Title: Postdoctoral Fellow, Institute for Systems Biology
Education: PhD, University of California, San Diego, 2005
Recommended by: Harris Lewin
By all accounts, Nathan Price had a pretty successful career as a graduate student. Upon finishing his PhD in bioengineering with Bernhard Palsson at the University of California, San Diego, where he published some 20 papers in computational biology, Price was offered a faculty position in the department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. The majority of Price's grad work was centered on metabolic systems, mitochondria, and red blood cells, but he wanted to do something more relevant to disease research, specifically cancer. So Price decided to defer the job offer and applied instead for a fellowship to work with systems bio guru Lee Hood.
“The rationale for that is that Lee is one of the best persons in the world for thinking about systems approaches to medicine,” says Price. “What I wanted to do was to harness computational models and high-throughput technologies to [be] able to get a better grasp of understanding of cancer from [the] systems perspective.”
In Hood's lab, Price has set about developing algorithms that harness information external to the cell. “What we really want to learn is how to read secreted protein patterns in blood, not only to distinguish between health and disease at various stages, but also we hope to be able to link that back into causal perturbations and networks to use these patterns to identify states,” he says.
He sees blood as a window into human health and disease that's just teeming with data. The problem, of course, is getting it. Price notes that one of the biggest challenges is measuring protein concentration in the blood. “It's very important that you identify your candidate markers in advance because when you go into the blood, it's much easier to measure proteins that you know you're looking for,” he says.
And although Price says that researchers often refer to “deluges” of data in biology, that's not a problem he faces. “Relative to what we want to do, and the kind of algorithms that we want to run and the kinds of predictive capabilities that we want to generate, we often find that we have much less data than we would like,” says Price. “That's a fairly universal problem that almost anyone in modeling faces.”
In the future, Price would like to see small microchips that can measure 1,000 to 2,000 proteins in the blood. His vision is to be able to take blood measurements, run them through various algorithms, and detect patterns that will essentially read out a person's health, what kind of diseases that individual might have, and the state of those diseases. “So we hope that eventually, the blood can be used as a window to track the development of various diseases and even to assess drug efficacy,” says Price. “We hope to be able to see those kinds of things very early on.” Price sees the blood not only as a way to monitor network perturbations and screen for diseases like brain cancer, but also as a means to determine how well a particular treatment will work.
Publications of note
Price and his colleagues recently submitted a paper entitled “Highly accurate two-gene classifier to differentiate gastrointestinal stromal tumors and leiomyosarcomas.” Using a novel classifier based on a simple relative expression reversal between the expression of two genes, the paper reported 100 percent accuracy in differentiating between GIST and LMS in all patients tested.
And the Nobel goes to…
Price says if he were to win the Nobel Prize, he would want it to be for developing good systems models and algorithms that could assess vulnerability in cancer as a dynamic system and effectively treat it in a variety of settings.