29.09.2010 Hausseminar: Kristoffer Rigbolt University of Southern Denmark
05.11.2009 Stefan Rotter (BCCN) "Activity Dynamics in Neocortical Neuronal Networks"
Artikelaktionen
19.11.2009 Hauke Busch "Using Time-Scale Separation of cellular processes to untangle biological complexity"
The venue of this lecture is: Room 00.043 BIO II/III !!LECTURE STARTS AT 4 pm!!
| Was | Lecture |
|---|---|
| Wann |
19.11.2009 16:00
19.11.2009 17:00
19.11.2009 von 16:00 bis 17:00 |
| Wo | BIO II/III Raum 00.043 |
| Name | Peter Pfaffelhuber |
| Kontakt-E-Mail | peter.pfaffelhuber@stochastik.uni-freiburg.de |
| Termin zu Ihrem Kalender hinzufügen |
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The development of mathematical models to predict cellular behavior is currently hampered by the enormous complexity of biological systems. Building such models thus goes hand in hand with strategies to reduce biological complexity to a computationally and experimentally manageable amount. Most strategies approach this task in terms of network topology. Cellular gene and/or protein networks are modularized and the individual subnetworks, such as signaling pathways, are then investigated in detail.
Here, we propose a different approach to reduce biological complexity based on time scale separation. Dynamic interaction processes within a cell can be categorized according to their characteristic time to complete: from seconds to minutes for protein signaling, to hours, days and months for gene expression kinetics and tissue growth, respectively. Focusing on cellular subsystems evolving on a particular time scale, slower processes then remain quasi static, while fast processes follow instantaneously and can be adiabatically eliminated. The decision time for mammalian cells to differentiate, migrate or proliferate is usually on the time scale of hours. Adiabatically eliminating faster processes such as protein signaling, we show how gene expression kinetics can be employed to obtain a global, holistic view on cellular decisions on this time scale.
Taking HGF-induced migration of primary human keratinocytes as an example, we infer a dynamic model from time-resolved microarray data that predicts in silico the time-ordered events necessary and sufficient to start, sustain or stop cell migration. Briefly highlighting further cell-fate examples, we propose that this approach provides a new way of obtaining insight into the dynamic orchestration of diverse signaling pathways and gene expression that control cellular decisions in general.