What do we do?
We study gene networks that control the proliferation and death of cancer cells.
What is the goal of our research?
We aim to better understand control mechanisms underlying various cell-fate decisions, and their connections to cancer development and aging, as well as to potential therapeutic strategies.
Welcome Eddie Khav, Clay Lanham, and Alexa Wollach to begin their undergraduate research in the lab! (05/2012)
Welcome Benjamin Horn to begin his undergraduate research in the lab! (11/2011)
Welcome Kimiko Della Croce to join us as a lab manager/ research specialist! (07/2011)
Welcome Colleen Carlotto (Chemical Engineering, UA) to begin her undergraduate research in the lab! (06/2011)
Our pilot proposal "Single-Cell Experimental Platform for Cancer Systems Biology" is funded. Thanks The Faculty Seed Grants Program of The University of Arizona Foundation and the Office of the Vice President for Research. (06/2011)
Our pilot proposal "Understanding Life and Death Decisions of Individual Cells" is funded. Thanks The American Cancer Society Institutional Research Grant program. (05/2011)
Our paper "Origin of bistability underlying mammalian cell cycle entry" is published in Mol. Systems Biololgy (04/2011)
Welcome Aishan Shi (Biochem and Biophysics & English, UA) to begin her undergraduate research in the lab! (04/2011)
Welcome Dr. Geoff Mitchell from Univ. of Arizona to join the lab as a postdoc fellow! (02/2011)
Welcome Dr. Chenglu Chen from Univ. of Miami to join the lab as a postdoc fellow! (10/2010)
Motivated individuals are welcome to inquire about training and working opportunities in the lab. Please see Join/Contact Us.
The Yao Systems Biology Lab in the MCB department at UofA will open the door in October 2010.
What is our approach?
We use an integrated approach of high-resolution single-cell experiments and computer modeling. Single-cell experiments can uncover dynamic and heterogeneous behaviors of cancer cells that often get buried in population-average analysis; modeling can help reveal emergent properties of gene networks that are hard to grasp intuitively. As demonstrated in our previous work, the combinatorial approach holds great promise in dissecting complex biological systems like cancer.