Search
  • nikitadesai17

Q&A with Professor Christine Orengo

A full feature profile for Christine can be found here

1. As you joined the field of bioinformatics when it was a relatively young field, would you say the fields of computer science and bioinformatics changed dramatically since you started your career? If so, in what ways, and what do you think are the most dramatic changes in the last few years?

Yes! When I started there was really just protein sequence and structure data on a large enough scale to do interesting data mining. Since then many biological data e.g. gene expression data, proteomic and imaging data, have expanded hugely due to incredible new experimental technologies. In the early days there was more focus on algorithms and classical machine learning and not so much on advanced machine learning. The scale of the new data, especially imaging data, is suited to more powerful AI/ML strategies like deep learning and the recent successes of DeepMind’s AlphaFold could be transformative for many areas of biology not just protein structure prediction. The AlphaFold results are probably the most dramatic I’ve seen during my career as their performance represents such a big leap over previous methods, although they obviously built on key bioinformatic concepts like those developed in the labs of Weight, Marks, Sander, Jones, and others.


2. As the first woman serving as president of ISCB and having consulted with several large research consortia, have you noticed any significant changes in the gender balance of the general field of bioinformatics, and your particular fields of structure classification and function prediction? If so, how has this balanced changed?

I’d like to see a lot more change. There has been some progress. About 30% of scientists in the field are women (approx. based on ISCB membership), compared to about 15% when I first joined. I’d like to see a lot more women coming into the field especially as I think computational science gives so much flexibility for managing your work/life balance. Also because women often combine great science skills with good social skills and since our field is very interdisciplinary it needs that. The importance of computational work will grow over the next decade thanks to the data deluge and advances in AI/ML. Most wet biology groups will need skilled computational biologists and bioinformaticians and this will increase the opportunities and hopefully increase the number of women choosing this field. Many women worry about how easy it will be to start a group especially if they want a career break to start a family. As computational biology becomes more central it will be a very safe and sensible career choice.


There needs to be more role models at higher levels. Past presidents of ISCB have done a fantastic job of encouraging women to participate in the governance of the society. The Executive Committee has 60% women and the Board of Directors about 40% which is a high percentage given the approximately 30% of women that account for membership. Hopefully, we will see these trends emerge in most research institutes and societies.


3. Did you ever find it difficult beginning and establishing a research career in bioinformatics? And did you find there were any particular assets or challenges to being a woman in bioinformatics?

I think it’s difficult for everyone! I didn’t experience any prejudice apart from internal in that I sometimes doubted my own capacity but in the end I was driven by a really strong wish to spend my life doing science! I spent a short spell in industry but really missed the scientific freedom and excitement of academia. I’ve had to work on being assertive and presenting a confident face and I think many women struggle with confidence issues. I was incredibly lucky to have two wonderful supervisors who mentored and supported me and I advise young women to choose their research labs very carefully. Obviously follow your scientific passions but try to find people who will believe in you and help you believe in yourself!

More initiatives are needed to increase access to childcare. We also need to develop better strategies for comparing women’s achievements with men’s, especially early in their careers when they may need to take a longer career breaks than their partners. On the positive side, it can help to be in a minority group where all contenders for a position have equally high merit but you catch the luck because you’re a special group. I can’t wait till the need for that disappears though!


An ironic challenge that I’ve had as I’ve got older, and the world has become fairer and more concerned about gender/ethnicity balance, is that you can get too many requests to sit on committees! You have to be careful not to take on too much too early!


4. Do you have any advice for young women/womxn starting out in academic research careers, especially in computer science, bioinformatics, and STEM?

Work out what you really enjoy doing scientifically and focus on that. Make choices that build on your existing strengths. I’ve already mentioned that it can help to choose supportive supervisors for doing your postdoc studies. Don’t worry if you’re not great at everything! I’m not a brilliant programmer but I enjoyed it and was competent, and I realised that interest in and understanding of the data is as important as coding style. It’s easy to stress about competence in particular areas. However, there are lots of fantastic on-line courses for everything now. One of the greatest enjoyments of academia is that you’re continuously learning new things!

As mentioned already be careful about taking on responsibilities too early. Some women can find it hard to say no. Its good to become involved in societies and help organise conferences etc but try to pace your involvement. Make sure it doesn’t distract from your science. Obviously, as you become established scientifically, you should contribute to the community and be good citizens where you can. We all depend on each other for reviewing grants and papers etc!


What recent developments in bioinformatics and computer science are you most impressed and excited about? What future areas and avenues of research do you find most exciting?

Oh dear – too long a list. It’s always exciting! There are always new methods and new data that bring amazing opportunities. The huge expansion in human genotyping and functional genomics data in the context of disease e.g. the hundred thousand genomes, and other related initiatives (recently Covid-19) going on round the world, will bring a lot of data. For example, on how modifications in proteins can influence their structures and functions and may ultimately lead to disease phenotypes. My group are increasingly using such data to understand what processes might be affected in conditions like cancer, ageing and immune response. I also think the recent deep learning technologies being used for predicting protein structures and functions are also very exciting. Not just because they are so powerful, now that we have so much data, but because they can also yield insights on which features are informative for the predictions and that reveals how proteins fold and function. So these AI/ML methods will become tools of discovery instead of ‘black boxes’, as some tended to be in the past, and they will hopefully help illuminate complex biological processes and synergies.


I think we are on the cusp of the most exciting era in biology, and computational strategies and data science will be absolutely key to the discoveries. Don’t miss out on this – I wish that I could start my career again now!

19 views0 comments