title: Complexity of generic biochemical circuits: topology versus strength of interactions.
abstract: The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.
authors.affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA. Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Mikhail
authors.initials: M
authors.lastname: Tikhonov
authors.forename: William
authors.initials: W
authors.lastname: Bialek
mesh_terms: D053263:Gene Regulatory Networks; D008954:Models, Biological; D008957:Models, Genetic; D008962:Models, Theoretical