Lorenzo: Opening Remarks
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 38 | |
| Author | ||
| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/20538 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
|
1
3
4
8
9
11
12
13
15
21
23
25
33
35
38
00:00
DNS-SyntheseRNAProteinFermiumStream gaugeSynthetic oilStuffingFunctional groupWaterfallCollectingCaffeineSpawn (biology)MethylgruppeMill (grinding)Volumetric flow rateHerzog August LibraryFoodDiethylstilbestrolInsulin shock therapyCuring (food preservation)GesundheitsstörungWaterTocopherolNahtoderfahrungGeneAleTeaBiotechnologyAgeingS-Adenosyl methionineDigoxigeninSilicon Integrated SystemsUsher 1CAsh (analytical chemistry)IndiumLibrary (computing)Ice frontTidal raceBeverage canMonoamine oxidaseWine tasting descriptorsChemical reactionDigital elevation modelStem cellLakeSecondary ion mass spectrometrySea levelBia RiverMeatOctane ratingFamily and consumer scienceGemstoneSynthetic biologySystems biologyWursthülleMolecular biologyOrigin of replicationTool steelDNS-SyntheseMaterials scienceSynthetic oilElectronic cigarettePerfumerProcess (computing)Human subject researchSystemic therapyOrganische ChemieTopicityComputer animationLecture/Conference
08:32
Synthetic oilDNS-SyntheseRNAProteinModul <Membranverfahren>EnzymeSolutionSetzen <Verfahrenstechnik>Pitting corrosionProcess (computing)WursthülleComposite materialElectronic cigaretteFunctional groupDNS-SyntheseSystemic therapyConnective tissueMultiprotein complexThermoformingSeparator (milk)CheminformaticsSystems biologySetzen <Verfahrenstechnik>Substrat <Chemie>River sourceTiermodellData conversionSolutionMetabolic pathwayPhysical chemistrySynthetic oilProcess (computing)Body weightRNASynthetic biologyCollectingHydrophobic effectVolumetric flow rateWine tasting descriptorsChemical propertyOrigin of replicationController (control theory)ErdrutschChewing gumFiningsMethylgruppeIceUsher 1CAtom probeResearch Institute for Mathematical SciencesTeaFamily and consumer scienceStorage tankTin canSaltMeatAcetylcholinesteraseBase (chemistry)Active siteBiochemistryAreaTool steelWalkingMonoamine oxidaseFertigteigHeck-ReaktionWaterfallGesundheitsstörungEndokrin wirksamer StoffSteelButcherAngiotensin-converting enzymeMayonnaiseTidal raceInsulin shock therapyStress (mechanics)Cell (biology)AdenineComputer animationLecture/Conference
16:59
ProlineBiochemistryMetabolic pathwaySynthetic biologySystemic therapyTopicityZunderbeständigkeitSetzen <Verfahrenstechnik>Breed standardSynthetic oilFunctional groupMoleculePaste (rheology)Connective tissueMetabolic pathwayBody weightCheminformaticsSystems biologyChemical compoundWalkingSolutionChemical structureWursthülleElectronic cigaretteHardnessBase (chemistry)Chemical propertyBleitetraethylAmineScaffold <Biologie>Tool steelOptische AktivitätSteelDyeingWettingÖlWine tasting descriptorsTarMill (grinding)FertigteigHydrophobic effectTin canGesundheitsstörungLamb and muttonEms (river)FiningsGeneElektrochemische ReduktionLeadNitrosamineTeaProlineS-Adenosyl methionineCigarHope, ArkansasIce frontLecture/Conference
Transcript: English(auto-generated)
00:17
So we have been asked by the organizers as chairs of the different sessions, not only to chair and control the time and so on,
00:25
but also to put some perspective in the contents of the various topics that will be discussed by the different speakers. So I take this opportunity to also share with you some of my thoughts on the matters
00:42
that we will be discussing today and the next few days and also to warm up a little bit the subsequent presentations. So I have been asked also by the organizers that to ask you as an audience to please refrain from asking questions on the spot after the different presentations,
01:00
but just to postpone them until the general discussion that will take a good 40 minutes after each session. So we have time to not only to interrogate and to ask specific questions to different speakers, but also to raise more general questions for a more general discussion as well. And this is exactly what I wanted to do this morning as the first little presentation or the first little talk that you are going to have.
01:30
So I will not be discussing the work that we make in my laboratory directly with details and so on. I just want to share with you some thoughts that were provoked by the
01:40
program once I had the chance to see the different contents and the different speakers. So while I come from Madrid, as I told you, this is the National Center for Biotechnology. Some of you have been visiting there and well, the matter and the object of this of the subject of this whole meeting for this week is synthetic biology.
02:01
So every meeting on synthetic biology may start with some discussion of what is synthetic biology. I will not get into that, but I want just to remind you that the term synthetic biology was coined in France, maybe with a different meaning. But indeed, the term is not new, despite some claims. It has been around for a while, but it has been subject to different trans significations.
02:25
And the most modern and the most popular meaning is about engineering biology and doing things connected to interfacing biology with biology. But I want to go a little deeper into this question.
02:40
So as a matter of fact, I think that the starting point of synthetic biology is this reaction that we may have when we find a complex object. So when you are in the wild and you find something that is there, you don't know very well how it works. There are two legitimate questions that you may want to raise.
03:03
One of them is where does it come from and second, how it works. And I think that in biology, we tend to mix these two questions and from my perspective, they are completely different. One thing is to ask the origin of things and the other is to ask how these things work.
03:23
Okay, so in biology, there is this general theory, evolution combined with modern genetics. And the two pillars of modern biology are the ones that you see there. On the one hand, the Arbenean evolution and all the rest of it. And then the central dogma through which DNA goes to RNA, goes to proteins and if you want also, it goes into metabolism.
03:44
So the main argument that I want to try to convince you, and you are very welcome to disagree, is that in fact evolution is great as a conceptual frame to understand biology, but it does not help us to understand how things work. They tell us where they come from, but not so much how they work.
04:01
So how biological entities come to work? Well, there is this concept that was developed by Francois Jacob, also in Paris, in the Pasteur Institute, that is called tinkering in English. I have to say that the word bricolage in French, in my opinion, is not identical to the word tinkering in English,
04:23
but still, you know, there is this idea that evolution just, you know, takes one thing that is available, wires it to another thing and then it works and then somehow it can be hooked up to another thing. And little by little, you end up having something that works, and this is what we see in biological objects today.
04:45
However, I have the impression that if you take this concept too seriously, at the end you end up with this kind of grotesque situation that is exemplified by the cartoons of Rube Goldberg. And this idea that you can concatenate, you can link various events and at the end get something working,
05:03
but at the end, the way, the process that brings about the outcome is very weird. So for instance, in this cartoon, and there are many like this, the question or the challenge is to wake up that gentleman there, and then the way to do it is that you have that bird, that, let me see if this works here.
05:20
So you have this, the day arises and you have this bird that eats a worm, then there is a thread here that will get a pistol, the pistol will get into a balloon, then a brick, the brick will go into this perfume box, then it will release some vapor, then there's a sponge that will come down, and then at the end there will be this cannonball that will go in the face of the guy there.
05:43
So sometimes one has the impression that in the biological system you have something like that. So as a matter of fact, maybe this is not sufficient to understand how things work, because of this phenomenon that in evolutionary biology is called exaptation. So exaptation means that one object, one biological object that appears in evolution to fulfill a given function,
06:04
in the next stage of evolution it may go into a different, completely different function. And the examples are feathers, for instance. So feathers were, say, invented to provide thermal insulation to dinosaurs, but they were invented for that, but their major evolutionary outcome had nothing to do with thermal insulation, had to do with flight.
06:23
So the fact that you know how something works at one stage of evolution will tell you nothing of how it works in the next stage. And therefore, here comes my argument that by following the genealogy of different biological objects, we in reality will understand, we may not understand, or it does not help as much to understand how these things work.
06:44
Then here comes synthetic biology. So synthetic biology, from my point of view, is about doing things and all this wonderful paper with Cas9 and CRISPR and everything, but I think it has a more profound scientific impact. Because it's no more and no less than taking a look to life systems using engineering as an interpretative frame.
07:04
So from that perspective, you know, like by the same token, that molecular biology was born out of the interest of physicists, and in some cases nuclear physicists for biology. In synthetic biology comes from the interest of engineers, real engineers, to look at biological systems using their own interpretative frames,
07:26
their own concepts, their own, even their own materials. So this has serious consequences, okay, because depending on what frame one uses to understand things, the kind of consequences and their resulting agenda might be quite different.
07:41
So engineering as an interpretative frame can be used for understanding. Then we can look at biological objects, looking at their, for instance, their composition, their relational logic, and other aspects that perhaps only engineers have looked at from that perspective. Also, of course, it's about doing, and this is doing and creating, and the two more visible outcomes of synthetic biology.
08:05
But let us not forget that there is this big aspect, and this important aspect of using engineering as a frame, as a tool for understanding the state of affairs. And this is something that I like very much. So we can return to biological systems and look at them as if they had a relational logic that explains its function.
08:27
And this is the twist that I think is important. In the case of synthetic biology, the emphasis is not in the evolutionary origin of things, it's in the composition and relational logic of the things that makes them to work here and now.
08:41
And, you know, that changes a little bit the historical, say, drive for biologists to be interested in evolution and to just concentrate in understanding the origin of things. The twist now is to concentrate in how things work and whether we can improve the way of working and whether we can even create things that using biological components still will work in a different fashion.
09:04
So the consequence of adopting this engineering frame is the kind of discourse that you may have seen in many places. This is a typical slide shown in every talk on synthetic biology, how in synthetic biology we adopt the abstraction hierarchy that is typical of engineers.
09:21
We start with parts, then it goes into devices, and it goes into systems, and then you get all this nice narrative of contemporary synthetic biology. And the consequence of that is that whereas in traditional biology, you have a central dogma that tells you that DNA goes to RNA and so on.
09:41
In the case of synthetic biology, the dogma or the tenet is changed a bit, and now the question is not to understand this flow of information, the question is to understand how parts make devices, how they make eventually models and perhaps systems, and by the same token that we understand them, we can also recompose them and generate new properties.
10:02
And this is something that is the kind of big core of synthetic biology. So the situation is that we want to start with an existing system, then by using this plethora of omics approaches, one ends up with a collection of parts, and then you can make a model, and then you go into this cycle,
10:20
or you can go out of this cycle and using the same parts with the characteristics and so on and produce objects that are not exactly what you had at the beginning, but still they follow a relational logic that follows the kind of tenets of engineering. So here comes now the interesting challenge for synthetic biologists.
10:41
That is how to construct complex objects, and one can take a look to biological systems as complex objects, and the challenge first to understand how they work, but also how to rearrange their connectivity and their forms and their parts and their layout to produce different still complex objects.
11:03
So here you have what one engineer would look at. In the case of biology, things can get a little more complicated because of various things. The main thing is this issue of how to connect, how to add different functions in a complex system
11:21
in such a way that at the end the whole system works. So biological systems are actually connected. Everything, not everything, but many components of a system is connected to the others. Directly and directly you have these systems, these actions at a distance and so on. So you can know a lot about the composition of different parts. For instance, in this case you have a pathway that you want to build
11:43
for conversion of this substrate into its product here, and it goes through a series of steps, and you may have all types of information about the regulator, the promoter, everything, but at the end you put them together and it doesn't work. And it's simply because you have to optimize many, many, many things at the same time.
12:02
So how do you handle this problem or this challenge of optimizing many, many things at the same time? Well, the typical engineering approach is that first you define what you want and then at the end you go into a type of design and then you have to explore a solution space that in many cases
12:22
involves an iteration of having a prototype, then testing the prototype, and then it doesn't work, identify the problems, then you have to fix it, and so on and so on, and at the end you end up with something that seems to work. Well, how can we do that in biology? In biology, keep in mind that we have an extreme complexity. It is difficult to isolate bits and parts of that complexity into separate models
12:44
even though there's this concept of orthogonality that in some cases is able to do that, but in many cases the optimization of a system that you engineer as a whole in biology is very, very complicated. So I have to take you into a different scenario that perhaps can give us some inspiration
13:04
on how you can really handle this creation of massive complexity on the basis of what we know to do. So the example I want to show you is this building that happens to be in Barcelona, and you have in there a so-called Sagrada family.
13:20
It's a cathedral that was started to be constructed in the late 19th century. 19th century means no computation, no simulation, no complex mathematics. However, if you look at this building, it's really, really extraordinary. You get inside, then you find all this amazing architecture inside with heads that are connected and everything.
13:44
And the important thing is that if you take a closer look, you see very, very few straight lines. Everything has strange angles here, and you have an extreme complexity here of connectivity that makes the building go up. So this is a case of an architectural object, an engineer object,
14:00
that had extremely complex, a lot of dimensions, a lot of parameters had to be calculated with no or very little mathematics and no or very, very little computation. Well, no computation at all because computers have not been invented by that time. So if you visit the place and I invite you to get there, it's really amazing. Now, how can you construct this amazing complexity with so little mathematics?
14:25
Okay, so this was the gentleman, Antonio Gaudí, that took this brilliant idea. Instead of making a mathematical model and follow the way that engineers and architects will follow, let's do the following. I'm going to make a model with strings and weights.
14:42
And then I put the dimensions that I want to introduce into the complex system and then in the strategic points of the building, of the model, I just put a weight here. And then I just let the gravity to make the job of calculating what is the best distribution of angles and dimensions and curves that will give this structure stability.
15:04
So this is a model, for instance, you can see if you visit the place in Barcelona and then, well, they have the weights and everything. So what he made, when you have this model, all you have to do is to turn it upside down and immediately the gravity will tell you the solution to your problem.
15:23
So no mathematics, no simulation, just gravity and embodiment of the construction challenge in a physical object. And this is something that, in my opinion, can be very inspirational to take the challenge of constructing biological systems without the real complexity, without really understanding and knowing every single parameter involved in the challenge.
15:45
So in more strict terms, this has been called, in some cases, heterotic computing. And heterotic computing is this idea that you raise a problem, but instead of solving it through mathematical computations
16:00
or mathematical calculations, what you do is that you embody the problem in a physical object and then you introduce into the object some type of physical action and then the result of that is not a number, is not a parameter, it's a physical measure. And this is something that, you know, it forms part of traditional technologies
16:21
and so on, but at the end, it's as useful to find the solution of a complex program as any other, say, more rigorous and more mathematical approach. So one of my kind of propositions for a discussion along the meeting is that perhaps we should not be too paranoid on having control in every single aspect of the design of a physical object
16:44
because at the end we may not be able to make it, but it's this trying to be creative and find solutions to the problem of designing multi-scale complexity perhaps by using other methods that indeed have been used by other individuals that have been challenged with the problem of constructing these type of things.
17:00
And at the end, you know, you get these amazing structures. Again, no computation, very little mathematics. So then here comes this idea that perhaps at the end of the day, the challenge of evolution is not so different of the challenge of engineering. And you might be aware that in some cases, engineers themselves are able to find solutions
17:20
to very, very complex problems, not by again making calculations, but by developing some type of evolutionary algorithms that will find the solution to a multi-task or very, very complex problem. In this case, these are some antennas that were developed by NASA for one of their missions. And the idea, instead of having a kind of top-down design of the corresponding antenna,
17:46
was to develop some evolutionary algorithms that, little by little, will change the shape of the antenna into something that was completely optimal for the role. And this is the ultimate design. You see that, you know, it's very weird. I mean, it's not the type of antenna that you expect to have made by an engineer,
18:02
but still, this is the best option that you have there. So at the end, what happens is that if you translate that into synthetic biology, you can think in terms of optimality also by using this type of heterodytic computing. So for instance, you have a pathway for production or degradation of one compound here,
18:21
and you have to have all these steps being run properly with an optimal action and so on. And then how can we implement this type of heterodytic computing in biological systems? Well, there are various procedures or various ideas around to do it. The idea would be to start putting together the basic components,
18:42
then try to connect them, of sorts, in one way. But then, and this is the most important, once they are connected, you put some weights, and we'll discuss in a moment what these weights might be in biology. You let the system to evolve to get the optimal, and then you manage to get the data from this, say the formation of the object, into something that has a perfect design,
19:04
and therefore, in principle, a perfect functionality. So one possibility, and this is more technical, and these are the type of things that modern synthetic biology can do for you. You can, for instance, start by defining a pathway, and then the components of the pathway, and you can connect the pathway through various connectors.
19:23
You need a promoter, you need Chandelgarno, you need the intergenic regions, and so on and so on. And then in the strategic points of the pathway, you have to introduce points where you put this weight. And then this weight, at the end, which is the one that will tell you the proportions and the characteristics of these intergenic spaces here,
19:41
that will give you a degree of optimality. So, ladies and gentlemen, this is basically what I wanted to share with you, these thoughts on how engineering biology might be easier, that we think, because even engineers and architects in the past have been dealing with the problem of constructing very complex objects
20:01
by using unconventional approaches. And that's fine, and I think that we should be happy to interface with our colleagues, engineers and mathematicians, and discover, perhaps, ways of solving these problems that we could not figure out before, if we just maintain ourselves in a real realm.
20:22
So, I think that this is some ideas that will pop up throughout the meeting. I just want to say a few words about the session for today. So, we will, after I shut up, then we will have a talk by Paul Freeman.
20:41
So, Paul Freeman comes from the Imperial College in London. He's been amazingly instrumental to promote synthetic biology, not only in the UK, but in Europe as a whole. He's what you may call one of the evangelists of synthetic biology at a global scale. And he's been working a lot on the problem of standards in synthetic biology.
21:00
He's been working a lot on the problem of formatting automated systems to monitor promoters and to monitor other scientific parts to generate the parameters on the basis of which one can really construct complex circuits. And I'm sure that you will enjoy very much his presentation.
21:22
Then we have these other topics by Daniel Tullman Erkic and by Ishiro Hirao that start giving us the type of flavor of the things that synthetic biology can do for us and how synthetic biology allows this repurposing of objects
21:41
that nature gives to us in a given format or in a given function, how we can really repurpose them to do something completely different or to enhance their properties or to use them in a different context. And viral capsids, as you will hear during the talk, is one of these scaffolds that nature have developed
22:01
for one given function, but in the hands of synthetic biology they can do amazing things, as you will have the chance to see. And finally we have this flavor on the world of chenobiology, how we start with familiar biology, with nucleotides, with tetra amino acids, with four bases and so on,
22:22
but then how we can start introducing and interfacing these natural systems with other molecules that are completely unnatural and still have synthetic systems and biological systems that work and do things that are new to nature. So we're in that part of engineering that has to do with creation of new things and new properties.
22:40
And then we have a discussion, and then we have a long discussion, and then we'll have the chance to have some reframed natural fiber, and in the meantime we'll have some natural fiber. Well, in any case, I hope that this sets the frame of the discussions
23:00
that in the best case scenario should provide some inspiration not only to the biologists that are present in this meeting, but also to the mathematicians and people that comes from the physical science and these other hard science disciplines. That's all I wanted to tell you. Ladies and gentlemen, thank you for your attention, and I will be happy to introduce Paul Freeman.
23:21
Thank you.