T
Tianhai Tian
Researcher at Monash University
Publications - 142
Citations - 3748
Tianhai Tian is an academic researcher from Monash University. The author has contributed to research in topics: Stochastic modelling & Computer science. The author has an hindex of 27, co-authored 126 publications receiving 3369 citations. Previous affiliations of Tianhai Tian include University of Glasgow & Huazhong University of Science and Technology.
Papers
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Journal ArticleDOI
Plasma membrane nanoswitches generate high-fidelity Ras signal transduction
Tianhai Tian,Angus Harding,Angus Harding,Kerry L. Inder,Sarah J. Plowman,Robert G. Parton,John F. Hancock +6 more
TL;DR: It is shown, using in silico and in vivo analyses of mitogen-activated protein (MAP) kinase signalling, that Ras nanoclusters operate as sensitive switches, converting graded ligand inputs into fixed outputs of activated extracellular signal-regulated kinase (ERK).
Journal ArticleDOI
Binomial leap methods for simulating stochastic chemical kinetics.
Tianhai Tian,Kevin Burrage +1 more
TL;DR: Numerical results indicate that the proposed binomial leap methods can be applied to a wide range of chemical reaction systems with very good accuracy and significant improvement on efficiency over existing approaches.
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Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation
TL;DR: This paper introduces delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation, and shows that this process may well explain more faithfully than continuous deterministic models the observed sustained oscillations in expression levels of hes1 mRNA and Hes1 protein.
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Stochastic models for regulatory networks of the genetic toggle switch
Tianhai Tian,Kevin Burrage +1 more
TL;DR: A previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations is developed.
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Numerical methods for strong solutions of stochastic differential equations: an overview
TL;DR: A review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations can be found in this article, where the authors give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational biology applications.