Many complex systems in biology can be conceptualized as networks. This perspective helps researchers understand how biological systems work on a fundamental level and can be used to answer key questions in biology, medicine, and engineering.
Blood flow in the brain is a prime example. Blood travels through a network of vessels and can be re-routed to specific parts of the brain as needed. Walking, for example, would require blood flow in different regions than talking or chewing gum.
It’s thought that networks perform such tasks by controlling connections within the network, called “edges.” What physicists hadn’t explored is how many tasks a single network can accomplish simultaneously.
A team of researchers from the Department of Physics & Astronomy published a study in PNAS that addresses this question. Graduate student Jason W. Rocks and former postdoc Henrik Ronellenfitsch, who is now at MIT, were the lead authors of this paper, and worked alongside physicists Andrea Liu and Eleni Katifori, as well as Sidney R. Nagel from the University of Chicago.
The Penn team had previously studied two types of networks. Katifori has examined how nature builds and maintains “flow networks,” such as blood flow, using approaches that are inspired by and related to biology. Liu studies “mechanical networks,” such as the arrangement of amino acids that form a protein, and how these networks can be changed in order to perform a specific biological function.
While these two systems differ from one another, discussions between the Liu and Katifori groups about how much multitasking each network could accomplish helped Liu and Katifori realize that they could study these two seemingly unrelated networks together.
“We were both independently studying the complexity of a particular function that a flow network could do and what a mechanical network could do,” says Katifori. “It was two entirely different physical networks, but in a way the same question.”
The authors developed a set of equations that described each system. They then used simulations to control or “tune” the network so they would perform increasingly complex functions. Rocks, Ronellenfitsch, and their colleagues found that both types of networks succeeded at multitasking.