Skip to main content

Nan Hao


In recent years, genomic sequencing and systematic analysis have revealed many molecular components that control gene expression at multiple levels, and detailed the myriad of interactions among and between these regulatory modules. However, it remains unclear how these molecular networks operate in time and space to carry out diverse cellular functions. The goal of our lab is to understand how network architecture governs the dynamics and function of regulatory responses in the context of stresses, aging, or diseases.

Our lab uses multidisciplinary approaches, integrating biology, engineering and computer science, in our studies. We develop and use novel microfluidics and imaging technologies to quantitatively track the dynamics of molecular processes in single living cells and construct computational models to describe and predict cellular behaviors.

In particular, our laboratory focuses on the following three major research themes:

Decoding the dynamics of stress responses

Cells could transmit environmental information and initiate cellular responses through regulating the temporal dynamics of signaling activities. Our current work focuses on understanding: (1) the network structures that encode the environmental information into signaling dynamics; (2) the gene circuits that decode signaling dynamics into functions; (3) the mechanisms that underlie cellular memory of environmental changes.

Probing the causes of cellular aging

Cellular aging plays an important role in many diseases, such as cancers and neurodegenerative disorders, but the mechanisms that drive the aging process remain largely unclear. We aim to combine transformative microfluidics and single-cell imaging technologies with computational modeling to obtain a quantitative and predictive understanding of the aging process and to develop new strategies to promote longevity.

Quantifying the heterogeneity in cancer cells

We have expanded our quantitative biology research into mammalian cell systems. We have developed new microfluidic and imaging technologies to track individual mammalian cells over a very long period of time. Our current work focuses on studying the heterogeneous signaling dynamics in cancer cells and how these dynamics influence cancer cell growth.

Select Publications

  • Zhou Z, Liu Y, Feng Y, Klepin S, Tsimring LS, Pillus L, Hasty J, and Hao N. (2023) Engineering longevity - design of a synthetic gene oscillator to slow cellular aging. Science 2023 April 28; 380:376-381.
  • Paxman J, Zhou Z, O'Laughlin R, Liu Y, Li Y, Tian W, Su H, Jiang Y, Holness SE, Stasiowski E, Tsimring LS, Pillus L, Hasty J, Hao N. (2022) Age-dependent aggregation of ribosomal RNA-binding proteins links deterioration in chromatin stability with challenges to proteostasis. eLife 2022 Oct 4;11:e75978.
  • Jiang Y, Hao N. (2021) Memorizing environmental signals through feedback and feedforward loops. Curr Opin Cell Biol. 2021 April; 69:96-102.
  • Mudla A, Jiang Y, Arimoto K, Xu B, Rajesh A, Ryan AP, Wang W, Daugherty MD, Zhang DE, Hao N. (2020) Cell-cycle-gated feedback control mediates desensitization to interferon stimulation. eLife 2020 Sep 18;9:e58825.
  • Li Y, Jiang Y, Paxman J, O'Laughlin R, Klepin S, Zhu Y, Pillus L, Tsimring LS, Hasty J, Hao N (2020). A programmable fate decision landscape underlies single-cell aging in yeast. Science. 2020 July 17; 369(6501):325-329.
  • Jiang Y, AkhavanAghdam Z, Li Y, Zid BM, Hao N (2020). A protein kinase A-regulated network encodes short- and long-lived cellular memories. Sci Signal. 2020 May 19;13(632):eaay3585
  • Jin M, Li Y, O'Laughlin R, Bittihn P, Pillus L, Tsimring LS, Hasty J, Hao N (2019). Divergent aging of isogenic yeast cells revealed through single-cell phenotypic dynamics. Cell Syst. 2019 Mar 27;8(3):242-253.e3.
  • Li Y, Roberts J, AkhavanAghdam Z, Hao N (2017). Mitogen-activated protein kinase (MAPK) dynamics determine cell fate in the yeast mating response. J Biol Chem. 2017 Dec 15;292(50):20354-20361.
  • Li Y, Jin M, O'Laughlin R, Bittihn P, Tsimring LS, Pillus L, Hasty J, Hao N (2017). Multigenerational silencing dynamics control cell aging. Proc Natl Acad Sci U S A. 2017 Oct 17;114(42):11253-11258.
  • Jiang Y, AkhavanAghdam Z, Tsimring LS, Hao N (2017). Coupled feedback loops control the stimulus-dependent dynamics of the yeast transcription factor Msn2. J Biol Chem. 2017 Jul 28;292(30):12366-12372.
  • AkhavanAghdam Z, Sinha J, Tabbaa OP, Hao N (2016). Dynamic control of gene regulatory logic by seemingly redundant transcription factors. eLife. 2016 Sep 30;5.


Nan Hao received his Ph.D in Biochemistry and Biophysics from the University of North Carolina at Chapel Hill. He was a postdoctoral fellow with Timothy Elston at the University of North Carolina and a postdoctoral fellow with Erin O’Shea at Harvard University/Howard Hughes Medical Institute.

portrait placeholder