Systems biology of quiescence entry

Abstract
This proposal aims to provide crucial training for the candidate’s long-term career plan to study how cellular
quiescence is established through decision-making processes. The decision to undergo quiescence in response
to stress or developmental signals is a fundamental and understudied property of living systems. Failure to
maintain quiescence can lead to cell proliferation disorders in humans, such as fibrosis or cancer.
Quiescence entry is triggered when multiple nutrient- and stress-sensing signaling pathways arrest the cell cycle
machinery. However, the molecular mechanisms that coordinate stress response pathways with the cell cycle
during quiescence remain largely unclear. This is, in part, due to the difficulties to simultaneously quantify multiple
stress pathways at the single cell level in vivo. To solve this limitation, the candidate will use a microfluidics-
fluorescent imaging system that tracks up to six different pathways simultaneously during the transition from
proliferation into quiescence. Using this approach, the coordination between stress responses and the cell cycle
machinery can be quantified with unprecedented temporal resolution in the model organism S. cerevisiae. A
computational platform based on machine learning and time series analysis will be used to process the large
imaging data derived from tracking six biomarkers simultaneously in single cells. An initial version of this
framework found that during the onset of quiescence the nuclear levels of the conserved DNA-replication kinase
Cdc7 are dynamically regulated. This approach also identified that the nuclear levels of the stress-activated
transcriptional repressor Xbp1 define how the cell cycle is stopped during quiescence entry. Combining this
computational approach with biochemical techniques will determine the molecular mechanisms for the
establishment of cellular quiescence by modulation of stress responses and the cell cycle machinery.
The candidate is to acquire crucial training in computational biology during the K99 phase of this proposal to
complement his previous training in biochemistry, cell biology and yeast genetics. The candidate will be
mentored by a leader in computational biology Dr. Gaudenz Danuser, whose lab develops advanced machine
learning and time series analysis to study cellular signal transduction. This proposal harnesses the commitment
of an entire bioinformatics core facility and the training environment of a world-class research institution at UTSW.
Establishing a unique computational and imaging framework, combined with biochemical approaches for the
study of quiescence, will support the candidate’s transition to an independent research academic position and
will lead to the discovery of biomedically relevant principles of quiescence and cell cycle regulation.