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25.1: Introduction to Synthetic Biology - Biology

25.1: Introduction to Synthetic Biology - Biology


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A cell is like robot in that it needs to be able to sense it surroundings and internal state, perform computations and make judgments, and complete a task or function. Synthetic biology combines technology, science, and engineering to construct biological devices and systems for useful purposes including solutions to world problems in health, energy, environment and, security.

Synthetic biology involves every level of biology, from DNA to tissues. Synthetic biologist aims to create layers of biological abstraction like those in digital computers in order to create biological circuits and programs efficiently. One of the major goals in synthetic biology is development of a standard and well- defined set of tools for building biological systems that allows the level of abstraction available to electrical engineers building complex circuits to be available to synthetic biologists.

Synthetic biology is a relatively new field. The size and complexity of synthetic genetic circuits has so far been small, on the order of six to eleven promoters. Synthetic genetic circuits remain small in total size

(103 - 105 base pairs) compared to size of the typical genome in a mammal or other animal (105 - 107 base pairs) as well.

One of the first milestones in synthetic biology occurred in 2000 with the repressilator. The repressilator [2] is a synthetic genetic regulatory network which acts like an electrical oscillator system with fixed time periods. A green fluorescent protein was expressed within E. coli and the fluorescence was measured over time. Three genes in a feedback loop were set up so that each gene repressed the next gene in the loop and was repressed by the previous gene.

The repressilator managed to produce periodic fluctuations in fluorescence. It served as one of the first triumphs in synthetic biology. Other achievements in the past decade include programmed bacterial population control, programmed pattern formation, artificial cell-cell communication in yeast, logic gate creation by chemical complementation with transcription factors, and the complete synthesis, cloning, and assembly of a bacterial genome.


Synthetic Biology: Principles and Applications

00:00:11.19 Hello, my name is Jan Roelof van der Meer, I'm a
00:00:14.10 professor in microbiology at the University of Lausanne in
00:00:16.23 Switzerland. Today I would like to talk to you about
00:00:19.20 synthetic biology. About the principles of synthetic biology,
00:00:22.17 and some of the applications. Some of you may have very different
00:00:26.10 perspectives and ideas about what is synthetic biology.
00:00:28.29 You may have heard of the word, you may have associated
00:00:30.26 it with plastic organisms or with organisms doing various
00:00:34.08 strange characteristics. But probably, this is not what
00:00:37.06 synthetic biology really is. So my goal of today is
00:00:40.22 to explain to you the concepts of synthetic biology
00:00:43.28 and contrasting them to the normal way that biologists
00:00:46.10 work when they try to understand living beings. After
00:00:49.25 that, I will tell you something about research directions that
00:00:52.23 are ongoing in synthetic biology, and I would like to explain
00:00:56.05 some of our own work, which is about synthetic bioreporter
00:00:59.17 cells that we think are useful for environmental purposes.
00:01:02.22 So, if we think about biology, it's really about understanding
00:01:07.05 living organisms in all their aspects. So you may think that
00:01:11.12 biology is about going out into the jungle and looking at
00:01:14.22 elephants, but as a microbiologist, we often look just at
00:01:17.19 bacteria, microscopic organisms. So what you see here
00:01:20.19 is this small growth chamber that we developed in order to
00:01:23.19 look at the behavior of single cell bacteria that you can see
00:01:26.06 here as these small rods. And what this small instrument is
00:01:29.08 doing is that we can feed the bacteria from the left side
00:01:32.24 and then look at their behavior on the right side. So it's really
00:01:36.04 very simple in the sense of looking at what the organisms are
00:01:40.18 doing. Biology often uses observation, just observational
00:01:45.12 techniques to study behavior. So here on the left side,
00:01:49.04 you can see for example how bacteria, even though they're
00:01:51.29 extremely small, use a flagella to move themselves forward
00:01:56.07 in search of nutrients or conditions that they have. You can see
00:01:59.25 the spirally movement of the flagella that propels the cells in one
00:02:03.14 direction, and if they want to change the direction, you can see that
00:02:06.17 the flagella become disordered and they can rotate the cell to go in
00:02:10.02 another direction. You can see on the other image here more closer,
00:02:13.17 that this is an observation of a single Daphnia individual. And Daphnia
00:02:17.22 is a small water creature that lives in most freshwater habitats.
00:02:22.11 And what you see here is the movements of its legs and of the
00:02:26.00 heart and of the internal organs. So the organism is sufficiently
00:02:28.20 transparent that you can keep it under the microscope. Here we
00:02:31.13 keep it in a small cage, where the organism sits and is fed
00:02:35.16 with fresh water. And we can observe how it reacts.
00:02:38.05 So observation really is one of the critical tools that biologists
00:02:40.28 can use. Another tool that biologists use a lot is understanding
00:02:46.18 from the creation of mutations. So what are mutations?
00:02:49.19 Mutations are changes that we make in the DNA, the hereditary
00:02:53.29 material of organisms. Again here you see a very simple
00:02:56.26 example, on the left side you see the cell of a wild bacterium
00:03:03.26 called Bacillus subtilis. Which is a bacterium that normally
00:03:06.19 dwells in the soil, it can make spores, it knows how to
00:03:10.04 survive very well. In order to understand how this cell
00:03:13.11 divides, researchers have made mutants that cannot make
00:03:15.28 proper cell walls. So for example, if you look at this particular cell
00:03:20.16 here, it's completely round and blown up because it carries
00:03:24.05 a mutation in a gene that is essential to make the cell
00:03:27.00 and that otherwise maintains the cell as a nice rod shaped
00:03:30.25 structure that you see on the left. So by knowing where these
00:03:33.29 mutations are, we can try to understand how the organism
00:03:36.18 organizes itself and makes this cell wall. The third important
00:03:41.06 aspect that biology uses is what we call dissection.
00:03:44.11 So we like to take things apart in biology in order to understand.
00:03:48.02 This can be an anatomic dissection, like you see here for a bee where
00:03:53.02 investigators of our own department dissect the bee to
00:03:57.05 understand how the bee gut and the internal organs of the bee
00:04:00.16 work. And how they interact with bacteria that live in the
00:04:03.20 gut of the bee. So you can see here, a researcher preparing
00:04:06.22 the gut of the bee in order to understand this. It's not only
00:04:10.19 anatomic dissection that biologists use, but more and more, we
00:04:13.24 also use genetic dissection. So we like to understand
00:04:16.26 what the DNA is made of in every living organism and how
00:04:21.20 this contributes to the whole body plan and how the whole
00:04:24.26 functioning of that particular organism. Maybe you have
00:04:27.13 seen genetic dissection of DNA before, what you can see here
00:04:31.02 for example, is a culture of cells on the left, it looks like if you
00:04:34.10 have a soup, a turbid soup. This is because the soup
00:04:39.20 culture contains millions and millions of bacterial cells
00:04:42.07 that you can break open by lysis and then you can isolate
00:04:45.29 the DNA that in solution looks sort of like this fluffy solution.
00:04:49.14 This fluffy material, this white-ish material. If you put this
00:04:52.22 white-ish material under a microscope, here under the
00:04:54.24 atomic force microscope, you can see that it forms sort of
00:04:57.22 a chain of pearls that you can observe. And you can draw certain
00:05:01.20 conclusions from it, but more importantly, for DNA, we often look at
00:05:05.13 the gene sequence. So we take the DNA apart, we determine
00:05:08.28 base by base what the DNA looks like. And that's shown here in
00:05:11.19 the trace below, where every peak that you see in a different
00:05:15.18 color, in red or green or blue, means a different base
00:05:19.17 of which the DNA is made up. Now if we take all that sequence
00:05:24.12 together, so then we try to convert it to code, just the
00:05:28.18 code of A, C, G, and T, you can get a very nice
00:05:31.22 and thick book. This is the start, if you like, of a
00:05:35.14 genome sequence of a single bacterium. This bacterium
00:05:38.16 doesn't have a very big genome, it's only like 6 million characters.
00:05:42.07 But if you think about this page containing 2000 characters
00:05:45.08 per page, then you would still need 3000 pages to print that whole
00:05:49.01 bacterial genome, which is quite a thick book. And if you think
00:05:51.23 that the human genome is a thousand times bigger, then
00:05:54.11 that would be a very big genome. So normally we don't
00:05:56.12 print that out, because it would take too much space.
00:05:59.05 Now the real goal in biology, in particular molecular biology is to
00:06:02.25 understand what does this sequence actually mean. All
00:06:05.07 these letters that are there. What do they do? How can this be the
00:06:08.26 important plan for the bacterium or the living organism that
00:06:12.28 is there? So what we often do is we try to gaze into the
00:06:16.23 sequence and do an analysis of important features that this
00:06:19.25 sequence can contain. So as you know, the sequences contain
00:06:23.08 for proteins, for RNAs, there's signals on the DNA that are
00:06:27.22 important to direct certain proteins to actually read the
00:06:31.11 instructions in the DNA and form the parts for the cell that are needed.
00:06:34.24 So what is really important is that we understand what
00:06:38.04 such a DNA sequence means. And as I said, this could mean
00:06:41.01 a really big sequence. So when we look at this particular
00:06:44.12 part, you can see some of the things that biologists
00:06:46.10 try to interpret. This is the case of a bacterial genome.
00:06:49.07 So what we are looking at here in what is called reading
00:06:51.25 frame, is actually the region that is needed for the cell to
00:06:55.18 recognize, oh this is the part of the DNA where I have to
00:06:58.00 make an mRNA and then a protein. A reading frame has to
00:07:01.11 have a start, like here is shown at the ATG, that's the start of that
00:07:05.14 reading frame. It's a signal to start building the protein
00:07:07.26 where it's needed. But then there are also other parts that are
00:07:10.25 needed, for example, what is shown here as an RBS.
00:07:13.29 This is a site that is recognized by the ribosomes, the
00:07:17.00 factories that produce the proteins, to begin the
00:07:20.17 synthesis of a protein. And then there are often other parts
00:07:23.11 on a sequence that do not code directly for a protein, but
00:07:26.11 are important for other proteins to know where to start
00:07:29.22 doing the task they have to do. So for example, here in green
00:07:32.26 is the protein binding site, it's a transcription factor binding site
00:07:36.01 that directs the machinery toward expressing that gene.
00:07:40.04 Next to it is a promoter sequence, that is a signal for the RNA
00:07:43.22 polymerase to start transcribing that gene and so on and so forth.
00:07:47.16 Now this is really the basis, or this is really where biology
00:07:51.10 ends and where synthetic biology starts. Because synthetic
00:07:54.22 biologists start to interpret this sequence in a different
00:07:57.22 schematic way. So one of the concepts of synthetic
00:08:01.02 biology is really that you break the DNA down into
00:08:04.00 biological parts. This can be DNA parts that you can
00:08:07.10 assemble in a particular way, or it can be protein parts
00:08:10.05 if you want to profit from these protein parts. So if we
00:08:13.08 look again at this sequence that I just showed you in a
00:08:15.14 different way, in a very schematic way, then it may look for a
00:08:18.29 synthetic biologist like this. A gene, so a coding region that is
00:08:23.06 needed for a protein, will look like a small arrow here in green
00:08:27.04 or there in brown. That codes for protein 1 or protein 2, depending
00:08:32.04 on what we need. The synthesis of those genes are driven by
00:08:36.26 promoters that we display by different other arrows, here
00:08:39.26 in small black arrows, and we have important signals for the
00:08:43.01 ribosomes to start the translation of such proteins that are
00:08:45.29 listed here as RBS. And there may be other things that a
00:08:50.06 synthetic biologist needs like here, a binding site for a regulatory
00:08:54.26 protein, and here a terminator that's a signal for the RNA
00:08:58.20 polymerase to stop. So it's really important to try and
00:09:02.11 understand. We can decompose the sequence into parts
00:09:04.24 that we can study as they are in a living organism in
00:09:08.18 the particular way that they appear, but we can also move
00:09:11.08 them into different parts. So if we take this sequence
00:09:14.05 apart, then we see really what the circuit parts are so that
00:09:18.02 the synthetic biologist would need. So we may need a part
00:09:22.01 for genes, we need a part for ribosome binding sites,
00:09:25.22 promoters that are signals, terminators that are signals,
00:09:28.19 binding sites for transcription factors on the DNA, these are the
00:09:31.25 parts that we need in order to assemble something. The
00:09:35.11 protein part that we need would be a structural protein,
00:09:37.25 a regulator protein that we can see that is important to
00:09:41.26 signal the cells "yes now you start transcribing that gene
00:09:45.17 or not." We need transcription factors, we need sensory
00:09:48.07 proteins depending on what we actually want. So it's really
00:09:51.14 important to realize that we can go from the sequence
00:09:53.07 to the parts, we can study the parts and then we can put them
00:09:57.07 back together in a different way. Now the second concept that
00:10:00.26 is very important for synthetic biology is rules and models.
00:10:04.01 So we do not only like to dissect the sequence and know the
00:10:08.02 exact sequence of the A, C, G, and T's in the genome of an
00:10:11.16 organism or a part of DNA that we want to construct,
00:10:14.16 but we want to understand how does this sequence work
00:10:17.23 together. So which are the rules that the cell is following
00:10:20.18 in order to make this sequence functional? So for that, synthetic
00:10:24.28 biology uses certain rules. This could be logic rules like that gene
00:10:28.11 is on or that gene is off. It could also be models like shown here
00:10:32.06 in the back, that tries to predict how a particular stretch of
00:10:35.18 DNA and promoters and terminators and binding sites
00:10:38.12 is working for the cell. Now if we go back to that same
00:10:42.11 DNA circuit, the same stretch of DNA that we have
00:10:44.28 seen before, with the two genes in green and in brown.
00:10:48.10 And the different parts that are needed to operate this particular
00:10:51.26 gene circuit, then it means for the cell the following, you can see that
00:10:56.26 in steps 1, 2, and 3. The first signal for the cell so that it starts
00:11:01.17 to interpret this DNA sequence is that it will try to transcribe
00:11:05.25 this particular gene. It does that because there is an RNA
00:11:09.12 polymerase coming. The RNA polymerase starts at
00:11:11.22 the promoter and then transcribes that gene until
00:11:13.29 it reaches the terminator. This mRNA is then translated
00:11:17.06 into a protein that you can see here schematically in green.
00:11:20.07 What this protein is doing is that this protein will bind
00:11:23.07 to the DNA at the particular site that is here in green.
00:11:27.00 Now that protein's not just any protein, it is a sensory
00:11:30.14 protein with also activating functions, so it is capable
00:11:34.17 of sensing for example, a particular chemical that
00:11:37.16 interacts with this protein and then tries to attract
00:11:40.14 RNA polymerase again, but to a different promoter.
00:11:43.13 So what this protein is now doing is that it attracts
00:11:46.20 RNA polymerase, but to a promoter that is here.
00:11:48.28 And then when RNA polymerase is there, it will then transcribe that
00:11:52.16 gene and make that particular protein. So this small
00:11:55.06 schematic structure is actually giving some instructions
00:11:57.24 to the cell, start here automatically, make a protein, bind
00:12:02.05 that protein that can intercept that signal, and then transcribe
00:12:05.05 another protein. So a very simple thing that follows a certain set of
00:12:09.00 rules. You can put these rules in a kind of model if you like.
00:12:13.25 If you have these simple circuits, you can sort of by
00:12:16.17 modeling, try to predict what they're going to do. Here's an
00:12:19.23 example of two simple circuits, in one case we have the
00:12:23.05 two genes that are located in the opposite direction.
00:12:26.11 In the other case, we have the same genes but located
00:12:28.28 next to each other. Now the rules that this small circuit
00:12:32.16 says is that this particular gene codes for a protein
00:12:36.04 that will then inhibit the transcription of the other gene
00:12:40.02 here. So in one case, this protein will inhibit its own synthesis
00:12:46.24 and the gene the gene that is in yellow behind it, in the other
00:12:51.19 case it, it cannot inhibit its own synthesis because it is not
00:12:54.07 binding there, it's not influencing this particular promoter
00:12:56.20 that would transcribe itself. Now the model now would predict
00:13:00.10 that in the case where you have this feedback, where FB means
00:13:04.25 feedback loop, then this would be dependent on a signaling
00:13:08.13 molecule, that is in this case arsenic. And as a function of the arsenic
00:13:13.06 concentration that is shown here below, you can see that
00:13:16.19 the more arsenic you add to the system, the more
00:13:19.00 of this protein ArsR you get. And the more of this protein
00:13:22.25 GFP that you get. In the case of the uncoupled systems,
00:13:26.20 so UN means uncoupled here, then this gene is not
00:13:31.11 under its own control, but it's under the control of something
00:13:33.13 else. You can see that it's always produced at a constant
00:13:38.23 level, which is independent of the concentration of in this case,
00:13:42.00 this arsenic or AsIII. But the other protein is still under
00:13:46.29 the control of this AsIII, so as you can see here, this increasing
00:13:50.13 amount when the concentration becomes higher. So this is
00:13:53.05 a simple model, it's a very simple genetic circuit
00:13:56.08 as we call it. It gives a set of instructions to the cell and the
00:14:00.05 cell will carry out these instructions if it is properly equipped.
00:14:03.19 The third concept of synthetic biology is really standards.
00:14:08.02 Standards? That sounds very, very weird. Why would you need
00:14:11.14 standards in biology? Well, think about it. Synthetic biology has
00:14:15.13 a fair amount of relation to electrical engineering, where people were
00:14:21.11 working in the beginning with electricity and trying to harness
00:14:24.20 electricity in forms that are useful. Like cameras, like
00:14:28.20 televisions, and so on. So the industry and the people had
00:14:33.13 to adopt certain standards that we now know as electrical
00:14:35.29 plugs. Now the electrical plug may still be different between Europe
00:14:39.05 and the U.S., but the essence is that there is an electrical
00:14:41.19 plug you can plug something in there and it gains the
00:14:44.26 electricity and can work. In synthetic biology there is a similar
00:14:48.10 concept in order to try to make it possible that people from
00:14:52.17 different laboratories and different industries can work
00:14:55.10 together on the same parts. So maybe we are thinking
00:14:58.14 about standards for gene expression. But how would that
00:15:01.07 look like? It's not electricity, it must be some biological
00:15:04.17 equivalent of electricity. And the plugs? What could they be?
00:15:08.18 They could be small fragments like here, promoter sequences
00:15:11.18 that can be adopted into one system or another system.
00:15:14.28 So standards is really an important part for synthetic
00:15:18.02 biology. Now having explained all this, what is synthetic
00:15:21.29 biology really about? So what is synthetic biology hoping
00:15:25.01 to achieve? There's two main things really, at this point.
00:15:28.22 One is that we can understand complex biological
00:15:33.01 processes not by dissecting them as normal biologists do,
00:15:36.12 but by reconstructing them. So we take parts and we
00:15:41.08 build something that is more complex, like here schematically shown
00:15:44.25 for Legos. It looks very much like Legos. So understanding
00:15:49.01 biological processes not by dissection but by their
00:15:51.26 reconstruction. The second thing that has appeared in
00:15:55.07 synthetic biology and that is maybe not so different as
00:15:57.24 people may know from genetic engineering or so
00:16:01.00 is to facilitate the construction of complex biological processes
00:16:04.18 that carry new functionalities. Not just producing one protein
00:16:08.07 but producing a complex pathway that you engineer
00:16:11.25 into the cell that was not previously possible.
00:16:14.02 So these two things are really what synthetic biology
00:16:16.29 is nowadays trying to accomplish. The engineering
00:16:20.20 idea, as I said, is really rather similar to what electrical
00:16:23.07 engineers do. They have their parts, they can be small transistors,
00:16:28.06 transformers, capacitors that they put together on an electrical
00:16:33.02 board. These electrical boards, if you put them into your computer,
00:16:36.17 can give your computer certain instructions. Biologists
00:16:39.18 and synthetic biologists are trying to do the same.
00:16:41.10 Take biological parts with some rules, models, and
00:16:45.05 engineering, we put them together. And then we
00:16:47.16 try to verify what this construction really is doing and what it
00:16:51.02 means. Now current research activities in synthetic biology
00:16:55.07 go consequently in all directions, I would say. There are
00:16:59.04 groups that work on making standardized parts, making
00:17:02.25 new models, trying to come up with complex engineering
00:17:06.20 strategies to put these parts together. That is really
00:17:09.07 important, because if we want to play with parts, we
00:17:11.14 actually need to have parts. So the more parts we
00:17:14.00 have, the better they are characterized, the better we
00:17:16.29 can produce new structures in synthetic biology. The
00:17:20.16 second part of synthetic biology has really started off with
00:17:23.26 DNA synthesis. So previously in genetic engineering, it was really
00:17:27.08 difficult to make mutations and really cumbersome
00:17:30.14 and took a lot of time. Now there are DNA synthesis
00:17:33.04 companies and biologists will simply write down their
00:17:36.01 sequence, send it by their computer to the DNA
00:17:38.11 synthesis company who will actually make the construct,
00:17:41.07 and that facilitates largely to put parts together in a
00:17:45.02 particular way. So consequently, there are people who try to
00:17:48.22 design whole genomes, which is still an important and challenging
00:17:51.10 task. Because we do not understand all the rules very well
00:17:54.18 to actually be able to put genomes together. In some
00:17:58.11 cases, people also use genome parts like complex
00:18:01.02 phenomenon that the cell does. If you remember the
00:18:04.02 example of the swimming cell, so the flagella synthesis
00:18:07.06 even for a bacterium takes a lot of power, it's a very
00:18:09.25 complex process with many proteins. So that's
00:18:12.22 something that a synthetic biologist may try to reconstruct.
00:18:16.26 The third thing is something that looks really bizarre
00:18:20.10 if you think about it. It's the production of minimal cells
00:18:23.01 and host production platforms, so synthetic biologists have
00:18:26.24 adopted this terminology that's called "Chassis," almost like a
00:18:29.26 car factory. You have your chassis that you can put in
00:18:32.00 this kind of chair or that kind of chair, and it doesn't
00:18:35.04 really matter because the car is still running. So the
00:18:37.23 same idea appears for biology as well. You can make
00:18:40.12 bacteria or yeast that are just a chassis needed to make
00:18:44.08 the motor for the cell. And everything else you can plug
00:18:47.04 in, colors, pathways, things and so on. So for that, very often
00:18:51.00 people find that the living beings that exist naturally are
00:18:54.10 way too complex. They contain viruses, they contain things
00:18:57.10 that you wouldn't really need, and that is why they want
00:19:00.10 to design minimal cells that have been devoid of all
00:19:03.29 the parts that are not really needed. A fourth direction
00:19:08.26 in synthetic biology really tries to go even beyond it,
00:19:11.28 that is trying to make protocells and artificial life.
00:19:15.19 There is a huge interest in trying to understand where is
00:19:18.24 life coming from. We do not know, but synthetic biologists
00:19:22.05 may be able to recreate certain life forms and that
00:19:25.14 would help enormously to try and understand where is
00:19:28.07 life coming from and what are the different paths that can
00:19:30.29 lead to life. Finally, there's a lot of effort in what's called
00:19:34.19 Xeno-DNA, and this may be sort of your fantasy dream
00:19:38.01 strains of DNA. But what it's really about is that biologists
00:19:40.26 and synthetic biologists are saying, you can alter DNA,
00:19:45.00 you can alter proteins, in that you incorporate different types of
00:19:48.18 amino acids that the cell normally doesn't like, but it could be
00:19:52.04 really important to try to incorporate all these into
00:19:55.13 proteins because it could give new functionalities to
00:19:58.01 proteins that we cannot currently make. So this is the
00:20:00.18 xeno-DNA/biology. And finally, there's an important point that
00:20:04.10 comes with synthetic biology that allows biology to
00:20:07.14 attach to a do-it-yourself community. So many people
00:20:10.13 also amateurs become interested in biology because
00:20:14.13 of the efforts in synthetic biology. Trying to understand
00:20:17.07 biology, making simple instruments that you can use
00:20:20.23 in organized groups and so on, to try and understand
00:20:24.11 biological phenomenon. So this is really an overview
00:20:28.10 of the general research activities in synthetic biology.
00:20:31.02 I would like to pick just one particular application.
00:20:35.02 To give you some idea of things that people are dreaming of,
00:20:37.20 and this is obviously one of the things where you may say,
00:20:41.11 okay will these dreams finally come true? But this is a bit of
00:20:45.04 marketing, if you like, by the biologists and the engineers
00:20:48.20 that are behind it. So there's a lot of hope that synthetic
00:20:51.29 biology will be able to help producing new things that
00:20:56.17 will be useful for human health, animal health, there's obviously
00:20:59.18 a lot of money going into it. In terms of pharmaceuticals, vaccines,
00:21:02.28 maybe gene therapy, tissue engineering, probiotics, diagnostics,
00:21:07.10 and so on. Another area of importance is agriculture.
00:21:10.10 Try to improve plants that are resistant to diseases,
00:21:14.06 resistant to drought, that give better feedstocks for animals,
00:21:18.14 that can maybe help sequestering CO2, chemical production,
00:21:22.28 diagnostics. Then there are things in industry, you may have
00:21:27.20 heard about bioenergy and biofuels. Things that can become
00:21:30.24 very important if synthetic biology is able to create better
00:21:34.20 organisms that do these kind of conversions with higher
00:21:37.20 efficiency. Production of bulk chemicals is very important
00:21:40.14 because maybe at some point, we'll run out of oil and
00:21:43.06 we need alternatives to actually produce the chemicals
00:21:45.24 that we need daily. Specialty chemicals, new materials,
00:21:49.11 people are thinking about building DNA and proteins together
00:21:52.14 to get new kinds of materials that might have properties that
00:21:55.23 we have not seen before. And there's also applications in
00:21:58.19 the environment, like biosensors, bioremediation, waste
00:22:01.20 treatment that may be helped by engineering specific
00:22:05.05 organisms that do tricks that we cannot normally achieve
00:22:07.20 in the natural conditions. So let me explain to you
00:22:10.19 just about one of the things that we do in our own
00:22:12.25 lab, which is called bioreporters. These are really
00:22:15.25 very, very simply engineered bacteria cells. Bacterial
00:22:19.23 cells that are not pathogenic, harmless in the lab.
00:22:23.28 And what we can do is that we can equip them with different
00:22:26.18 colors like here, this is called bioluminescence, it's really
00:22:29.01 a cell that gives off light. Or with fluorescent colors,
00:22:32.08 you shine light on them and they produce another
00:22:34.19 color back that you can measure. Or just regular
00:22:37.07 colors like blue, red, green, and so on. The idea
00:22:40.04 with these bioreporters, as we call them, is that the cell
00:22:43.14 can signal for us the presence of, for example, a toxic
00:22:46.26 chemical in the environment. And then what the cell is doing is
00:22:50.08 it has a small circuit inside, so it will recognize the compound
00:22:52.23 that will diffuse inside the cell and then this compound
00:22:56.04 is bound again by one of these sensory proteins that I talked
00:22:59.14 to you about before that can bind the DNA and can direct
00:23:02.18 the synthesis of a new protein in the cell. And the new
00:23:05.23 protein is often one of these proteins that we have seen
00:23:08.00 here, that gives off light or fluorescence and so forth.
00:23:11.08 So we think that these are very simple cells that can do
00:23:14.19 very useful tricks for us, because they can help us to make
00:23:17.10 analytical devices to sort of interrogate parts of the environment
00:23:21.08 where we think there is contamination that may occur.
00:23:23.24 One of the systems that we have been working on is
00:23:26.07 to construct cells that would detect arsenic. So
00:23:29.04 you know arsenic from the novels of Agatha Christie,
00:23:32.14 it's a really nasty toxic chemical. But unfortunately,
00:23:35.14 it has not only been used in novels of Agatha Christie,
00:23:38.10 but large areas in the world are contaminated with arsenic
00:23:41.23 from natural resources. So it's an abundant metal
00:23:45.10 that exists in the earth's crust and can come up in the ground
00:23:48.11 water. And people like here, shown in this picture in a village in
00:23:52.01 Bangladesh, suffer enormously because they do not know
00:23:54.16 if the drinking water that they take from their household
00:23:56.29 pumps is actually contaminated with arsenic or not.
00:23:59.23 So we sat together in the lab and with a small
00:24:03.02 spin off company called ARSOLUX, that is a collaboration
00:24:06.04 of the Helmholtz Institute in Leipzig in Germany, to make
00:24:09.20 bacterial systems that would be able to measure
00:24:12.07 arsenic in drinking water. And then could be used
00:24:15.00 on the field to measure the water that comes from the
00:24:17.22 pumps and analyze this for arsenic. So what we do is
00:24:20.26 we make small glass vials, and you can see here
00:24:22.24 sort of a powdery stuff. This powdery stuff is really the
00:24:25.24 bacteria that are dried inside such a vial. The vial is
00:24:28.24 closed with a stopper, and that's important because that
00:24:31.10 makes it a closed system and the bacteria cannot
00:24:33.23 escape. We inject the water directly through the
00:24:36.10 stopper inside it, this reconstitutes the bacteria, as you can
00:24:39.13 see here. It makes this sort of watery suspension, if
00:24:42.16 there is arsenic in this water, the bacteria will react
00:24:45.13 to it and will start to glow. So they will make this famous
00:24:48.17 bioluminescent signal that you cannot see by eye unless
00:24:51.08 you are in a very dark chamber. But you can very easily
00:24:54.02 do this by putting these small vials into a small instrument that's
00:24:57.02 shown here, that is called a luminometer. This is a
00:24:59.28 portable luminometer that we can use in the field.
00:25:02.16 It has a battery capacity, you close the cap, you wait
00:25:06.04 a little while, and it measures the light that comes from
00:25:08.06 the cells. So what we have been able to do is, if we are
00:25:12.18 in such villages, then we can sample all the wells from those
00:25:16.13 different households. And that is really the problem, that they don't
00:25:18.28 have a central drinking water supply, but individual
00:25:22.00 households are pumping and you have to test all that
00:25:23.29 water. And not just once, but multiple times. So what we can do
00:25:27.05 is go into such a village, fill all the different vials that are
00:25:30.11 necessary for each of the pumps. Fill them one by one,
00:25:33.04 and then wait until the cells react, and then measure them
00:25:36.29 one by one by one. And in an afternoon, you can
00:25:39.10 measure all the water wells in the whole village.
00:25:41.11 Obviously if you try to do such a test, it's very
00:25:44.19 important that you can actually show that this is working.
00:25:47.08 So in the first test that we tried to do, this was done in Bangladesh
00:25:51.01 and in Vietnam, in different settings with different types of
00:25:54.08 ground water. We compared that at the same time, the
00:25:57.01 response from our engineered bacterial cells with the response of
00:26:01.22 classical chemical analytics by ICP-MS, or with
00:26:05.10 atomic absorption spectrometry. And as you can
00:26:07.12 see here, there's a very good dependence between the
00:26:11.13 signal that's given by the biosensors and the signal
00:26:13.14 that is given by the chemistry. So there is almost
00:26:16.04 a one to one ratio of the concentration that you measure
00:26:19.10 by chemistry and with the biology. And that tells us that this method is
00:26:23.24 potentially very good and very interesting because
00:26:26.10 the bacteria multiply by themselves, so to produce such a
00:26:29.13 biosensor is extremely cheap and doesn't require a lot of
00:26:31.18 engineering. Whereas to make a GC, MS or atomic
00:26:35.02 absorption spectrometer, it costs a lot of money
00:26:37.13 and you cannot deploy it in the field. So that is why
00:26:40.08 we think that this test could be very interesting
00:26:42.08 to do this. As another example, we use bioreporters
00:26:45.08 to measure pollution at sea. So here we engineered
00:26:48.02 a set of bacterial reporters that could measure different
00:26:51.08 compounds that come off of oil, like alkanes, solvents,
00:26:55.02 basically aromatic hydrocarbons. For this we worked together
00:26:58.11 with the Dutch government on an exercise in the North Sea.
00:27:01.04 The Dutch Government has what is called responder
00:27:03.17 vessels, they go out whenever there is an oil pollution and
00:27:06.04 they scoop the oil and bring it back to the refinery if
00:27:09.12 they can. But much of the oil, particularly smaller spills, go
00:27:13.00 undetected and floats there. And nobody really knows
00:27:15.11 how dangerous this can be. So what we set out to
00:27:18.04 do with these responder vessels is that we got permission
00:27:20.17 to actually make an artificial spill out in sea with a limited
00:27:25.02 amount of crude oil. And then we went onboard with our
00:27:29.16 small portable luminometer that you see here again,
00:27:32.00 the different cell lines in the vials that we can directly
00:27:34.29 incubate with the sea water to try and measure
00:27:37.10 what is the oil pollution that really occurs at the sea.
00:27:41.02 This sampling was quite challenging, as you can imagine,
00:27:43.13 we had to go out with a rubber boat from the responder
00:27:45.04 vessel to actually approach the oil slick, that you can see for example
00:27:49.11 here. Because the ship itself is so big that it cannot
00:27:51.14 go into the oil slick, because otherwise it would be
00:27:53.17 horribly contaminated as well. So here is an example
00:27:56.20 of the results that we found in these exercises.
00:28:00.04 Again, in the top you see the chemical analysis, and in
00:28:03.14 the bottom, you see the analysis of what we call the
00:28:06.25 reporter cells that were done onboard. The chemical
00:28:09.05 analysis was obviously extremely good, but it took two
00:28:11.28 months to actually get to that. Whereas the bioreporter
00:28:14.24 signals could be obtained directly onboard the same
00:28:17.14 afternoon. So here is shown the results of two
00:28:20.17 experimental spills, we had one opportunity in 2008.
00:28:24.17 And one opportunity in 2009. And then there are the spills
00:28:28.05 that we encountered on the way, because the North
00:28:30.16 Sea is a very busy traffic route. And ships from time
00:28:34.12 to time, they clean their insides and they throw away overboard
00:28:38.25 some oil, which we can also analyze. So importantly,
00:28:42.14 what you can see here in the diagram below with the different
00:28:44.29 colors is the different parameters that we measured
00:28:47.12 with the reporter cells. So you can see that in all
00:28:49.25 cases, our samples from the sea water that were far
00:28:52.25 below the oil slick that we measured important concentrations
00:28:56.10 of toluene, benzene, methylene, alkanes, etc .
00:29:00.23 So that told us again that what we measured with these
00:29:04.11 cell lines is very, very relevant and can help to address
00:29:07.17 the situation of samples at the site immediately. And we
00:29:11.14 hope that these sort of results are convincing to the
00:29:15.17 authorities to give permission and perhaps to companies,
00:29:18.18 to say, oh this is an important way of trying to analyze
00:29:22.06 and apply synthetic biology efforts. So finally, I would like
00:29:25.20 to give you sort of a prediction or report. So this is a report
00:29:29.17 that was commissioned by the European community
00:29:32.01 to estimate the global value of the market for synthetic
00:29:36.07 biology. This report was done in 2011, and obviously
00:29:40.10 these things are always a bit predictive in the sense that
00:29:43.17 maybe they're not too conservative, but you can see that
00:29:45.20 the estimates for 2011 were already $1.6 billion USD in various
00:29:51.17 fields like pharma, chemical products, agriculture, and
00:29:54.11 energy. In 2016, it's rising up to $10 billion. So this
00:29:58.13 is really something that everybody has high hopes, that
00:30:01.19 synthetic biology is going to be a globally important market.
00:30:04.10 I hope that I have shown you a little bit about how synthetic
00:30:08.08 biology works, how the concepts work, with the bottom up
00:30:11.10 construction, not the dissection and destruction of organisms,
00:30:14.10 but taking parts and building something again. Synthetic
00:30:17.28 biology has many useful applications, potentially useful
00:30:20.19 and that's how the research is going. Some of the things may not
00:30:23.17 make it in the end, whereas other things come surprisingly
00:30:26.16 and will in the end deliver important results. Several results
00:30:29.26 are within close reach, so it's not something that we have to
00:30:33.02 wait 25 years to deliver. No, no, there's important applications
00:30:36.20 and some of them, like we demonstrated with the small
00:30:39.13 bioreporter cells to measure the environmental quality
00:30:41.26 can be used immediately. Thank you very much for your attention.


Extreme Genetic Engineering: An Introduction to Synthetic Biology

A new report by the ETC Group concludes that the social, environmental and bio-weapons threats of synthetic biology surpass the possible dangers and abuses of biotech. The full text of the 70-page report, Extreme Genetic Engineering: An Introduction to Synthetic Biology, is available for downloading free-of-charge on the ETC Group website.

"Genetic engineering is passé," said Pat Mooney, Executive Director of ETC Group. "Today, scientists aren't just mapping genomes and manipulating genes, they're building life from scratch - and they're doing it in the absence of societal debate and regulatory oversight," said Mooney.

Synbio - dubbed "genetic engineering on steroids" - is inspired by the convergence of nano-scale biology, computing and engineering. Using a laptop computer, published gene sequence information and mail-order synthetic DNA, just about anyone has the potential to construct genes or entire genomes from scratch (including those of lethal pathogens). Scientists predict that within 2-5 years it will be possible to synthesise any virus the first de novo bacterium will make its debut in 2007 in 5-10 years simple bacterial genomes will be synthesised routinely and it will become no big deal to cobble together a designer genome, insert it into an empty bacterial cell and - voilà - give birth to a living, self-replicating organism. Other synthetic biologists hope to reconfigure the genetic pathways of existing organisms to perform new functions - such as manufacturing high-value drugs or chemicals.


Synthetic biology in the UK – An outline of plans and progress

Synthetic biology is capable of delivering new solutions to key challenges spanning the bioeconomy, both nationally and internationally. Recognising this significant potential and the associated need to facilitate its translation and commercialisation the UK government commissioned the production of a national Synthetic Biology Roadmap in 2011, and subsequently provided crucial support to assist its implementation.

Critical infrastructural investments have been made, and important strides made towards the development of an effectively connected community of practitioners and interest groups. A number of Synthetic Biology Research Centres, DNA Synthesis Foundries, a Centre for Doctoral Training, and an Innovation Knowledge Centre have been established, creating a nationally distributed and integrated network of complementary facilities and expertise.

The UK Synthetic Biology Leadership Council published a UK Synthetic Biology Strategic Plan in 2016, increasing focus on the processes of translation and commercialisation. Over 50 start-ups, SMEs and larger companies are actively engaged in synthetic biology in the UK, and inward investments are starting to flow.

Together these initiatives provide an important foundation for stimulating innovation, actively contributing to international research and development partnerships, and helping deliver useful benefits from synthetic biology in response to local and global needs and challenges.


Introductory Courses Without Prerequisites

MCDB 040b. Science and Politics of Cancer.
Robert Bazell
TTh 1.00-2.15
Fundamentals of cell biology, Darwinian evolution, immunology, and genetics that underlie cancer the history of cancer science and treatment historical and current policy issues. Enrollment limited to freshmen. Preregistration required see under Freshman Seminar Program.

MCDB 050a. Immunology and Microbes.
Paula Kavathas
TTh 1.00-2.15
Introduction to the immune system and its interaction with specific microbes. Attention both to microbes that cause illness, such as influenza, HIV, and HPV, and to microbes that live in harmony with humans, collectively called the microbiome. Readings include novels and historical works on diseases such as polio and AIDS. Enrollment limited to freshmen.

MCDB 065a, Science & Politics of HIV/AIDS.
Robert Bazell
TTh 2.30-3.45
Study of the basic virology and immunology of HIV/AIDS, along with its extraordinary historical and social effects. Issues include the threat of new epidemics emerging from a changing global environment the potential harm of conspiracy theories based on false science and how stigmas associated with poverty, gender inequality, sexual preference, and race facilitate an ongoing epidemic. For all first-year students regardless of whether they are considering a science major. Prerequisite: AP Biology or equivalent. Enrollment limited to first-year students. Preregistration required see under First-Year Seminar Program.

[MCDB 103b. Cancer.]

MCDB 105a or b/MB&B 105a or b. Biology, the World and Us.
Fall: John Carlson, Joshua Gendron, Anthony Koleske
Spring: Donald Engelman, Scott Strobel, Shirin Bahmanyar, Jacob Yannick, Candice Paulsen
MW 11.35-12.25 1 HTBA
Biological concepts taught in context of current societal issues, such as emerging diseases, genetically modified organisms, green energy, stem cell research, and human reproductive technology. Emphasis on biological literacy to enable students to evaluate scientific arguments.

MCDB 106a/HLTH 155a. Biology of Malaria, Lyme, and Other Vector-Borne Diseases.
Alexia Belperron
MW 1.00-2.15
Introduction to the biology of pathogen transmission from one organism to another by insects special focus on malaria, dengue, and Lyme disease. Biology of the pathogens including modes of transmission, establishment of infection, and immune responses the challenges associated with vector control, prevention, development of vaccines, and treatments. Intended for non–science majors preference to freshmen and sophomores. Prerequisite: high school biology.

MCDB 109b. Immunity and Contagion.
Paula Kavathas
TTh 2.30-3.20 Meets RP
Introduction to the basics of the immune system strategies to fight pathogens while maintaining harmony with our microbiome. Discussion of specific microbes such as influenza, HIV, and HPV historical analysis of the polio vaccine and the AIDS epidemic. Enrollment limited to freshmen and sophomores.


Clusters

Instructors:
Curt Schurgers, Associate Teaching Professor, Department of Electrical and Computer Engineering, UCSD
Leo Porter, Associate Teaching Professor, Department of Computer Science and Engineering, UCSD

Karcher Morris, Assistant Teaching Professor, Department of Electrical and Computer Engineering, UCSD

Algebra II or Integrated Math II (The focus of this cluster is students with little or no prior programming experience)

Description:These days computers are everywhere, from our coffee makers and thermostats to our cell phones and televisions. They make our cars safer and more efficient they perform advanced image processing in intelligent devices they are the engines behind creating our movies, television shows, and our video games and they fuel the Internet of Things. This course will focus on the basics of computing and coding, making it accessible to students who have no prior programming experience. It provides an introduction to computation through lectures, guest speakers, and projects. It starts by teaching the fundamentals of programming where students use a puzzle-like programming language called AppInventor to create mobile phone applications. Students then learn one of the most commonly used programming languages in the world, Python, and use it to perform image manipulation (e.g., image blurring, green screen substitution) and later to author video games. The complexity of the projects grows each week and culminates in a substantial final project where students form small teams and create a project of their choosing.

System requirements (minimum): Laptop with 4GB of RAM. Windows, Mac, Linux, or Chromebook. (Minimum)

System requirements (strongly recommended): Laptop with 8BG of RAM. Windows, Mac, Linux, or Chromebook.

Cluster 2 - Engineering Design and Control of Kinetic Sculptures

Instructors:
Raymond De Callafon, Professor, Department of Mechanical & Aerospace Engineering, UCSD

Prerequisite:
Algebra I and 8th-grade general science or equivalent

Recommended:
Algebra II or Integrated Math II, Trigonometry, Physics

Description:
Mechanical Engineering and Computer Control are brought together in many modern products that have moving parts, ranging from heavy automobiles to light-weight drones and robotic vacuum cleaners. In this cluster, students will analyze, design and build Kinetic (Moving) Sculptures operated under Automatic Control to get a comprehensive introduction to mixed disciplines in the field of engineering. Students design and analyse a pendulum clock during the first week to become familiar with Inventor, AutoCAD, running 2D dynamic simulations, and (remote) manufacturing capabilities of a LASERcamm and a 3D printer. In the following weeks, Mechanical Engineering methods will be used to analyse, design and build three dimensional kinetic sculptures where marbles move along ramps, bounce on trampolines and drop in baskets. The sculptures are augmented with sensors, motors and computer control to emphasize the mix of engineering skills needed to design a reliable and automatically controlled kinetic sculpture. The students attending this cluster will walk away with valuable engineering experiences that include the use of modern micro-processor controller to measure and analyze timing and mechanical behavior of their design and integrating engineering design and control principles throughout the curriculum of this cluster. Moreover, student will be able to (remotely) use the state of the art facilities at the Mechanical and Aerospace Engineering (MAE) department that include the MAE Design Studio, LASERcamm and 3D Printers for rapid prototyping along with advanced computer laboratories for creating computer drawings, running dynamic simulations and programming a microcontroller. Examples of prior year projects can be seen here.

In case of VPN/remote connection to UCSD network, we may be able to support any hardware and OS (except for Chromebook)

Cluster 3 - Climate Change

*Adult supervision is strongly recommended for some cluster activities.

Instructors:
Robert Pomeroy, Associate Teaching Professor, Department of Chemistry and Biochemistry, UCSD

Climate Change is one of the most important and controversial issues facing our world. This cluster will break Climate Change into four parts. The first section will focus on the science of Green House Gases, GHGs, and their impact on the atmospheric energy balance. In the next section we will introduce the current research conducted at UC San Diego examining the role of aerosols on the energy balance and climate. These aerosols are influenced by the biology in the ocean and are subsequent chemical transformation in gas phase reactions which serve as the third section. The cluster will explore how global industrial human activity has impacted health, food security, and land utilization. We will also review how we might mitigate climate change through reduced utilization, alternate energy sources, carbon abatement and geoengineering.

Sample projects for this cluster include:

GHG Climate change simulations

The Direct and Indirect effect of atmospheric aerosols

The other carbon Problem: Ocean Acidification

Atmospheric VOCs and Secondary Organic Aerosols

Bending the Curve: How do we reach carbon neutrality?

Nuclear Energy and the Lithium Fluoride Thorium Reactor

Replacing Petroleum: Biofuels and Bioplastics

Cluster 4 - Structural Engineering: Building Better

*Adult supervision is strongly recommended for some cluster activities.

Instructors:
Lelli Van Den Einde, Associate Teaching Professor, Department of Structural Engineering, UCSD

Prerequisite:
Two years of Algebra or Integrated Math I & II (with Trigonometry component)

In Cluster 4, we like to build AND break things. We build small scale models of all types of structures (bridges, buildings, foundations, soils, underground pipes, aerospace structures, wind turbines, automobiles, human body, etc.) to see how we as engineers can put together different components to build strong structural systems. Every crack and every snap is exciting! We want to understand how and why it failed, discuss what it means, and consider different methods of improving the design to build it better! To further the understanding of building materials, the effects of natural forces such earthquakes, blasts and wind, and project planning and building, we will also do a number of hands-on laboratories. No matter what the structure is, we want to learn to build it better. We will introduce you to structural engineering and immerse you in the design and problem solving process. By the end of this cluster, students will be able to:

Describe the structural engineering major at UC San Diego and explain the role of a structural engineer.Design, build and test a structural component or system, analyze its performance, and evaluate and recommend a possible redesign from initial failures. Interpret structural engineering (SE) concepts such as mechanics and materials, and apply them to the structural design of a component or system. Demonstrate proficiency in the soft skills such as oral and written communication, teamwork, and engineering ethics required to succeed in a multidisciplinary engineering field. See last year's Cluster 4 video.

Windows/Mac PC with at least 8GB of RAM and 100GB of free space

Cluster 5 - Photonics: Light-based Technologies in Everyday Life

*Adult supervision is strongly recommended for some cluster activities.

Instructors:
Charles Tu, Distinguished Professor, Department of Electrical and Computer Engineering, UCSD
Saharnaz Baghdadchi, Assistant Teaching Professor, Department of Electrical and Computer Engineering, UCSD

Prerequisite:
1 year of Physics preferred

We seldom realize how much our everyday life uses photonics, or light-based technologies, such as in cell phone display, traffic light, DVD player, solar cells, microscopes, endoscopes, optical fiber transmission, etc. The progress of photonics is rapid, similar to Moore’s Law for electronics. One recent aspect of this progress is the integration of photonics with electronics to produce high-speed Silicon photonics integrated circuits. Other advances in photonics includes the integration of artificial intelligence with computational optics and the development of optically assisted diagnostics or therapeutic medical devices. While the economic driver for the 20th century was electronics, the economic driver for the 21st century is photonics.

In this Cluster, we shall study the generation, manipulation, transmission, detection, and applications of light. Students will first conduct experiments with LED, laser, prism, lens, and spectrometers to study wave properties of light, such as polarization, diffraction, and interference, and also particle properties of light, such as photoelectric effect/solar cell. They will then work on “workshop” projects, including plastic lens, solar cells, etc. Afterward, they will work on final projects of their choice as a team, and present the results to the Cluster and their families before the virtual closing ceremony.

Any computer with internet access will work

**Not offered this year due to COVID-19

Cluster 6 - Biodiesel from Renewable Sources **Not offered this year due to COVID-19

Instructors:
Robert S. Pomeroy, Associate Teaching Professor, Department of Chemistry and Biochemistry, UCSD

Prerequisite:
Introductory high school chemistry – Basic knowledge of ionic and covalent bonding, electronegativity and intermolecular forces of attraction.

Description:
This course will introduce students to renewable biofuels. This is a laboratory intensive experience where the students will extract and purify oil (lipids) from biomass, convert the oil into Fatty Acid Methyl Esters, FAMEs, also known as biodiesel, wash and purify the biodiesel, and then analyze the quality of the finished product. They will use advanced instrumentation such as FTIR, GCMS, Chromatography, and Bomb Calorimetry to determine the quality of their fuel.

Sustainable energy engages scientists, entrepreneurs and consumers searching for a renewable form of energy that will also not place the Earth's ecosystem at greater risk. Biofuels can be generated from biomass. This biomass can range from terrestrial, agricultural, forestry and municipal wastes, energy crops like soybeans, rapeseed, switchgrass and algae. Biodiesel has gained attention in recent years as a renewable fuel source due to its reduced greenhouse gas and particulate emissions, and it can be produced within 10 states in the US. For projects students will create higher value materials from plant lipids to produce renewable and sustainable bioplastics which economically serve as a bridge to large scale biofuels production.

Cluster 7 - Synthetic Biology

* This cluster is First Choice only.

*Adult supervision is strongly recommended for some cluster activities.

Instructors:
Mauricio de Oliveira, Adjunct Associate Professor, Department of Mechanical & Aerospace Engineering, UCSD
Carlos Vera, Lecturer, Department of Bioengineering, UCSD

Prerequisite:
One year of high school biology.

Synthetic biology is an emerging engineering field that aims to produce novel organisms in scalable and reliable ways to do something useful for humankind. For example, treat diseases, sense toxic compounds, produce new fuels or valuable materials.

After thousands of years of genetic manipulation by selective breeding, genetic engineering has finally developed techniques to read and modify the genetic code. Synthetic biology enriches genetic engineering by applying the basic engineering principles (design, build, test) to modular systems built from simple Lego-like standardized biological parts obtained from an open source catalog.

Taking advantage of the increased capabilities to "write" and "read" genetic code, it is now possible to assemble large DNA sequences in a minimal amount of time. These coding sequences can be incorporated into plasmid vectors and introduced into the cells to reprogram the DNA original instructions. This new genetic code will produce new proteins that may modify the structure and/or function of the cell. In that sense, synthetic biology develops software that builds its own hardware!

One of the newest techniques of synthetic biology, CRISPR-Cas9, is revolutionizing biomedical sciences by allowing the editing of genetic information in living complex organisms. This revolution has created myriad ethical dilemmas that will also be analyzed in the course.

In this course we will introduce the basic concepts and techniques of synthetic biology, apply engineering principles to design, build and test modified organisms, and develop mathematical models that quantitatively describe their behavior. The students will learn basic recombinant engineering techniques to clone specific DNA sequences in plasmid vectors, how to transform E.coli bacteria and S. cerevisiae yeast with plasmid vectors to produce fluorescent and bioluminescent proteins, purify recombinant proteins, produce proteins in cell-free systems, test a basic CRISPR-Cas9 system, and predict the behavior of modified organisms using predictive mathematical models.

In this online version of cluster 7, students will be provided with electronic components to model biological circuits and will learn how to simulate basic genetic circuits from the molecular to the cell level. Students will work in small teams with an instructor to work on their circuits and to develop their own programming code to perform simulations. No prior programming experience is required. Many of the wet lab techniques learned will be demonstrated by our instructors remotely from a laboratory and will provide opportunities for the students to interact live with the experiments so that the results can be used to test the students’ models. The course ends with students developing their own projects using their newly learned engineering skills.


Introduction

For most of its history, developmental biology has been mainly an analytical science with a strong focus on uncovering detailed mechanisms of embryogenesis. Early work was purely descriptive but, particularly from the mid-19th century, descriptive embryology was supported by hypothetico-deductive approaches in which researchers proposed hypotheses and tested them by manipulating embryos. Experimental techniques have included: surgery, resulting in the discoveries of regulative development and induction (see Glossary, Box 1) genetics, resulting in the correlation of genotype and phenotype and the implication of specific molecules in particular events environmental perturbation, resulting in an understanding of the influences of external signals and the production of chimaeras and mosaics (see Glossary, Box 1), resulting in an understanding of cell fates and potencies. The details of embryonic development have turned out to be complicated, particularly at the molecular level, and this has encouraged researchers to integrate results and formulate abstract principles through which embryonic development is thought to occur. These principles are expressed in terms much simpler than the fine details of any real embryonic event. Examples include the use of gradients to specify positional information [e.g. the French Flag Model (Wolpert, 1969), see Glossary, Box 1], the use of reaction-diffusion (see Glossary, Box 1) for de novo patterning (Turing, 1952), the use of feedback by trophic signals to balance cell populations (Raff, 1992), and the use of a landscape of creodes (see Glossary, Box 1) and Boolean networks (see Glossary, Box 1) to determine transitions between states (Waddington, 1957 Kauffman, 1993). There are, of course, many more. These principles stand above the level of the specific details of any particular developmental system, analogous to the way that the principles of rhythm and harmony stand above the specific details of any particular symphony. Together, the principles form a framework for our current understanding of development, even though, as in the musical analogy, it may be that real embryos demonstrate the pure principles only approximately, each being cluttered with different detailed variations.

Boolean network: A network of entities (e.g. genes) that can be in one of two states, 0 or 1, and that are controlled by the statmorphoes of certain other entities (genes) in the network, with controls from several genes on the same controlled gene being combined according to a Boolean rule. For example, ‘Gene D will be in state 1 if genes A AND gene B are in state 1 OR if gene C is in state 1.’ [see Kauffman (1993) for more details].

Chimaera: An embryo formed from a mix of cells from two embryos of different genotypes.

Creode: One of a range of possible trajectories in state space that might be pursued, in normal development, by an embryonic cell as it develops towards one of a choice of fates. Croedes are akin to branching railway tracks in a marshalling yard, down which wagons can be switched.

French Flag Model: An illustration of the principles by which morphogen gradients work: the idea is that a morphogen gradient extends across a blank flag and the cells therein read the levels of morphogen to decide whether to be red, white or blue.

Hysteresis: A response that follows one pathway in the forward direction but a different pathway in the return direction (e.g. a thermostat that turns ‘on’ at 20°C but ‘off’ at 22°C). Hysteresis can be used to avoid vacillation.

Induction: In developmental biology, triggering the development of one tissue using signals coming from a different tissue in genetics and synthetic biology, triggering gene expression using an exogenous factor.

Inverting path: A signalling pathway in which activation at the start causes inhibition of the output.

Lateral inhibition: A cell following a fate choice makes a local signal that inhibits its neighbours from making the same choice. This is one mechanism for regulative development (q.v.).

Morphogen: A diffusible signalling molecule, the local concentration of which influences development.

Mosaic: An embryo or tissue formed from a mix of cells of different genotypes, usually made by mutation of one or more cells in a normal two-parented embryo.

Orthogonality: The (ideal of) non-interaction between two systems (e.g. synthetic and natural).

Oscillators: Devices (natural or engineered) that generate an output that rises and falls repeatedly.

Phase-locking: Keeping the oscillations of multiple devices or cells in step with one another.

Quorum sensing: Cells detecting the size of the aggregate in which they are located.

Reaction-diffusion: A mechanism for generating patterns in which the local concentration of signalling molecules depends on both the local reactions (synthesis and destruction) of the molecules and also their diffusion.

Regulative development: A mode of development in which feedback controls cell fate so that, for example, deletion of cells fated to make a specific structure is followed by their automatic replacement by neighbours not initially fated to make that structure.

Segmentation: In development, the division of the body into segments (e.g. those obvious even from the outside of an earthworm).

However, in any field limited to analysis, the verification of derived principles is problematic. Confirming the details of a particular developmental event – for example, that gene X is necessary for process Y – is straightforward, but proving that the conceptual principles are fully adequate is more of a problem. The field of synthetic biology can help to overcome this problem: if a complex system is believed to achieve its action according to a simple principle, then constructing a new system based on that principle and assessing whether it performs the required action provides a powerful verification. This idea that biological understanding is best understood by using it to construct artificial systems is by no means new: in 1912, the pioneering synthetic biologist Stéphane Leduc stated ‘when a phenomenon has been observed in a living organism, and one believes that one understands it…one should be able to reproduce this phenomenon on its own’ (Leduc, 1912). Many years later, Richard Feynman made a similar point, in the context of mathematical equations, writing ‘What I cannot build, I do not understand’. Leduc was most interested in the biophysics of morphogenesis, and the main focus of his book La Biologie Synthétique (Leduc, 1912) was the construction of non-living analogues of biological forms. This work, which drew on the work of earlier synthetic biologists such as Traube (Traube, 1866) and which in turn gave strong inspiration to the pioneer of theoretical embryology, Thompson (Thompson, 1917), was itself a scientific dead-end, because the similarities of shape between organisms and inorganic forms turned out to be mainly coincidental and not due to common morphogenetic mechanisms. For this reason, synthetic biology largely disappeared after the First World War.

The 21st century has seen a dramatic resurgence of synthetic biology, now with a focus on the construction of designed genetic systems. Much of the work in this modern era of synthetic biology has been concerned with industrial applications, such as the construction of new metabolic pathways for production of drug precursors (Ro et al., 2006) or biofuels (d'Espaux et al., 2015), or the construction of systems to detect traces of pollution (Webster et al., 2014). There is also discussion, and some preliminary data, regarding the potential use of synthetic biology for tissue regeneration and regenerative medicine, both for creating better disease models and for constructing treatments (Ruder et al., 2011 Hutmacher et al., 2015 Davies and Cachat, 2016). The technology also lends itself, however, to being used as a tool for the basic sciences. In particular, it can allow developmental biologists to construct ‘developmental’ systems based on the current principles in the field, and to verify that mechanisms that seem realistic in computer models are realistic in living cells. It can also allow the testing of new ideas derived from imagination rather than analysis of real embryos these ideas might be alternative methods for performing a task that seems not to be used in naturally evolved organisms.

Although the application of synthetic biology to development is a young field, it is clear that progress has been made and that the field is expanding. Here, I review this progress with the aim of bringing together the results, engaging greater numbers of mainstream developmental biologists and, hopefully, stimulating interesting collaborative research. The processes of development are often divided into patterning, differentiation and morphogenesis, and this Review is organized according to that structure, with the principles of each topic being described first, followed by presentation of the synthetic biological systems that have been built to better understand these principles. In each section, at least one seminal synthetic biological mechanism is explained in detail and, to save space, related systems are described only in sufficient depth to convey the developmental biological relevance of the later work: details can be found in the cited papers.

Before going into details, one thing should be made clear: I am not arguing that a synthetic biological approach will be the best way to discover the mechanistic details of any specific embryological event. The only way to do that is to study the event in the real embryo. Rather, I argue that synthetic biology allows us to test and further develop high-level principles of biological self-organization that underlie embryogenesis in general. Synthetic approaches have been used in this way in other sciences: it was experience with synthetic chemistry, rather than the analysis of natural compounds, that finally illuminated the nature of the chemical bond (reviewed by Asimov, 1979). Similarly, discoveries made when building and testing engineered electrical apparatus led to an understanding of electricity in general that could then be applied back to complicated natural phenomena such as electrophysiology (Piccolinoa, 1997). This article will make an argument that synthetic biological systems will be of similar use to developmental biology.


References

Tasic B, et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature. 2018563(7729):72-8.

Hie B, Peters J, Nyquist SK, Shalek AK, Berger B, Bryson BD. Computational methods for single-cell RNA sequencing. Annu Rev Biomed Data Sci. 20203:339-64.

Davie K, et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell. 2018174:982-98.

Dong X, et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat Methods. 201714:491–3.

Cao J, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science. 2018361:1380–5.

Karemaker ID, Vermeulen M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 201836:952–65.

Rotem A, et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat Biotechnol. 201533:1165–72.

Stoeckius M, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 201714:865–8.

Rodriques SG, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019363:1463–7.

Kiselev VY, Yiu A, Hemberg M. Scmap: projection of single-cell RNA-seq data across data sets. Nat Methods. 201815:359–62.

Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 201937:685–91.

Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 201836:421–7.

Barkas N, et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods. 201916:695–8.

Korsunsky I, et al. Fast, sensitive, and accurate integration of single cell data with Harmony. Nat Methods. 20186(12):1289-96.

Stuart T, et al. Comprehensive Integration of single-cell data. Cell. 2019177:1888–1902.e21.

Welch JD, et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell. 2019177:1873–1887.e17.

Svensson V, Teichmann SA, Stegle O. SpatialDE: identification of spatially variable genes. Nat Methods. 201815(5):343-6.

Edsgärd D, Johnsson P, Sandberg R. Identification of spatial expression trends in single-cell gene expression data. Nat Methods. 201815(5):339-42.

Sun S, Zhu J, Zhou X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat Methods. 202017(2):193-200.

DeTomaso D, Yosef N. Identifying informative gene modules across modalities of single cell genomics. bioRxiv. 2020:2020.02.06.937805. https://doi.org/10.1101/2020.02.06.937805.

Argelaguet R, et al. MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data. bioRxiv. 2019:837104. https://doi.org/10.1101/837104.

Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood Components Analysis. In: Advances in Neural Information Processing Systems 2004.

Davis JV, Kulis B, Jain P, Sra S, Dhillon IS. Information-theoretic metric learning. In: ACM International Conference Proceeding Series 2007. https://doi.org/10.1145/1273496.1273523.

Weinberger KQ, Saul LK. Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res. 2009 https://doi.org/10.1145/1577069.1577078.

Xing EP, Ng AY, Jordan MI, Russell S. Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems 2003.

Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 201815(12):1053-8.

Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ. Single-cell RNA-seq denoising using a deep count autoencoder. Nat Commun. 201910:390.

Lotfollahi M, Wolf FA, Theis FJ. scGen predicts single-cell perturbation responses. Nat Methods. 202016(8):715-21.

Gayoso A, Steier Z, Lopez R, Regier J, Nazor KL, Streets A, Yosef N. Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells. bioRxiv. 2020 https://doi.org/10.1101/2020.05.08.083337

Wu M, Goodman N. Multimodal generative models for scalable weakly-supervised learning. arXiv Preprint arXiv. 2018:1802.05335. https://arxiv.org/abs/1802.05335v3.

Shi Y, Siddharth N, Paige B, Torr PH. Variational mixture-of-experts autoencoders for multi-modal deep generative models. arXiv Preprint arXiv. 2019:1911.03393. https://arxiv.org/abs/1911.03393v1.

Kurle R, Günnemann S, Van der Smagt P. Multi-source neural variational inference. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33 2019.

Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 20199:1-12.

Mather A, Pollock C. Glucose handling by the kidney. Kidney Int. 201179:S1-S6.

McInnes L, Healy J, Saul N, Großberger L. UMAP: uniform manifold approximation and projection. J Open Source Softw. 20183:861.

Drysdale R, FlyBase Consortium. FlyBase. Drosophila. 2008:45–59.

Saunders A, et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell. 20184:1015-30.

Fabregat A, et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 201846:D649-D655.

Singh NK, et al. Emerging concepts in TCR specificity: rationalizing and (maybe) predicting outcomes. J Immunol. 2017199:2203–13.

Dash P, et al. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature. 2017547:89–93.

Thakkar N, Bailey-Kellogg C. Balancing sensitivity and specificity in distinguishing TCR groups by CDR sequence similarity. BMC Bioinformatics. 201920(1):1-14.

Shugay M, et al. VDJdb: a curated database of T cell receptor sequences with known antigen specificity. Nucleic Acids Res. 2018D1:D419-D427.

Murugan A, Mora T, Walczak AM, Callan CG. Statistical inference of the generation probability of T-cell receptors from sequence repertoires. Proc Natl Acad Sci U S A. 2012109:16161-6.

Pijuan-Sala B, et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature. 2019566:490-5.

Argelaguet R, et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 202021:1-17.

Hochgerner H, Zeisel A, Lönnerberg P, Linnarsson S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat Neurosci. 201821:290-299.

Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020:1–7. https://doi.org/10.1038/s41587-020-0591-3.

Hie B, Cho H, DeMeo B, Bryson B, Berger B. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst. 20198(6):483-93.

DeMeo B, Berger B. Hopper: a mathematically optimal algorithm for sketching biological data. Bioinformatics. 202036:i236-i241.

Argelaguet R, et al. Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature. 2019576:487–91.

Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 201112:2825-30.

Wolf FA, Angerer P, Theis FJ. SCANPY: Large-scale single-cell gene expression data analysis. Genome Biol. 201819(1):1-5.

Haghverdi L, et al. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 201613(10):845.

Elad M. Sparse and redundant representations: from theory to applications in signal and image processing 2010. https://doi.org/10.1007/978-1-4419-7011-4.

Mallat SG, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process. 199341:3397-15.

Singh R, Hie B, Narayan A, Berger B. Source code for “Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities”. Github. 2019 https://github.com/rs239/schema

Singh R, Hie B, Narayan A, Berger B. Schema release v0.1.0. Zenodo. 2021 https://doi.org/10.5281/zenodo.4521803.

Lajoie BR, Dekker J, Kaplan N. The Hitchhiker’s guide to Hi-C analysis: practical guidelines. Methods. 201572:65-75.

Davis CA, et al. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 2018D1:D794-D801.


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Book chapter on ‘Taking Care’ in synthetic biology

Synthetic biology, from its early stages of development, has had an explicit focus on biosafety and biosecurity concerns. These concerns are being identified and addressed in different ways, including strategies that ‘take care of’ them by getting them off the mind, and approaches that attend to and ‘care for’ concerns in more open-ended ways. In this chapter, we look at the shaping of concerns and non-concerns relating to biosafety and biosecurity in two high-profile synthetic biology initiatives: the US-based Synthetic Biology Engineering Research Center (Synberc), and the international genetically engineered machine (iGEM) student competition. We identify a variety of examples and strategies by which actors within these initiatives are rendering safety and security concerns visible and invisible. We suggest that each reflects a particular, situated understanding of and approach to ‘taking care,’ with different implications for how the institutions, epistemic structures, practitioner identities, and objects of synthetic biology may develop. In these examples, we also strive to account for our own involvement as social scientists in the activities of Synberc and iGEM.

This chapter is part of a book that explores the absent and missing in debates about science and security. Through varied case studies, including biological and chemical weapons control, science journalism, nanotechnology research and neuroethics, the contributors explore how matters become absent, ignored or forgotten and the implications for ethics, policy and society.

Evans, Sam Weiss, and Emma K. Frow. 2015. “‘Taking Care’ in Synthetic Biology.” In Absence in Science, Security and Policy: From Research Agendas to Global Strategy, edited by Brian Rappert and Brian Balmer. Palgrave.

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