Take 10 with... Jane Allison

1. Describe your research topic to us in 10 words or less.

Watching atoms and molecules jiggle.

2. Now explain it in everyday terms!

We use computer simulations to study how biological molecules move and function, by following their constituent atoms over time. This helps us understand how they work, and what’s gone wrong when they don’t (e.g. in disease), which in turn might help us design better drugs. We also investigate how proteins evolve.

3. Describe some of your day-to-day research activities.

Mostly, I just fill in forms, go to meetings, and read, write and delete emails – it’s my students and postdocs that get to do the science. My group’s research is entirely computational, so this means they are seated in front of a high-spec desktop computer with access to a supercomputer. They use a range of computer programs, many of which are operated from the command line (no graphical user interface) to set up, run and analyse their simulations of biological systems. The preparation phase typically involves background reading and a lot of careful design of the simulation. Analysis typically begins with watching a ‘movie’ showing the atoms moving around – from this, we form ideas and hypotheses about what is happening that we then test through further simulations. We also have to figure out how to quantify what we see, and how to visualise it in a way that can be published.

4. What do you enjoy most about your research?

I really like to understand how things work and why. I was drawn to biochemistry and biophysics through a combination of wanting understand how biology works, and some inspiring lecturers and supervisors. I was never a big fan of lab work, especially the way it organises your time for you, so computer work suits me well. I particularly like using computer simulations to investigate biological molecules because you can investigate systems that are difficult to study experimentally and test ideas that might not be feasible in real life, such as switching off specific parts of the underlying chemistry to test whether it’s important for a biological mechanism.

5. Tell us something that has surprised you in the course of your research.

How assumptions are unintentionally propagated through scientific research: I remember asking my postdoctoral supervisor how the value of the isothermal compressibility that we used as an input parameter to our simulations was determined. I’d noticed the same value being used by pretty much everyone, no matter the biological system being simulated, and often with no justification. It turned out he’d done a back-of-the-envelope calculation decades ago to come up with an approximate value for a protein surrounded by a thin layer of water, and this value was then adopted universally, often for completely different systems, without anyone bothering to check if it was appropriate or not.

The isothermal compressibility can vary by several orders of magnitude before it has a noticeable effect on the simulation (I know this, because I tested it once), but I wonder how many situations there are where there are potentially large effects of an incorrect assumption that aren’t realised.

6. How have you approached any challenges you’ve faced in your research?

It’s easy to get bogged down in the minutiae of problems and end up going round and round in circles. A better approach is to work slowly and carefully, take notes on what you’ve tried, and take breaks so that you can mull over what you’ve tried and zoom out to the big picture and check whether it’s even necessary to solve that problem, or if there’s another approach, or you’re chasing the wrong outcome. I seldom manage to put this into practice myself, but it’s a model I use often with the students that I supervise.

7. What questions have emerged as a result?

In one recent example, a student was concerned that while her simulations of a cell signalling system produced the same two key outcomes as previously published work, their ratio was different. When we zoomed out we realised that the previously published work only included a subset of the signalling system components, and the additional components that we had included meant that the outcome that was most favoured in the smaller system was actually less favourable when more components were included, and might well be irrelevant to the complete signalling system that exists in cells.

8. What kind of impact do you hope your research will have?

Our research is mostly quite fundamental, but I hope it’s building the base knowledge and understanding that will inspire future work. On rare occasions, however, we do get closer to real life impact, mostly through helping to rationalise why e.g. some drug molecules are more effective than others.

9. When collaborating across the faculty or University, or even outside the University, who do you work with and how does it benefit your research?

We collaborate extensively with experimental groups both within the University and at other Universities and Crown Research Institutes (CRIs). I think this is beneficial for both parties – we can often help to explain counter-intuitive results, as well as propose hypotheses that can be tested experimentally, and we can study aspects of a system that are difficult or impossible to study experimentally, such as short time-scales and specific (atomic-level) interactions. Ultimately, however, our simulations are only as good as the models and methods that we use, and working with experimentalists gives us a chance to validate these.

10. What one piece of advice would you give your younger, less experienced research self?

Take the linear algebra and physical chemistry courses – these subjects underpin almost every aspect of my research, but I avoided them as an undergraduate. But then if I had studied them, I probably wouldn’t be doing what I do now...