Modeling Life - Using Math to Understand Biology

Chris Miles speaks at the Science @ Breakfast series 

 

Chris Miles, an associate professor of mathematics at the University of Utah, brings a mathematician’s precision to one of science’s most complex frontiers: biology. In his recent lecture for the College of Science’s Science @ Breakfast series, Miles described how mathematical modeling can be used not just to interpret biological data, but to guide experiments and deepen our understanding of living systems.

Miles began with a simple analogy using an image of rush-hour traffic on I-15 in Salt Lake City. “Even though there is no data to say what these cars are doing, because it’s literally a still image, by using your understanding of the process, you can really tease apart things in data that are not necessarily there,” he said. “And what I want to convey for the rest of the talk is that that’s a very powerful idea for biology.”

The focus of Miles’s current work is transcriptomics, which is the study of RNA molecules that reveal which genes are “on” or “off” within a cell. These patterns of gene expression help explain why a skin cell behaves differently from a brain cell, even though both contain the same DNA. “Biology is the study of living things,” Miles noted, “but the issue with taking this type of data is that you have to kill the cells to put them under the microscope.” Mathematical models, he explained, can help reconstruct these dynamic biological processes that traditional methods destroy in the act of observation.

Advances in transcriptomics have given researchers an unprecedented view of life at multiple scales, from entire organs down to individual molecules. “It’s an unbelievable amount of data,” Miles said, “and unbelievable resolution that lets us zoom into the finest details of cells within the brain to ask, well, what is going on here?” By quantifying which genes are active, scientists can trace how stem cells mature into specialized cell types, and, potentially, how to reverse that process to create stem cells for therapy. 

The list of applications for this research doesn’t end there. Miles shared an example of a striking medical case of a patient who suffered a life-threatening inflammatory reaction to a common antibiotic. By comparing the patient’s transcriptomic data to that of healthy volunteers, researchers discovered that a handful of genes in his immune cells were unusually active. One of those genes was linked to a pathway already targeted by an existing drug. When the treatment was administered, the inflammation quickly went away. “This is personalized medicine through the lens of transcriptomics,” Miles explained. “You can see what genes are expressed that are causing diseases in individual people.”

To make sense of such enormous and complex datasets, Miles turns to mathematical modeling, particularly stochastic models that capture randomness in biological systems. “Random doesn’t mean unpredictable,” he clarified. “If I flip a coin, individual outcomes aren’t predictable, but overall patterns are.” By modeling these patterns, Miles and his collaborators can uncover the underlying “rules” that generate observed biological data. This mathematical framework, he said, allows scientists to essentially reverse-engineer cellular behavior in their models. 

Looking ahead, Miles also recognizes exciting opportunities at the intersection of mathematics and artificial intelligence. “Math modeling is very good at describing biology, and AI is very good at finding patterns and scaling,” he said. “Historically, these have been separate approaches, but combining them is really the necessary thing to unlock the full potential.”

To conclude his talk, Miles emphasized that math should not play a passive role in biology. “The math is not a passive player after the experiments are done, but it can be a really active player in shaping the experiments,” he said. By working hand in hand with experimental biologists, mathematical models can help identify which experiments will yield the most meaningful results, saving both time and resources. “Biology has all of these challenges that are really rich for a mathematician to think about,” he said. “It’s heterogeneous, it’s random, it’s big scale. I find cool math problems to solve, and hopefully I’m helping biologists solve theirs.”

by Julia St. Andre

As a new faculty member at the University of Utah, Chris Miles is tasked with elevating the new Bioinformatics major (BS) as an avenue for undergraduate students to get involved in interdisciplinary science at the interface of math, biology, and computing as described above. You can read a separate story about the new major here.