Research interview – Professor Ruth Baker
By Dr Robin Thompson
Robin Thompson talks with Ruth Baker, Professor of Applied Mathematics at the University of Oxford and member of SMB Board of Directors, about her research and the advantages and challenges of an academic life.
Your research focuses on modelling biological developmental and cell biology. How did you get into these fields?
I studied maths as an undergraduate at Wadham College in Oxford, and took the third year mathematical biology course lectured by Philip Maini. I went on to a DPhil with Philip and (former SMB President) Santiago Schnell, and it was at that stage that I became interested in developmental biology and somitogenesis – the segementation of the head-tail axis of vertebrate embryos – in particular. I was then awarded a UK Research Council 5 year fellowship alongside a Junior Research Fellowship in Oxford, during which I spent six months in each of Germany, USA and Australia, before taking up a permanent position in Oxford.
What do you foresee as the biggest challenges in developmental biology?
Initial modelling studies in this field provided insights into the mechanisms underlying biological development. But at that stage, links with data were mostly qualitative. Now, with increasingly detailed datasets from a number of sources – such as single cell sequencing and microscopy – there are many more opportunities to integrate models and data. Learning how to make use of the vast quantities of available data is one of the biggest challenges facing mathematical biologists. Another significant challenge in developmental biology is linking biochemical and biomechanical models, and understanding the interactions and feedback between these types of process.
Do you have a favourite research paper written by another mathematical biologist?
The first paper that springs to mind is “Models of dispersal in biological systems” by Othmer, Dunbar and Alt. The paper provides an excellent introduction to stochastic modelling of spatial dispersal with two different types of model, considers diffusion limits and parameterising models using data. It is a great read!
What are you currently researching?
Members of my research group are conducting a wide range of projects at the moment. Some of those projects are examining specific questions about particular systems, and others are focussing on developing mathematical methods. As an example of the second of these, I am currently interested in developing methods for model parameterisation, with applications in developmental and cell biology.
Have you encountered any surprising results in your research?
Some of my research has been about neural crest development – and models have changed the way that this system is thought about. Interestingly, during one of my projects on that topic, we ran models at the same time that experiments were being conducted. The modellers and experimenters did not want to influence each other’s thinking, so we worked independently and did not discuss our results until the experiments were complete and the models had been run. One of the experiments “failed”, and cells did not migrate – but we had found the same result under identical conditions in the model! So that was a surprising result for the experimenters, and a good example of modelling providing insight into the behaviour of a biological system!
Do you find the complexity of developmental biological systems daunting?
In general, I try to keep models as simple as possible. I tend to start with the simplest model that I think can recapitulate observations of the biological system under consideration; even very simple models can generate useful predictions.. Sometimes additional complexity is required, for example a model might need to consider individual cell behaviours if the datasets being used to parameterise the models contain data at that scale. A related question that I am interested in is, given a particular dataset, what is the most complex model that could be parameterised? And how much mechanistic insight can be obtained given available data?
Have you found working with experimenters challenging?
I have found collaborating a very positive experience – and I have learnt a huge amount, especially at the discussion stage before a model has even been developed. Collaboration has also led me to think about different systems in ways that I had not previously.
What advice would you give to a junior mathematical biologist?
Good question! I think that a key piece of advice is to be aware of the goals you need to achieve to be successful. For example, if you are planning to apply for a fellowship in a few years, it is worth reading the application form now to understand precisely what you have to do to put yourself in the best position to successfully obtain the fellowship. This requires a lot of organisation – but these days it is perhaps more important than ever before to be strategic when seeking out academic success. I would also recommend that junior mathematical biologists collaborate with other academics, since that can be very rewarding, and that they build a presence for themselves within their field not only by publishing papers but also using social media.
What is the best part of your job?
I love the freedom and flexibility to work on problems that interest me, and the opportunity to change focus if I come across interesting questions slightly removed from my current work. I love learning and expanding my knowledge and skillsets. I also enjoy training others in mathematics and mathematical biology. Oxford is a very good place to work – the department is excellent and I have great colleagues.
What is the worst part of your job?
Academia tends to be a high pressured environment that places multiple, competing demands on your time – you have to be careful to make sure to maintain a good work/life balance!
What do you do in your spare time?
I have two children – aged two and four – so I spend most of my spare time with them. That’s another great part of the job – the flexibility to arrange my schedule so I can spend time with my kids!