American Journal of Bioinformatics Research

American Journal of Bioinformatics Research publishes all the newest and distinguished research articles, reviews and letters in all areas of bioinformatics and computational biology. Each issue contains a series of timely, in-depth written articles by leaders in the field, covering a wide range of the integration of biology with computer and information science.


Tianhai Tian

Editorial Board Member of American Journal of Bioinformatics Research

Associate Professor, Monash University, Australia

Research Areas

Mathematical Biology, Bioinformatics, Systems Biology, Modeling Biological Systems, Infectious Disease Dynamic System, Biostatistics, High-Performance Bio-Computing, Computational Genomics, Computational Proteomics

Education

2001Ph.DMathematics, The University of Queensland
1988M.Sc.Applied Mathematics, Huazhong University of Science and Technology, China
1982B.Sc.Computational Mathematics, Huazhong University of Science and Technology, China

Experience

2011-presentAssociate Professor and Australian Research Council (ARC) Future Fellow, School of Mathematical Sciences, Monash University
2009-2011Reader and Lord Kelvin Fellow, Department of Mathematics, University of Glasgow
2008-2009Senior Research Fellow and ARC Australian Research Fellow, School of Mathematical Sciences, Monash University
2007-2008Lord Kelvin Fellow, Department of Mathematics, University of Glasgow
2007Senior Research Fellow and ARC Australian Research Fellow, Institute for Molecular Bioscience, University of Queensland
2004-2006Senior Research Fellow, Advanced Computational Modelling Centre, University of Queensland
2001-2004Research Fellow, Advanced Computational Modelling Centre, University of Queensland
1988-1998Associate Lecturer, Department of Mathematics, Hubei University of Technology, China
1991Lecture, Department of Mathematics, Hubei University of Technology, China
1994Associate Professor, Department of Mathematics, Hubei University of Technology, China

Academic Achievement

International Postgraduate Research Scholarship, Feb. 1998-Feb. 2001
University of Queensland International Postgraduate Research Scholarship, Feb. 1998-Feb. 2001
Excellent Researcher Award, the Hubei Provincial Government, China, 1998
The Top Ten Teachers Award, Hubei University of Technology, China, 1996
Excellent Teaching Award for Youth, Department of Education, Hubei Province, China, 1995
The Top Ten Teachers Award, Hubei University of Technology, China, 1994

Publications: Journals

[1]  Duff C., Smith-Miles K., Lopes L. and Tian T., Mathematical models of stem cell differentiation: the PU.1-Gata-1 interaction, to appear in Journal of Mathematical Biology.
[2]  Li W., Luo X., Hill N.A., Ogden R.W., Tian T., Smythe A., Majeed A.W. and Bird N., Cross-bridge apparent rate constants of human gallbladder smooth muscle, to appear in Journal of Muscle Research and Cell Motility.
[3]  Tian T., Olson S., Whitacre J.M. and Harding A., The origins of cancer robustness and evolvability, Integrative Biology, 3(1):17-30, 2011.
[4]  Qiao M., Qi H., Liu A. and Tian T., Analysis of Stability and Permanence for a HBV Model with Impulsive Releasing Immune Factor, Chinese Annals of Mathematics, Series A, 32:173-184, 2011. The English translation of this paper will be published in Chinese Journal of Contemporary Mathematics, 2011.
[5]  Tian T., Plowman S., Parton R.G., Kloog Y. and Hancock J.F., Mathematical modelling of K-Ras nanocluster formation on the plasma membrane, Biophysical Journal, 99 (2), 534-543, 2010.
[6]  Qiao M., Qi H. and Tian T., Steady state solution and stability of an age-structured MSIQR epidemic model, Intelligent Information Management, 2 (5), 316-324, 2010.
[7]  Wang J. and Tian T., Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53, BMC Bioinformatics , 11(1), 36, 2010.
[8]  Tian T., Stochastic models for inferring genetic regulation from microarray gene expression data, BioSystems , 99(3), 192-200, 2010.
[9]  Tian T., Effective stochastic simulation methods for chemical reaction systems, Journal of Numerical Mathematics and Stochastics, 1(1):85-101, 2009
[10]  Shalom-Feuerstein R., Plowman S.J., Rotblat B., Ariotti N., Tian T., Hancock J.F. and Kloog Y., K-ras nanoclustering is subverted by overexpression of the scaffold protein galectin-3. Cancer Res., 68, 6608-6616, 2008.
[11]  Tian T., Harding A., Inder K., Plowman S., Parton R.G. and Hancock J.F., Plasma membrane nano-clusters generate high-fidelity Ras signal transduction, Nature Cell Biology, 9, 905-914, 2007.
[12]  Tian T., Xu S., Gao J. and Burrage K., Simulated maximum likelihood method for estimating kinetic rates in genetic regulation, Bioinformatics, 23, 84-91, 2007.
[13]  Tian T., Burrage K., Burrage P.M. and Carletti M., Stochastic Delay Differential Equations for Genetic Regulatory Networks, J. Comput. Appl. Maths., 205, 696-707, 2007.
[14]  Tian T and Burrage K., Stochastic models for regulatory networks of the genetic toggle switch, Proceedings of the National Academy of Sciences (USA), 103, 8372-8377, 2006.
[15]  Barrio M., Burrage K., Leier A. and Tian T., Oscillatory regulation of Hes1: discrete stochastic delay modelling and simulation, PLoS Comput Biol, 2, 1017-1039 (e117), 2006.
[16]  Tian T and Burrage K., An efficient stepsize selection procedure for discrete simulation of biochemical reaction system, ANZIAM J. 48, C1022-C1040, 2006
[17]  Harding A., Tian T., Westbury E., Frische E. and Hancock J.F., Subcellular localization determines MAP kinase signal output, Current Biology, 15: 869-873, 2005.
[18]  Tian T. and Burrage K., Binomial ├čleap methods for simulating stochastic chemical kinetics, J Chem Phys 121, 10356-10364, 2004.
[19]  Tian T. and Burrage K., Bistability and switching in the lysis/lysogeny genetic regulatory network of Bacteriophage lambda, J Theor Biol, 227, 229-237, 2004.
[20]  Burrage K., Tian T. and Burrage P.M., A multi-scaled approach for simulating chemical reaction systems, Prog Biophys Mol Biol, 85, 217-234, 2004.
[21]  Burrage K., Burrage P.M. and Tian T., Numerical Methods for Strong Solutions of Stochastic Differential Equations: an Overview, Proc. Royal Soc. London A 460, 373-402, 2004.
[22]  Tian T., Robustness of mathematical models for biological systems, ANZIAM J. 45(C), 565-577, 2004
[23]  Tian T., Burrage K. and Volker R., Stochastic modelling and simulations for solute transport in porous media, ANZIAM J. 45(C), 551-564, 2004.
[24]  Burrage K. and Tian T., Implicit stochastic Runge-Kutta methods for stochastic differential equations, BIT 44, 21-39, 2004.
[25]  Tian T and K. Burrage, Accuracy issues of Monte-Carlo methods for valuing American options, ANZIAM J. 44(E) ppC739--C758, 2003.
[26]  Tian T. and Burrage K., Two-stage stochastic Runge-Kutta methods for stochastic differential equations, BIT, 42 (2002), 625-643.
[27]  Burrage K. and Tian T., Predictor-corrector methods of Runge-Kutta type for stochastic differential equations, SIAM Numer. Anal., 40 (4), 1516-1537, 2002.
[28]  Tian T. and Burrage K., Implicit Taylor methods for stiff stochastic differential equations, Applied Numer. Maths., 38 (2001), 167-185.
[29]  Burrage K. and Tian T., Stiffly Accurate Runge-Kutta Methods for stiff Stochastic Differential Equations, Comput. Phys. Commun., 142, 186-190, 2001.
[30]  Burrage K. and Tian T., The composite Euler method for solving stiff stochastic differential equations, J. Comput. Appl. Maths., 131, 407-426, 2001.
[31]  Burrage K. and Tian T., A note on the stability properties of the Euler methods for solving stochastic differential equations, New Zealand J. Maths., 29, 115-127. (Special issue for the retirement of Professor John Butcher), 2000.
[32]  Burrage K. and Tian T., Parallel half-block methods for initial value problems, Applied Numer. Math., 32, 255-271, 2000.

Publications: Conferences/Workshops/Symposiums

[1]  Tian T., Estimation of kinetic rates of MAP kinase activation from experimental data, Proceedings of IJCBS 457-462, IEEE Press, 2009.
[2]  Burrage K., Mac S. and Tian T., Accelerated leap methods for simulating discrete stochastic chemical kinetics, Proceedings of POSTA06, Lecture Notes in Control and Information Sciences 341, 359-366, 2006.
[3]  Tian T. and Burrage K., A mathematical model for genetic regulation of the lactose operon, in Proceedings of the International Conference on Computational Science and its Applications, Lecture Notes in Computer Science, 3481, 1245-1253, 2005.
[4]  Tian T. and Burrage, K., Parallel implementation of stochastic simulations for large-scale cellular processes, Proceedings of the 8th International Conference on HPC-Asia, 621-626, IEEE Press, 2005.
[5]  Burrage K. and Tian T., Effective simulation techniques for biological systems, in Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems II (Proceedings of SPIE Volume: 5467), SPIE, 311-325, 2004.
[6]  Tian, T. and Burrage, K., Stochastic neural network models for gene regulatory networks, Proceedings of the 2003 Congress on Evolutio nary Computation, 162-169, IEEE Press, 2003.
[7]  Burrage, K., Burrage, P.M., Jeffrey, S., Pickett, T., Sidje, R. and Tian, T., A Grid Implementation of Chemical Kinetic Simulation Met hods in Genetic Regulation, Proceedings of APAC03 on Advanced Computing, Grid Applications and eResearch, 2003.
[8]  Tian T., Numerical simulations of solute transport in porous media on parallel computers, Proceedings of the 5th international conference on HPC-Asia, 2001 (published in a CD).
[9]  Burrage K., Burrage P.M. and Tian T., Numerical methods for solving stochastic differential equations on parallel computers, Proceedings of the 5th international conference on HPC-Asia, 2001 (published in a CD).
[10]  Tian T. and Burrage K., Numerical simulations of a financial market model, Proceedings of the 3th operations research conference of the Australian society for operations research Queensland branch, E. Kozan and R.Beard ed., 168-179, Brisbane, 2000.

Publications: Books/Book Chapters

[1]  Wang J., Tan A. and Tian T. (ed) Next generation of microarray bioinformatics, Humana Press and Springer, in press.
[2]  Wang J and Tian T., Effective methods for inferring genetic regulation from microarray gene expressio data, in Next generation of microarray bioinformatics, Wang J. (ed.), Humana Press and Springer, in press.
[3]  Tian T., Stochastic modelling of genetic regulatory networks, in Applied statistics for Biological Networks, M. Dehmer (ed), 13-37, Wiley-VCH.
[4]  Burrage K., Burrage P.M., Hamilton N. and Tian T., Computer-intensive simulations for cellular models, in Parallel Computing in Bioinformatics and Computational Biology, A.Y. Zomaya ed, Wiley, 79-119, 2006.
[5]  Burrage K. and Tian T., Poisson Runge-Kutta methods for chemical reaction systems, in Advances in Scientific Computing and Applicati ons, Y. Lu W. Sun and T. Tang eds, Science Press, Beijing/New York, 82-96, 2004.