Michael McLaren is a graduate student in Marc Feldman’s lab in the Department of Biology. He received his BA from the University of Pennsylvania, where he studied mathematics and physics and worked on the ACT and BLAST telescopes. His research uses mathematical modeling to study evolutionary dynamics.
This content has been transcribed from an interview that took place on Stanford campus Tuesday, October 20, 2015 with CEHG’s Director of Programs, Cody Montana Sam and Communications Manager, Katie M. Kanagawa.
Can you start by telling us a bit about your research?
My thesis projects consider how spatial structure affects the ability for populations to evolve complex adaptations—adaptations that involve several functionally interacting mutations. For such adaptations to evolve purely by natural selection, there must be a sequence of mutations where each step increases the fitness of the organism. But, if the mutations have a synergistic effect on one another, such a path might not exist. For example, if two mutations are needed for a particular adaptation, having one mutation may have a neutral or even a deleterious effect on fitness.
Adaptation in such situations has been described as crossing a fitness valley or plateau, depending on whether the intermediate genotype is deleterious or neutral. Besides having implications for how complex biological functions can evolve, valley crossing is increasingly being recognized as important for the evolution of human pathogens, and it also has close parallels with how human somatic tissues acquire multiple mutational “hits” needed to develop into cancer.
Natural populations of organisms (unlike most of the theoretical populations we modelers study) exist in a spatial context: each individual competes for resources and mates and disperses its offspring within a limited geographic area. My research uses mathematical modeling to determine how this spatial context affects the crossing of fitness valleys and plateaus, as well as evolution more generally.
It sounds like you are leveraging math to solve biological problems. Would you call yourself a mathematician first and a biologist second? Or how would you characterize yourself?
I would call myself a mathematical biologist, emphasis on the biology. I studied pure math—math for math’s sake—in college, but now find that I appreciate math the most when I can connect it to the questions in biology that interest me.
What do you think drew you to evolution and the field of biology, in particular?
I guess I have always been interested in origins. Curiosity about the origin of the universe first drew me to physics. But after studying physics for a while, I became more interested in life than the stars, and, specifically, in the origins of the complexity and diversity of life.
Has there been anyone in particular you have looked up to on your path to evolution and mathematical biology?
My approach to evolutionary theory is heavily influenced by many physicists now working on evolution. For instance, Daniel Fisher (Stanford CEHG) and former Stanford graduate student Daniel Weissman. Their approach to developing and analyzing theoretical models emphasizes developing intuition over building the most realistic model and making the most accurate predictions. The intuition is often broadly applicable and transferable to nature and experiments.
Is there advice that you would like to offer to other trainees, drawn from your own experience?
I think mathematical biologists who want to have a broad impact need to take special care in learning how to effectively communicate their work to biology audiences. Many math talks at biology-related seminars and conferences fail to connect with the audience.
It’s important for a speaker to keep in mind what an audience of biologists (even one of mathematical biologists) cares about most—it’s almost never theorems or equations.
The experience I’ve had of being in a Biology department, interacting with many types of biologists, has made me better able to communicate my work to them and learn from their work. Giving talks to a variety of audiences has also given me invaluable practice.
Where do you see yourself in the next 5 or 10 years?
I would love to be able to teach and run a research lab at a university. I will continue to study the dynamics of adaptation and the effects of spatial structure using mathematical modeling, but I hope to develop skills and collaborations that will allow me to combine modeling with genomics or laboratory experiments, specifically in bacteria, where much of the complexity and diversity of life originated.