ABSTRACT:
Abstract: Many biological systems employ physically constrained networks of chemical, electrical, or mechanical signals to perform complex functions. Recent advances across multiple disciplines have begun to elucidate the role that these embedded networks play in guiding and enabling system dynamics. A critical remaining challenge is to harness the predictive role of complex networks in tissue, brain, and behavior to support the control, rescue, and imitation of system function. Such predictions can be extracted from network structure, network dynamics, or the properties of the signals that propagate through the network. I will illustrate these efforts and the associated mathematical tools they employ using examples drawn from material, biomedical and population systems. For example, the network structure of soft materials constrains sound propagation, informing the development of non-destructive testing and design techniques. The network dynamics of human brain activity predicts adaptive behaviors like learning, potentially enabling the monitoring of disease progression and rehabilitation. The information passed between individuals on a social network drives behavioral variability in the population, impacting information dissemination policies. I will discuss the ramifications of these findings for critical questions in theoretical biophysics and outline some of the outstanding conceptual, experimental, and mathematical challenges that will propel research in the coming years.