What I want it to start? I want it to
start with the little metal philosophy. Christian,
I'm going to give you talk with you about some details
of the practice experimental practices of the termining protein structure as well as,
the algorithm make practices of generating predictions, a protein structure.
And one my desk, why all of these details,
from of a last of her of science.
And I think one thing, I wanted to suggest,
was that for last of first of science,
have for a long time, try to figure out,
how science, is but done best.
So that we can make explicit the roles of reasoning,
the logic, the infrances, that drive the development of true,
that we think a course in science, in fact,
that's an old story, as you probably all thought, even
newton had is regular, princippey a regular, philosophicals,
regular, which with the rules of reasoning in for last,
we to document what he that, he
was had been doing right. And there are hands been
a turn from logical reconstruction of the reasoning of scientists to one
that focuses in a way on the practices of science,
not just there, people like propositional output.
And I wouldn't suggest that these are so very different things
that all in fact where I take the joble the follows of or who's looking at the actual practice of science,
to do is to be making explicit, what aspects of science,
we should be analysing philosophafically normatively and conceptually, what's going on,
what kinds of a sumptions or are generating the various stages in the scientific percilit.
And what kinds of theories, where do the accord,
we are might assumptions that we revise have an impact on,
what we think, we know.
So I think the practice is in the service of doing the philhasselfical normative cognitive analytical project.
And so that's two cussions of you for all the details,
we're going to get into nights talk. In fact,
the story one it tell to to night is,
is what is the role of data in generating scientific knowledge.
And this if for those view been here before, I'm,
I do a couple, there are some card knowledge.
And so all those there are many different inputs if you like to the scientific enterprise to determining what theory,
we should accept what we should believe is in the world
and what we should believe about how what's in the world behavious,
what are the last, if you like governing,
the change of state of processes, that were interested in.
And so I think both reason,
are basically logical inference concepts or pre-suppositions,
matt medical reasoning in this case two and pragmatics or the goals questions in problems,
we persue all contribute to the shape,
if you like, of scientive egnoledge,
but to night I want a focus on this part on experience,
abservation experimentation.
And in particularly apparent experimentation to ask the questions,
about how do we understand data? What's the role of
data? It's particularly sailing it in some respects these days,
because there's this, we know,
have data intensive science and we have an explosion of data
and we have experimentation emerged emans of data looking around for
a theory to accommodate them
So the Slanguage of Data and the Roll of Data maybe changing.
I'm not put right, in this lecture going to address the big data
of issue. But rather, just the data issue.
How do we think about Data what's it's roll?
And for last offers, so here's a very ecruity history.
If we started with the kind of possite of this to view,
the view with that, observation experience and experiment service,
nutral arbiters between competent theories, you see
this ecodant in the vagical positives that tap possite vision the latical quant pierces,
who said that we can take as are foundation,
either something operational or opservational and build and try and beal
two have translations in to their radical terms and we will have a strong imperiocal
foundation and not falling to some unempirical matterphysical stands with respect
to what we think nature is like soon.
So in the inpiracism tradition
Abservationen experience, are taken is the arbiters of theory of
which theory we should accept. Now,
there was a postpowser of this few that are current that
included people like hands and and coon and others who challenge
the role of data that was projected in that positive is
few. And argue that instead Observation was theory laden,
that wasn't objective that everytime you actually did in Observation and discribe,
but also it's of theore radical present positions camian to play.
And in in indeed that theorys there is themselves are under the termine by appedance,
this is sometimes called that do him quine problem,
that even if you got all the evidence, it wouldn't did dictate,
by which the radical account, we should accept.
And thirdly, this is my name, which is not
very lovely. I think we're in the post post
positive is view about data and observation theories. In which,
there's been a number proposals to provide more complex to dedetail
the more complex relationships on our visorvations experiment, data evidence in models.
And that's we were going to be going into night slecture.
But let's go, there are alreased in two steps.
So the logical reconstruction of the relationship up what we glean
from our reservations the data.
Have the following kind in this tempt to reconstruct
scient to agreasoning in terms of logic get at the core rationality up it.
High parthesis were, taken to have test implications if they're imperial call,
which is one of the foundations of emperial science that
they had to be testable
Poppers lineway to refugeable or have test implications that might at been hamples lock language.
And so the largic went that if conditions see a car
or they're realized in my speare matter in my abservational setup, then even
you will core, that's the test implications of my high parthesis age.
So largically, if age just true then under conditions see
ewill a car right. And the propagand the challenge to that,
was that the largic didn't capture in fact,
some of the complexities of the epostemic situation in science. Namely,
if you have serve c&E and then you can't actually diductively,
you can't do do sage,
because you have the problem of alternative high parthesies around you did termination namely confirmation,
confirmations not deductives, there's always other all turn to hypothesis,
I can explain the very same facts.
It so the fact that you're high pothesis capture,
these events doesn't these experience is doesn't guaranty it's true, logically.
And also refugeation has a problem. So if you aboservacy and not eye,
then you cannot about upsolutely refuged age, either logically,
because this is the problem of a oxilary high parthesies,
namely refugeations not specific. Some assumption in generating,
the abserved counts of clients, the experiment of refuges,
is at falt for the falsness of eye,
but it may not be the theory, or high parthesis a test.
So this largical rease construction would matter. This is a part of the,
I think, empathis for their postposit of this view,
that we need that more complicated in new anst account of
the relationships between data and model or high part this is and theory, yeah.
Thank you, keep that, keep questioning these things,
because I have experts point for. Refutation just means,
I can prove it's wrong, light it prove.
So everybody want it prove in the vain at mathmatical proof,
because it's such secure nowledge, you really are guaranteed,
your conclusions if infact you have a logically valid argument.
And so by using logic, that was a very strong,
rationality, rational frameworking, which to think about they.
So can I prove with out it doubt that it's true,
no, can I prove with out it out that it's falls,
refutation, now.
So the logical framework needed more a laboration and there's in interest,
there's been industries to do that.
So there are many more refined views of the logic
of experimentation
But in fact, other terms have gone,
to try to to look at the extra practice of science
to had a fill in those more a laborate accounts.
And one I want to deal with talk about today,
I mean, talk about two different approaches,
is one that was developed by boogin and wood word in a paper call,
saving that for namen
on in 18, and sorry, pups, we not that all, in
1988. And then there's been a series of other developments of
their view, as well as criticisms and etc.
And actually, I think, in fact, all the way,
didn't bring these into the conversation to know it.
These really are in do him,
if you read do I'm same instructure, physical theory, he
was already on too many of these kinds of complications.
But boogin' word word, suggest to that,
one of the problem is with looking at observations,
Spiral Manal Results in Relation to Theories, was that that they left up some layers.
So they were going to add some layers. And what
they suggest to this that they are red of commodelor Theorie.
Makes predictions about for namen out that I in the world.
So if you want to know classically and they you get this from Nago,
you want to know what the melting point of lady is.
You want to know what that is in nature right.
And it order to find out,
what the melting point of lady is or to test of theory
that posits a particular melting point of like you do experiments.
And you do you experience you take all these measurements.
And in your measurements, you get a whole band to numbers.
None of perhaps, none of which or a percisely what
the predicted or actual melting point of lady is,
if you think that's a fixed point.
And so that's the data experiments give you data,
lots of different measurements, of you like.
And you use that to the measurements to test the theory
but not directly. So they suggest that there,
what was missing in the story,
is a distinction between data in the phänomenal,
that theory is doubt make specific predictions about the readings on meters,
but they make predictions about the phänomen and the world.
And what data do is try to reparisat the phänomen on in the world,
by means of their experimental, the experimental output of those experiments, the measurements.
And so what they suggest? And they said of you
things about this relationship that I want to challenge into nights talk. They said data,
which plays the role of evidence for the exxistence of the phänomenal,
in this case the melting point a lator,
the existence of in for to nights talk,
they actual more more to more attomics structure other given proteam,
which we think there is and atto funktional structure oder given protein.
Date play the role of evidence for the existence of the phänomena,
for the most part, date, can be straightforward,
they abserved, how ever, date it typically connot be predictored or systematically explained by Theories.
And I think both of those following claims art exactly correct.
And what I'm going to do is show you that if
you did a little deeper into the practice or science,
you see both that things are quite so straightforward and that there's lots of Theories,
that are predicting the results of experiments.
And I think the rest of this, we can we
can skipe for now. So one of the issues is
that most for lots of us have have been concerned with the epistamic relationships,
between experimental results, data and Theories, can we can firm or disconformed that?
And in fact, there's a whole nother store story to be told about data,
that been completely enord in the philosophical corpose, I think,
hacking like doing to this two certain excenten, they were some others.
But this is about, how data is actually produced.
So there's both in epipstammic story and a causal story.
And what's important, in distinguishing those to is that,
if you ask the question, what is good data?
The standards by which you judge them, I'm going to
argue, a very different.
If you're talking about the causal part of the production of data,
compared to the epistammic part of the use subdatas as evidence.
And so by doing this a little more fine grain analysis of the practice,
and has implications for normative arguments about how we can judge with that,
what is good data what of the standards,
that data needs to meet in order to to be used
Are scientipic enterprises. So the first part is to look
at from phonaman on to data. So had we get data,
actually mix a difference to how we can used data.
And here's the suggestry, I'm going to put forward to nite is that it's a type of signal detection process. So if you interact,
causally with a top of namen on in or some sergeant for the phänomen on,
that you're trying to explain by your theories,
if you interract with it carsally, you it is the source phonamana,
that's going to be sending out,
if you like signals about it's properties,
that are are a vailable for detection in different ways,
by different detecting devices that are available for detection in different ways by different detecting devices,
that's perspect tive all right, you can hear it,
you can see it, or you can dos.
You can detected in some matter. And that the process by which it data is we is produced,
is through this kind of course, it's not just to cause,
it's the causal plusion influential process,
where you have a source phänomenal that generates signal,
they have some way of detecting that signal, measuring it.
And then with those with that, which we my call data,
you signed this can then, using, the radical sumptions,
in fire, to a data model,
that's a model of the phänomen on from just the data, plus sometheors.
Okay. So in the case,
I want to talk about how that get spell down in the case approtents,
is that, and I'm going to just you nucle your magnetic residence,
spect trust coupy to night, the nMR,
is that you start with the protein,
that's the phänomen of who structure,
you want to understand to know what idea is,
you think there's a structure in the other party in the world you want in other ideas,
and you could try to predicted
Or you could try to detected. Okay, so this is
the detection part, we do the protection part afterwards.
So the detection parties is start with the protein, typically in contemporary,
animal experiments. These are cloned from Synthetic DNA.
So you actually can produce a protein that has really good detectable features,
in fact use, I said tops of the Adams,
that are going to be eneracting with the magnet.
That are better, good it clear signal, if you like,
then if you just did the old fashion way,
before there is cloning and just, you know,
try to get the proteins, mostly at a chaos, right?
So, he muglob in the first to, you know,
the first protein detected by extra request of largerfy,
was, was taken hard of cowblet.
And it's not a surprise, it was he muglobing,
concerts of lot of blood.
It into periofy the protein out of that natural
resources udely
Okay. So you need a like to start with you
get something out you can use in your experiment.
But now we can a clonedom from Synthetic DNA to produce
just the string of minor assess that we might,
cause that's what the DNA codes for,
we just right the scripts and the way,
the some place, junk out the protein.
And thank out a lot of it,
because you still need a lot of protein,
milicals in order to perified, it for an experimental.
As hey, Assay. So you start with this comes and you get your get
your periofy protein that you want to know the structure out.
You put it into the NMA experimental laborators.
And that's going to one more layer of detail it.
Any point in the story you can did down further,
by the way. And in this what basically you do,
it's you disrupt the attamic spin,
which is in that quanomicanical property of the new clears
of the addams
You disrupted from ecoliveryum, the ecolivery edities in the big magnon.
I hed so there alpe, if you like pointing,
different ways. And then you disrupted with a radio frequency
polls and watcher, if you like, watch.
What it return to ecoliveryum? And what you can do
by this intervention on the new nugliers of the adams,
is you can use that information to show the data,
which is the decade curves of the different adams,
because what affects there decade curves, is not just the big magnet.
But all the other haddams around, okay.
So what you want to do is try to figure up,
where all the adams are, you focus on the new clears,
you focus on this property, you do this,
it's magnetic properties.
And you disrupt them, and from that that
the measurement of their decade curves
You can, here is the influential part, that that
there's no influence before. They influential parties that you can
infere the relative distance in Rotation, Angols between Adams.
Okay. Like, that's how it works.
And hear it is in the more flash.
So again, even to get to the peer-fied protein
ticks some, they are radically in beaut prote processing.
And want you get in the test to be goes into the magnet,
and here's the spectre. So this is what if you like,
we're might call the data. Okay. And least,
we might call it, that is data,
they're maybe other parts of the story that we might call data,
but this is data, which is the spectre of the different Adams.
And that we can now use to infered the milecular attomic
structure or mardel of mardel of the protein.
And that's how that process works. Now one of
the things that's interesting about it is that
It use is a lot of theory. It's not very straightforward.
So it least just a balken and would word in national characterization and
was. It's not very straightforward. How you get to
that data? And there's a lot of theory.
So here's a blill picture of some of the Theories.
No, what is the Theories tories?
These are Theories about how the experiment works?
How other spin of the nuggliers of nine-to-gin 13 13er,
Carbon 15, you can works, and here's
the radio freequence you polls. And you can see that
it's going to be detect the shift right as it shifts from
equal librarium to dissical librarium to back to equal librarium.
And these shifts are what are measured as a signature,
or you providing information, but the relative distance between different adams,
because there are fact to buy achother as well as the big werden.
On in the things, I would like to suggest here
is that what's important is and that we all know,
datast theory laden, we can use it,
it's not objective or the Theory is art predicting or explaining the data.
But rather, we have to ask to different question, which is which theorys.
Which theory is are involved in the production of data.
And you turn that this or theory is about the quanomicanical properties
of and the magnetic properties, of nuclear I and Adams, okay.
These are not, the Theory of will, I think,
that predicting that structure of the protein those this way,
because of it's function, and I have a competing theory,
that's as a goals this way.
Those I'm the Theory is that are involved in that experiment.
These are Theorys out of the experiment of how we get the data.
And so the fact that there are steorys here do not puren,
it's use, as grounds for judging between competent theories,
of what the structure of the protein as.
But you don't have to go the other direction,
say, there are no theorys explained,
because you need the theories to explain, how you get the data.
Secondly, what you get has a major minute of any
these complex experimental setups. It's just the first pacit data
of the right. It includes, noise, which is random
fluctivations in the data. And it includes something else,
which is other signal from other sources in the experimental setup,
cake which are systematic and not random.
And if you want to be able to get good product,
produce good data, you would you're goal list to try to,
oisely,
the signal that's coming just from the protein
Nugle yai, who structure you want it detect?
And that the signal that's coming from the main electricity line in to which you plugin your machine.
Or not in this some temperature flexiblations case here,
conditioning capt on, well, you're doing our experiment,
or not or all these other or some of sources of what's called instability in the data,
come from features that you can out, get rid of, right?
There just features of the limitations of the experimental equipment,
to I sulate as signal, as just from the source,
the targeted source für Namen an. And so you need to understand it,
how to manage the data, by either,
getting a better experimental setup,
controlling for those factors that are influencing that the output the detector,
what's coming out of the detector,
and your stea and the stea and the character of the production of these namen phenomen and sources of signal.
And calculated out, their effects. And so the way
and which you get data turns out to require theories about
the sour other sources of the signal.
And not just theories about how this signal,
the phonamen our works in the our experience.
So there's the large of their radical work in the experiment,
that are a used in order to to go from their removal of no ways
or non-source signal in order to use just that,
those measurements that data in order to infor or generate your model of the phänomen on.
So again, just going in here to this opening up,
if you like the boxes in between that are in the data parks,
in the measurements of DK only the first.
One, that's also data, but the data that you
want to use for the influence to the model from the from the experiment.
Are once that have been refined to Elliminate noise and non-protein signal.
So, the point of these details is justiced just the fauling.
The norms for good data, causally and episthemically or different.
And so if you want to know what you've got good data,
that's in itself a kind of end, big you
was question. And I don't know how many be
we're theoreticians and how many you were experimentalists.
And if you're comfortable being in the same, rumothy jother,
but it turns out that there are are experimentalists who who's
life is a devoted to creating,
clean detection of signal
To building better experimental apparates to refining their tools for cleaning
the data of extranious signal. And they have know interest
in what the predictions based on dynamics or function,
a protein suggest the structure of a protein should be.
Peach is want to get a good signal. Right,
they want a build a better, better equipment.
They want aselve the carsel problem love the generation of data.
And so the norms or they want to have more accurate.
Detection, carsel detection of the signal. And they want
to make, try to a chae,
what is use the language is in terms of stability,
which is, I think, interesting.
And so I think stability alcome in The letter tomorrow two,
that's stability of the data versus art of the sources artifactual sources or non signal sources.
Nine, the namen and sources of data. The multiple
sources of signal that are in this, in what the detector device,
the tee tags and also two get what's stable compared to noise.
Now, often the way,
you do turn what stable in nonstable in the data,
is every selt of doing multiple experiment? And you see,
what the data shows each time you do it and you can see,
what features of that data or stable,
what are not stable and that,
but that's the beginning of the story and then you have to go in try to figure out,
what the sources are for the instabilities.
So that you been need there, remove them our culture late out, they're effects.
So I think that those are a little different,
then what you would think this you are theoretician, you want,
you need some good data to decide between your theory.
And somebody else systerious. Or you're the remakes one production and another
competent theory makes another production. And you need data to determine,
which want it, you should exapt, which one,
what is the data favor, which are hypothesis or theory
or model, they're ready commodel is favorite,
by the servations, that's the episthemic story,
that's the evidential story that full hassipers have been telling.
And it seems to we that those are different criteria,
that you want the data to be percise, or maybe they're additional,
you certainly want the data to reflect the phänomena,
that you're theory is, and it's balking would be correctly said,
explaining and discribing.
But you need that data to be percise namely at that it discribes,
the relevent features of the phänomen that phänomena,
that disting with your hypothesis your prediction from the competent production.
And so help person is it has to be clearly is relative to your tearies.
If you have theories that have domatically different predictions,
he might need very crew data to determine which one of those is getting at right.
But as your theories might be giving you more fine grained predictions,
the data you need to this skriminate between them is also
going to have to be more fine grained.
And the interaction between their editions and experimentalists is one of the things,
it drives, experimentalists to develop,
better big big bigger my hagnets to do more fine grained, analysis.
But it's not the only raiser, experimentalists,
when it do that, because they have a project or ready.
They're project is it helping out the skriminate to twin competen theories,
they're project is to get a better coustle signal to detector
Happening in their experiment. So I think that by separate in edition,
sorry, to having the data is good data,
epistamically, if it does the epistamic job, right,
of discriminating, between competing are alternative predictions,
it also is important that it be somewhat generic.
This kind of pulligancy chother, in the sense that it
can be evidence for are against different theories in models.
It has to be have a certain outconomy, maybe that
better than generic, has to have a certain outconomy.
So that somebody with that this data can also speak to
other theories or models, set our generated, in the future.
So and it was, so the epistamic goals of data
has evidence as a post to the callsal goals of data are as signal detection.
I think expose that different norms that can be use to
develope stehanders for judging the goodness of data.
And only by and also, I so lading,
or identifying, which their radical components are build into the
generation of data and which once or at stick in the Apple, in the episthemic
job of testing alternited theories. So I think this kind of move tour separating the causal from the Aposthemic,
isn't just giving you a more fine green discription and what's going on in the sign to big practice,
but gives us normative purchases, philosophically relevant,
places to both make explicit,
what the assumptions are what the standards are what the judgments should be,
right, in a way for for last first to further engage with sign tos.
So the lessons from Experiment, quickly,
are that boggen in wood words clime that data is for
the most part straight for rightly episode
That's so much in contemporary complicated experiment. And so it's important to
look at how they art and why what we learn from sing,
how they art. And secondly that date it typically can
up be predictive or systematically slain by Theories.
I think also fails to partition that Theories of the experiment
from the Theories to be tested by the experiment.
And so again, if you like general climbs about theory laidness,
are just going to be two crew to cut in order
first to see how to judge the goodness of the epistemic role that data complay.
So there's another, a tempt, there's many,
that these are just two. There's another attempt,
to for the explacate from the original if Age than if
see than e-account of the role of data to get at
it a different understanding of How we should think about data.
And that is run gary, run gary someone who have,
I like very much,
I share a lot of his views and he left lots of from for further development of those views,
which I hope by it been engaged in over the last number of years.
So he has a different account of this,
no distineother is more, we had age and an e-will, you see e-o-e,
then we had data and for namen are and,
and theory, and now we have principled models,
representational models, specific high partificies and generalizations, coming top down,
and badem up, we have the world experiments and data,
to models of experiments and data,
that also generate specific high partificies and generalizations.
And so now in this framework, it's all talk about
in a little bit more detail in a minute,
in this framework
When you compare if you want to test of a Theory,
Newtons Theory, against the data, there is no direct.
I mean, it's a laboration, beyond, balgan in with it.
It's not direct the Theory, says what I should see.
But the Theory generate certain models, that generate certain predictions,
the data is generate certain models,
if you like that are predictions from the data.
And what you have in the middle is a model model comparison.
Okay. So this is again it different a tempt to
explicated in a more new anstway, the relationship of data two Theory.
It's still episthemic in many of its forms.
So the prince build might ultray that in minute.
So now I want invest to get that direction of what,
what how is data werk here?
And it wants a price, you cause for last first are boring to criticize.
That I'm suggess that guery other we gets a lot of it.
It's going to, not get a all of the story
So I'm going to introduce some important use is of data inscience.
That are not capsured. In this account either out,
I think you goes for that. So one of the things,
I like about Guerius that he defense the views, at I have,
I also defined namely that models all the models or partial,
perspectively in pragmatic.
But danny, at which is, what gives you plurality and models?
But then he hads that they higher archoical,
that's his like which for capturing this picture.
So in this picture, what he suggest is that principles
models, are just theories that are extremely abstract.
So newtons there, lots of motion for example, streamly abstract.
And they applayed a lot to different things say,
played a falling bodys, they he played a planet,
say, played a props to light particles,
they applayed are sorts of things drag.
And so if you going to test a theory that that's
you have to do it least one level of interpretation,
where do I look in the world detest this theory?
If it's just a math medical formula, I have to interpret it,
in some context in order to go how, and perform some
kind of experiment to tested or observation detested.
So I think that's what you do,
you take your every strack math medical structure,
he's a semantic theorious, at theories are are semantic structures.
You take, you say, a math medical structure,
you fillion, what there it refers to that your representational model.
So your newtons last, can we fill than vice springs
or pengible or pengible or pengible or pengible or playonets,
so what are you want? And that's your representational model,
that's your telling you were you should go in the world to try to tested.
And from your representational model, you can than make a prediction.
Super specific high part this is about what the planets are
going to do, if the newtons laster true,
what the spring's going to do? I etc. Okay.
So this is here's more a laboration of how you get from Matheory,
which is primemarily mathmatical to something you can look at in the world.
And then from the bottom, you have the actual world,
there it is. The experiments, which I guess to spose,
we the in causal interections, I mean, terpriting him,
he doesn't going to this much deep to the causal interections in the world.
And the data, which are partle, the causal upshot of those interections.
And from those you can do velople model of the experiment in the data,
that's what I just show do on the NMR,
that model of the data,
is the prediction of what the structure of the poeting is given liked and my innemare experiment on it.
For my NMR, this is what the protein, should look like.
And usually, it's a class of model,
it's not a single model
We know have to going to that for now. So,
if we apply guerries are can't to the prouding case.
And most of the about national, the top down,
modeling approachings, is my like it or dynamics.
It's new tony. But so you start with newtons a max will laws
or equasions. You have an energy potential. And then,
you want to do rive, you want to have a model,
that represents a particular prouding, namely or proudings in general plus,
the initial conditions of the initial sequence of a mino assets.
So the abonn issue attomic model from I mean,
I have sit sequence,
is what you want to developing account of the list mean,
give's energy state, as the most stable configuration of approaching.
And there for what the structure of the
prouding should be
So they want to say, that's most stable their modernal this stable structure reprotein.
Should be what ideas? That's what we should find in
the world. Okay. And I can do that.
Right, abonnation you. I can take the string of
a mino assits that define a protein, tell you were
all the Adams are, do all the possible configurations.
Of what shape three the mentional shape those Adams good take,
calculate, there are energy.
And then search for that configuration that has the least energy.
That's the game. Turns at it's kind of hard to do.
Okay, but we're going to get to that.
That's relevant to the story. And from that at given that, I've,
if you like fertig and a particular here me now,
I has it sequence that defined a particular their protein,
road doubs and or look at in two elf,
or whatever the protein us, right our new clears, put in approaching.
The fine by it's, and mean, I has it,
put it in
two this
The radical framework, that's says okay, well,
what's it's configure, which shape gives me the least energy.
Okay. Okay, I can call you late that.
And that's my prediction for what the functional structure that Pertin
to be, from the top to the bottom. Now,
from the bottom, as we've saying,
you start with an actual protein, you refined it,
you pure afy, you put it in your animal experiment,
you start with it's actual spin orientations before in after the freak with the radio frequency innervention,
these are, you know,
things you're can up server about the real protein.
And from those you can develop a model of the experiment in the data,
as we saw a model of decay course, a bottoms,
reality battomic distanzers, that you can infer from the animal experiment.
And from those,
you can develop a model of the experiment in the data,
that those formers of the data, you can,
then in for a prediction if you like a predicted model
from the data of what the structure is given what it's a done in your enemor magnetic experiment.
So now you have two models, yes. Well,
from that which direction from the badem up,
what it is is you get a set of,
you get you get data,
which a number of models will set as five and not all.
Okay. So I'm much a what to call that it's not.
It doesn't to terminary, unique structure for the protein.
When that just fertignote interestingly and there is that there's a
just a Cultural von Namen anthat, when NMA, Spectrascupers,
Public-Seriesalt, sie publishen die Collection-Models, die ihre Services fragt.
Das ist das, was die Daten haben. Und die
Extra-Kurse der Lagerfests, die die same influentialen Strukturen haben,
pickt die besten one.
Und sie publishen one. So, es ist lux different
. But in fact, it's still under determine.
Thank it for the question. Okay, so that looks like,
this looks like a really good filling out. I mean,
I think we've learns that that garries model,
addresses many more of the steps and they early year tapps,
subtilling us the role of data and how data is not used evidentially.
And also per helps shows how the causal processes give rise to
also in in first class of models that service fized that data.
And those it the ones that hopefully your see
our educal count has auch so predicted in then confirmation
Not, Garretteed, but that's good. Now,
how ever, he leave something out, okay.
So I want to argue in the time evel left,
that they're a two roles of data here here as well.
Namily, there's the role of testing theories as,
we've just seen, that's what guerry was,
guerry didn't say they work, he just didn't explicates.
So that's filling it out, it's not reprejecting his view,
that there's two roles for data, even in this process,
there's testing theories, the principled models and guerry's framework,
via they're interpreted representational models, which make specific predictions.
Right on ugly years, should look like this. Right,
okay, he might go wing should look like this, that's the least
mean gives lienergy state configuration. But in addition,
there's another role that can that data place into a protein science community,
that I think is probably,
it's really Why it's spot the scientist here all those,
it better the nideo, which it pays around of constructing,
the very predictions themselves.
Okay. So it plays the specific predictions or that are made from the data,
sorry, I should be that in the data models,
the models from the data, right?
In conjunction with the theories, are used to generate the
representational models, that predict the structure of the data.
Okay, so now that would still that will confusing.
So let's go down all little bit more in terms of
what's going on here. But let's let me just feeling the testing part,
the testing part happens and this is an important domain of the contemporary scientivic products,
with the critical assessment approaching structure caspe, caspe, licensany words would pea.
Caspe, that is work
Contests, every two years, since 1978, this organization,
performance has a contest, we are they use experimentally their call derived,
but I know for us, for us,
it means, something different for us.
So experimentally driven models from the experiments, animal exray at all
the other experimental things, also the two down, it once.
So you do all these experiments,
they come up with a model of the structure a class of models of the structure,
what the take a protein,
but before they put it in the protein databank,
which now is about 120.000 improte instructors, in it,
before they published their results, they give it to the contest.
Okay, so only there are web knows,
what the experimental results on this structure are.
And the contest, then then then invites all those predictive algorithms,
to see of thinking, what they can get as their predictive structure.
Okay. So in, and so their blind,
right, their blind predictions of structure. They don't know
. Even do, it's been experimentally determined what it is.
They're going to brilianly predict the structure from abonnational or from principles
of physical theory. Okay. And even in 2006,
they were over 200 prediction teams,
some of our now automatics, that more complicated,
but they were least to there were over two and our prediction teams,
set to work to see, who can get add?
Who can get closers to the experimental model of the...
The protein. And what happens is that it turns out,
that the abbonniciour modeling according to gearries from formilar
Putin-Structure-Works by exploring all the physically possible attamic configurations of approaching,
defined by next equance of a minor assists to find the lowers energy state.
That's how it works. And it turns up that they,
I'm in a comeback that the slide. This is what
they do, right? They have to look for the minor,
they have to minimize the energy of the system, right?
By looking at all the configuration, in configuration space,
and selecting those with the potential function and selecting those,
that have the least or that one that has the least energy state.
And that will be the predicted structure. But it turns
that that there's really problems with that. Surprise, surprise
. I'm going to comeback to this and my imperial,
because that's a response impart to the problems,
they are their radical problems.
This actually came up earlier in
the week of aero
For Total potential energy. So we go back to the
slide. You can see that the Total potential energy is
made up oval these subkinds of energy for that configuration.
And not all of a limit of very well, now,
no, we'll end your study in the sunish you are things that have more,
that's an last certainly to them right,
what you get is the Total energy maybe in a fact infected by those and certainties.
And so what you might be predicting,
you have the right theory,
what your feeding in as the as the potential energy might not actually capture,
the real potential energy that this a minus as it sequence should have,
because you can get all of the pieces about nail down.
And so even that you run your,
you run your helgorythm through this theoretical representation or model.
You may and that with there result that is and actually correct.
Okay. So that's one problem. The second problem is computational sampleling.
So it's computationally really hard because the space of confirmations,
that are possible is astrynamical. And so you can't like going on,
that they can't gelin and measure, they energy, of every possible configuration.
And then search that space in find the lowerst one.
Okay. So you have to have some kind of sampling algorithms,
some ways of reducing that problem,
in order to get at the right analcer.
And so the computational one is,
the problem is really called the sampling problem,
is generate you have to generate, and not confirmation,
soufficially close to the firma dynamic minimum to a liably identify the stable configuration.
So you've got some, you looken it's a same
bow cas you can look at all about.
And in your single, you pick out, your lowerst one.
But that if your sample doesn't in cloud the ones
near they actual those energy configuration, it's not
kind of you the right an to eater.
But see it these are really difficult problems to sound.
So had we sell them
It turns that and this is the constructive use subdata,
through another form of generating predictions,
which is homological or knowledge based modeling a protein structure.
And this is where, you use experimentally detected actual structures,
right, from the experimental models.
And not of the protein you looking at it all the proteins.
And what you look get to sequent semiliarity.
So you have a 300, I mean,
I has it proacting. And these 20, I mean,
I has it's a right danacall to the 20,
minute assits in the suther protein, this at the protein,
maybe 400, minute assits long.
But it's got the same stretch of sequence.
And the assumption is of the has the same sequence,
it should have that build of the structure should be the
same is the one we've already abserved.
Okay. And so that it's a homology, so it's similarly
instructure. And by using this similarity instructure, right?
You can use that
to directly
If to basically, reduce the search space for the least
education configuration, you make you reduce the space.
And since many of the problems of the nicio of top
down marling that it's computationally impartment.
It's nearly empossible. Then by reducing the space of confirmations,
by our the fixing parts of the protein,
through the use of the data marles of other proteins with those shared sequences,
it turns out.
That you do a lot better. So here's a homology modeling,
I've given you a very quickshort hand thing it includes many
steps, right? About recognizing the template that share,
the cross, the two tefferent proteins, right?
Generating, or then the generating using,
how you use it actually and putting it in
the predictive model has many steps three
But now me just sure you the pay off.
The pay off is this, that's good back to caspe.
The critical assess many approaching structure prediction contest.
So here's in example where. You here on there,
they collect correct, which are makes me nervice,
but it's not my my my image. The correct answer,
namely the target to protein that the expert, the experimental model,
that's what that is experimental model of the structure reporting is this why.
Rosetta is an ebenecial prett modeling algorithm, that doesn't news any
imperiate data and it's not a comparitative model, home logical algorithm. This is,
which you get if you struct from first principles,
given all the problems are are are in trying to gentnerate
the model other protein, compared to this one.
This is what you get if you add the results of
animal are experiments on other proteins,
to the reset are algorithm, hey,
no,
this have much closer it us
Ups. So, what they found is that in these contests,
the algorithms, the predictions that are more successful and increasingly successful are all homological ones,
or all same my empirical ones,
or all ones that used data constructively, not just to test,
but to constructely algorithms at generate the prediction.
So, this is a really different story.
This is a very different relationship of data to theory,
right, then just to straight forward,
it's sits down here and the theory sits up there and they meet it in the middle.
Because in how that the delicon somedata are are guin parted titioning,
which data. Some data is now used directly up here,
to generate the prediction, that's compared to other data.
So, one of the interesting things,
I two slights life to him going to be on go
Is that need turns out that you through these casper series
of casper contest experience. You can see a change,
each of them are getting better, actually three sites.
But one thing you can, if you can see
on the slide it all, you can see pretty good,
is that they know vole who is abonnation your prediction, right,
without any the use of any of the our ready depositive structures from experiment,
is not to bet that really small malocials.
Okay. Big abysely, because the search space is smaller.
So they're pretty good at up to,
up to better 100 read residues, 100 emino assets.
And then, I'm any talk about the one.
And then sequence that the comparative modeling, right,
is considered to be really pretty good at larger,
size proteins or multiles,
agetting their astructures to be pretty to be I dan of fine.
And then at the very talk is an emorian extra,
that they're art any really. And need do not another predictive
algorithms are getting very close to the drived models from experiment,
when those Malikills are proteins of very large.
And so you can see that we need for different size
Putins, right, also for different purposes.
These different ways of modeling at or generating predictions up at
are going to work better words.
In the last, there was a 2016 cast contest,
but the results are yet in, left I chat.
So the 214 after each one,
they predict all the results and who did better and cockitay all the data,
but who's doing better and which all grhythms are working.
And all the algorithms, all the algorithm designers,
use this contest to then tweak their models right to get
them to go to, you know,
it's a feedback process to get
better predictions
So mall those one of the organizers of the cast sells,
the accuracy of a mology models,
as managed by cast have improved dramatically through a combination of improved melthods,
larger to data basis,
of structure and sequence and feed back from the cast process.
So how do I get a better prediction? Also,
now is feeld impart by getting more data,
because then you have more likely defined a sequence home of lock
and other protein with that sequence. And the more
sequence homology, there is the better you are.
And abonnity your modeling methods have also improved substantially from a
more data low bass in the first casts experiment.
It is now not an usual, see this is freddy
not on usual to see topo
allogically accord, that means on the crude,
the cruder,
aspects of the structure
Atten indeed, typologically accurate models for small less in a 100, I mean,
our assets, that are regular and single domain non-template proteins.
So the simple is proteins out the Abonnier she once are getting,
pretty good at, the simple small once,
but they really still without the imput of data in the
construction of the prediction all the rhythms,
they don't get very close at all.
So conclusion. What is data and how is a used?
Turns of datas really interesting in contemporary side,
it's it's not just ee in the logical representation, right?
It's not just the straight forward signature of phanamena,
that is easy to see. It is turns that to
be something that it has both a causal characterization of the
detection of a signal firm of the namen on,
which require stereos are instrumentation.
Theories of other sources of signal
And it somes this stability of the signal from the phänomen and that's one of the assumptions.
It has a huge a never dentials character, data does,
which is that whatever is taken is relevant in comparing the eradical models,
or whatever is relevant in comparing their eradical models, can be data.
And it is somes this stability of the mapping from the representation to the phänomen on. So if you going to use it to,
whether it's a mathmelical mapping or a linguistric mapping,
what you take the data to be to characterize it,
has to be true of the phänomen on as away to bring it into avidential contact,
this is the guery model to model, relationship in history,
right, the model of the data and the model of the phänomen on,
sorry, the model, the data in the model from the the theory right,
can that's where evidential relationships accourse.
So they have to be represended in ways at a
addiquite to
Valth their sources and their compares and. But I wanted
this suggest, as it's true in contemporary sign.
It's there's also a constructive role for out for data.
Namely, it's the direct in the directing corporation of experimental results in building predictive algorithms.
And they do that in reduced computational intertractability. And they as soom,
this you have indies to do work to do that,
as you have to assume the stability, you like,
of the parts of phänomena a corss different phänomena.
So that sequence distructure relationship, I see and one,
Protein, has to also be true when either curse in
other proteins for the hammer vagical modeling to a curr.
And with that, I want I think you,
where your time in a video there's questions