Cell scientist to watch – Kirsty Wan

Kirsty Wan

What inspired you to become a scientist?

I think I was always destined to become a scientist. Both of my parents have science PhDs and even my grandparents had PhDs and were university professors. Growing up, my parents would try to inspire me with bedtime stories about famous scientists rather than fairy tales, and our bookshelves were full of books about science and the wonder of the natural world. From an early age, there was the assumption that I would have to at least get a PhD, but there was never any sort of expectation about which topic I should pursue. Early on, I wasn't at all clear about what kind of scientist I wanted to become. At some point I was quite keen to become an astronomer, but swapped the telescope for the microscope when I realised I wanted to be physically much closer to my data. I will always be grateful to a remarkably dedicated group of teachers I had in high school, who went above and beyond to nurture and encourage my interest in science. When they couldn't offer a certain course (this was a small town in the Scottish Borders), the head teacher taught me himself!

You completed your PhD and postdoc with Prof. Raymond Goldstein in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, UK. What drew you toward studying mathematical biology in particular?

I did my undergrad in mathematics at Magdalene College, Cambridge. Most of my cohort went on to banking, consultancy or industry, rather than academia. One year I took a course on mathematical biology, taught by Raymond Goldstein, who would become my PhD supervisor. Honestly, as an undergrad, I didn't realise that was even a subject, let alone a career option. When I started his class, I thought ‘wow, this is fascinating!’ There's this untapped field with fascinating biological phenomena that are largely unexplained at a mechanistic level, and there are so many insights that the language of physics or mathematics could provide. The influence of that lecture series really piqued my interest in not just that subject, but in research in mathematical biology in general. I was quite keen to do a PhD in the subject, specifically in Ray's lab, and from that point I never imagined doing anything else.

Another thing that drew me toward mathematical biology was when I discovered the existence of a secret underground laboratory in the mathematics department. On the surface, it looks like a perfectly normal mathematics building, but there are all these labs underground! I think the proximity to the lab was really comforting for me. The opportunity and potential for constant dialogue between theory and experiment was something that that really attracted me to interdisciplinary research in this field.

Much of your work has focused on biophysical modelling of cilia. What do you find most interesting about these molecular machines?

I kind of fell into this subject by accident. I started my PhD studying the synchronisation of the two cilia in Chlamydomonas from a physical perspective. This is when I realised how important cilia are and that they are literally everywhere. In so many different organisms, cilia have basically the same geometric form and similar structural composition. But they are found in all sorts of different configurations, whether in single-celled organisms or in multicellular species, and diverse functions can arise from essentially the same structures, depending on the context. Also, I find cilia aesthetically pleasing in the sense that they create amazing spatiotemporal patterns. This is something that especially mathematicians find fascinating – the discovery and explanation of patterns in nature.

For motile cilia, there have been remarkable recent advances in resolving axonemal structures with cryo-electron microscopy. The next question is how do all these parts actually come together. It's the classic phenomenon of the whole being more than the sum of its parts. We might know exactly what the components are, but we're still quite far from understanding what they do together in a live cell, and what, if anything, is coordinating them. Which aspects of these dynamics are controlled and which are emergent? Emergence is another concept that physicists love. Fundamentally, I'm just curious about how cilia operate in different systems. I find this a rich problem full of novel physics, mathematics and genuine scientific interest. I think that's the key for me – it's not necessarily that these questions are driven by a need to solve some biomedical problem, even though exploration of the important role of cilia in human health and disease is a future direction for my group.

How do you balance pursuing curiosity versus utility in your research?

Part of the role of science should always be to advance the quality of human life, but at the same time, scientists should be given the freedom to pursue interesting questions. This is how natural philosophers used to operate. Thinking back to the days when the microscope was first invented, scientists used to just look at organisms and record or draw what they saw, simply because it was fascinating. You can find many examples of important scientific discoveries, even Nobel Prize-winning discoveries, that arose not from any specific application, but just completely accidentally. I hope that more funders recognise that and provide support specifically for these types of projects, because I think down the line it will be extremely rewarding in ways that we may never have expected.

On the other hand, what I just described is a very idealistic view of science. In reality, you have to recognise and accept that funders want specific outcomes and downstream applications. Ideally, you should do something that allows you, within the scope and constraints that are placed upon you by society, to still have some freedom to do discovery-led research. I think I'm very fortunate because I get to do this as part of my primary research goal. It's not easy to strike the right balance, but if you're really interested in a problem, find a way to convince other people that what you're trying to do is interesting and worthwhile.

More recently, you've looked into what the mechanics of cell motility in simple eukaryotes might tell us about the evolution of animal cognition. What do you find the most exciting – or challenging – about interdisciplinary projects like this?

Sometimes as scientists, we are too focused on our own tiny question. If you step back a bit, and marvel at the immensity of life, you realise that cilia have existed for such a long time. The last eukaryotic common ancestor (LECA) was probably a single cell that had cilia, and that was well over a billion years ago. How did we get here, from those tiny creatures? If you look under the microscope at some pond water, it's full of life. All these different organisms are swimming around using cilia, and a lot of them are single-celled species. In traditional neuroscience, the preferred model organisms tend to be multicellular animal species. But we don't even understand how a single-celled organism ‘thinks’. In terms of evolution, some of these earliest cells discovered ways to react to environmental cues at speed, which parallels the dynamics of action potentials and neuronal firing. These organisms are constantly making choices and interacting with their environment through a mysterious combination of physical and biological processes. If we're going to understand biological cognition or the evolution of intelligence, it might not be a bad idea to start with single-celled organisms. I think there's definitely increasing appetite from the scientific community to revisit these concepts.

In what ways do you think scientists with backgrounds emphasising maths and physics bring new perspectives to biological research?

Hundreds of years ago scientists used to be polymaths. They weren't told, “You're a mathematician, therefore, you must study this topic. You're a biologist, therefore, you have to think like this.” Now there's a new trend again toward increasingly interdisciplinary pursuits. If you have a slightly different background, you might have a different perspective on the same observation. A biologist could study protist behaviour, and potentially write down a qualitative description of what they observe or make a few sketches of what they interpret to have occurred under the microscope. But someone with a physics or mathematics background might look at the same thing and ask entirely different questions; they might create mechanistic explanations or quantitative characterisations, and maybe even models to explain how and why the cell performs this type of behaviour. With different training, you're automatically provided with a different lens with which to view the world. This is not to say that any perspective is superior to another. Often people think that maths and physics are the hard sciences, and biology is a soft science, which is a major misconception. I think all these perspectives are equally valid and valuable. Scientists from different disciplines must talk to each other to combine these diverse perspectives, to get at the truth. Science is forever enriched by its diversity.

How do you approach clear communication between theoreticians and experimental biologists when organising new collaborations?

What's most important is just to have the communication in the first place. Often, theorists will have a quite idealised, perhaps reductionist, understanding of the system they're studying, which may or may not match what is actually happening; while experimentalists and biologists sometimes can be disillusioned about what is actually possible in a mathematical or computational model. I think that all sides just have to keep talking to each other with an open mind and mutual respect. It's not about thinking ‘I have the answer’, but about being open to accepting what the other side is telling you. I increasingly enjoy playing the role of the ‘interpreter’ in some collaborations, and it can be a privilege to understand what both sides are saying. I think physical proximity is also quite important. Even if you and your collaborators are at the same university, it doesn't mean that you will automatically talk to each other. Just being in the same physical space increases the probability of encounters – having coffee together – that really is the key to seeding unexpected collaborations. This is what we're trying to do here at the Living Systems Institute (LSI) in Exeter.

In 2017, you started your own lab at the Living Systems Institute at the University of Exeter, UK. What did you find to be the biggest challenge as a new PI?

Along the usual path of becoming an academic, you're never trained in a lot of the things that you end up having to do as a PI. This includes very mundane things, such as how to order equipment or how to negotiate with suppliers. How do you manage large financial budgets? How do you manage people? What do you do when there's conflict? No one teaches you that. Learning how to counsel lab members was a challenge especially during the COVID-19 pandemic, when everybody was going through the same uncertainty. Throughout all of this, an important thing I've learned is to just be patient; things will happen. Maybe it's okay if the item that you ordered doesn't arrive the next day, or your experiment doesn't work – and maybe never will! You have to give things and people the time and space to succeed. I am fortunate to have recruited such a talented group of people to my lab, and I'm very proud of everything they have accomplished. I am also grateful for the help of many mentors. In the research environment at the LSI, one unusual aspect is that we have access to mentors from different disciplines. Going back to the benefits of having access to different perspectives on the same thing – that also applies to life! Having mentors who are biologists or mathematicians by training, again, gives you different perspectives on how to be a PI. I'm quite lucky to have had the support of that network.

How has the mentorship you received during your training shaped your career, and how do you approach mentoring your own lab members?

Throughout my time in Cambridge, I had the benefits of mentorship from my advisor. What I found particularly inspiring was his approach to choosing a scientific problem and framing it in the context of the wider scientific literature. With my own group, one of the questions that I'm always asking myself is how much I should be influencing a particular project. As a PI, you might have a better intuition as to what works and what doesn't. You could just go and tell the student directly, and it might save them a lot of time, but you're also trying to allow them to learn and experience the scientific process. Sometimes you have to do things wrong, discover why you did it wrong, and then do it again. The right balance is not something that's easy to establish. On one hand, the whole point of having a PhD supervisor is that they can be there to tell you what to do in some cases. But a PhD is also meant to train you to become more independent as a scientist and learn how to think for yourself. What I've discovered over time is that you shouldn't apply the same algorithm to everybody. There are people that prefer more to be guided, whereas there are others that want the freedom to explore things for themselves. In the end, you have to find the balance that is right for that individual to maximise their chance of success.

What is the best science-related advice you've ever received?

The piece of advice that I find maybe the most memorable and amusing was given to me by my former advisor. He used to quote this famous baseball player, Yogi Berra. I am not American, and I've never watched baseball, so I didn't know who he was talking about, but he said this guy used to say “you can observe a lot by just watching”. Regardless of the provenance of the quote, this really applies to what we do, because a lot of it is about watching living organisms under the microscope and not necessarily always having a specific question in mind. By watching you develop intuition; suddenly a question occurs to you that is worthy of pursuit.

You've attended a lot of scientific conferences as an independent scientist. What is your approach toward getting the most out of the meetings you go to?

What I want and what I've gotten out of these meetings has evolved over time. As a student, you're primarily just there to present your research and get noticed. Later on, it's a lot more about consolidating relationships with great scientists all over the world. Since the pandemic, this has become a lot easier, because you can just call someone on Zoom and immediately pick up where you've left off. But nothing beats the face-to-face experience. Science takes you to wonderful places and it's a privilege that we get to travel and visit all these respected institutions.

Now that I'm a PI myself, I think it's important to talk to students and postdocs at meetings. Why should there be so many artificial barriers between different career stages? I think conferences can do more to help remove those barriers. For a PI, a chance chat with a student is maybe just another conversation, but for the student, maybe they will think ‘the PI is actually talking to me!’. And they can feel much more comfortable about asking questions directly. I recently helped organise an EMBO/EMBL symposium on theory and concepts in biology. We only had about 80 or 90 attendees on site, so it was a small enough group that we could try a few crazy experiments. We ended up splitting people into discussion groups deliberately designed to have a good mix of people from different career stages. Students could go off and have intimate conversations with professors who could be much more senior. Inspiring and engaging with the next generation is a valuable part of going to meetings.

Your lab website and social media are full of beautiful videos of ciliated cells and organisms in motion. How does social media influence how you share your research?

I enjoy visual media in general. I love seeing videos of weird and interesting organisms (both microscopic and macroscopic), and I think maybe other people would enjoy seeing them too. I love sharing that excitement. I also find social media to be a very effective platform to reach certain people and broadcast scientific results, and it helps to connect scientists. Everybody has a platform, so a student can be heard just as well as an eminent person in the field. It also removes the barriers between disciplines. If you publish something in a traditional physics journal, it's not necessarily going to reach a biologist. But if a biologist follows you on social media, then they'll see that your paper could potentially be of interest, and it doesn't matter in which journal the work was published. That level of direct contact between different disciplines is something that I hope social media, whatever form it takes in the future, will maintain. I once wrote a grant with somebody who reached out through social media because we both showed interest in a very niche topic, and that led to a collaboration. Going back to the importance of physical proximity in collaborations, virtual proximity online is very useful too.

Finally, could you tell us an interesting fact about yourself that people wouldn't know by looking at your CV?

I quite enjoy creative writing. One day, if I ever have the time, I think I would like to write a fantasy or sci-fi novel. For now, I try to sort of channel it through my scientific writing. Nowadays, you have to write in a certain style. Sometimes I read papers from hundreds of years ago, and they'll describe the weather that day, the colour of the pond the day before, or the degree of their enthusiasm. I kind of miss that! There's a certain level of restraint I have to impose when writing about science, and I think it would be fun to be able to write more creatively or emotively. But who knows? Maybe we can change that!

Kirsty Wan was interviewed by Amelia Glazier, Features & Reviews Editor for Journal of Cell Science. This piece has been edited and condensed with approval from the interviewee.