162,853 research outputs found
Burrage, J A, 400643
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/374980Surname: BURRAGE
Given Name(s) or Initials: J A
Military Service Number or Last Known Location: 400643
Missing, Wounded and Prisoner of War Enquiry Card Index Number: 22773186361
Item: [2016.0049.07288] "Burrage, J A, 400643
Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method
No full textBackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need for numerical methods that are both fast and accurate. The Bulirsch-Stoer method is an established method for solving ordinary differential equations that possesses both of these qualities.ResultsIn this paper, we present the Stochastic Bulirsch-Stoer method, a new numerical method for simulating discrete chemical reaction systems, inspired by its deterministic counterpart. It is able to achieve an excellent efficiency due to the fact that it is based on an approach with high deterministic order, allowing for larger stepsizes and leading to fast simulations. We compare it to the Euler ?-leap, as well as two more recent ?-leap methods, on a number of example problems, and find that as well as being very accurate, our method is the most robust, in terms of efficiency, of all the methods considered in this paper. The problems it is most suited for are those with increased populations that would be too slow to simulate using Gillespie’s stochastic simulation algorithm. For such problems, it is likely to achieve higher weak order in the moments.ConclusionsThe Stochastic Bulirsch-Stoer method is a novel stochastic solver that can be used for fast and accurate simulations. Crucially, compared to other similar methods, it better retains its high accuracy when the timesteps are increased. Thus the Stochastic Bulirsch-Stoer method is both computationally efficient and robust. These are key properties for any stochastic numerical method, as they must typically run many thousands of simulations
[Monnaies antiques grecques et romaines. XVII. Coll. R. Burrage, Dr. J. S., Sir Arthur J. Evans]
No full text[Vente. Numismatique. 1934-10-03. Genève][Collection. Numismatique. Burrage, R.. 1934][Collection. Numismatique. Evans, Arthur J.. 1934]Avec mode text
Portrait of H.W. Burrage [picture] /
No full textTitle from inscription on reverse.; Condition: Good, but mark on mount.; Inscriptions: "Sincerely yrs, H.W. Burrage" --Signed on reverse. "The Sears Studio, Melbourne" --Printed on mount.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.pic-an24229797
Lettre de G. J. Mountain à G.H. Ryland sur un nommé Burrage qui cherche un emploi
No full text4 pages, originalLettre de G . J. Montreal [G . J. Mountain, évêquede Montréal,] à G.H. Ryland sur : un jeune homme, nommé Burrage, qui cherche un emploi dans le bureau de Ryland et la possibilité que Jacob [le fils de Mountain] puisse obtenir un poste dans le même bureau
Parallel implementation of stochastic simulation for large-scale cellular processes
No full textExperimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
Stochastic chemical kinetics and the total quasi-steady-state assumption: Application to the stochastic simulation algorithm and chemical master equation
No full textRecently the application of the quasi-steady-state approximation QSSA to the stochastic simulation algorithm SSA was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions Rao and Arkin, J.Chem. Phys. 118, 4999 2003 and further work has led to the nested and slow-scale SSA. Improved numerical efficiency is obtained by respecting the vastly different time scales characterizing the system and then by advancing only the slow reactions exactly, based on a suitable approximation to the fast reactions. We considerably extend these works by applying the QSSA to numerical methods for the direct solution of the chemical master equation CME and, in particular, to the finite state projection algorithm Munsky and Khammash, J. Chem. Phys. 124, 044104 2006, in conjunction with Krylov methods. In addition, we point out some important connections to the literature on the deterministic total QSSA tQSSA and place the stochastic analogue of the QSSA within the more general framework of aggregation of Markov processes. We demonstrate the new methods on four examples: Michaelis–Menten enzyme kinetics, double phosphorylation, the Goldbeter–Koshland switch, and the mitogen activated protein kinase cascade. Overall, we report dramatic improvements by applying the tQSSA to the CME solver
A spectral method for time-fractional diffusion systems
No full textThe time fractional derivative of a function y(t) depends on the past history of the function
y(t), and so time fractional differential systems are naturally suitable to describe evolutionary processes with memory. Fractional models are increasingly used in many modelling situations including, for example, viscoelastic materials in mechanics, anomalous diffusion in transport dynamics of complex systems and some biological processes in rheology. Here we consider a time-fractional reaction diusion problem [2]. This is a non-local model and as the solution depends on all its past history, numerical step-by-step methods are computationally expensive. We propose a mixed method, which consists of a finite difference scheme through space and a spectral collocation method through time. The spectral method considerably reduces the computational cost with respect to step-by-step methods and is exponentially convergent [3]. Some classes of spectral bases are considered, which exhibit different convergence rates and some numerical results based on time diffusion reaction diffusion equations are given [1].
References
[1] Burrage, K., Cardone, A., D'Ambrosio, R. and Paternoster, B. 2017 Numerical solution of time fractional diffusion systems. Appl. Numer. Math. 116 8294.
[2] Gafiychuk, V., Datsko, B. and Meleshko, V. 2008 Mathematical modeling of time fractional reaction-diffusion systems. J. Comput. Appl. Math. 220(1-2) 215225.
[3] Zayernouri, M. and Karniadakis, G. Em 2014 Fractional spectral collocation method. SIAM J. Sci. Comput. 36(1) A40A62
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