Welcome to the Yao Lab! -- MCB, Univ. of Arizona

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 to the Yao Systems Biology Lab at UofA

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.

Single-cell measurement. In the example shown on the left, genes A and B exhibit distinct expression patterns (the expression level of gene A in all cells is around 11, and that of gene B in half of the cells is around 2, and in the other half of cells is around 20). However, the single-cell level distinction between A and B is buried when gene expressions are measured at the population-average level (expressions of both A and B = 11).

Modeling. Our human brain quickly loses track of complex dynamics of biological systems. Verbal reasoning of biological processes is often vague and incomprehensive. Computer modeling provides a powerful tool for data integration, quantitative/comprehensive analysis of system behaviors, simplification of complexity, and hypothesis generation. (Shown in the figure: modeling a gene circuit using the same method for modeling an electronic circuit.)

Cancer cells make aberrant cell-fate decisions: they grow when normal cells would not given the same conditions; they refuse to die when normal cells would. What are the biological switches underlying these aberrant behaviors?