Tian Hong joined the BCMB department as an assistant professor two years ago. He is a welcome addition to our growing computational biology group. Tian came to Knoxville from the University of California, Irvine where he was a postdoc with Professor Qing Nie. Although he has been in the department only a short time, he has published four papers, became a Co-PI on a funded grant, and established multiple collaborations with other faculty in BCMB.
Q: Tian, how did you become interested in your current research?
I love biology and mathematics. I started my research career as a mathematical biologist because I was amazed by the deep insights that we can obtain by describing and analyzing biological systems with mathematical tools.
There are many types of mathematical models. In my research area, scientists use equations to describe systems and analyze them to get useful information. In biology, both intuitive models and mathematical models are important, but the rigorous and quantitative nature of mathematical models is essential for understanding many complex biological systems.
One of the most influential mathematical models built for biological systems is the reaction-diffusion system that Alan Turing proposed in 1952. The models that Turing built predicted robust pattern formations arising from simple chemical reactions, and he explained the mathematical basis of such phenomena. The concept of Turing pattern became a major theory in biology, because this mechanism may hold the key to understanding a wide range of developmental processes, such as the formation of the digits in the limb.
Q: What are you working on now?
We are working on models for a variety of biological systems. The overarching theme of our research is to understand how gene regulatory networks control the dynamics of cells. We ask why certain types of network structures were selected, and what kind of performance advantages these networks can bring to specific biological systems. For example, we are interested in how epithelial cells gain motility during development and metastasis and how gene regulatory networks influence this process. We build mathematical models to analyze these systems, and these models provide critical information on how the behaviors of cells may change when we perturb gene networks in a quantitative manner. With these models, we will gain better understanding of our cells at the fundamental level, and ultimately, we will be able to use them to develop better therapeutic strategies to cure diseases.
I hope that we will uncover the functions of several network structures that occur frequently in biology but are not well understood. I hope that we will have some unifying theories that may help to understand a wide range of biological systems containing complex gene networks.
Q: If a student wants to learn mathematical modeling, what skills are necessary? What classes that student should take?
The student should have basic understanding of differential equations and nonlinear dynamical systems, which in turn require some understanding of calculus and linear algebra. They will also need some basic computer skills. This list of topics may look intimidating, but my observation is that many motivated students can learn most of these important materials in a couple of semesters.
Q: What classes do you teach?
I teach programming for biological data analysis in the fall, and molecular and cellular biology in the spring. I really enjoy teaching courses that benefit many students with very diverse career goals. Students in my programming classes include those who have decided to go to medical industry and those who are starting their academic research career. It is satisfying to see that the skills that they learn in the class will help all of them to achieve their goals.
Q: What do you like to do in your spare time?
I play flute and I like traveling. At the moment, our two-year-old son keeps us busy most of the time.