OK, so I would term that person
an adversary, not your friend. It’s just too hard to do that. Now, an easier task would be
give your adversary or friend limited choices and
say in the freezer, I have these DNA
sequences that consist, let’s say, of
specific promoters, specific ribosome binding
sites, specific proteins with specific degradation
tags on the proteins, OK? And that’s– you’re allowing
that adversary or friend to only use those elements
in the design of a circuit. And then they come back
to you and they say, now predict what
the circuit will do. You still– it still
doesn’t work yet. But I think– but I
would say that’s how we would phrase
the challenge, OK? Stick to things that we know and
allow us to even characterize those things ahead of time. What we have that–
unfortunately, I don’t have that here. But what we have
done– so people can get a kind of a
general characterization. So they can say it’ll be roughly
this input output behavior. And when I say
roughly, the errors could be on the order
of five to 10-fold. That’s approximately what’s
been published so far. Now, five to tenfold
depending on your perspective could be great because
it’s biology or could suck if you’re an engineer. It just depends on if you’re
trying to do something like get green
fluorescent protein to turn on and off,
tenfold is probably great if you’re doing this
in a Petri dish. If you’re trying to
create a cancer classifier circuit you put into
humans to kill cancer cells but not harm healthy cells,
tenfold is probably not great I wouldn’t take
a circuit like that into me, especially if that
circuit controls, for example, the production of a
killer protein, which I’ll try to show you a
circuit that does that. We recently have
been able– we’re in the process of submitting
a paper about this– been able to show that if you have
really good characterization of regulatory elements such
as these repressor devices– and you have to do a lot
more characterization and you do the– we can
get within 20% on average on predicting the behavior
in mammalian cells, actually. And so as an
engineer, I would be happy with 10%, 20% percent
for many, many applications. So I think we’ve
gotten better at it. But it’s not quite perfect yet. One of the things
about that approach is that we don’t
necessarily know the rate constant
for everything. What we do know, however,
is a very detailed behavior, both steady state
and dynamic behavior, of a repressor promoter pair. So we don’t know, for example,
what’s the binding affinity or what’s the rate
constant for the repressor binding the promoter,
what’s the rate constant for RNA polymerase
binding that promoter, what’s the exact translation rate
or transcription right too. But we do know what’s the
input output behavior. And that’s actually been enough
to get really good predictions. But I would say these
kinds of predictions are one of the most important
aspects and challenges and bottlenecks of
synthetic biology. So those include–
again, the challenges include how fast
can you build DNA. But wouldn’t it be nice
if you can actually predict what the DNA does? So it’s just as
important, if not more. And also having–
so those are two of the important challenges. I’d say another
one would be– OK, so if you can predict
things how many parts do you have in your freezer that
you can actually put together and they’re
well-characterized actually now build the circuits? Probably three of the
most important challenges. And so if you go back to what
we’ll call the post generator, this is a loop tape of bacteria
that now respond to the pulse. So sender cells that then
secreted the small molecule then went into receiver cells. And they light up. So one of the things to
note here is that it works. The other thing to note here is
that it’s not perfect, right? And so the amount of
heterogeneity here I think is quite astounding. So if you take the
average behavior, it’s actually quite predictable. But if you now start
looking at the distribution in the response,
it’s staggering. And we quantified that. And so we quantified what’s
the distribution in terms of the fluorescence levels
that– the peak and also the buildup and so on. And we then correlated
that also– we created the stochastic
simulations that then correlate reasonably well with the system. So we can get simulations
to generally correspond with what we’re seeing
at the population level. But I do want to
bring up this point that when you think about
engineering biological systems, don’t try to figure out how to
engineer a single cell to do something reliably, OK? So you always want
to think about kind of statistical engineering. You want to think about, I’m
going to create a circuit. And when I put this circuit
into a population of cells, this is the distribution
of behaviors that I’m going to get. Because if you’re
trying to depend on any individual cell giving
you exactly the behavior that you’re looking for, it
is just– it’s going to fail. So you have to really
think about distributions. And that I think changes
things a little bit. So that’s not
normally the way you think about– maybe
that’s the way Microsoft thinks about programming. So if 90% of the time, the
computer doesn’t crash, that’s pretty good. Probably Bill Gates
agrees with that, right? But that’s not what we want. That’s not what we
typically do with software. So to kind of further
think about this in terms of populations,
we program something else which was a pattern formation. So now we have the desire to
create senders and receivers where the senders send the
same message to the receivers. Now we have a longer
feed forward motif. OK, so this feed forward
motif has two branches. And so these two
branches actually have a different impact
on the final output. One has– there’s two repressors
meaning that input comes in. It activates a fluorescent
protein and another one represses the
fluorescent protein. OK, so it’s an incoherent
feed forward motif. And so what we use that here
is not for post generation, but rather to define a
range of concentrations that would turn on
the final output, OK? So it would be activated–
the range of concentration would be activated starting
with this branch right here and then ultimately
repressed by this. So this defines the
low threshold and this defines the high threshold. So under the low threshold,
nothing gets activated. Whenever you have
just the right amount, it activates this
which represses this which allows this
fluorescent protein to get turned on. OK, so this branch right
here is more sensitive. So it defines when
this thing goes up, when the response goes up. And then this is less sensitive. So this defines under
high concentrations when the output goes down. So we basically have a
non monotonic response to the input, which is
low then high then low. So that’s the design
that we had in mind. And the idea is that
whenever you put, let’s say, receiver cells
everywhere in a Petri dish and you put senders
in the middle, then the communication
signal basically builds up. There’s a chemical gradient. Each cell interprets
the chemical gradient and then decides whether to
make a fluorescent protein. And then only– because there’s
this steady chemical gradient due to diffusion and
decay of the signal, then you would get some
kind of a bullseye pattern. So that was the hope at least. And so to give you again
a timescale, so it took me about I think three hours on
a plane to make the slide. It took us about three weeks to
create the computational model. And again, for whatever
reason, three years was the magic number to create
the actual functional circuit. So that was an older
version of PowerPoint. But I haven’t tried
it on the new. But anyways, so we created this. This is a computational model. We actually used a
computational model to predict how changes
in rate constants would affect this band detect,
the region where we’re actually responding to the signal. And we used that to engineer
different responses. And so we created eventually
three different responses input versus output. And we put different
fluorescent proteins on them– a red fluorescent protein and
a green fluorescent protein. This is the experimental
set up over here. And after 16 hours of waiting,
this is basically what we got. So we got a lot of bacteria
to make all kinds of patterns. And we were very
happy about this. We danced around in the lab
a little bit– you know, yay! So this was fun on those rare
occasions where things actually work. So we said, let’s
have some more fun. And so we put senders
in other configurations. And so we have
programmable patterns of bacterial communities. So I’m not sure
of is this useful. I’m not sure by itself besides
having some fun with it. But one of the things we’re
using this for right now– and depending on time,
I may get to that later– is this notion of
embedding these circuits in mammalian stem cells or
actually also in human IPS cells so that we engineer these
human IPS cells to communicate with one another
to make decisions. And then those
decisions actually lead to differentiation
patterns, right? So you can imagine
in principle, if you can create three dimensional
versions of these and use those to cause the
cells to make differentiation decisions so that red
would mean make neurons, green would mean make muscle,
different colors– yellow would mean make bone and so on. So in principle, you might
be able to create tissues by design. OK, so that’s something that
we are working on actively in the lab right now. So we don’t quite have a working
heart in a Petri dish yet. We won’t for a little while. But we taking some baby
steps along the way. And so we have been able to
get cell-cell communication to work. We’ve been able to get
programmed stem cell differentiation to work. And hopefully, I’ll
be able to show you some images that we have of
some recent examples where we take human IPS and actually
created these embryonic liver buds that have lots and
lots of interesting– and actually all
the cell types that are known to exist in
the embryonic liver. So there are some
progress along the way. Now, we don’t anticipate
to replace your liver, you know, any time soon. So don’t destroy it. So actually one near
term application that we’re
specifically looking at is if we can take– imagine
taking your own fiberglass, de-differentiating them
into human IPS cells– those are your human IPS cells–
and then differentiate them into, like, this
liver-like environment and put that in a
Petri dish and then test out the effect of
drugs on your mini liver. OK, so maybe it’s a
good idea to test drugs on things that resemble
human tissue as opposed to some random
mouse that may or may not be as correlated with what
the drug would actually do to actual human cells. And if we actually even do it
in the patient specific manner, I think that really
changes the way drug development would actually work. And that’s something that I
think within the next few years could become a reality. They’re talking about
the next– beginning to do that within the next one
to three years in a laboratory setting. So I think that is near
term and realistic. So one of the things
that we did notice is that when we engineer
the small systems, its intuition works quite well. So I can look at
this circuit design and say, if I modify this,
this is what’s going to happen. If I modify this, that’s
what’s going to happen. So you can use intuition and
it works reasonably well. And what happened in I would
say the first 8 to 10 years of synthetic
biology, every paper would have a
computational design. But most of those
would build something. And in order to publish, we also
tacked on a computational model that correlates really well
with the experimental results. And we are just as guilty of
doing that as anyone else, OK? So it wasn’t critical to
have a computational model to create something
successfully in the lab. And I think that
that is changing. So we have examples right now
of designs where– I’m not sure if I’ll get to that today,
but we have published on that– designs
where it involves about 20 to 25 components. And it’s related to
a diabetes system the retired engineer where we
can have intuition about it. But our intuition doesn’t
work great anymore, guys. So we might have some
intuition about the system. But the computational
analysis would all of a sudden shed light and
provide insight that is very difficult to
get this by drawing this thing on a blackboard. OK, and so I think that
computational design tools are becoming absolutely essential. They can provide insight
into system behavior that you can’t get just
using intuition alone. But in another aspect
of computational design is one where imagine being able
to specify want this behavior. And then the
computational design tool says, here’s 1,000 different
versions of circuits that you should build and test. OK, and so it’s still
difficult for a human to generate easily 1,000
different versions of a circuit to build and test. It is becoming easy
to actually build– I wouldn’t say– maybe
easy is a strong word– feasible to generate
1,000 versions of a particular circuit. I’ll give you an example. Very recently in my lab,
one of the graduate students has come up with a
framework that– he is generating 200 versions of
a circuit in three hours, OK? And they’re pretty much
gotten to the point where they’re all correct. In three hours–
so that really, I think, changes what you
do in synthetic biology. And so again, having
that be connected to a computational design
tool that tells you which ones to build
would be rather useful. And so specifically
recognizing that– this is a collaboration
with some folks at BBN. And actually Jake Beal was one
of my former graduate student colleagues. So he was in the same lab as me. And then Doug Densmore is
from Boston University. And so the notion
here is that this is what we want synthetic
biology to look like. OK, so if you’re
trying– or maybe all of biological engineering. But we’ll start with
synthetic biology. So if you want to
program biology, should you really care
what the ribosome binding site is for the
lambda repressor? You know, hopefully not, right? What you should
do, just like when you program your simulations in
MATLAB, you don’t think about, well, here’s the shift register
in this Intel Pentium chip. And this is how it’s working
to simulate this ODE over here. That’s just not the level
at which your program. So you think really
at a high level. And then you have compilers
and lots of infrastructure that takes care of
everything in between. And so maybe someday in the
future, the graduate students maybe five to 10 years will
look back at synthetic biology graduate students
now and would just have a lot of pity for
them and, oh my god. You actually had to
know what elements you were using in circuit and
actually build them by hand? You know, wow. And so here’s a notion that
we start with a high level description. And by the way, this
is now– there’s a website that you can go
through right now and get a free account and then type
in a high level program. And it will actually create
a low level genetic circuit representation. It will also give you MATLAB
simulation files of this. And so, in this course
I’m teaching to undergrads right now that many of them
didn’t even hold pipettes before they started the
course, one of the first things that we taught
them was this tool. So before telling
them, for example, this is the way the lac repressor
works by DNA looping, we said, there are these things
called repressors. And when there’s more of
them, there’s less output. Now let’s design with that. This is, I think, heresy to
biology as a whole probably. I don’t know that– this is the
first time that we’ve actually tried to do that. And I think it’s
actually worked out OK. But teach them enough so
that they can move forward. And then, yes,
let’s simultaneously be teaching them about
the underlying biology and mechanisms as much
as they need to know. But what can they
do if they just know that there’s
this thing called a transcriptional repressor? And then the bio
compiler will figure out everything that needs to happen. And so we’re actually– so they
did a whole bunch of designs. And starting next week, we’re
going to be testing them out. So we will find out whether
that was a useful way to teach biological engineering. But I think so. I think it is. Because they seem to understand
what design means, OK? Because that’s a thing
that we focus on– design as kind of a first class object. OK, so what you do is you write
code that looks like this. Has anybody programmed in
code that looks like this? It should be– so Lisp. OK, one person only? Any computational? OK, we usually get
one or two people. But I was hoping for more here. So anyways, for the people that
should be ashamed of themselves and haven’t programmed in
lisp, this is a simple program. This is, if the
input is high, it produces cyan
fluorescent protein. Or else, it will produce a
yellow fluorescent protein. And so the bio compiler then
automatically takes that and first translates
that into a data flow. So you have a data
flow– and I’ll actually go through
an example of that– and then creates an
abstract gene circuit and then looks at essentially
what you have in the freezer and says, well, this
is the actual DNA sequence that would
implement this. And it creates robot
instructions to assemble this so that God forbid you
would have to actually touch a pipette to build this. And then we have a robot,
liquid handling robot, that does most of the assembly. Now, this pipeline is
not fully end to end yet. So it’s not– if you came to
my lab right now, the still– I could tell you that
this works but then I would be slightly lying. Is– mostly works. But it’s actually– there
are companies right now that will go from this level–
about this level– not this level, but this level. And actually, this
is mostly automated. So– to the point where the
only thing that’s not automated is somebody, not necessary
your op, but maybe a technician goes from a robot
and takes a plate and puts it in another robot. And, like, everything
else is essentially automated in DNA assembly So those companies have, I
guess, more money than us. But eventually, this is the
way DNA assembly will happen. So we’re collaborating
with some people. I’m doing this
with microfluidics. The problem with this
robot– it’s $150,000. So we can’t put it on
everyone’s bench yet. But we are collaborating
with Lincoln Labs to have microfluidic devices
that would cost around $3,000 that would do this. And we’ve already demonstrated
with the microfluidic devices we can do DNA assembly
of large circuits. So this is not that
far away that everyone will have microfluidics. They program here. And they get the DNA assembled. And then they realize
it doesn’t work. But at least there’d
be a lot faster to realize that things
don’t work, which is good. So let’s look at how this
compiler, bio compiler, works to see that
it’s not magic. So this is saying
green if not IPTG. So IPTG is a small molecule
that we have a sensor for. And then the
information gets routed to an inverter which
then gets routed to activate a green
fluorescent protein. So you can take a program
specified like this and automatically convert
it into a data flow graph. And then the data flow
can be translated– again, this is automated
and this is also automated in a
rather simple way– to an actual portions
of the gene circuit. So IPTG sensor is
simply this motif. So you have a repressor
that responds to IPTG. And then it basically
inactivates the repressor. So more IPTG, more output. And so this is the
IPTG sensor box. And this is how
you implement this. Not gate– so I showed you
how a not gate already works. So a repressor
represses the output. Green fluorescent
protein, you just have to have an
activator that activates a green fluorescent protein. So every data flow
box can automatically be converted into a small motif. And then what we’re
missing is the glue. And so the glue is just these
transcriptional activators and repressors. And so once you put them in
there, then this is a circuit. And so this is an automatic
way to go from here to here. And it can do rather
complex circuits and logic. It can’t do everything yet. But it can do an
interesting set of things. OK, so this is– again,
it’s not completely finished in the sense that I
haven’t told you what A is. I haven’t told you what B is. So there’s a whole
bunch of things that still are– we
have– either published on or still need to be designed. But there is, for example,
tool called matchmaker which will decide what’s
a good protein A, what’s a good protein B. So things like
that– so those things already exist. Anybody look at this and figure
out why this is not perfect? So we have an input that
represses a repressor, which means that more input,
more activator– sorry. More input, more
protein here, which represses B, which
activates GFP. So the compiler spits
that out automatically. But you may not want to
build this right away. Any ideas why? I’m sure John would. AUDIENCE: So basically, you
have this A can directly repress object, so you activate B? PROF. RON WEISS: So this is what
it– the first version of B. But A represses B.
B activates GFP. So why not just hook A up
to regulate GFP directly? So this seems like a
non-optimal solution. And so you can get the compiler
to figure that out too. And so what you do is
you say copy propagation. So these are actually just
tools that are available. These are mechanisms
that are part of normal compilers–
normal software compilers. So they do something called
copy propogation– means that A, if it sees that A–
the compiler sees A regulates B, A represses B,
and B activates A. So you can just say, well, let
A just directly regulate this. And then the compiler realizes,
well, B doesn’t do anything. Let’s get rid of it. And then the promoter
doesn’t do anything. Let’s get rid of it. And now the compiler
figured this out. And that’s using just
basic compiler technology. OK, so it’s able to do that. So the difference in your
life between four and three may not be huge. But the difference in your
life between 15 and five could be the difference
between getting your Ph.D or going insane, dropping out,
and then starting a company and becoming a billionaire. OK, so– but this is
what the compiler can do. And that does make a difference. And it may be able to come up
with optimizations that you wouldn’t be able to really
easily come up with or even at all under a reasonable
amount of time. So the compiler can do
combinatorial logic. It can do state. There’s also some aspects that
can do spatial things as well. And again, this is
available online right now. I’m going to– maybe I
should skip a few things. So very quickly, I’ll tell you. So in the lab– so
it’s nice [INAUDIBLE] But if you can’t do
anything in the lab, then nobody believes
you in the world of biology or synthetic biology. So we can build big things. So here, you can– there’s a
library of promoters and genes that we have available. And you can decide,
I want to build a new circuit has these
promoter gene pairs. And within five days, you can
create in the lab that circuit. And we’ve been able
to demonstrate things that are 61, 64 Kb that
you build in five days. And you build them efficiently. And then the undergrads
that we’re teaching also have been able to–
again, these are people to barely
knew what pipette are in the beginning a semester
can now efficiently build large circuits. So that’s become an
easy to use technology. And then I mention
this notion that you can build this one at a time. But this very recent
development where you could build 200
versions of these at a time. OK, so you can build them. We can then– again,
I’ll skip this part. You could build them. You can put them
into mammalian cells. These things work. And we have lots and lots
of parts– regulatory parts. And so let me skip this
particular example. So this is– the two
sentence explanation is, OK, you build lots of parts. You can build modules. And then you put
modules together. And guess what? This is biology. So these modules actually
affect each other, potentially in undesirable ways. So they can place
things like load. So whenever you have a module
that works really well– maybe I will show you one slide. But I won’t show you
how we solved it. But one slide– so
this is a circuit. Any idea what this
circuit might do? So this is a regulatory circuit. They have an activator
activating itself and a repressor that
represses the activator. Have you seen this motif? So it’s a– some people are
whispering to themselves, doing this. So this is a
relaxation oscillator. When you do it by
yourself, it works great. So these are simulations. But then if you
connect it– so it’s nice to have an
oscillator in a cell, again, if you want to
have blinking bacteria or mammalian cells
which is, again, fun. But then you typically want
to connect it to something. So you spent three years
building an oscillator, got it to work. And now your adviser says, OK,
let’s just get a paper on this. But before we get
the paper, I want you to connect it to
something meaningful. Because we want to go for
a high impact journal. And then you connect
it to something. And you realize that it
doesn’t work anymore. I think you’re really
pissed off at– you will be pissed off
at your adviser. I don’t know if your adviser
would want to be mean to you. But anyways, so that’s a
real problem in biology or synthetic biology
is that these things have impacts on each
other that, first of all, are going to be
undesirable but because of unique aspects, for
example, of the substrate. Now, they’re not
as unique as you might think because
these load issues also come to play in
electronic circuits. And so in electronics
circuits, these things have been solved decades
ago with these notions of load drivers. So if you have a module that
has, for example, high fanout and it controls many
things, then guess what? Those things that it’s
trying to control, even though the arrows are
pointing one direction, they actually have an
upstream effect too. So those modules that you think
you’re– the downstream modules that you’re just controlling,
they actually have an impact on the upstream. So what you do– in electronics,
you just build a load driver. And it basically
takes care of things. So now there’s no kind
of parasitic effect. And so we’ve demonstrated
that we can also build– so this is a
notion of retroactivity. And this is work with
Domitilla Del Vecchio here. And we’ve demonstrated
that we can– so I wont’ go into the details here– so
we can actually solve this. So this is a real problem
even in simple circuits experimentally. And then it uses this cool
notion of timescale separation to solve it. But we can take these things
that are highly affected to go from black to red
and put a load driver in and then it fixes it. So this should hopefully lead
to the generation of much more predictable circuit
construction targeting one of the real challenges in
scaling going from simple toy modules to large scale systems. So I’ll skip that and then move
on to another example– oh, sorry, an example
of an application. So in this particular
case– again, besides turning GFP on and off, what
can we do with synthetic biology that we can’t necessarily do
without synthetic biology? And so one of the most
important challenges in cancer therapeutics
is specificity– perhaps the most important. There are other things
that are important such as delivery of
a therapeutic agent. But as you improve specificity
that it can actually change how you do delivery
of a therapeutic agent. So if you have a therapeutic
agent that’s much more specific and has no side effects, you
can deliver lots and lots more. So these things
are highly related. So imagine a therapeutic agent
that recognizes something on a cell surface and then
says, this is a tumor cell. Kill it. So there are actually a
lot of efforts ongoing that have this particular approach. So whether it’s small molecules,
whether these vesicles that contain various cell surface–
various molecules that bind to cell surface receptors,
a really hot area right now is these engineered
killer T cells. So this notion that
you can actually engineer your immune system
by placing various receptors on these killer T
cells which then go and then bind tumor cells
by recognizing something on the cell surface
and then kill those. Sounds great, maybe, depending
on your perspective, or not so great, depending
on your perspective. So what’s happened with
those is they’re really sometimes great at
eliminating your tumors. But then the side effects
can be horrendous. And so what happens is that
those cell surface markers that exist on the tumor
cells– guess what? They’re also present on
healthy cells as well. And so the side effects for
those killer T cells approaches have been, I mean, just
terrible in various patients. So one– this should
not be a shock. One marker is typically not
enough to distinguish a cancer cell versus a healthy cell. Seems pretty obvious. So actually, what they’ve been
doing with these engineered killer T cells is
now just saying, OK, has to be this cell
surface receptors, but also cannot be this. So it’s like, and biomarker
one and not– you know, biomarker one, and
not biomarker two. OK, so they’re starting to
engineer more logic into these, more multi-input logic
into these things. And so they haven’t done
any clinical trials. But I saw a couple weeks
ago where they’ve actually made progress in Petri dishes. So we recognized this as
an issue several years ago. And this is work
with Coby Benson. And so really, all the
information that you want is actually inside the cell to
make a highly precise decision about whether this particular
cell is cancerous or not. OK, so the idea is when
the therapeutic agent, does the computation
by integrating multiple pieces of the
sensory information and then decides whether
to express a killer protein or not. Now, even if this is not
the cure for cancer– can’t really
guarantee that, right? But even if this is
not, just this notion of being able to create multi
input circuits to go into cells and analyze in live
cells real time what’s going on in the cell
with various molecules that might be interesting– that
has applications I would again say just about anywhere you
could imagine in biology. So one of the
things, for example, we’re looking at– I mentioned
this notion of organs on a chip or programmed
organs on a chip. So one of the things
we’re looking at now is placing these types
of sensory circuits into cells within
this organ on a chip. And so the idea would be expose
cells to these drug candidates. And then the cells
light up in different colors to tell you how
they’re responding to it. So for example, certain color
green would mean just fine. Green with red and
yellow would indicate that some apoptotic
pathway is being expressed or this drug is affecting
this proliferation path or this pathway
or that pathway. And so these
mechanisms can tell you in real time in
single cells– also specially– what kind
of impact there is to, let’s say, the drug
candidate you’re looking at. So again, this is
something that I think would have an impact
today if it was available, but realistically
making prototypes within the next
year to three years. OK, so we started with
looking at HeLa cancer cells as an example. And so we did some
bioinformatics. And we saw that based on
microRNA profiles that were available to us, this
would be a bio program that would distinguish HeLa cells
from all other cell types. And so some of you are
familiar with this way of annotating logic. Pretty ugly if you’re not
used it, but it’s simple. This just says, these
microRNAs have to be low and these microRNAs
have to be high. And that’s a HeLa cancer cell. Because that’s the
basic logic in this. So it’s a rather
simple logic statement. What we would argue is
that every cell type would have a logic
statement that would be true of that cell type
and not true of other cells. It’s almost by definition. There’s something different
about that cell than other cell types. Now, it gets
interesting when you start talking about
heterogeneous population. So for example, and that’s–
I won’t get into details about that. But there is heterogeneity. There’s heterogeneity in tumors. So the cool thing you can do–
this is a six input and gate. So if you have heterogeneity,
where you can do is you can have a six input
and gate with an or operation. So you can identify this
sub population of the tumor has this microRNA profile. And this cell population has
a different microRNA profile. And all you have to do is
create a new logic circuit and just combine them as like
a cocktail drug as an or gate. So this is a really
general approach that I think should
be relevant, again, for any kind of population. And you can also
set the thresholds and have all these fun things
that you can do with it. So I think the question really
is, is that microRNA expression profile sufficiently
small so that it can be encoded on a circuit
that you can deliver into cells? And can you do it reliably? That really is the challenge. OK, so the idea is that you
have a therapeutic agent, goes into a cell
does the computation, and then decides whether to
make a killer protein or not. OK, so how does
this actually work? So how do we implement an and
gate with inverted inputs? OK, so that’s one
portion of the circuit. So it’s actually– for microRNA,
it’s actually pretty easy. So what you do is you
put microRNA target sites on the gene of interest
or the output gene. And so the idea is that the
only way that this gene is being expressed, this output
protein is expressed, is when this is low and
this is low and this is low. So in principle, this
is pretty easy to do. OK, so that’s how we have
a three input and gate with inverted inputs. And now we can
add logic to that. So now if we want to make sure
that the output is high also when this input as high as well. And so what we do– so
remember the first circuit that I showed you
was this cascade. And the cascade was a bunch
of repressors that would then cascade that did the
not not operation. So when you think about
the not not operation, it doesn’t seem like a
very useful operation. But this here, the not not
operation, is actually useful. So it can convert
a microRNA sensor into something that can be
integrated with other sensors to create the four
input logic function. So this microRNA has
to be high in order to repress this
repressor which then represses the final output. So the only time that the
final output can be high is when this is high and
these three are low, OK? And then you can continue. And you can add another one. This is slightly more
complex in the sense that now, essentially the
sum of these microRNAs has to be high in
order for this branch to allow expression here. So it’s slightly more
complex in the sense that it’s not just–
it’s like a plus– again, like a plus operation,
which we found to be actually the
relevant operation here. And so the only
time the output high is when this microRNA is
high, combination of these two is high, and these are low. Anybody see a
potential problem here? A potential crosstalk? So everybody here
understand the circuit? Raise your hand if you actually
understand the circuit. OK, that’s not a lot of people. OK, maybe I should
go back for a second. Everybody– raise your hand
if you understand this. OK, so if microRNA is
high, they repress– they result in
degradation of the RNA. So all of these have to be low
in order for this to be high. OK, now if we had
this, this RNA– let’s focus just on
this maybe to simplify. So this microRNA
represses– this is a repressor which
represses this, OK? So if this microRNA is low,
then this repressor as high. And as a result of
that, this repressor represses this
promoter regardless. So it doesn’t matter
what these are. If this is low, this
repressor is high. And then there’s no way that
this output can be high. Now, if this is
microRNA is high, then this becomes low, allowing
this to potentially be high. Doesn’t guarantee it, but
it has a potential of doing. OK, now raise your hand
if you understand that. OK, more. OK, cool. That’s progress. By the way, this was
not a trivial circuit. There’s a lot of
connections here. And then we connect the same–
now we have the same repressor. So this microRNA–
these set of microRNAs repress the same repressor
which is now repressing this. So why is it the
case that you need– let’s just assume
this is one microRNA, this is another micro. Why do you need both microRNAs
to allow high output? And then I will not
let anyone leave until you answer the question. And I can sit down. So why– and then
we’ll stop there. And everybody will believe
me that the cure for cancer, you got it figure it out. Otherwise– so
there’s– oh, thank you. Letting– you will be the hero. AUDIENCE: So if the
microRNAs are not present, then you’re going to get an
expression of Lacks E. So– PROF. RON WEISS: Lack I. AUDIENCE: Yeah, like eyes. So only if both the microRNAs
are high and present do you repress both
of the lack I’s which then allow for
the expression of the– PROF. RON WEISS: Exactly. And what happens if
one of them is present and the other one is not? So if this microRNA is
present and this one is not? AUDIENCE: Then the lack
I still gets expressed because the other one
is still not repressed. PROF. RON WEISS: Exactly. So the only way that
you can actually have no expression of lack
I is when this is high and this is high. So this is essentially
like a two input and gate. These two have to be high
to allow this promoter to potentially be high. And in the other three logic
cases where one of those is low, lack I is
expressed and hence does not allow the output
protein to be expressed, OK? Yeah. AUDIENCE: So why do
you have the partition? All three are
acting the same way. PROF. RON WEISS: Ah, great question. So what happens if microRNA
21, 17, and 38 were all here? So that’s a great question. So that’s a question, right? So if you put microRNA
21 here, 17, and 38? What is the logic function here? Yeah. AUDIENCE: Then you
only need one of them in order to degrade the RNA. And then you won’t
get the same logic. PROF. RON WEISS: Great. So then it becomes
essentially an or. This will be an or
operation of the microRNAs where what we want is an and
operation on the microRNA. So that by having
two separate paths that have the same repressor
where they converge on the same repressor, we
achieve the and operation. Where we have them on
the same repressor, they do the or operation. That’s a great question. OK, so this works. And maybe I’ll say thank you. I’ll stop there.