A quantitative and dynamical approach to understanding cell state transitions

Cell state transitions, whereby cells move from one state to another (i.e. from progenitor to differentiated cell), sometimes reversibly, are of key importance to most areas of biology, including development, regeneration and cancer. Our understanding of this inherently dynamic process is limited by the molecular approaches that are commonly used, such as “omic” approaches which are sophisticated and powerful, but essentially static, relying on “snapshot” data. My research vision is to pinpoint the mechanisms by which cell state transitions take place in real time by applying live molecular imaging, quantitative data and dynamical mathematical approaches. We will study ultradian gene expression oscillations of key transcription factors (TFs) because recent data suggests that they represent a prevalent yet under- appreciated mode of regulation and are important in enabling cell state transitions. We will focus on how TF oscillations are decoded, the area that we know least about. We will study mammalian and zebrafish neurogenesis, ideal systems for capturing the impact of oscillations in a developmental context. The proposal has three aims: Aim 1. To visualise and characterise oscillatory dynamics during cell-state transitions. Aim 2. To understand the function and decoding of oscillatory gene expression. Aim 3. To uncover new oscillatory gene expression.