Modelling metabolism: an interview with Keren Yizhak

Keren Yizhak majored in computational biology at the Hebrew University of Jerusalem and is currently a PhD student at the School of Computer Science at Tel-Aviv University, where she uses computational techniques to study biological phenomena, focusing on the metabolic changes that occur in cells during cancer and ageing. She will move to the Broad Institute at Harvard and MIT in March 2015 to begin her first postdoctoral position. Her main interest outside of science is ballet dancing, which she finds a source of inspiration and discipline.
Keren Yizhak
Keren Yizhak uses computational techniques to study biological phenomena. Image credit: Keren Yizhak.

What attracted you to studying computational biology?

I believe that developing new and creative ways for analyzing and modelling the enormous amounts of biological data that are being generated is of great importance. With the right analyses we can potentially shed light on many biological phenomena that could not have otherwise been revealed.

How do you describe your research to your family and friends?

I am working on a computer model that includes all of the metabolic reactions that take place in a human cell. I develop computational methods that help us to integrate large amounts of detailed biological data collected from cells with this model. We use these methods to predict how cells will respond to different genetic and environmental perturbations, focusing on aging and cancer.

What was the main finding in your recent eLife paper?

Our work describes a new way of creating cell-specific metabolic models of normal and cancer human cells. These models allow us to predict some important things about these cells, such as how quickly different types of cancer cell will divide, and to identify new cell-specific cancer drug targets. Our top-predicted selective gene target, together with its underlying mechanism, was validated by our collaborators, Edoardo Gaude and Christian Frezza, in experiments at Cambridge University (Yizhak, Gaude et al., 2014). This target could not have been found by conventional data analysis.

Why is this finding exciting?

The cell- and patient-specific metabolic profiles we create with our models have many uses: by describing the unique metabolic activity of each cell, they can potentially predict individual drug responses, as well as a patient’s prognosis. Considering that abnormal cellular metabolism is regarded today as one of the hallmarks of cancer, our approach could be of great value in our ongoing efforts to develop precise, personalized approaches to treating cancer.

What are you working on at the moment?

I’m working on extending and improving this methodology by integrating additional data sources collected from clinical samples to build more accurate and predictive models using cells from different cancer patients. I’m also working on a new method to identify cancer-associated metabolites that could become drug targets.

What’s been your best moment in the lab?

There have been many but the best come when our computational predictions are validated experimentally. Given all the variables and the noisy nature of biology, the fact that we still manage to identify targets that have a true biological meaning is always very exciting and rewarding.

And the worst?

The hardest moments professionally come when a paper is rejected or a hypothesis is proven wrong. However, these moments have taught me how to deal with such rejections, which are an integral part of scientific work.

Who has most influenced your career so far?

Without doubt, the greatest influence on my career has been my advisor, Eytan Ruppin. His enthusiasm for science has really inspired me and turned me into a more curious person. His endless support throughout my studies has greatly helped me to overcome many moments of desperation.

What single change would most improve the way that science is done today?

The unification of all biological databases would be hugely beneficial. While there are some big and organized databases, each has its own format, data identifiers, etc. The generation of one big resource that contains all types of molecular data, organized by certain criteria, accessible to anyone who wishes to explore it, could significantly improve the way science is done today.

What single change would most improve the professional lives of early career scientists?

If the scientific community could come up with another metric for evaluating researchers, one that extends beyond the number of publications in high impact journals.

What are your main interests outside science?

Classical ballet has been an integral part of my life since I was four years old, although I had to take a break from it during my military service. Despite the fact that I have never danced professionally, dancing has always been a great source of discipline for me and a wellspring of wonder and passion, very similar to my work in science.

Do you find it difficult getting the work-life balance right?

Generally, no, I don’t. I do, however, spend much of my time thinking about my work after normal working hours, probably much more than the average person does.

Where would you like to be ten years from now?

I don’t know yet if I will follow an academic career or an industrial one, but in either case, being able to learn something new every day, and to use this knowledge to create new knowledge is something I personally find very fulfilling. I would also be very happy if, in ten years time, I could be a scientist in Israel.

What would we be surprised to learn about you?

At different stages of my very brief career, I claimed that I would not do a PhD and then later that I would not do a post-doc… I was obviously very wrong and happy that things didn’t turn out as I expected.

Keren Yizhak CV

  • 2011 – present: PhD student, Computer Science, Tel-Aviv University, Israel
  • 2009 – 2011: MSc, Bioinformatics, Tel-Aviv University, Israel
  • 2005 – 2008: BSc, Computer Science and Computational Biology, The Hebrew University, Israel