Complex biological function as an inverse input-output problem
Andrea Liu | Dept of Physics and Astronomy at Univ. of Pennsylvania School of Arts & Sciences
Abstract
In neural networks, parameters that govern interactions between nodes are tuned to obtain desired input-output relations. I argue that it is useful to think of systems with biological function as having effective interactions that are tuned to achieve the biological function. Such systems can be viewed as members of a large class of systems that I call "tunable matter." In some cases, biological evolution has tuned the interactions, but in other cases, systems tune their effective interactions by local rules in order to maintain function on time scales much shorter than evolutionary ones. I argue that tunable matter provides a unifying conceptual framework for understanding the emergence of collective function in a wide range of living systems.