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.
Original language | English (US) |
---|---|
Article number | 066012 |
Journal | Physical Biology |
Volume | 13 |
Issue number | 6 |
DOIs | |
State | Published - Dec 5 2016 |
All Science Journal Classification (ASJC) codes
- Biophysics
- Structural Biology
- Molecular Biology
- Cell Biology
Keywords
- Boolean networks
- complexity
- genetic networks