Can you describe your current position at Princeton?
Currently I am a Lewis-Sigler Fellow at the Lewis-Sigler Institute for Integrative Genomics at Princeton. This position gives an opportunity to recent PhD graduates to start their own lab without necessarily going through a regular post-doctoral training. The great advantage of this fellowship is the complete independence: you can work on anything you want, as long as it concerns genomics and quantitative biology and has a solid research plan. A huge help is that the fellows don’t have to worry about funding: our institute provides us with an annual research budget that covers expenses, laboratory space and financial support for a small team of students and/or technicians.
Princeton also has a very specific focus on teaching and, as a fellow, you are usually asked to contribute some time to that end. What you teach varies a lot from case to case. I gradually progressed from serving as a TA to co-lecturing to designing and running my own course. It is quite an experience and it made me realize that, just as I heard from others, teaching does make you a better scientist.
Can you describe your lab’s research?
My main interest is to map out the functional organization of the cell on a global scale. This interest stems directly from my graduate work on yeast genetic interaction networks in Charlie Boone’s laboratory and I continue to work extensively on this problem with Charlie and his team. However, I also realize that a true understanding of a cell’s functional wiring depends on analyzing genetic interactions in the context of other biological networks that provide complementary information about genes and their relationships. As a first step, I am currently focusing on large-scale phenotypic analyses of yeast single deletion mutants. Because of the availability of the yeast deletion collection (a library of mutants where every gene has been systematically inactivated), screening the entire genome for loss-of-function phenotypes has become fast and affordable. However, the data derived from thousands of these phenotypic experiments are scattered all over the literature, with no central place to house it and therefore no hope for global analysis. This first challenge defined my research plan: I set to collect all of the data published so far and then to analyze them as a comprehensive dataset. What I would like to understand is: which genes share similar phenotypes? What does it say about their function? How are different phenotypes related to each other? Which ones are redundant and which ones are complementary? Can we use this information to optimize future experiments, especially in novel and more complex organisms? This way of approaching things has given me a completely new (basically, unpublished) dataset and an exclusive chance to glance at the most complete phenotypic landscape of an organism.
When did you first realize that you wanted to become a scientist?
There was never a conscious decision of becoming a scientist. I was always attracted to collecting things and looking for patterns. Seeing my interest in that, my father directed me towards science because, he said, it is the ultimate form of gathering information, analyzing what’s similar and what’s different, and understanding how everything works. That sounded like something I would enjoy doing, so I signed up.
Why did you choose the University of Toronto and the Department of Molecular Genetics for your graduate studies?
As an undergrad, I spent most of my time at a research institute affiliated with the University of Milan in Italy. One day Charlie Boone came to give a talk about functional genomic networks and, although I had no idea what that was, I was blown away by the scale and the breadth of questions they could address. My supervisor at the time, Marco Foiani, had no doubts that, given my interests, I should do my PhD in Charlie’s lab and helped me get in touch with him. So the decision to come to Toronto really stemmed from that one visit. Later on, I learned more about the University of Toronto and the Molecular Genetics department and realized what a scientific hub it was. And when I tried to find out what life in Toronto was like, I got such enthusiastic responses that I knew I had made the right decision.
What were some of your favourite memories from graduate school?
Oh, there are tons. Graduate school has been the most intense and fun time of my life so far. It was a very difficult time, especially at the beginning. We had a huge project with a lot of data coming in every day and very little experience managing it. Even simple questions like where and how to store the data and what kind of quality control we needed to do – those were all big challenges. Also, we needed to develop new data normalization methods to address new types of experimental noise that we had never encountered before and that were preventing us from asking the real biological questions we were interested in. It was intense and quite stressful at times. But I always thought that, if it were easy, why would anyone want to work on it?
These challenges make good memories because eventually we solved all of our problems. We designed a good database with an easy interface that allowed us to store and quickly access all of our data. We put together an amazing quality control pipeline. And the algorithm that we developed to correct for experimental noise became the first chapter of my PhD thesis.
These are also good memories because no one was facing these challenges alone. We had, and still have, a fantastic team of people that includes Charlie, Michael Costanzo, Charlie’s research associate and the leader of the genetic interaction project, and Chad Myers, our very close collaborator and friend at the University of Minnesota. And, of course, none of it would have been possible without the hard work of our technicians, who ran all the experiments, and the key input from Brenda Andrews and her lab.
How did your experience in graduate school influence your career path?
What I expected from grad school was to get a sense of what it would be like to be a professional scientist. Every time I had a major failure or a problem, I would ask myself: can I live with that? Would I be able to deal with things like this if they happened day after day after day? Similarly, after every success, I would think: do I care? Does this make me happy? Grad school answered all of these questions and my current career path is a direct consequence of that. I have also been extremely lucky with the people I met during grad school, in the lab, in the department and at the conferences I attended. Their work and their views on life and science have changed my work and my views, and I am very grateful for that.
You won the Barbara Vivash Award in Molecular Genetics for most outstanding PhD thesis. How has winning that award had an impact on your career?
The award is quite recent, so it might be a bit too early to tell how it affected my career. What I can say for sure is that being recognized for your work has an incredibly positive impact on your self-esteem and your optimism looking towards the future. And I hope that seeing it awarded to someone like me gives a similar energy boost to the students who are still in the middle of their PhD struggles.
What scientific discovery are you most proud of?
Our lab generated an unprecedented volume of genetic interaction data that describe how mutating yeast genes alone and in combination affects cell growth. It was known for quite some time that genetic interactions are very informative for uncovering genes that work in the same pathway or protein complex, as well as genes capable of compensating for each other’s absence. However, at the back of our minds we always had a question: how scalable is this idea? Does this only work on the level of individual pathways and protein complexes? Or can we actually use genetic interactions to analyze the functional organization of the entire cell? My most important scientific contribution was coming up with a way to look at the data on a larger scale. The idea came from the fact that functionally related genes often share similar genetic interaction partners. Now that we had a genome-scale dataset, I was able to analyze partner similarity for almost every pair of genes in the genome and to visualize them as a network. This network formed about a dozen very large clusters that corresponded to broad biological processes and were interconnected in a way similar to our current understanding of cellular organization. Within each large cluster, we were able to identify smaller clusters that corresponded to more specific groups of genes, and within each of those we could see individual pathways and protein complexes. In other words, the genetic network showed us that the idea of functional relatedness is valid on any level, regardless of how broadly you define “function”. This finding provoked a lot of thinking, both from us and from other people. Several labs have used our network as a roadmap for their own research and I think that is our most important accomplishment. You would like your discoveries, no matter how big or small, to push the field forward so that other people can use them, build on them and make further progress.
What was the most important thing that you learned in graduate school?
I learned to be patient and persistent. And, also, that a job is done when it’s done, not when you’re tired of working on it. These are very valuable lessons, both in science and in life.
What was the best advice you received while you were preparing to graduate and searching for a job?
The most important career advice I got was to never worry about my career, but instead, focus on my work and be as good at it as I can, and everything else will take care of itself. Also, I was encouraged to choose my mentors among the founders of my field and listen carefully to their advice. Your mentors, I was told, will help you see the bigger picture of where we are, as a scientific community, and where we are going, and they will help you place yourself onto this picture in the best possible position to maximize your potential and make your most significant contribution, for your own sake and for the sake of the entire field. I thought it was a great advice and I followed it.
What advice would you give to current and prospective students interested in pursuing a career in academia?
I would pass along the same advice that I received. Figure out what you like and what you are naturally good at (those two things are usually related). Develop your talents. Get better. Choose your mentors wisely. Take grad school as an opportunity to see what academic research is all about. Keep in mind that there are many ways of doing science and academia is only one of them. And don’t take your career path too seriously: it looks more like a highway interchange than a one-way street.