Teaching
Most complex systems are hard to describe and understand because they are composed of large numbers of elements interacting in a nonordered way. A good example is cellular biology; diverse cellular components (genes, proteins, enzymes) participate in various reactions and regulatory interactions, forming a robust system.
A very useful representation of complex systems is given by graphs (or networks), where we denote the components with nodes and their interactions by edges. The properties of these interaction graphs can then be analyzed by graph theoretical and statistical mechanics methods and this information can lead to important conclusions about the dynamics of the system.
Courses
PHYS580: Elements of Network Science and its Applications (graduate), Fall 2011  2015,
Fall 2016  PHYS/BIOL 497
10:10  11:00 am MWF, 112 Osmond
Network analysis of biological systems
Lecture Notes
Current lecture notes are distributed via Canvas. The materials covered in the course will change over the years as we incorporate the newest advances in the field. Below we list lecture notes for a few prior years.

Lectures notes for 2012: phys5802012.zip

Lectures notes for 2009: phys5802009.zip
Topics
 Elements of graph theory: node degree, distances between nodes, clustering.
 Node betweenness, subgraphs, directed graphs.
 Random graph theory.
 Network models and theory: lattices, smallworld networks,scalefree networks, evolving networks.
 Network robustness and vulnerability.
 Percolation and flow processes on networks.
 Introduction to cellular networks: gene regulatory networks, signal transduction networks, metabolic networks; methods of network inference.
 Modeling reaction networks: elements of chemical kinetics.
 Signal transduction network models.
 Modeling gene regulatory networks using continuous and discrete dynamics.