@article {Razooky144964, author = {Brandon S. Razooky and Youfang Cao and Alan S. Perelson and Michael L. Simpson and Leor S. Weinberger}, title = {Non-latching positive feedback enables robust bimodality by de-coupling expression noise from the mean}, elocation-id = {144964}, year = {2017}, doi = {10.1101/144964}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV{\textquoteright}s fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIV{\textquoteright}s promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit{\textquoteright}s non-cooperative {\textquoteleft}non-latching{\textquoteright} feedback architecture is optimized to slow the promoter{\textquoteright}s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV{\textquoteright}s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.}, URL = {https://www.biorxiv.org/content/early/2017/06/13/144964}, eprint = {https://www.biorxiv.org/content/early/2017/06/13/144964.full.pdf}, journal = {bioRxiv} }