Welcome to Tianhai Tian's homepage

Research Interests

Stochastic modelling of genetic regulatory networks

There is a growing body of evidence that suggests genetic regulation in single cells is a stochastic process. Stochasticity in gene expression may result from small numbers of gene products, intermittent gene activity, and variability of transcriptional factor activities. In addition to intrinsic noise, which is derived from uncertainty in biochemical reactions, extrinsic noise arising from environmental variability has significant influence on the system dynamics. The major goal of my research in this exciting area is to investigate approaches for introducing noise into mathematical models and to develop stochastic models for realizing experimental results showing phenotypic variations in isogenic populations. I have proposed a general modelling technique for developing stochastic models based on macroscopic reactions, and applied this technique to develop two stochastic models for the genetic toggle switch interfaced with either the SOS signal pathway or a quorum sensing signal pathway. Both models successfully realized experimental results with bimodal population distributions. In addition, I have studied the bistability property and genetic switching in the lactose operon and lysis/lysogeny regulatory network of lambda phage.

One of the major challenges in systems biology is the lack of quantitative information which is very difficult and expensive to be obtained from experiments. Fortunately, recent developments in high-throughout technologies provide valuable descriptions of gene activities under various biochemical and physiological circumstances and give the possibility to evaluate kinetic parameters in biological systems. The focus of my research is to develop reference methods to estimate kinetic rates in mathematical models and to infer genetic regulation from time-series experimental data. The simulated maximum likelihood method represents a major step in this direction and will lead to more robust and effective estimation methods.

Mathematical modelling of cell signal transduction pathways

The mitogen-activated protein (MAP) kinase cascade has been implicated as one of the major pathways mediating signal transduction for a diverse group of extracellular stimuli. Activation of this pathway results in changes in gene expression and leads to regulations of the fundamental cellular functions such as cell proliferation, survival, differentiation and motility. The MAP kinase signalling cascade has profound effects on a number of diseases and plays an important role in cancer pathogenesis. In the last few years I have studied mathematical modelling of the MAP kinase pathway based on the collaboration with Professor Hancock at the Institute of Molecular Bioscience of the University of Queensland. We have analyzed the signal output of the MAP kinase cascade at different subcellular locations and the critical functions of nanoclusters in signal transduction. Our research indicates that Ras nanoclusters function as nanoswitches and that clustering is essential for Ras signal transduction.

Stochastic simulation of biochemical reaction systems.

In recent years, the stochastic simulation algorithm (SSA) has been successfully applied for simulating genetic/enzymatic reactions in which the molecular population of a critical reactant species is relatively small. The tremendous success of the SSA has led to it being applied to much larger systems than it was originally designed for. This has stimulated the development of effective methods for simulating large-scale stochastic systems. I have developed a number of efficient methods to reduce the large computational time of the SSA. The proposed binomial tau-leap and multi-scale methods have achieved significant improvement on efficiency over the existing approaches and have very good accuracy. These methods have been successfully applied to the simulation of genetic regulatory networks and cell signal transduction pathways. In addition, we have designed the delay stochastic simulation algorithm (DSSA) for stimulating gene networks with time delay which can be used to describe slow reactions that may involve a number of multistage reactions in genetic regulatory networks.

Research Grants

Tian T., Stochastic modelling of telomere length regulation in ageing research,
Australian Research Council Discovery Project, A$327,000, 2012-1014.

Tian T., Faculty of Science Equipment Grant, A$14,600, 2011.

Tian T., Stochastic modelling of genetic regulatory networks with burst process,
Australian Research Council Future Fellowship, A$688,630, 2010-2014.

Tian T. and Harding A., Multiscale stochastic modelling of tumour robustness,
Australian Research Council Discovery Project, A$240,000, 2010-2012.

Tian T., Spatio-temporal modelling of the mitogen-activated protein (MAP) kinase pathway,
Scientific Visit Grant for International Travel (North America), Australian Academy of Science, A$9,500, 2010.

Blatt M.R. and Tian T., Systems analysis of oscillatory mechanics in stomatal dynamics,
UK Biotechnology and Biological Sciences Research Council (BBSRC) Research Grant, 408,450 GBP, 2007-2010.

Hancock J.F. and Tian T., Spatio-temporal modelling of Ras-dependent MAP kinase activation.
Australian Research Council Discovery Project and Australian Research Fellowship, A$704,542, 2007-2011.

Burrage K., Pailthorpe, B. and Tian T., Multiscale stochastic modelling of genetic regulatory networks,
Australian Research Council Discovery Grant, A$210,000, 2004-2006.

Tian T., Mathematical modelling of cellular processes based on microarray gene expression data,
University of Queensland Early Career Research Grant for 2005, A$20,000.

Tian T., Burrage K. and Nielsen L., Stochastic modelling of genetic regulatory networks, Genetic regulatory network is a noise business,
University of Queensland Research and Development grant, A$17,000, 2003.

Beveridge C., Burrage K. and Tian T., Inter-organ communication in plants: is polar auxin transport so important,
University of Queensland Research and Development grant, A$37,000, 2002.

Tian T., Stochastic modelling in finance,
University of Queensland start-up funding, A$10,000, 2001.