Synthetic Biology and engineering multicellular systems
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00:00
SyntheseölHalogeneMannosePrimärelementStoffwechselOrganische ChemieAllmendeChemieanlageChemische EigenschaftStereoselektivitätSpezies <Chemie>GenSenf <Lebensmittel>Anomalie <Medizin>Bukett <Wein>Chemischer ProzessZunderbeständigkeitUmlagerungThermoformenZellwachstumKohlenstofffaserSubstrat <Chemie>MeeresspiegelDurchflussKunststoffMaßanalyseVorlesung/Konferenz
02:26
ZellwachstumChemieanlageEukaryontische ZelleThermoformenChemische StrukturPrimärelementChemischer ProzessStammzelleZunderbeständigkeitVorlesung/Konferenz
03:59
PrimärelementBohriumChemieanlageMannoseNatriumhydridGenexpressionZuchtzielDNS-SyntheseTransformation <Genetik>SyntheseölCycloalkaneSetzen <Verfahrenstechnik>PrimärelementChemische EigenschaftChemische StrukturDNS-SyntheseChemisches ElementMetallmatrix-VerbundwerkstoffMeeresspiegelAbschreckenChemischer ProzessMonomolekulare ReaktionWursthülleEukaryontische ZelleChemieanlagePolymorphismusDNS-SequenzZellwandZellteilungSystembiologieZellwachstumMaßanalyseBindegewebeElektronische ZigaretteQuellgebietDiazotypieSpaltungsreaktionStratotypAbbruchreaktionGenexpressionAzokupplungExtrazelluläre MatrixThermoformenOrganische ChemieComputeranimationVorlesung/Konferenz
10:08
DNS-SynthesePeroxyacetylnitratDifferentielle elektrochemische MassenspektrometrieMannoseBohriumAnsatz <Physiologie>CadmiumsulfidNatriumhydridZuchtzielKorngrenzeChemisches ElementPrimärelementGenexpressionSystembiologieEukaryotenFunktionelle GruppeGrün fluoreszierendes ProteinCytologieAbschreckenIsoliergasChemisches ElementEukaryontische ZelleFarbenindustrieProteineDNS-SyntheseBindegewebeTransformation <Genetik>GenErdrutschChemischer ProzessMultiproteinkomplexMannoseAssemblyZunderbeständigkeitElektron <Legierung>Physikalische ChemiePrimärelementThermoformenZuchtzielFormulierung <Technische Chemie>NucleotideGenexpressionWerkzeugstahlSubstrat <Chemie>Synthetische BiologieChemische StrukturDNS-SequenzMonomolekulare ReaktionVorlesung/Konferenz
16:17
PrimärelementMannoseGenexpressionRNSProteineTranslation <Genetik>SterblichkeitMethylmalonyl-CoA-MutaseBohriumGraukäseBodeninformationssystemStickstofffixierungSyntheseölMaischeHydroxybuttersäure <gamma->Substrat <Boden>Setzen <Verfahrenstechnik>AcepromazinChemischer ProzessSingulettzustandMeeresspiegelSpanbarkeitChromosomRNS-SynthesePräkursorFluoreszenzfarbstoffElektronische ZigaretteContainment <Gentechnologie>Eukaryontische ZelleGenZellwachstumKohlenstofffaserQuellgebietAssemblyReflexionsspektrumGesundheitsstörungPrimärelementAlphaspektroskopieOberflächenbehandlungPlasmidKüvetteOktanzahlProteineGrün fluoreszierendes ProteinSystembiologieZunderbeständigkeitWursthülleGenexpressionRNSSonnenschutzmittelChemisches ElementSubstrat <Boden>Chemische EigenschaftZuchtzielFunktionelle GruppeDiagramm
24:33
Adenomatous-polyposis-coli-ProteinPrimärelementBukett <Wein>AcepromazinMannosePropanHydroxybuttersäure <gamma->MaischePrimärelementPolymorphismusFarbenindustriePhysikalische ChemieChemischer ProzessZellwachstumKnickfestigkeitSekundärstrukturReibungWeichgelatinekapselEukaryontische ZelleZellteilungGrün fluoreszierendes ProteinErdrutschSubstrat <Boden>SchönenProteineBaustahlFleischersatzChemieanlageElektronische ZigaretteAzokupplungGenexpressionTrennverfahrenComputeranimation
28:15
MannoseHydroxybuttersäure <gamma->Chemischer ProzessAgar-AgarWeiche MaterieEnzymkinetikZellwachstumWursthülleKlonierungPrimärelementZellteilungSubstrat <Boden>Eukaryontische ZelleComputeranimationVorlesung/Konferenz
30:05
Toll-like-RezeptorenPlasmidSpezies <Chemie>Fülle <Speise>Glättung <Oberflächenbehandlung>Primärer SektorWursthülleSetzen <Verfahrenstechnik>ZellwachstumPrimärelementVolumenhafter FehlerRingspannungZellteilungErdrutschBukett <Wein>ZellwandGrün fluoreszierendes ProteinGangart <Erzlagerstätte>ProteineSeleniteFluoreszenzfarbstoffComputeranimationVorlesung/Konferenz
33:00
MannosePrimärelementGenexpressionSyntheseölArabinoseSetzen <Verfahrenstechnik>Chemischer ProzessOberflächenchemieEukaryontische ZelleGenAntigenThermoformenPrimärelementReglersubstanzRingspannungProteineZelladhäsionComputeranimation
33:49
Adenomatous-polyposis-coli-ProteinPrimärelementMetallmatrix-VerbundwerkstoffMannoseChemieanlagePrimärelementMähdrescherChemischer ProzessExtrazelluläre MatrixZunderbeständigkeitChemische VerbindungenMolekülstrukturComputeranimationDiagramm
34:32
ThermoformenHomogenes SystemTransdermales therapeutisches SystemFiltermittelFülle <Speise>PlasmamembranComputeranimation
35:12
PrimärelementSyntheseölMannoseAminosalicylsäure <para->MutationszüchtungElektronische ZigarettePlasmamembranEukaryontische ZelleLactonePrimärelementSerinHomocysteinChemischer ProzessDNS-SynthesePropagation <Chemie>GesundheitsstörungBindegewebeFilterMolekülProteineBindungsenergieEnzymQuellgebietReglersubstanzSetzen <Verfahrenstechnik>FiltermittelWursthülleHeterodimereBaseComputeranimation
38:24
Aminosalicylsäure <para->MutationszüchtungMannoseBohriumHydroxybuttersäure <gamma->FlammeEukaryontische ZelleB-ZelleFilterChemischer ProzessGrün fluoreszierendes ProteinPropagation <Chemie>PrimärelementMischenSetzen <Verfahrenstechnik>WursthülleSeafloor spreadingSeleniteEnzyminhibitorComputeranimation
41:08
MähdrescherGenomChemieanlageChemisches ElementMultiproteinkomplexEukaryontische ZelleSetzen <Verfahrenstechnik>Chemischer ProzessFunktionelle GruppeRNS-SyntheseFunktionsmorphologieZunderbeständigkeitPipetteChemieanlageMitochondriale DNSWursthülleThermoformenGangart <Erzlagerstätte>Single electron transferStoffpatentComputeranimationVorlesung/Konferenz
44:06
SyntheseölModifikation <Kristallographie>Bukett <Wein>Single electron transferZelldifferenzierungRiesenzelleSystembiologieChemieanlageFülle <Speise>Hydroxymandelonitril-LyaseChemischer ProzessBukett <Wein>Seafloor spreadingChemische StrukturPrimärelementZunderbeständigkeitSaccharomyces cerevisiaeInlandeisSubstrat <Boden>VerhungernSetzen <Verfahrenstechnik>MutationszüchtungVakuoleÖlPloidiegradZellwachstumAgar-AgarVulkanisationZellteilungOberflächenchemieVorlesung/Konferenz
47:03
Modul <Membranverfahren>MannoseVerhungernBiologisches MaterialHydroxybuttersäure <gamma->RingbrennkammerBukett <Wein>ChemieanlageGasaustauschPrimärelementLichtreaktionLängsprofilEukaryontische ZelleRiesenzelleChemischer ProzessChemische StrukturKeimzentrumOberflächenchemieGen notchZellteilungZellwachstumAdenomatous-polyposis-coli-ProteinComputeranimationVorlesung/Konferenz
49:10
Modifikation <Kristallographie>PrimärelementMannoseHydroxybuttersäure <gamma->InsulinZellfusionMitochondriale DNSGenZellwachstumGenexpressionPhysikalische ChemieChemischer ProzessZellfusionSyntheseölGentechnologieChemieanlageSaccharomyces cerevisiaeMultiproteinkomplexChemische ForschungEukaryontische ZelleInitiator <Chemie>OberflächenbehandlungWerkzeugstahlPrimärelementGenStoffwechselChemische EigenschaftStratotypZellteilungZunderbeständigkeitOrganische ChemieVorlesung/Konferenz
Transkript: Englisch(automatisch erzeugt)
00:18
I'd like to thank the organizers for the invitation to be here.
00:23
So Anne's talk was focusing on the way genes and substrates and catalysts interact within cells, the way they create networks, the way you get the flow of carbon from one place to the other in plant cells. What I want to talk about is the next level up in the scale is cells themselves
00:43
and how the different arrangements, the properties of cells can be used to create different forms and how we might be able to engineer things. Now with plants we have a kind of close relationship with plants on a day-to-day basis and it's usually in the garden or in the supermarket and here we've got a picture
01:03
of a range of vegetables of course, cauliflowers, broccoli, cabbages etc. And you might be forgiven for seeing this picture and thinking well they're quite different plants but rather these are all the same species. These are species or varieties that have been derived by breeding and selection from
01:23
a common wild species. So this is a wild mustard plant and through, you know, many millennia often of breeding all the common crop plants that humans use have been derived from natural antecedents by virtue of continual selection and breeding to create very different outcomes.
01:45
And these are all, these plants are all derived from these same ancestral plant where you've got, say, floral meristems or vegetative meristems creating different arrangements of cells in the context of a growing system to create very different outcomes.
02:03
And I show this picture to emphasise the plasticity. So even as you see in metabolism the different flow of genetically controlled flow of carbon into different substrates, at the cellular level you see the arrangement, the rearrangement of cells during development, during growth to create different outcomes at the end point
02:23
of these developmental processes. And I want to show this image from the BBC Natural History Unit which shows here this is a leaf that's growing and as you see in this accelerated time-lapse image you've got this leaf and this stolon or stem-like arrangement which is growing from the tip of the leaf and as it grows you'll see this small nub of tissue here which
02:44
itself will continue to grow and expand. And I use this as an example to describe, to kind of illustrate the dynamics of growth. This of course is a plant structure which is emerging, growing and creating this large structure as you see it growing you can see it forming the picture of a pitcher plant.
03:01
And I think partly without human perception is not good at understanding or conceptualising what happens at the scale of plant growth because plant growth is a little bit slow for us. So we're used to morphogenesis or movement in animal systems but in plants of course we take them for granted because they don't do this very quickly.
03:21
But when you start to see things accelerated, crammed together, you can see the dynamics in a very concrete way. And I think it illustrates, for me anyway, the processes that are going on inside this process where there are literally millions of cells initiating essentially from a single cell to create this structure which is a very defined and a constrained structure.
03:47
And of course this process of morphogenesis is driven by growth and it's driven by the growth and dynamics of individual cells interacting as a population or society. And so at the root of this process is this in cartoon form.
04:02
So we have a plant cell here which has grown and is about to divide to create two daughter cells and I use this cartoon to illustrate a couple of the salient features of plant growth which make it different to animals and help explain why plants look different from animal systems.
04:22
So plant cells are immobilised. So just like the macroscopic plant which is rooted in the ground, plant cells are immobilised. They have this extra cell in the matrix which locks them in place with their neighbouring cells. So you have a single cell here which is attached to its neighbours. As it divides it will form two daughter cells by essentially subdivision.
04:43
So the new wall that's formed is actually built inside the existing cell to create this new cell wall structure and these two daughter cells and all of their descendants will be locked together for the life of the plant. So this boundary here, these cells can of course continue to expand by division but
05:01
this boundary marks a clonal boundary which will not shift because these plants can't move. The plant cells can't move with respect to each other. So this also brings another consequence which is that because of the immobility of these cells there's a relationship between the properties of cells and the local cell or anatomy.
05:22
And if you imagine going back to that picture plant which starts at a single cell and grows and you've got millions of the cells all coordinating their behaviour to create this final terminal structure and it's composed of these individual elements in the population, individual cells which are of a particular type at a particular time.
05:41
And we know that in the case of plants, plant fate or gene expression is largely controlled by interactions. It's the neighbouring interactions which tell plant cells what they're to be and in return those cells will communicate with their neighbours and it's this social interaction which and the passage of genetic information between cells in a local fashion which controls
06:04
gene expression. And so there's this network of exchange of information which takes place between cells within that growing picture plant and it's that network, that population, that social interaction which describes the properties of that final structure.
06:20
So here we've got a cell which has been, obviously a schematised cell, which has been essentially told or promoted or programmed to divide. So as it divides it forms two new daughter cells and those two daughter cells are in two different positions of course with respect to each other. So each cell will pick up different information from its immediate surroundings, from the
06:42
cells around it. It will also deliver information to those surrounding cells which will put those surrounding cells in a different context. So by this kind of binary fission you're creating a breaking of symmetry, creating new information which can then be used to feed back on the process.
07:01
So the whole process of building a picture plant is a highly parallel and feedback-driven process. It's much more like the process of organisation of a social network or an economic network or a political network than it is like constructing an aeroplane for example. So most of our conventional engineering paradigms are based on blueprints.
07:24
You have an end point that you specify, you then have parts that you assemble to create the individual elements, the subsystems and final product of what you, you know, this final end point. In the case of our biological system we need a DNA program which is not just implemented
07:42
in a cell but implemented in millions of cells and where the key is not so much what is encoded in that individual cell but how those cells interact with their neighbours dynamically across this process of emergence, building a population which is growing by division, proliferating and at the same time building, putting in place interactions
08:05
which self-reinforce and bootstrap this final process. And of course the process that we're talking about is a source of the most organised structures that we know. The human brains in this room each started from a single cell and end up with trillions
08:21
of connections by virtue of this kind of bootstrapping process, this local process. And of course if you look outside the room, if you look in terms of human constructs, our social constructs are some of the most complicated things that we know and they're not designed as such, they're not based on a blueprint, they grow, they emerge from
08:42
local interactions just like an economic network does based on local transactions and you've got this process of growth. So I think it's actually quite a deep problem and a very broad problem and I think in the case of a biological system you have the huge advantage that the kind of interactions that we can build here are based on parts which have molecular specificity.
09:04
Of course the kind of biological parts that we can create have the level of accuracy, molecular specificity and accuracy which is beyond almost anything else that we can machine or create and using evolved systems. This will hopefully become more clear as we go on here.
09:22
And so this whole talk is about trying to get towards a process where we can engineer cell populations to create some kind of supercellular structure and as you'll see, most of the talk is about microbes, it's not about plants, it's about taking systems which are as simple as possible and engineering at the lowest level DNA parts and other
09:46
interactions which can come together to create more ordered structures. And so we adopt this kind of, it's a variant of the engineering model if you like where you've got a test or a build design, build test cycle but in the case of a biological
10:02
context, the parts that we're using that we design are based on DNA, DNA elements that are there inserted into cells by virtue of transformation. So we have a biological system which we then need to be able to interrogate and derive precise parameters from and those parameters can then be used to parameterise computational
10:24
models which can then be used to design improved circuits hopefully. And in our, broadly in the synthetic biology field, there's this paradigm which has been referred to already which I think certainly correctly underpins the whole business of
10:41
synthetic biology where you start with DNA elements in a biological context, you can modularise or abstract DNA-based functions as parts. These can be put together to create devices, larger scale devices, circuits and systems and implemented in a multicellular context.
11:00
And these processes, this hierarchical view of systems I think works. It also comes with the kind of principles that are found in every form of human engineering, the benefits of decoupling design from fabrication and the abstraction that comes with creating standardised elements that can be used where you get the social aspect of building complicated
11:24
systems by using abstraction as a way of going forward. And I think just segueing away from the problem in more precise biological terms to this more general aspect of how do you create a way of going forward here if you're
11:40
to tackle this quite ambitious problem. And if you go back to 1958, this is a picture of Jack Kilby's first integrated circuit. It's quite a famous image of the first circuit in what became Texas Instruments. And there are five logic devices on that rather crude device. And this is 1958. By the early 60s, you had the formulation of precise physical and electronic standards for
12:05
the way these kind of devices could be put together in larger scale circuits. And this diagram shows going from Jack Kilby's circuit through the first planar transistor through to the first integrated circuits. And now a modern microprocessor, a modern integrated circuit will have four, five billion
12:24
logic elements on it. And this process of moving from very simple architectures through to more complicated structures started of course the way we are sort of tackling things in biology, which is often with hand design, using direct interaction with the substrates and the design
12:45
principles where you've got individuals who are designing processes where they have to know everything. They have to understand the whole process of the biological components they're using. They start with raw nucleotide sequences, they synthesise, they assemble this, implement it in a biological system where we're certainly at this stage.
13:03
And certainly in the electronic systems, you move very rapidly from manual design and assembly through to computer-aided design processes, which Patrick mentioned earlier. And clearly we're in this process of trying to assemble the tools which allow us to handle complexity in a reasonable way.
13:21
And I think this slide in a way encapsulates both the challenge and the opportunity for physicists and mathematicians and biologists to come together in a way that I'm sure we haven't quite plumbed the depth of the kinds of interactions that are feasible here. But clearly the opportunity is there. But there's one thing that I want to emphasise also in that the superficial similarities
13:46
between say semiconductor assemblies and design and biological systems breaks down very quickly. And if you think of individual circuits in an electronic context, compared to the kind of circuits that exist in biological systems, you have insulation and a top-down design
14:07
process for electronic circuits, whereas in a biological system, and this could be either at the genetic scale or the cellular scale or population scale, you have networks of interacting elements which are often not insulated.
14:21
The kind of, if that's for example a genetic circuit inside a cell, all of the components have access to all of the other components. So you require molecular specificity to derive and separate elements from each other. And so for example here we can have a circuit which is essentially comprised of identical
14:42
elements which are simply wired up differently. And here in the biological context we need a different element for each of the logic components that you're using inside that cell. And I think this is in a way, well both obvious but also important, because it also highlights the potential benefits of working with cells rather than genes.
15:04
So if that's a genetic circuit then you're constrained with each one of those logical elements needs to be insulated by molecular specificity. If you're working in a cellular context there's a natural form of insulation where cells are the unit of gene expression and the interactions between cells are necessarily
15:22
more limited. And it's those connections which provide the edges between these nodes in this kind of network. So they're much simpler networks because of the physical arrangements of cells. And this will become very clear as we go on. So the question is, if you have a network of interactions, so you have a eukaryote cell
15:41
where it might have 30,000 genes, you want to install the circuit where you want to examine the behaviour of one of your synthetic genes in that circuit, what do you do? Well of course there's a revolution in cell biology with the use of these fluorescent proteins which come from corals and jellyfish which now have quite an elaborate palette
16:01
of different colours. And these directly visualisable proteins of course are genes that can be used to insert into a circuit similar to a flag in a computer program. So you can look at the behaviour of individual genes inside a cell. And so if you imagine that, well you can do that of course, you can put your gene in,
16:22
we would commonly use, look at for example if you wanted to look at a population of cells, and this is a real example I'm going to show you with bacteria, you can insert a single gene for a fluorescent protein, have these population of bacteria growing inside a small container being observed in an automatic fashion in say a microplate reader, where
16:43
you can get multi-parameter measurements very accurately, continually, you can get very accurate measurements of say for example increase in fluorescence or increase in the optical density of the culture as the cells proliferate, the more cells there are the more light scattering there is the more the optical density goes up.
17:02
So you can, and you can have multiple fluorescent proteins for example. So you can use this to measure say for example two genes and the relationship between those genes and what they might be doing in a particular circuit as these cells are growing. And one of the things that we've been trying to do in this sort of effort to try and create
17:23
computational systems which create cellular interactions is to get at the lowest level understand what individual parts are doing and to get intrinsic values or estimates or measurements of what say promoters are doing or other circuit elements are doing inside a cellular context.
17:41
But here we're not looking at individual cells, we're looking at a population of things and the process of creating fluorescent proteins schematized here. So here's our gene, here's fluorescence at the end of the day. But of course if you're looking at a biological system like this which is about as simple as you can find, in that cuvette or that well, the process of getting fluorescence
18:03
and you're looking at the transcription rate which is actually the intrinsic value that we want to get out of this system is what of these promoters are doing to and what the rate of that transcription rate is creating in terms of amount of RNA which is then translated to make a protein precursor which then needs to be matured
18:24
and folded to create the fluorescent signal that we can measure in the machine. So in order to get to this point we have to also deal with various side issues. These genes are in cells, on chromosomes in cells, the chromosomes are dividing,
18:42
proliferating, the cells are dividing, they're also proliferating. You've got transcripts which are being made which are also being diluted by cell growth. They're creating proteins which are being diluted by cell growth. And there's degradation rates of course for all of these compounds. So we've been struggling with how to express these processes in a formal way
19:04
but also in a way that allows one to make measurements, allows one to interpret the data and combines a description of the process of gene expression and production of something you can measure and allows us to get to an intrinsic value at the end here. So one of the first things that we've discovered both experimentally and theoretically
19:23
is that the product of fluorescence, the thing that you're actually measuring in these devices is a linear relationship between the rate of accumulation of the intensity and the absorbance, the ratio of those two is a constant product.
19:43
So if you have for example a construct, a plasmid that you make with one of these fluorescent proteins on board growing inside a cell population which is then proliferating inside a cell, you can see for example that over time the absorbance, that is the amount of cells in that cuvette will increase with time and then flatten off as the growth rate
20:05
finishes. At the same time the amount of fluorescence will also accumulate with time and it will follow a similar kind of trajectory, not quite the same but similar. But if you then express these two functions, essentially the rate of accumulation of
20:22
fluorescence and the rate of accumulation of absorbance and plot them against each other, you get a straight line relationship which makes it easy to measure. And this provides essentially a parameter we call alpha which is essentially an estimate of for a particular condition what proportion of the cell's effort is going to make this
20:42
fluorescent protein and the rate at which it accumulates. So what is being devoted essentially to the assembly of this fluorescent product which is a reflection of the intrinsic value of the promoter that drives this expression since that's the main variable in these experiments. And that gives you a view of the value for the intrinsic ability of
21:08
this promoter to create or drive expression. But when you start looking at these values and then changing the conditions, you get a lot of variation which is due to the difference in load on the cells both because you're creating some kind of output, you're now using here
21:25
either different carbon sources or using cytostatic agents to create different loads. And the promoters themselves respond differently according to the kind of perturbation that you see inside the system. And so for example the amount of variation which is due to say
21:41
for the media or for other experimental features is that the vast majority of the noise, if you like, the variation in the experiments comes from extrinsic processes, things which are outside and not related to the way the promoter's working but rather to the way the cells are operating,
22:01
the way that the cells are feeding themselves off the media or the way other extrinsic processes are moderating this process. And so what we've done is to take another process where we take these values for the intrinsic value of the promoter and then have two genes, now looking at a test gene with respect to a standard gene, whereas standard gene as a promoter here is a fixed
22:26
reference point and that we can correct for other extrinsic variables by comparing the alpha values for each of these two genes. So we have this factor rho which now where you're looking at very different media properties you still retain these straight line relationships
22:44
by looking at, by using this ratiometric estimate which essentially corrects for the transcriptional load and the other, essentially the extrinsic factors which affect cell growth as opposed to the intrinsic value of the promoter. And so if you look at the variation now
23:04
where you're correcting for these extrinsic factors you can remove a lot of the variation which is associated with this external, say for example the different media which is shown up here as well, is now smoothed and this is our standard promoter up here is the, of course is sort of fixed value, but you can see also for the other promoters you've got
23:24
this intrinsic value you can use for circuit design. And so you have this relationships now which you can have a value for the intrinsic properties of the promoters which can be used for design. And so when you're looking at this level of say the nanometre scale if you like,
23:45
where you've got genes, you've got gene elements which are interacting, gene regulatory networks, you can start to develop ways of measuring parameters that can be used for modelling. And the rest of this talk is really about trying to move up this scale. So it's all very well to have a gene regulatory network which will operate inside a cell,
24:04
but then what happens after this? How do we get to a reasonable way of modelling or understanding cell behaviour and then think about patterning those processes on a larger scale? So you can see here the scales here which run from molecular to cellular to population run
24:21
from nanometres to microns to millimetres and run from seconds to minutes to hours as a rough estimate of the kind of scale of elements that we're trying to model. So in the case of the cellular scale, this is an image from a confocal microscope which shows a field of bacteria. So they're just simply taken out of a shaking flask, put onto a
24:43
microscope slide and visualised, where they're yellow because we've got both of these two different colours of fluorescent proteins that we're measuring inside these cells. And you can see even in this population of cells there are variants and size and colour depending on the different genetic behaviours inside the systems.
25:02
And this is essentially this idea of stochasticity is embodied in this slide, but there's also the spatial aspect because of course bacterial cells in many of the circumstances that we want to work with them are on a solid substrate or in some other non-homogeneous system.
25:21
So this shows a couple of colonies that have grown on an agar plate and have merged at the end here. And you can see here the individual cells, again the stochasticity of gene expression and the physical arrangement of cells inside the colonies. And in a way this is a very simple analogue of what happens in plants as well. So if you're thinking about bacterial cell growth you have this slight quandary
25:43
because those of you who have done any microbiology will know that if you plate individual cells on an agar plate and come back the next morning that single cell will have grown to make this very regular hemispherical colony, geometrically very regular, but the way it gets there, the way it produces that
26:03
regular colony is anything but radially symmetric. So for example bacterial cells like E. coli or bacillus have a bacilliform shape and they grow uniaxially. They don't go in a radially symmetric way, they grow uniaxially. So the growth of a cell is mediated by this
26:21
extension in a single axis followed by a septation and this septation is mediated by Turing-like systems, the min proteins which will calculate the midpoint for the cell. And so you end up with a capsule-like process where these daughter cells are created and then undergo the same process again after the cells separate.
26:42
So if you think of modelling that and what we've done is to create physical models for this division process. You can start with single cells, this shows part of a few sequences in a time-lapse sequence where we've got cells, individual cells growing. And in a friction-free environment of course if you plated a single cell like this and came back the next morning there'd be a single colony, one cell wide and several meters long
27:05
which would be the result of this cell just undergoing its axial extension. But of course in reality you can't extend indefinitely, the frictional forces build up, so you generate buckling processes which are well described in many physical systems, these kind of processes. But here it's driven by growth,
27:23
growth of individual cells. And as these cells grow and form these columns of cells they buckle, they buckle very quickly and they create these effectively rafts of cells and you can make out as these mini or micro colonies are growing. This is a simulation at this point but we see this same thing under the microscope.
27:41
You get these buckling processes that create cells in opposition, cells which have easier or more difficult ways to grow and you build up physical tensions or forces inside these small micro colonies which create inhomogeneities and create fractal-like patterns as we'll see
28:03
in a minute. So this model, for those who are interested, there's a GitHub open source software package called Cell Modeler which is online and that's the website there cellmodeler.org. And you can create, it's a three-dimensional model and it's based on rigid body kinetics.
28:21
This shows the microscopy data. This is real cells growing under a microscope, under a cover slip on soft agar so you get the cells growing to form these micro colonies. This shows a computational model showing a similar kind of process where the cells are growing and pushing against each other to create these constrained three-dimensional arrangements.
28:42
And so you can grow colonies, you can grow hundreds of thousands of cells if you want. I think this one has about 50,000 cells on it. And you can simply grow those processes. What's more, you can also mark the dynamics of the process. You can visualise what happens during the growth process. So if you take this little micro colony and tag the first two
29:05
divisions, you start from a single cell, two cells, four cells, and at the four-cell stage, if you colour those cells and then watch their daughters progress through the growth process, this is what those four cells have created. The clones of those four cells have created
29:21
this fractal-like arrangement in these early colonies. Because of the underpinning uniaxial growth of the cells, and because of the physical dynamics and the competition between cells, you end up with these different directions and nature of growth. So these are tiny colonies. I think in these cases where they've got on a rich nutrient medium,
29:55
we don't see any limitation of growth at this early stage. These are all possible, but
30:05
we go to the real stuff. So we go to the experimental data and we always tie together theoretical observations with direct experimental observations. Sure. But I think at a first approximation, clearly these things happen, but at a first approximation we see similar phenotypes
30:20
where we don't have nutrient limitation in the small colonies, which are only an hour or two old. So this shows, for example, an image of mature colonies that are after an overnight growth, which have been densely seeded on a plate. And we've got three different species of bacteria which have red, blue or green fluorescent proteins being expressed.
30:46
And even here you can see where the colonies have collided early, you get these fractal-like boundaries being formed. And the nature of confocal microscopy is that you can go and look at these in detail. So you can zoom in very effectively, identify subsets of the field,
31:03
and zoom in to look at the individual details. And as you zoom in, you can start to see individual features here. And you can see the kind of behaviour and arrangement of cells due to these fractal boundaries that are being produced. And so these are real. These are not a model. This is real data, real cells. And we have colonies, well, methods
31:25
now for creating split colonies. This is a model, not data. So you see these fractal boundaries being produced as a result of two cells segregating the plasmid, or actually two plasmids segregated into two daughter cells at the first step of indivision. And we can also do that in vivo.
31:46
So I think I've got the next slide. But we can use not just the different colony assays, but also start to use different bacterial strains. So this is a mutant of E. coli. It's called Rod A mutant, which creates spherical cells. As a defect in the nature of cell wall growth,
32:06
it forms spherical cells. It doesn't have the same kind of uniaxial growth. And this shows a colony, a small colony growing inside some normal wild type cells, which are marked in the blue here. And so you can create these split colonies. This is, again, is real data.
32:22
This is not a model. Where you've got plasmids, one bearing a red fluorescent protein, one a green fluorescent protein, which is segregating at the first point of division. And the consequent daughter cells are interacting at the boundaries to create these fractal-like boundaries. In the case of the Rod A mutant, where you've got these roughly spherical cells,
32:42
you get a much smoother boundary. And see the fractal dimension is much lower in this case. And this shows, again, colliding colonies. So this is a clonal sector. And these are colonies that are growing together. And you see the fractal boundaries in one case, and smooth boundaries in the other. And again, the models back this up as well.
33:01
As well as this, you can also start to explore these models by looking at adhesion. So this is a wild type micro colony. This is a bacterial strain that is now expressing under arabinose control one of these adhering genes. This is an antigen 43, which is a
33:21
gene that is, or protein that is exported to the outside surface of the cell. And it allows aggregation between cells. And this aggregation creates these kind of extended processes where the cells, once they contact each other, form an aggregate. And then are pushed away from each other. And that's both found in the physical model and in the
33:43
microscopy data, the experimental data. So we've got all these different potential contexts. And we will try to use these, in fact, to create a plant-like context. Where I mentioned plant cells where they aggregate and form an extracellular matrix. And we're trying to use the combination of spherical cells and cells that adhere to each other
34:04
to create more plant-like processes. But these are quite difficult to work with because they're very sick cells. But that was our, partly our goal in this. So we have these models for cellular interaction. And that allows us to look at the scale of, you know, the sort of
34:23
micron scale interactions between cells. But if we want to move towards population-based interactions, we have to move up in scale to sort of millimetre scales. So we've developed this, I think, quite interesting system for experiments, which is very simple. But it's based on use of membranes, which have this black stuff here as a
34:44
hydrophobic ink, which is printed onto these filters. And the hydrophobic ink allows the bacterial populations to be inoculated and to grow on the hydrophilic patches between the ink and form these small quadrants of homogeneous bacterial populations, which can then interact
35:02
from population to population where the geometries of the bacteria are highly constrained. So they can only grow within that geometrically constrained quadrant. And you can generate longer-distance signaling interactions. And we've used a number of different signaling processes, but focused mainly on the quorum sensing signals, which is about as simple
35:23
a signaling system as you can make between two cells. We've got these homo serine lactone producing enzymes and receiving enzymes. So you've got a two-enzyme system, one of which is the catalyst that produces the signal. The signal will diffuse across membranes,
35:42
and it moves directly from one cell to the next by diffusion. And there's a protein that you can express, a cognate protein that you can express inside the cells, which will recognize this, dimerize, and then bind to DNA on the basis of that dimerization, catalyzed by the interaction with the signaling molecule. So you can start to create systems where you combine
36:03
these quadrant-based filters, where you can inoculate different cell types onto the filter. And you can then use the genetic circuits to condition the signaling across these systems. So this is actually the control over here, where we have cells that are receiving a signal.
36:21
In this case, it's a particular homo serine lactone. I won't go into details. But that signal then diffuses from a source across the membrane and triggers a response in the adjacent cell quadrants. And you can digitize these, and so you see the signal being conditioned as it diffuses across the system. And this is in, these are about,
36:44
so that would be about 20 millimeters, no, about 10 millimeters from there to there, across this quadrant, series of quadrants. And here we've got a similar kind of experiment, where we've got the same kind of signal diffusing from one end of the filter into the other. But here we're looking at a system where we've got a signal that's being responded.
37:03
These cells are conditioned to respond to this, but there's a negative feedback interaction with an alternate signaling process here, which I'll describe in a minute. And that gives you a conditioned signal, where you get a much sharper cutoff between the cells that are receiving the signal and those that are not. And there's a feedback relationship there. And so the kind of signals that you can make, and we've got now,
37:24
in this diagram, we've got two different signals. So we've got these AHL signaling systems, but now there's two of them. And they're hooked up, they're connected. So we've got a signal system here, which is producing a signal, which cannot be received by the same cell, but it can be received by the other side of the circuit, if you like.
37:45
So you've got two signaling systems, call them A and B, where A needs to be received by the B conditioning cells, and the B conditioning cells produces the A signal. So you have the relationship which is governed by propagation of a signal, if you like,
38:02
where A can signal to B, B can signal to A, but they can't signal to themselves. So you have a process where you can create sort of a leapfrog arrangement, where this cell type will signal to those and vice versa, but not to themselves. So if you start out with a system like this, if you've got a short range interaction,
38:23
these cells can interact with each other if they're close to each other, because they can then interact and feed back on each other, create an excited state by virtue of mutual induction. If they're separated, that doesn't happen. So here we've got essentially a checkerboard arrangement on this quadrant arrangement.
38:40
We've got cell types A and B across the whole field, and they're too far apart to excite each other. Whereas here, we've seeded the process with a mixture of these, right in the centre portion of this checkerboard. These are all mixed with A and B signals, and here we've got this process amplifying and then spreading.
39:01
As cells signal to the adjacent quadrant, adjacent quadrant produces the opposite signal, which then signals to the next one along. So you have this feedback-regulated propagation of a signal across centimetres across the filter, based on these local interactions which are then propagated across the material. And of course, that's using this leapfrog-based process.
39:23
You can also do the other. Instead of having mutual induction, you get a mutual competition. So for example, if you have a system where we have now our two states, A and B, but now repressing each other rather than activating each other, you create a very different logic, where if you have cells which can be preconditioned
39:40
to be in state A, they will inhibit state B. So they will promote themselves and repress the opposite state. Similar to this one that's here. So the B state will repress the A state, but will excite itself. And this is the circuit. I won't go into details of the circuit down here. And all of these are visualised by virtue of these fluorescent proteins that we're using.
40:02
And so in this case, if you have one of these filters, because of the nature of the system, they're isogenic strains, but you can either push them into state A or B by giving them the signal. And so these cells are preconditioned to state A or B, decorated onto the filter, along with a field of cells that are unconditioned.
40:23
So they are neutral in their process. And this particular circuit, they tend towards the green state, we'll call that the A state. But here we're starting out with these preconditioned cells, and as they are allowed to grow across the system, these cells in red, the B cells, will continue to propagate, will have a signal that then spreads to adjacent quadrants,
40:45
those cells will be recruited to the B state. And so you get this recruitment of cells to this state, and you have this boundary formation as they're competing with cells of the opposite state. And so you have this population-based effect with a very simple circuit.
41:01
And I think this is the kind of testbed that we want to take and use to create more complicated circuits, to create the kind of systems that describe schematically up here. So for example, if you have an AND gate expressed in this population context, so an AND gate of course you can use within a cell, have two different molecular functions that can create
41:26
a state as a consequence of the interaction. In a population context, where you've got states A and B coming together to create a new transcriptional state by virtue of an AND function, you can create state C, for example, as a new transcriptional state.
41:42
And the idea that A plus B equals C plays out in a population context to create the kind of creation of a novel band of cell types, which of course can then be also used to bootstrap another set of interactions based on the interaction between C and B and C and A,
42:02
so the whole process can reiterate itself in a way that's quite familiar to say segmentation or other patterning processes that developmental biologists would be familiar with. And I think this idea that you can start to think of quite simple ways of dealing with patterning is quite interesting to us at least. And one of the other implications here is that these patterns,
42:26
which I think it comes to the way humans deal with things, that this is the kind of element that you can deal with because a lot of the complexity is underneath the process. So you can start to think of these being described phenotypically in a way that's hierarchical. And if you start to put those simple systems together in different order, you get different outcomes.
42:45
And this is just a very simple example here. We've got two patterning processes, which are completely theoretical and arbitrary. So one is a radial patterning process, and one is a bilateral asymmetric process. And if you take those processes A and B in different order, so the idea that you can capture this idea and have a transcriptional
43:04
state as a result of one type of patterning and use that to trigger the next form of patterning. So here we have radial patterning where the outer state here now undergoes bilateral patterning. And here we've got the bilateral patterning followed by radial patterning where one of
43:21
the transcriptional states in each case is active for the next step in the process. And of course you end up with very different outcomes depending on the order with which you apply those different patterning processes. And so I think this is quite an interesting test bed for ideas that could be implemented in plants. And of course if you think about plants, you've got issues here. Plants are slow,
43:43
the generation times are slow. They've got complex genomes. There's often a lot of redundancy in systems. They've got diploid or polyploid genetics. Tissue culture regeneration is quite slow and difficult. And complex tissue morphologies, which makes it really quite difficult to look at early simple stages. And they're quite difficult to analyze at the
44:37
arrangements. There's no shoots. There's no roots. There's no flowers. There's no seeds.
44:43
You have a very different mode of growth for these lower plants. But they are packed with all of the genetic equipment that you see in higher plants pretty much. So these are haploid. They're not diploid. They're haploid like E. coli and yeast. They have male and female plants. This is the male plant here. This is the female plant.
45:01
If you cross them, you end up with not flowers and seeds, et cetera. You end up with as a product of the crossing, sporangia. And so they make not seeds, but spores. So each one of these sporangia has about a quarter of a million spores in it. So you have from a single cross, you can make millions of progeny. Those progeny look like this.
45:20
They look roughly like yeast cells. They have all these energy-containing vacuoles full of oil. And they can be stored at minus 80 for indefinitely. If you put them onto agar media, they start growing. So they grow into these, if you go back one, so this is what they start out. A day later or so, they look like this, where the pluses,
45:40
the chloroplasts have started to differentiate. And they're much larger. After another day or day and a half, they start dividing. You can see the first division here. This is one of the ungerminated spores. So after about a day and a half to two days, this is the change already. And the change accelerates. So you get the formation of differentiated cell types. This is what passes for a root in these lower plants. This
46:03
is a rhizoid. So they're just single cells. And you can see this continuing cell division at the top here. And as they continue growing, this top part of the plant, the photosynthetic plant, elaborates to form a flattened sheet. And so after about five to seven days, you have this flattened structure, which will form this flattened sheet,
46:21
which is the body of the plant, with these giant cells, which are the roots of the plant underneath it. And so you end up with this flattened structure, which continues to grow. This gives you an idea of scale. So instead of having a bacterial or a yeast colony, you've got a little baby plant after you've spread the spores. And as it continues to grow,
46:42
you look at the top surface in detail, and it has this very modulus architecture. So you have repeating three-dimensional units, which repeat one after the other in an identikit fashion. And so each one of these units here has one of these donut-like structures. This is an air pour. So this is the photosynthetic unit of these lower plants.
47:01
You can call it the equivalent of a leaf. But it's separated into these small identical chambers, which are spread across the top portion of the plant body. And it has this very nice simple three-dimensional architecture, a pour for gas exchange, highly photosynthetically active cells inside a hollow chamber, which undergo photosynthesis.
47:21
On the bottom of this green surface, which has this on the top, you have what passes for roots in these plants, these single giant cells, which emerge from the bottom surface. And on the top, you start to see specialised structures, like this cup-like structure here. You may notice that you see these small photosynthetic chambers on the top surface.
47:41
And inside here, you can see these very unusual asexual propagules, which form inside these cup-like structures. This is a cross-section of the cup-like structure. So they start from single cells, which grow inside these cups and create these little groups of propagules,
48:02
which grow to a certain size and then fix their position. And you end up with a structure like this, which is one of these propagules. And inside, you've got this cell specialisation that takes place, which is easily observable by microscopy. But the key thing is that the process of growth is directly observable. So similar to the E. coli colonies I showed you,
48:24
you can observe growth. This is from day two to day three of growth or germination of these asexual propagules that grow very quickly. And you can use image processing techniques to take images from day two and day three. Here, we've stretched day two image with a warp
48:41
registration algorithm to make it fit over the top of day three. And then you can match them, overlay them, and you can see that now the two images are overlaid on each other. And what's different between them are the cell divisions that have taken place in the subsequent 24 hours. So they show up as red lines here, which you can see more clearly here.
49:00
So you can measure or visualise cell divisions quite clearly inside, around one of these apical notches. So you can visualise cell dynamics this way. Of course, you can segment and quantify the process of growth. And you can also map gene expression onto this. And so you can see these small gemma. These are some of the promoter fusion, synthetic promoters that
49:24
we've been using to mark genetic processes on top of the physical processes of growth. And so we have, we think, we're getting towards at least, a plant-like system which embodies some of the benefits of working with simple bacterial and yeast-like processes, not just with the physical and direct observation of the systems, but also in terms of genetic modification.
49:46
And to summarise, what we are aiming to do, and what we think with this Mark Antje system we are in a good position to do, is to start getting systems both for measurement of local cell properties, of the way those local cell properties relate to interactions locally between
50:03
cells, the way gene fates and gene expression are established by local interactions, and the way growth and metabolism results in tissue-wide interactions with the chemistry and physics of interaction. And of course, there's a feedback within this whole process where individual cell
50:22
properties, and say the geometry of cells are constrained by the physics of tissue arrangements, the shapes of cells are governed not just by genes themselves but their context inside the physical organism, and shapes of cells we know constrain patterns of cell division, therefore constrain arrangements. So there's an unholy arrangement here where a lot of the
50:44
complexity of the system emerges from local interactions playing out across these multi-scale linked processes. And I think now with Mark Antje we have a system where we can encapsulate all of these processes in the same system in a simple way using the analytical
51:00
techniques and models. And I'll finish there, hopefully on a hopeful note, just to give thanks to the people who are doing the work. These are people still in the lab, and the green folk are working on plants, and the ones in darker text are working on the microbial systems. And to the collaborators, including things which I haven't talked about today, which Anne alluded
51:22
to in her talk, which is the open plant initiative. And you'll see this funny plant spanner thing around at times, and that's really related to an effort to try and build open tools for engineering plants at different scales, both at the genetic and the cellular scale, and to have ways of distributing those tools in a more open fashion.
51:41
And thank you very much for your attention.