Systems Biology

 

A living system like any complex entity is more than the sum of its parts. It can be as “simple” as a virus or as complex as an ecosystem. We aspire to gain an understanding of how such systems function, adapt to and shape their environments over different time scales. We are an interdisciplinary group of engineers, computer scientists, physicists and evolutionary biologists taking a multi-pronged approach to understanding living systems. We develop and combine experimental and computational methods to study diverse problems, ranging from interactions between organisms (e.g., between hosts and pathogens, and within diverse microbial communities) and interaction networks within organisms (e.g., regulatory and metabolic interactions). A common theme to our research is to view these systems through the lens of evolution.

 

Our focus areas

John Yin, the Systems Biology Theme leader, is seeking to better understand how viruses grow, how they spread and how they persist in nature. He and his co-workers are advancing new experimental measures and computational models of virus growth, activation of anti-viral cellular defenses, and infection spread. This work has applications toward the improvement of anti-viral strategies, more effective vaccines and emerging virus-based approaches to treat cancer.

Laurence Loewe investigates questions in the new field of evolutionary systems biology, which merges systems biology and population genetics. To enable this, his group is developing two major tools. The first, Evolvix, is a new programming language that makes it easy for biologists to build simulation models linked to real data. The second, Evolution@home, is a globally distributed computing system that is being redesigned for analyzing the flood of simulation data generated by Evolvix models. His group works ‘in silico’ on diverse topics like circadian clocks, antibiotic resistance evolution, the population genetics of harmful mutations and species extinction.

Sushmita Roy’s research focuses on computational methods to construct and analyze regulatory networks and to understand how such networks change over different spatial and temporal contexts. She develops machine learning algorithms to integrate diverse types of genomic datasets to infer the structure, function and understand the evolution of regulatory networks. Her methods are broadly applicable to many organisms including yeast, flies and mammals.

Kalin Vetsigian investigates the ecology of bacterial communities that are responsible for many of the naturally existing antibiotics. He studies the dynamics and interactions of natural and synthetic communities by using numerous experimental and theoretical approaches that allow him to make targeted manipulations, collect precise measurements and provide a mechanistic understanding of how such communities take shape.