Computational Modeling of Pluripotent Stem Cell Transcription Factor Networks
Stem cells hold great potential for clinical application. The molecular control of their self-renewal, differentiation and other cell fate choices is therefore of great scientific and therapeutic interest. Transcription factors and their networks are powerful regulators of stem cell fates in health and disease. Many components of these networks and their possible interactions have meanwhile been described. However, the exact network topologies, their stability, expression dynamics and the underlying regulatory mechanisms remain elusive. We therefore aim at quantitatively understanding the transcription factor network underlying the control of self-renewal and differentiation of pluripotent stem cells.
We plan to use bioimaging approaches to quantify the dynamic expression of transcription factor proteins involved in the pluripotency transcription factor network. This quantification will be done non-invasively in individual living embryonic stem cells after manipulation of signaling or transcription factor inputs, over generations, and with high temporal resolution. These measurements will be used for the development and falsification of computational models of the transcription factor network. They will quantitatively describe the mechanisms underlying transcription factor network regulation, with a focus on inferring connections between the core transcription factor network and key signaling pathways, and on elucidating sources of cell-to-cell variability.
We expect an improved quantitative understanding of the dynamic molecular mechanisms underlying stem cell self-renewal, differentiation and plasticity. In addition, this project will contribute to the development of improved mathematical methods for the network inference from single-cell and cell lineage data and to improved workflows for bioimaging, computer vision, single cell tracking and quantification, and model generation.
Mot-clé: Stem Cell, Transcription Factor, Dynamics, Imaging, Computational Modeling, Network, Inference, Pluripotencyretourner