[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 ßleap 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. |