The Human Genome Project, which mapped the entirety of the genetics of our species, was a watershed moment in science. One of the greatest scientific feats in history, the project was a biological voyage of discovery that significantly advanced our understanding of disease, health and the functionality of our bodies, paving the way to improved medical practice.
The landmark global scientific effort was based on foundational research by University Professor Emeritus Michael Waterman. Known as the father of computational biology, Waterman joined USC Dornsife in 1982, where he founded one of the world’s first PhD programs in that discipline.
Genome. Image courtesy of Ernesto del Aguila III, National Human Genome Research Institute, NIH
Building on Waterman’s legacy, USC Dornsife created the Department of Quantitative and Computational Biology in 2021, drawing some of the top researchers in the field. We caught up with founding department chair Remo Rohs to talk about ongoing research, the role of artificial intelligence (AI) and why he believes the future of biology lies in computation.
How has computational biology advanced since Michael Waterman founded the field in the early 1980s?
Computational biology uses data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. Forty years ago, it was considered a pioneering discipline — today it is fundamental to the field of biology. Not only are we now able to collect vast quantities of data, but the evolution of biological research demands increasingly sophisticated methods to analyze and interpret this wealth of information.
The field began with genome sequencing in humans and other species. From those beginnings, it has evolved in a number of directions. We now design new drugs by using structural data to figure out how chemical compounds will work. We use mathematical modeling to predict outcomes in cancer. It’s all a part of the interdisciplinary work the department is doing. I am a physicist by training with a PhD in chemistry. Many of my colleagues are mathematicians, statisticians and computer scientists. What unites us is that we all study biological questions.
How is the department’s research improving health care outcomes?
Here is just one example from my lab, where we’re using AI to create new, more effective drugs.
Past drug design was limited to incremental improvements on existing compounds. But now, with the help of AI, we’re identifying proteins or nucleic acids within cells that drugs can affect to treat diseases. And we’re designing previously unknown drugs using machine learning. It’s like finding a new lock and designing the perfect key for it. This means we can create entirely new drugs that are more likely to work and cost less to develop.
In collaboration with colleagues at the USC Michelson Center for Convergent Bioscience, we are already synthesizing some promising new cancer drugs that are based on our work.
What can we expect from the field of quantitative and computational biology in the next 20 years?
Projects across specialties such as molecular biology, evolutionary biology, neurobiology and marine biology already require complex math and computer modeling methods. I believe that all fields of biology will become increasingly reliant on computers, databases, mathematics and statistics. That’s why I call our department “the biology department of the 21st century.”
AI is dramatically altering many areas of research, but it is poised to have a particularly huge impact in computational biology, since the field is already so rich with data. There’s no question AI will drive change in a wide range of areas, from revolutionizing basic scientific research to determining how hospitals are run. Because this is ultimately all about data. And it doesn’t matter so much if we’re looking at a protein structure or a patient file. At the end of the day, AI is a supremely efficient way to analyze data, interpret it and draw conclusions. And that holds tremendous promise for innovation in all areas — from biological discovery to health care.
Source: USC