Can you just give me your name and say what? Your title here? Yes.
My name is Brian Marston, an associate professor for Data Management and Research Informatics,
and I'm also associate head of Division for Digital Information.
Great. And I don't think you mentioned what Unity were attached to, because I'm attached to multiple units.
Yes. So I'm attached to the Kennedy Institute for Haematology and Dorms and the Centre
for Medicine's Discovery and DM and the Welcome Centre for Human Genetics in India.
Right. Great. Thanks very much. So can you just start by telling me a little bit about yourself, starting from what?
How you first got interested in science? Yes.
Pretty like the main staging points in your career up to until the end of 2019, 2018.
Yes, that seems like a long time ago. So, yes, science has always been my thing.
My father was a scientist. He studied at Leeds, the structure of penicillins.
Back in the fifties and sixties. There was an expectation that I would be a scientist, but nevertheless it came out that way.
So I did my undergraduate degree in natural sciences at Cambridge, focusing primarily on biochemistry,
then came here to Oxford to do a dphil in while biochemistry, looking at extracellular matrix proteins and the dynamics of those within.
Campbell Then I moved to San Diego.
I worked at the Scripps Research Institute for a number of years, and then I kind of focussed more on the computational side of things.
So that's where you first got interested in?
Yeah, they actually happened in the undergraduate end of things because we soon found out I was very dangerous in the lab.
And so there was a what is called a Silicon Graphics machine, which is a very fancy high end PC effectively corner with no one's using.
And I managed to get it to work. And the rest, as they say, is history.
So as a consequence, I have, you know, the,
the wet lab experience as well as the computational experience and you can speak both those languages is a little bit unusual.
So yes, the scripts where I help them build some of the first high performance compute clusters over there worked in the lab of Ruben Avigan,
who was interested in computational chemistry. So I built a number of algorithms and then I moved to industry for a number of years by a focus where
I led the computational chemistry team on the design of ion channel and kinase inhibitors and well,
briefly for for the ignorant, what is computation? It's a great question.
It's a computational chemistry. It depends on who you are and how you define it inevitably.
So you can imagine that there is a computational chemistry in terms of the fundamental understanding of how small molecules,
particularly organic molecules, how what their shapes may be, what their properties might be.
And that's not so much on the drug discovery pathway.
That's more like pure theoretical chemistry and use computation to be able to calculate these values and simulate them.
That's not what I do.
What I do is focusing more on using computation to better understand ways to develop small molecules or biologics in a drug discovery pipeline.
And so this involves not just the small, organic, small molecule itself and the drug,
but also the protein that that small molecule may actually want to bind to and how it may inhibited and so on and so forth.
And so that's much broader, tends to be a bit more practical.
So it really is a case of in an industry you might do something called a high throughput screen where you screen many millions
of these small molecules against the protein you're interested in you think is associated disease and you get some hits.
And the question then is can one then modify those hits,
those small molecules so that they're more potent and selective so in other ways
that they stick better to your protein and they only stick to your proteins,
you don't have side effects. And that's partly due to your screening in the lab or computationally both.
So that's at the screening. In the screening, the lab is done at the Centre for Medicines Discovery or with our partners in industry,
and then the results from that then come to us for then to age computation,
interpret it and to make suggestions because they're not always right,
but suggestions to the chemists about how they may modify those small molecules to make them better.
And that's the beginnings of the drug discovery process. So I guess I'm what's the it's just drill down a little bit.
What what is the data that you're putting into your algorithms?
Yeah. So as I said, there's the high throughput screening.
Yes. Whereby you know what your small molecule is and you know whether it had some
impact upon the assay that you're using within the actual throughput screen.
And then you can start looking at the shapes and the structures and the properties of these small molecules.
You might start seeing patterns, You might see see patterns about particular.
Types of substructures to be columns of particular atoms or collections of atoms on that molecule.
That might mean that they are more sticky or they are more polar, or they are something, or they behave in a certain way electronically.
And at that point you can try and correlate that with an understanding of the protein that they bind to.
So the analogy I tend to use is that of Lego bricks.
I mean, when we solve the structure of a protein,
we are effectively writing the manual for a LEGO model and we're kind of working out where all the bricks go, what colours, what size, what shapes.
And then when we have a small molecule, we've got another piece of Lego sets, a piece of Lego on.
What we have to do is design those sets, a piece of Lego to fit as much as possible into the cavity that exists in the main Lego protein model.
So that's that's what this is all about. It sounds very visual and even very 3D, the way you very 3D use it, very thin.
I think what I'm trying to get at as a non information scientist is how you do that computationally how
you describe the entities are they described in terms of a three dimensional shape or in the forces?
Yes, All all of all of those those things, yes.
So yeah, the 3D structures, sentiments, discovery, we we solve the structures of human proteins often for the first time.
No one's ever seen them before. And from that, we can then start to understand how those proteins actually work.
So the mechanisms and mechanics of them, we can also correlate that with no mutations.
So we might know that in a particular position in the protein sequence, there is a change in what is called the residue.
And that residue means that there's a change in one of the Lego blocks effectively.
So at that point you could then look in your protein structure in 3D and you can rotate it around on a computer screen and you can see how either
the change of the colour or the change of the shape of that particular Lego brick is going to have an impact on what that protein actually does,
what it switched on cause it switched off less. You know, all these sorts of things.
So yes, that's, that's how that works. Yeah. So I completely interrupted your life story.
So we got as far as scripts and you said that was. Yeah. Into the computational.
Yeah that's right. So then then moved to by focus that.
Oh yes, Yes that's right. Yeah. And then work there for two and a half years and then got very frustrated with the,
the, the lack of blue sky thinking and the risk averse nature of industry.
And I got called up in 2004 by a person called Michael Sundstrom who helped co-found the SGC here.
And he said, We need someone to come and do something called research Informatics.
I thought, Well, we'll have a discussion about it.
So I came over and he he sat me down and, you know, sat in front of the PowerPoint slides, and he ran through the PowerPoints.
And it became very clear that what they were trying to do is highly ambitious.
They were trying to solve the structure of human proteins, had never been done before at scale.
So hundreds of years. And this was something that no one thought was possible.
This is just after, you know, the human genome sequence. So everybody wanted to get this done.
So you said SGC, this Structure Genomics Consortium.
It was, well, let me explain.
So as I said, the human genome was solved to an extent, and we kind of then knew what proteins could be made in a human cell.
And so the question then was for the pharmaceutical companies, well,
how can we leverage this information to make smart decisions about drug discovery?
And that the existential risk was that there were a number of new Start-Up companies dotted around the world,
but particularly on the west coast of the US, which said,
well, we've got access to human genome, we're going to solve the structure of the human protein,
I'm going to license them to you for large amounts of money. And the pharma companies said this is not a good thing.
What we need is a pre competitive effort that is going to solve these structures, put them out of the public domain without any burden of use.
You know, you don't pay for it, it's just freely available. And that led you to that problem and everything is good.
Now it turns out that solving these human protein structures is a heck of a lot more harder than anyone thought it was going to be.
So back in 2000, this originally sort of kicked us around about 2000, 2001.
At that point, there were about seven or eight farmers involved. But by 2003 became clear this wasn't such an existential risk.
And then there was only GSK involved and um, and the welcome or welcome trust as it was then.
And the Wellcome Trust wanted to leverage its investment into the, you know, the solving of the human genome.
And that seemed like the next big step.
So they said, well, we need to set up a pre competitive structured genomics consortium, which is what they did here in Oxford and also in Toronto.
So yes, so I then came in and helped to.
Build mostly the data management and some computational chemistry around that.
And the idea was that the SGC would make all these humans and we did.
Yes. Yup. And make them. It was an open access project.
Totally open access, Yes, exactly. And, you know, I, I think at the last count, we've sold over 2000 of them, and we are the world leaders on this.
Um, so. Yes, very exciting. Mm hmm. Mm hmm.
And the the technology to solve those structures was, again, partly computational and partly using things like the diamond light source to.
Yes, well, diamond didn't exist in those days, so we had to use a place called SRF in France.
But there is some computational aspect to that.
That was not what I was responsible for. As part of the solving of protein structures, you can use a method called crystallography,
which is where you have your protein very pure and very small amounts of it,
and you put it into something called a crystallisation experiment whereby you try and coax the protein to turn into a crystal, a very uniform crystal.
These crystals is often smaller than the naked eye can see, but yet you can shoot very strong X-ray beams through them,
which from a synchrotron like SRF of diamond,
and from that you get diffraction of the X-ray beams according to how the atoms are placed within your protein.
So using some very fancy maths and some computation,
you could then infer roughly where all the atoms are and therefore you know where your Lego bricks are and therefore you've got the structure.
So there is some computation involved with that, but not as much as you might imagine.
Actually, the hard step is actually making the protein, making sure it's stable,
making sure you can get the crystals and actually solving a structure.
It actually is not that hard in the grand scheme of things. So what was your role?
Is that your role? Yeah, So so SGC, we wanted to do things a little bit differently.
We knew we were an academic organisation, but yet we have industry milestones and deliverables in the sense that,
you know, our funders expected us to deliver X structures per year, right?
Or actually X structures per month actually. Um, and we knew that we, we couldn't do everything in a standard academic process.
And so a number of us that were hired, I was employed like number three,
I think there were four or five of us who came from industry and knew how to work in a process oriented kind
of approach and in an industrial approach in terms of making sure you meet the deliverables in the mountains.
And as part of that, it became increasingly clear that what we needed to have was an appropriate
data management infrastructure underneath that to capture information about,
um, what proteins were working on, what ways we have tried to make the protein, um,
what ways we've tried to solve the structure of the protein, what work and all that sort of infrastructure around that.
So electronic lab notebooks, databases and so forth. So that's what I built over the first four or five years within the SGC,
and that was used in Oxford, but also the other SGC nodes around the world as well.
So yeah, so I that, that was the point at which I tend to move away from the computational chemistry and more to the
what I call the standard data management expects to work I guess in a library you call meta data.
Well, yes, that's right. So metadata being information about data, right?
Yeah. And making sure you're capturing as much metadata as possible because you can never go backwards.
And so, I mean, what what were your main concerns?
Well, I came to the timing is interesting. Because you've I mean, you moved from the SGC to the.
Centre for Drug. Sorry, I can't keep you. No, let. Let me expand this.
Been a bit. There's a bit more history. So the CDC here not said existed until 2020?
Yes. And in 2020, the university made the decision that we should no longer be part of the CDC.
Various reasons. And so but we still exist.
And so we had to relaunch ourselves. And now we're known as the Centre for Medicine and Discovery.
We're still is still the Oxford node. That's right. But we are not we're not affiliated with SGC anymore,
but we are still doing CDC like things in the sense that the science is the same and the ethos is the same,
and that we are still very strong proponents of open access science and,
you know, making reagents and know how available to other scientists to really catalyse the drug discovery process and the data centre,
because I tend to talk to people about what they did up to the end of 2019 and then January 2020.
COVID is on the horizon and so that changes everything. But that you were saying this change happened during 2020.
Was it already in the wind before the pandemic came along?
It was mooted probably it became clear that it was a possibility around November, December 2019, I think.
And then there were a lot of very difficult conversations that occurred over the following period
and how that was going on and how let's just cover this now and we'll get on to it in a minute.
Who who are your customers? Essentially, because you work as a essentially as a service to others?
Well, what we do and we don't yeah. So we we we are not like the SGC back in what we call phase one, which is years 24 to 27.
And then we had a single mission which was sold the structures, that was it,
There was nothing else is not right papers or bringing grant funding that was.
But of course, you know, that cannot last.
And we were fortunate to be funded, you know, four times by welcome, three times by up to nine pharma companies at any one moment.
You know, that's a heck of a lot of money. But those days have moved on and that's not possible anymore.
And so we now are much more academic in the sense that we are having to bring in the grant funding to do what we want to do.
And there are clear academic projects in the classical sense that are being run.
Having said that, we still retain a lot of the best things, particularly around platforms.
So we have what the university calls small research facilities around protein crystallography, around what we call biotechnology.
So that's the production of the proteins around screening of the proteins with small molecules and around the data management actually.
And those are self-sufficient in the sense that they provide services to the rest
of the CMG as well as of the people in the university and industry as well.
So our customers are ourselves, um, local collaborators, local biotech and semi and industry.
And actually the data that we're producing collectively with CMT, our customers, the world, because you know, the structures,
the know how, the expertise that we put out freely available, we know is highly prized by people because they know it is reproducible.
And you're probably aware that there's a bit of a reproducibility crisis in academic science, particularly discovery science.
Um, one of the reasons why we, we got it to the point where we had nine farmer funding at any one moment and they
kept coming back was because they knew that if they asked us to do something,
solve a structure that they were interested in, they would be able to replicate it first time in their lab.
And if they couldn't, they could pick up a phone and talk to us.
And so that has been transformative to many of our farmer partners because they can never really describe what the actual impact is,
because, you know, they're not gonna tell us how many proprietary programs we've we've enabled, but we know we've enabled very many.
We know we've enabled a number of drugs and critically we know that we have, um,
provided data or capability to generate data that has terminated projects early, which in industries particularly important, saves money.
Yes. Yes. And time and duplication of effort. Yes. Yes.
Yes. Yeah. So let's finally arrive at COVID.
I'm asking everybody this. Can you remember where you were when you first heard that there was something happening in China and and
how soon it was before you realised that it was going to be serious and be it was going to involve you?
Well, so I don't remember where I was when it really started to pick up.
I mean, it became clear early March that something was going on.
Obviously by mid-March it was clear something was going on and we didn't really know what that meant for us.
We were just, you know, there was lots of concerns, understandably.
Um, and one of my bosses, chest bound.
Brian, you need to go and help out in this time other than what people want.
And then it was on a Sunday in the first week of April, April the fourth or something like that.
And Dave Stewart called me up and said, Brian, we need you.
Okay, What do you need? We need a data platform because we're going to do a serology platform.
We need it in two weeks. Okay.
Would it normally take you? Oh, about nine months. And I said, Dave, you do realise it's going to take nine months?
We don't have time for that. You just got to do what you can. This is an international emergency.
That date. I get it. But I'm just warning you, I'm trying to. And of course, it didn't take us two weeks.
It took us six or seven. But nevertheless, that's when it all really kicked off.
And then life became very, very busy, very, very quickly. And what was that platform set up to do?
So I have spoken to Dave. I have spoken to Terry. Let me let me give my perspective.
My perspective. So as you will remember with the discussions for them,
this is a platform to take participants or patients blood samples and to determine how
much what what to what degree they had antibodies against the spike protein in COVID 19.
And yeah, so it became very clear that the government needed a way to do this in a kind of surveillance mode.
And it became very clear that Office national Statistics was going to be involved. And then Sarah Walker got involved.
And, you know, at that point it's like, well, there's half a million in the cohort.
It's going to be about 6000 samples a day. Thank you very much. And it's going to may even be a 24 seven things is what we thought at the beginning.
And then, of course, we we had to commandeer the the robotics in the Target Discovery Institute for Dan and his group.
And there were lots of problems associated with that. No reflection upon him.
It's just really hard to set up. And what we had to do and I'm fortunate my team at the same D is just so flexible is we were not in a position to
be able to define what the requirements were for the data management around that because it was changing daily.
We knew we were going to have to work with the Office for National Statistics,
with the company IQ view that was dealing with the logistics of receiving the samples and getting them to Oxford.
You see that name again, IQ, IQ VII.
So they were the company that worked with the government to do the logistics around, you know,
it was originally going to be Amazon and it didn't work out and Royal Mail and so on and so forth.
So it was very complicated in those days. And so we had because we would we had to know what the samples were that were coming.
And so that when we reported the samples back to the Office of National Statistics and the UK Government,
we were, you know, using the right identifiers so that they could then merge that data back into what they had.
So it obviously became very complicated in three ways in the sense that we had to be able to take data
in on a daily basis from a third party organisation who were in just as much blind panic as we were,
and we had to provide data to an organisation which were also just in as much of blind panic as we were and lots of discussions around them.
And they do not necessarily speak academic academia and we do not speak government or industry.
And so one of my roles was to try and have these conversations and to develop these conduits for this data.
So that was challenging.
But the most challenging thing was we actually didn't know why I said it was going to be we knew was going to be analyser assay.
We knew that that's fine, but we didn't know how it was going to operate.
We didn't know how the data was going to come off. We didn't know how to make decisions about quality control.
We didn't know how to make decisions about how to analyse the data, and we didn't really know how to report it.
And this we were learning on the fly. And that all sounds great, right?
But what we had to build was a database and a web platform that sits on top of it,
which everyone across the whole team in Oxford would have access to,
to be able to, you know, upload the data and be able to track how things were going, check the quality control and so on and so forth.
And I'm very fortunate that, you know, the couple of people on my team who did this pretty much 24 seven for the first
couple of weeks were really open to the idea of things changing all the time.
So, you know, they'd write a bit of code and it was right for yesterday.
But this morning we've changed things slightly and that code is going to have to change.
And in the normal circumstances, my team will get very frustrated by that because it's a waste of time.
But this is just a function of how things were operating at that stage.
So normally you would have done all that and prep and testing and setting up to even start it.
Yeah. And then we would have to sit down and say, well, you know what you want to do now.
So just tell us precisely, you know, how you want to catch the data, work with data, present the data and we'll build something around.
You know, this was this was a deep collaboration where they were saying, now we're going to do it this way now.
And we're saying, well, we can't. You sing that quick. But what if we do it this way and then they change their approach?
So there was a lot of to and fro, and that worked incredibly well.
Mm hmm. And how long did that? Six.
6 to 7 weeks. Yes. Did you get the initial thing up and running?
Yeah. To be fair, we probably had something working within four weeks, but the the the on the data management.
But on the actual assay platform itself, it's more like 6 to 7 weeks.
So it was more like end of May, early June, before we really had something that we could go to owners and say, It's good, let's go.
Mm hmm. And so was it from that point that the UN started publishing that national data So you'd get that?
Yes, exactly. Week. Yeah. Yeah, exactly. And did the platform have a name?
Just Oh, the data platform is called Eliza LIMS. Right. Because I couldn't think of any better library information management.
No. So yeah, so there's Eliza and then LIMS stands for Laboratory Information Management System, also known as Electrum member.
But it was bespoke. Um, yeah. So. Mm hmm.
Mm hmm. But that was only the first project, was it not?
Oh, yes. Well, yes, and there have been a couple of projects, so let's talk a little stick on that one and just say that,
um, you know, the little platform is now used out in Thailand at Morro.
They've been using that for their Oxford research.
Yes, it is. So that's that's been a real success for us, is being able to take what we built with lots of sticky tape and elastic bands and to be
able to transplant that somewhere else and get it to adapt to the way that they're working,
which is very different from the way that we were working here. The volume is different, expectations are different.
That's been a huge success. And there's they're also looking at coded and other pathogens as well.
In terms of malaria. Yes, I mean, they've just finished going through the COVID, but there are other pathogens that they're starting to look at now.
So I think that's been a huge success, too. So there's that and it's still running.
So the the UK one is running until the end of this march, right?
By which time we'll have done three years and that'll be enough. I mean it's great.
But when, when you have to look at the data, I've looked at the data every evening for the last three years without exception,
because I'm the one that keeps whether the data is good enough to go or not.
Um, and it's fine. We've, we've got it down to find out. And I can tell within 10 minutes whether we got problems or not.
But if there are problems that I need to be calling up the lab managers and saying, Well,
this play didn't run very well, do we understand why are the robots behaving properly?
Have we got issues with reagents? There's a lot of moving parts that can go wrong on a daily basis.
So yes, I shall not miss that. And does that mean and is that going to stop collecting from that household survey?
Correct. Correct. Right. There's no more funding. There's no there's no more funding.
Yeah. Oh, yeah. Interesting. So the other thing that I did during the pandemic was to support a really unusual project called Combat,
um, which is basically what is called a multi modal blood atlas.
Not only yes or multimodal in this case I think.
Yeah, but I'll explain why I call it multimodal in a minute but yes multi I make it and the idea behind this was that
there was already a recognition really early on that there may be some similarities between the way COVID um,
has an effect upon your, your respiratory pathway, um, and flu and sepsis similarly.
And there was a wonder whether there might be really common reasons why these things are happening between these different diseases effectively.
And so what was remarkable this project was um, well, a number of things were remark about it.
Remarkably, we had access to around about 150 patients with flu, sepsis and COVID and COVID, different levels of severity, including unfortunately,
those who died and were able to get blood samples longitudinally at different time points during their disease progression.
And um, we were then able to amass a lot of expertise and capability across the university to be able
to use those blood samples on different pieces of kit to ask different sorts of questions.
Carry on. Yes. So we have the blood samples that we were able to run across different types of technologies to understand what features
there might be in the blood samples that would describe the different severity levels of the COVID or the flu or the sepsis.
And why this was remarkable was a couple of reasons.
First of all, you know, everybody wanted to help out in the COVID context.
So we had I think it was over 120 researchers from 20 different departments across three different divisions in the university.
That has never happened before. And we were all able to work together because none of us were in, while very few of us were in.
Right. And then I think I'll talk maybe talk more about this later.
This was the beginnings of a really strong recognition of the way collaboration can occur without walls going forward.
So that, that, that was very cool. Um, I think the other cool thing was that we made a decision early on that, um, yes, the,
the pieces of kit, the laboratory platforms were in different platforms around the university and,
you know, we'd have to get the blood samples to them, they would run them there, they would get the data, but the data wouldn't sit there.
And so the data would be put into a central data warehouse,
which everyone would have access to and everyone would be able to operate upon in the same place.
And the beauty about this is that we didn't have we avoided the usual problems of people not knowing what the gold standard data is.
And is this a the copies of the right version? So we had to put things in like identifiers for particular data releases.
So, you know, we made clear that if anyone presented something within the combat team that was used,
the identifiers, everybody knew what they were talking about and how they got there.
So the provenance was well captured, um, that it was structured in a way that we understood who and what was structured in
a way that we knew what had happened to that data and that really paid dividends.
And that was a new way of working for many people, something I've done for a long time.
But across so many departments and so many people that was quite revolutionary here in Oxford,
it might be more normal now that universities where it's it's a little more top down, but in a federated place like this, that's very challenging.
So that was pretty cool. Um, and being able to take the clinical data,
the clinical metadata and be able to integrate that with molecular and cellular data and together, that's what we call multimodal data.
It includes the MULTI-OMICS, but it also includes the clinical data. And that's why it's multi-modal.
That really hasn't been done in this way before either.
So in that the people who are looking at the particularly the genomic data were able to stratify
that what they were seeing relative to what the clinicians had marked down by the sample.
Normally there's a big chasm between and you know, you can't do that dynamically.
So that was very cool. And of course what you're saying is that somebody say someone who was especially sick with COVID
or a group of people who seem to be especially sick might have a particular genetic signature,
for example. And that's that's, you know, the technical terms.
We were looking for interesting biomarkers and yet being able to segregate what you're seeing relative to the actual symptoms of the disease.
Clearly, I mean, it's it's it's common sense. That's the right thing to do.
But you'd be surprised how hard it is to do that in normal practice. Yeah.
And I don't know if you had anything to.
I did look at their papers, had anything to do with how the data was represented graphically, because that seemed no active.
I didn't, but I had not directly.
But I did have some input into bringing in Steve Taylor in particular, who has, um, some really cool ways of presenting quite complex,
multi dimensional data in a way that if you're not computational but you are a biologist, you might be able to interpret and navigate.
And that that was pretty cool. Yeah, exactly. And how long did that?
Oh, um, so I think we started it.
It was kind of mooted around June-July time that we were going to do this.
We got emergency funding from the university or the division to do it.
Um, and things really, it really took a year to get most of the work done.
And for the first draft of the cell paper, um, to be pulled together, I mean, just even pulling that cell paper together was a,
a feat in itself with so many collaborators and so much data and trying to come up with the
one story that made sense because there were so many interesting sub stories out of it.
But what is the one story that you can sell in the cell paper, and what do you think has been the impact of of that paper?
Not as much as I think we might have imagined.
I think in the end, a lot of the data that we found, um, replicated many other papers, but we did it all in one go.
So I'm not sure there's anything that was, uh, unexpected by the time we had the paper accepted.
But when we were writing the paper, there were the papers out there that said otherwise.
But as unfortunate as we went through the reviewing process and other things came out, so.
So I think the impact from that perspective was a lot lower than perhaps you would have liked.
But I think the biggest impact has been, as I mentioned earlier, the, um,
the change in perspective about how collaborations should be done and the recognition that
it is possible to work across departments and it is possible to be less insular and,
and it is almost expected that you have a multi skilled, multi, you know, talented approach to solving a particular problem.
Um, and so that's one aspect. And the other aspect is the data management.
Now everybody wants to do what we did and so I'm very fortunate that we're in really high demand to replicate this on that project by project basis.
I was definitely including that in yes, I was thinking of talking about this as in so so that's giving you a lot more work to do.
Oh, yes. So I just wanted to pick up on that collaboration point again, because I think it's very important.
I mean, even even within labs and certainly between institutions, academic life can be quite competitive.
But clearly what was going on during COVID,
a lot of that fell away because of the urgency of putting minds together to crack, but not just the urgency.
I mean, the that there was no competition because none of this was anybody's field.
So this and everybody knew that if they didn't play, it was going to have a negative impact upon their career anyway.
So a lot of the boundaries broke down as a consequence,
and many of them have stayed down because many of my colleagues have recognised that this is they might not felt comfortable doing it this way,
but it worked really well and is now expected and there are always going to be some that don't see that,
but they're going to fall to the wayside because this is just the new way of
working and it's fine to have to be one name among 200 000 people within reason.
Well, for something like that, yes, absolutely.
But the pressure then is to get it in the very best, general, right? Yeah.
Um, but the data that we generated in combat and even the data we generate in the serology platform has spawned so many subprojects and,
and papers with small authorship. Everybody's benefited in some ways, somehow.
Mm hmm. And so what are you mainly working on now?
Well, uh, so my Sandy research informatics team is busy providing lots of data management solutions to collaborators around here,
collaborators around the world, particularly around the early, early stage drug discovery aspects.
Um, I've just joined the welcome Centre here to help set up a big new data hub platform,
which is kind of the kind of like that the son of the it's a big thing that we did in combat.
Um, I and I work very closely at a divisional level to make sure that we're doing the right thing around digital information as well.
Mm hmm. And so I think we've we've got there very fast that you'll tell me if we've left
anything but the how did working through the pandemic impact on you personally?
Um, you mentioned one point that none of you were in. Um, well, that was the least of our problems.
I mean, it was, it was really interesting how course many colleagues who could not find things to do during pandemic, um, really were bored silly.
And, you know, there's only so many papers you can write and papers you can read and but when you can't be in a lab and
you can't generate data and this is very damaging not only to their careers but also their mental health.
Um, I, you know, I had students who. Could not be in the lab and need to be I students who were purely computational and therefore could work at home.
But yet they were stuck here in Oxford, isolated.
They couldn't be around friends, they couldn't be around family. And, you know, one of them basically lost a year as a consequence of that,
forced the university, put in some fairly good safeguards around that and we could mitigate it.
But nevertheless, that that had a huge impact upon people.
For those of us that were working at kind of like the coalface, we never worked so hard in our entire lives.
And it was, you know,
there were some days when I was up to two or three in the morning trying to unravel why the data from the previous day wasn't right.
And then, you know, you you have an 8:00 meeting every morning just to summarise where you are.
There wasn't an awful lot of sleep and that had a major impact upon my family.
Bluntly, they didn't see much of me and that was tough for them too, I'm sure.
I'm sure. And was there anything you were able to do to support your, your wellbeing through that, or was that just not time?
This wasn't time. I mean, to be clear, I mean, we weren't as busy as the the people in the.
Jenna, right? I mean, they really were I mean, they made us look like we were hardly working at all,
But, um, you know, it was just constant and it was even at weekends,
phone calls and, you know, something's broken or we need to think about this,
or the government's asked for this and they want it with the next 3 hours.
You know, it's tough when they things like that happen, so you just have to plough on.
But, you know, I was at home, right? So I wasn't coming in.
So the family saw me, but they kind of didn't see me. And how threatened did you feel by the virus itself?
By the possibility of infection? I assumed I was going to get it. There was no doubt about it.
But I was concerned about that because my wife is in a high risk category.
Um, actually, to this date, none of us have had it.
I hope it stays that way. But yeah, it was always a concern.
Yeah, but you were. Yeah. You were staying at home.
You weren't, actually. Well, I mean, one of the things I'm responsible for is a lot of i.t.
Infrastructure, um, which support was supporting what we're doing.
So I did have to come in occasionally, but of course the place was deserted anyway,
and you were just scrupulously careful about what you touched and how you did things.
And it was fine. It worked okay. Yeah.
And what I didn't pick up earlier, you mentioned the government was one of the things you were dealing with.
Was that something you, you were you personally interacting with civil servants?
Yes, yes, yes. Because that was a new experience for you, Of course.
Yes. Um, but I think, you know, and in normal times, I think it would have been harder, but because we were all in the same boat, you know,
we recognise that civil servants were under enormous pressure by the Government to deliver answers and to deliver data to be able
to explain to the population what was going on and why it was important that they were making the decisions they were making.
And, you know, it wasn't unusual for, you know, to find yourself on a call with civil servants very, very stressed out, taking it out on you.
And you know, you completely understand that and you find ways to work collaboratively with them,
um, to help them out, because that's, that's how it worked.
And in the end, I mean, there was quite a lot of turnover of civil servants over that time because I think a lot of them found it really hard.
Um, but we had lots of very good working relationships with them and, and you know, it was good.
And did the fact that you were working on something that was so important to you,
do you think, that supported you or your own wellbeing through this very stress?
I don't think I didn't realise when we first started.
I didn't realise how important it was and actually it really wasn't until it really wasn't until probably end of 2020, early 21,
when it became clear when the big national lockdowns were occurring and the numbers were appearing,
you know, in the Downing Street, it was only then that I realised I hang on, that's my data.
And because there's so many steps removed from, you know, you producing the data,
giving it to an S own s does the analysis, then they give it to DHS agency, then talk to the civil servants.
You then talk to number ten and you don't see it.
Right. And, and actually we on the serology platform really didn't understand what happened to the data afterwards.
And we weren't about to go and ask go and ask because they were stressed out enough as it was.
So it wasn't really until the middle of 21 end of 21 that we were able to get a
sit down and actually show us how were they using the data in their modelling. And it's like, Oh, that's very cool.
But at the time we didn't know how important it was.
Then it seemed like far more important things going on with the world just felt like we were just a small thing that Matt might be helping.
Oh, Um, and are there any, any particular stories that stick in your mind from the time that, um.
I'm not good at remembering these things. Not really.
I think it was just trying to make sure that we could deliver,
trying to look after people and people's mental health on the team because they were going above and beyond even those on shift work.
You know, they were staying longer to make sure that things were done.
They were getting very stressed when things weren't working, which was good but bad.
Um, and just the way that, you know, if, if there was a problem,
we all kind of mucked in together and made it happen and we didn't always get the right solution,
but we got to the point where the data was always acceptable to those who cared.
So I think. I think I've reached the end. Oh, there you go.
Which is in the last question is really whether the experience of working through
those code projects has changed your attitude or your approach to Absolutely.
100%. Um, in what way?
In the way that I can now have confidence that if someone throws something big at me, there's a way in which we can get it done.
And it's not about me. It's about the team and the collective.
And is there anything you'd like to see change in the future with regard to how things are organised?
Well, I mean, I mean, I mentioned the aspects of collaboration and, you know,
the university set up in a particular way that has served it very well over a long period of time.
I think there is a lot of understanding that things change are changing and I don't think there's a good solution to this,
but we have to find approaches and methods that support that collaborative way of working in a more seamless
way and to lower the energy barrier to catalyse sort of research that we can do if we work together.
Thanks very much. That's right.