Abstract

Stem cell driven regenerative systems are highly complex and dynamic, consisting of larges number of different cells expressing many molecules controlling their fates. Therefore, mathematical models are necessary – both to aid the interpretation of experimental data, and to simulate the behavior of stem cell systems based on hypothetical assumptions about their complex cellular or molecular composition. However, the generation of models is hampered by the lack of precise experimental data. Even moderate numbers of unknown variables quickly lead to uncertainties within the models which can render them largely useless for solving biological questions. In particular, it is a major problem that stem cell systems are usually followed by analyzing populations of cells – rather than individual cells – at very few time points of an experiment, and without knowing individual cell identities. Continuous real-time tracking of individual cells would be an important prerequisite to fully understand the developmental complexity of stem cell driven systems. We have therefore developed culture and imaging systems to follow the fate of individual cells over long periods of time. Our approaches also allow the continuous long term quantification of protein expression levels and activity in living stem cells. This novel kind of quantitative data of single cell behavior and molecule expression is used as the basis for the improved generation and falsification of models describing complex stem cell systems. _

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