Class Inspiring Students To Uncover the ‘Rules’ That Govern Life Itself
Thierry Emonet was trained as an astrophysicist, but this winter he found himself teaching a Yale class full of biology, computer science, engineering and math students who want to learn how to predict the behavior of living organisms.
The life sciences are undergoing a change as profound as that experienced by astronomy and physics after Galileo and Kepler described how the earth orbits the sun, Emonet told his students in the opening lecture for his “Systems Modeling in Biology” class.
Just as 400 years ago stargazers armed with telescopes described planetary motion in unprecedented detail, today’s life scientists are collecting staggering amounts of data, said Emonet, assistant professor of molecular, cellular and developmental biology and physics.
And just as Newton used that data to write the mathematical principles that explained and predicted planetary motion, today’s students might soon find the rules which govern life itself, he told his class.
“It may be possible to discover rules governing life processes, just as Newton did with planetary motion,” he said.
But if this revolution in biology is to take place, he added, experts from a host of academic disciplines will be needed to fully explain the extraordinary complexity of biological processes.
The enormous potential of the field has inspired Yale to offer an increasing number of classes - such as Emonet’s systems modeling course — that throw together students from a host of different backgrounds.
“Efforts to understand the operations of biological systems will depend upon defining our concepts about these systems as mathematical relationships,” says Tom Pollard, Sterling Professor and chair of the Department of Molecular, Cellular & Developmental Biology, and professor of cell biology and of molecular biophysics and biochemistry. “Then we can test these concepts with computers to learn if they explain observations and predict the outcome of future experiments.”
This is already happening in Emonet’s class.
One of the course’s teaching assistants, graduate student Michael Sneddon, is an expert in computer science who has had an abiding interest in biology. As an undergraduate at the University of California San Diego, he became an expert at writing code that helped link biological activity and various types of genes. However, Sneddon realized, as many biologists have in the past decade, that the old textbook “cartoon” models of molecular biology, represented by linear one-dimensional arrows showing activation or suppression of proteins, falls woefully short of describing the overwhelmingly complex reality of cellular behavior. A cell’s behavior depends not just on which specific genes are active, Sneddon knew, but on the relationship of the proteins they produce in time and space, the biochemical environment in which they exist, and the complex interactions between them.
Engineers have given scientists the tools to collect massive amounts of data, while mathematicians and computer scientists have devised calculations necessary to make the data accessible, notes Sneddon.
“I went from thinking about genes as static lists that have loose correlations to other genes, to thinking about proteins connected together in large networks with dynamic interactions that change and respond in time,” he says.
He became interested in the work being done by Emonet, who helped build a mathematical model to account for many of these variables in order to understand how a cell harnesses these diverse forces and moves about in response to chemicals, a process called chemotaxis.
Sneddon says he was enthralled with the idea that such a computer modeling of cellular behavior would allow scientists in a single day to run hundreds or even thousands of virtual experiments, which in turn would lead to even theories to test. He joined Emonet’s lab and signed on as his teaching assistant. And in doing so, he notes, he has discovered a new biology problem to work on.
He is teaming up with Jamie Schwendinger and Andrew Lawton, two graduate students in Emonet’s class who are interested in the precise timing of the development of somites - i.e., precursors of vertebrae and muscles. Even in a relatively simple organism like the Zebrafish, the intricate molecular dance of interacting proteins is so complex it is difficult to assess the entirety of the process with traditional tools of biology, the students explain, as the “clock mechanism” governing formation of these somites is as intricate as a Swiss watch and much less accessible. The three graduate students will be working to create a computer model to help unravel that complex process.
Schwendinger, who has a background in biochemistry, took Emonet’s class because she believes modeling might provide some quantifiable rules that govern the development segments which, growing sequentially, form a spine.
“It has tremendous predictive power. Modeling will help us ask the right questions,” she says.
Lawton believes that to pursue a career in biology, “this is something I need to know.”
The breadth of knowledge required on this new frontier is reflected in guest lecturers for the class. Lecturing this spring were Yale scientists Steven Zucker, applied math; Simon Mochrie, physics; Xiao-Jing Wang, neurobiology; and Steven Kleinstein, immunology - as well as James Faeder, a computational biologist from the University of Pittsburgh and creator of BioNetGen, a math-based modern language to describe biological processes.
Running experiments on computer screens is exciting, notes Emonet, and has the potential to speed up the pace of research by orders of magnitude. Yet tools of traditional biologists will not disappear, he asserts, as the findings will still need to be tested experimentally against living organisms.
“Scientists like Emonet are on the leading edge of a new era in biology,” says Pollard, “where mathematics and computer science will help biologists test with unprecedented rigor their ideas regarding the molecular basis of life.”
— By Bill Hathaway