No Simple Linear Pathways in Biology
Why have the mathematical techniques used in finance suddenly become so important in medicine? According to Dr. Schadt, for most of mankind’s history, we’ve tried to explain the complex and the incomprehensible by telling ourselves fairly straight-forward linear stories, the kind that don’t require a lot of math. We explained lightning as Zeus hurling light bolts from the sky, or more recently, we explained type 2 diabetes as a linearly ordered pathway, starting with a defective gene and ending with the disease.
However, people with the same defective gene, whether it’s a gene for type 2 diabetes or virtually any other disease, often have remarkably different outcomes. With many single-gene disorders, there is a spectrum of phenotypic expressions, with some individuals barely touched by the disease while others experience the most extreme and horrific versions of it.
The linear explanation doesn’t account for this variety of outcomes, and it can’t because it doesn’t take into account the full, nonlinear complexity of diseases.
“DNA variation,” notes Dr. Schadt, “is just one dimension among many that define living systems. DNA doesn’t directly cause disease, but instead it has effects at the molecular level. It changes transcription. It changes proteins and it changes metabolite levels.
“Those different variables aren’t acting in isolation. They’re acting in a network, and it’s this network that senses the genetic and environmental perturbations that cause shifts in the system that lead to disease.”
He goes on to say that it requires the tools of advanced mathematics and unprecedented computing power to take the deeper snapshots of biological processes needed for constructing and generating models that will predict states of the system.
The phrase “multiscale biology” in the institute’s name comes from its goal of modeling the complexity of living systems, understanding not only all of the processes taking place at the molecular, cellular, tissue, organ, organismal, and community levels, but also how information flows between these levels.
“We generate big data (DNA sequencing, RNA sequencing, and so on), but then we also pull in lots of big data generated by others,” says Dr. Schadt. “We then have access to all of the clinical data from electronic medical records, and then we integrate the data to build complex network models that can tell us how best to diagnose and treat disease.”
Dr. Schadt points out that, for example, in the case of an individual with liver cancer, “The information on the liver tumor for just this one individual can lead to a terabyte or more of raw data. However, if we’re trying to find out where a given individual’s sequence is abnormal, we may want to compare it to similar information from 1,000 other people. Now you have a petabyte of information—that is one followed by 15 zeroes—that has to be analyzed for just one type of cancer.”
And for a still deeper understanding, researchers need to look at a hierarchy of levels, at multiple scales, from the small molecular level to the cellular to the tissue to the organism and then up to the large community level.
“As we move away from single genes and into networks, the complexity increases exponentially, and the scales of data we generate will exceed everything that is in the digital universe today,” continues Dr. Schadt. “We used to think a terabyte was a big number, and now we have petabytes and even exabytes of data.”