报告题目: The heterogeneous brain network of the biological clock
报告人: Prof. Jos H.T. Rohling
The biological clock regulates daily and seasonal rhythms in physiology and behaviour in humans and most other living creatures, and malfunction of this clock is associated with numerous neurological (e.g. depression) and metabolic (e.g. obesitas) diseases and even with cancer.his clock is located in the suprachiasmatic nucleus(SCN), which is a heterogeneous network of cells. Specifically the interactions in the network of the SCN show emergent properties which cannot be traced back to the properties of the individual cells.
The talk will be about studies that investigate the network of the SCN, and explain the network properties of the clock using a number of analytical methods, such as clustering methods based on random matrix theory, and detrended fluctuation analysis to determine a power-law distribution. Also modeling techniques have been employed to gain more understanding of the mechanisms of the SCN network.
For the clock, experimental data is available for different levels of organization, from behavioural data to ensemble electrical activity data of groups of neurons, to single cell molecular data. All data have the same characterization in phase, period and amplitude, and are thus complementary to each other.
I will explain how we used the different analytical tools on the different data-sets and how we obtained information using the analytical and modeling tools.
Jos H.T. Rohling received his B.Eng degree from Hogeschool Drenthe in 1993 and his M.A. degree from the University of Amsterdam (UVA) in 1996. He obtained his PhD in computer science from Leiden University in 2009. He is currently assistant professor at the Department of Cell and Chemical Biology at the Leiden University Medical Centre (LUMC). His research focusses on investigating complex networks in the biological clock, a tiny brain region involved in day-night rhythms and seasonal rhythms, and itsemergent functional properties. His interests include time series analysis and complex networks in Neuroscience, Physiology and Chronobiology.