Daniella Schittler

Research interests

Modeling stem cell differentiation as subsequent decisions by genetic switches Differentiation of stem cells can be viewed as subsequent cell fate decisions, corresponding to increasing specialization. Each ''decision'' is represented by a switch module, i.e. interacting transcription factors that get switched on (upregulated) or off (downregulated), respectively. The goal of this modeling framework is to construct subsequent switch modules, where each switch influences parameters of the successive modules. In this way, undifferentiated states become unstable and have to switch to the one or the other stable differentiated state. Biologically established system properties such as observed cell types and transitions between them set requirements to be met by the theoretical model. Systems theoretic approaches allow to test hypotheses about gene regulation networks, to analyze or even predict the system behavior under given hypotheses, and eventually to deduce control strategies.
Structural requirements and discrimination for cell differentiation networks In order to select possible minimalistic networks that can reproduce cell differentiation via genetic interactions, we perform model selection based on a qualitative modeling framework. This allows to deduce necessary and sufficient requirements on network structure also in the absence of detailed quantitative knowledge.
Cell-cell interactions and cell population effects Additional effects arise on cell population level due to cell-cell signaling, as well as stochasticity and heterogeneity in cell populations. We aim to develop models to incorporate these processes and investigate the arising effects, especially their role in stem cell maintenance and differentiation.
Cell proliferation Understanding cell proliferation is also a very important aspect to control stem cells and employ them for therapeutical applications. We develop models that are well suited to deal with CFSE data in order to deduce cell division properties.
Signaling pathways to identify key components and effects of stimuli Signaling pathways describe the biochemical dynamics of cell signals, i.e. how a signal (input) is passed on and causes a certain response (output). Quantitative knowledge or hypotheses are used to derive a set of ordinary differential equations for the set of relevant molecular species. This allows a more detailed representation of biochemical reactions, molecular concentrations and system dynamics, but also requires a high number of parameters and accurate knowledge about interactions. For example, observed effects of stimuli can be used to derive a model of the causal relationship, or vice versa an established or hypothesized model can be used to predict the effect of a stimulus. The goal here is to use biochemical signaling pathways to model particular ''hot spots'' that are crucial for transcription factor activation, repression or other interactions governing cell differentiation. Eventually, obtained system properties (such as feedback strength, sensitivity) can be fed into the corresponding switch module (see above) and therefore into the overall differentiation model.
Application to bone tissue engineering The research interests mentioned are particularly motivated by tissue engineering of bone transplants: In this project, mesenchymal stem cells are differentiated into bone cells (osteoblasts) which should finally build a complete human bone. The high number and complexity of intra- and intercellular processes, and the still limited knowledge require interdisciplinary research and modeling.