Paul François: From Science Fiction to the Basic Mechanisms of Biology

Paul Francois is a big fan of science fiction. But not just any. “What I love is that science fiction has turned into biology and the nonbiology, which depicts our interactions with extraterrestrial societies. If you can imagine something in the living world, it must be out there somewhere,” he says.

Exploring the Game of Possibilities: This is the passion that drives a biophysicist even in his lab, where he is busy creating mathematical models using computer tools to better understand biology. In short, it “provides simple answers to complex problems”.

The new full professor path for Department of Biochemistry and Molecular Medicine from the University of Montreal returns entirely to this search for simplicity in complex terrain.

“At first, I did the math, but it wasn’t concrete enough. Then I turned to physics to find direct applications. My new jobs in medical school allow me to open up and relate to medical biology.” miles [Institut québécois d’intelligence artificielle] It gets me engaged with machine learning. Recent advances in artificial intelligence can teach us about biological phenomena and lead us to a better understanding of some diseases,” explains the man, who has a degree in engineering from the Ecole Polytechnique Française, a Ph.D. in theoretical physics from the École Normale Supérieure (Paris) and a postdoc in Bioinformatics from The Rockefeller University (New York).

Plus interdisciplinaires and transdisciplinaires men in UdeM in sciences quantitatives – mathematical, physic, apprentissage automatique, biologie, medecine – ont convaincu the ancien professeur de physique of the University McGill de faire the south campus of the montagne, at the same again Biomedical Innovation Centerwhich brings together researchers from the five basic science departments of the UdeM School of Medicine.

Deciphering the language of immune cells

A follower of “cross-pollination” between disciplines, Paul François continues his research on adaptive immunity, which he began more than 10 years ago. How does the immune system recognize pathogens? What information do you encode to trigger an immune response? The scientific approach generated by interactions between colleagues has given rise to new mathematical models. “At first, I didn’t understand the collaborative model of Gregoire Altan Bonnet, who heads the Immunodynamics Laboratory at the US National Cancer Institute. [NCI]. By talking to him, I found a way to simplify it to make the data more consistent,” explains Paul François.

In collaboration with Grégoire Altan-Bonnet’s group, his team is now using an automated platform combined with machine learning and mathematical models that quantify the immune response. “With clinicians, including Naomi Taylor of the National Cancer Institute, we are making mathematical predictions about how immunotherapies will adapt to cancer. Our research can quickly lead to concrete applications,” he hopes.

Japanese fish and embryonic development

Another favorite topic of the researcher is the mathematical modeling of embryonic development. “You should know that genetic oscillator [appelé horloge de segmentation] It directs the formation of syllables, one paragraph at a time, without us really knowing how. I am working to describe this phenomenon from a mathematical point of view, in particular from a study of Japanese fish that develop under different temperatures, he said. Our collaborators at the European Laboratory of Molecular Biology altered the temperature cycles in order to disrupt the formation of its spinules. These manipulations allow us to understand how the body is formed, what disturbs it and how it reacts to disturbances. If it does not resist, diseases appear.

This research is a precursor to other experiments on cell differentiation from embryos recreated in Petri dishes, for example. “If we can control embryonic development, we will be able to test treatments more easily,” stresses the scientist.

Machine learning in the service of biology

A subset of his lab is finally working on evolution algorithms. “We designed computer tools to simulate the evolution of biological networks that we applied, among other things, to control cell size,” he says. These models show us that if senescent cells are very large, it is because they are dividing too late or too early; The oscillator that controls the cell cycle is defective.”

Professor François hopes that all of this work will encourage his students to break down barriers between disciplines. “I find that young people today are more open to interdisciplinarity. My role is to help them develop their scientific tastes and get them to do concrete things.”