Program

(updated October 11, 2019)

Monday, 18 May, morning (9.30-12.30)
Introduction to Complex Networks (Mendes): [abstract]

Monday, 18 May, afternoon (14.30-17.30)
[to be defined]: [abstract]


Tuesday, 19 May, morning (9.30-12.30)
Critical Phenomena in Networks (Mendes): [abstract]

Tuesday, 19 May, afternoon
short talks by students (see page Application)


Wednesday, 20 May, morning (9.30-12.30)
Computational Human Dynamics (Karsai): [abstract]

Wednesday, 20 May, afternoon (14.30-17.30)
Analyzing the structure of biological networks (Albert): Biological systems are excellent examples of complex interacting systems with emergent dynamical properties. We will present the different ways in which the interactions of biological systems are represented as networks. We will discuss the challenges of analyzing and interpreting these networks, for example the existence of inhibitory edges and the inter-dependence of certain edges. We will review the observed properties of biological networks and the network models that most closely reflect these properties.

Wednesday, 20 May, evening (20.00)
social dinner


Thursday, 21 May, morning (9.30-12.30)
Biological Networks (II) (Pržulj): [abstract]

Thursday, 21 May, afternoon (14.30-17.30)
Analyzing the dynamics and attractors of biological networks (Albert): Biological systems can be fruitfully described as nonlinear dynamical systems, where each node is characterized with a state variable and a regulatory function that describes how its regulators (i.e. its parent nodes in the network) determine the change in its state variable. The attractors (long-term states) of the system have clear biological interpretation; for example the attractors of within-cell interaction networks determine cell types. Thus, characterizing the attractor repertoire of a biological system and the trajectories that lead to each attractor enables an understanding that can be translated to control. We will summarize models used to describe the dynamics of biological systems, with an emphasis on models that closely reflect the structure of the biological network. We will exemplify how integration of network structure and regulatory logic can be used to drive the system into a desired attractor.