A developing nerve cell does not simply switch genes on or off—it flickers them, pulsing in irregular rhythms that change over time. Current biology textbooks describe cell states as static snapshots, but live imaging now shows that individual cells express genes in dynamic, fluctuating patterns that are invisible when scientists average measurements across thousands of cells. This project proposes that a cell’s decision to differentiate or remain dormant depends not on whether a gene is active, but on *how* it is active: whether it pulses, oscillates, or stabilises. The researchers will watch living vertebrate nerve cells under microscopes, track gene expression in real time, and build mathematical models to decode these temporal patterns. This is fundamental science—there is no immediate medical application. Yet understanding how cells interpret dynamic signals could eventually explain why some stem cells fail to repair damaged tissue, or why cancer cells resist re-entering a quiescent state. Similar work on oscillating gene networks in bacteria has already reshaped synthetic biology; this project could do the same for developmental biology.
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During development, cells transition from a progenitor state to differentiation at defined times, or to quiescence, from which they may be reactivated. Understanding how such transitions are regulated is key in understanding how tissues are built, maintained, repaired or subverted in disease. Our current understanding of gene regulatory networks has been built largely on static gene expression profiles of cell states. However, recent advances in imaging have revealed a surprising degree of d ynamical gene expression at a single cell level, which evolves over time and is masked by static measurements of population averages. Here, I propose that cell state transitions are not simply driven by genes being on (or off), but by a change in the dynamics of gene expression, for example, from fluctuating or pulsatile expression to a more stable state. We will use single cell, quantitative approaches, live imaging, multiple experimental model systems and mathematical modeling, in order t o understand how changes in gene expression dynamics underlie cell state transitions. My vision is to establish a much more dynamic view of development than is currently available, by understanding how the output of gene interactions evolves, and is interpreted, over time. I will focus on vertebrate neurogenesis, although the emerging concepts are likely to apply to the development of most tissues. Key goals Characterisation of gene expression dynamics during cell state transitions. Understanding how dynamical, pulsatile, gene expression is controlled, at the transcriptional and post-transcriptional level. Understanding how the output of dynamical gene expression is interpreted over time.
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