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 reducebiological 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.