A comparison of computational efficiencies of stochastic algorithms in terms of two infection models

H. Thomas Banks; Shuhua Hu; Michele Joyner; Anna Broido; Brandi Canter; Kaitlyn Gayvert; Kathryn Link

doi:10.3934/mbe.2012.9.487

Article Contents

2012, Volume 9, Issue 3: 487-526. Doi: 10.3934/mbe.2012.9.487

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A comparison of computational efficiencies of stochastic algorithms in terms of two infection models

1.
Center for Research in Scientific Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212
2.
Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614-70663
3.
Department of Mathematics, Boston College, Chestnut Hill, MA 02467-3806
4.
Department of Mathematics, State University of New York at Geneseo, Geneseo, NY 14454
5.
Department of Mathematics, Bryn Mawr College, Bryn Mawr, PA 19010-2899

Received: October 31, 2011

Revised: April 30, 2012

Published: June 2012

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Abstract

In this paper, we investigate three particular algorithms: a stochastic simulation algorithm (SSA), and explicit and implicit tau-leaping algorithms. To compare these methods, we used them to analyze two infection models: a Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunodeficiency Virus (HIV) within host infection model. While the first has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational efficiency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.

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Mathematics Subject Classification: 60J27, 60J22, 92D25.

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References

[1]	B. M. Adams, H. T. Banks, M. Davidian, H. Kwon, H. T. Tran, S. N. Wynne and E. S. Rosenberg, HIV dynamics: Modeling, data analysis, and optimal treatment protocols, J. Computational and Applied Mathematics, 184 (2005), 10-49.doi: 10.1016/j.cam.2005.02.004.
[2]	B. M. Adams, H. T. Banks, M. Davidian and E. S. Rosenberg, Model fitting and prediction with HIV treatment interruption data, CRSC-TR05-40, NCSU, October, 2005, Bulletin of Mathematical Biology, 69 (2007), 563-584.doi: 10.1007/s11538-006-9140-6.
[3]	L. J. S. Allen, "An Introduction to Stochastic Processes with Applications to Biology," Second edition, CRC Press, Boca Raton, FL, 2011.
[4]	P. Bai, H. T. Banks, S. Dediu, A. Y. Govan, M. Last, A. L. Lloyd, H. K. Nguyen, M. S. Olufsen, G. Rempala and B. D. Slenning, Stochastic and deterministic models for agricultural production networks, Mathematical Biosciences and Engineering, 4 (2007), 373-402.
[5]	H. T. Banks, M. Davidian, S. Hu, G. Kepler and E. S. Rosenberg, Modelling HIV immune response and validation with clinical data, Journal of Biological Dynamics, 2 (2008), 357-385.
[6]	D. S. Callaway and A. S. Perelson, HIV-1 infection and low steady state viral loads, Bulletin of Mathematical Biology, 64 (2001), 29-64.
[7]	Y. Cao, D. T. Gillespie and L. R. Petzold, Avoiding negative populations in explicit Poisson tau-leaping, The Journal of Chemical Physics, 123 (2005), 054104.
[8]	Y. Cao, D. T. Gillespie and L. R. Petzold, Efficient step size selection for the tau-leaping simulation method, The Journal of Chemical Physics, 124 (2006), 044109.
[9]	Y. Cao, D. T. Gillespie and L. R. Petzold, Adaptive explicit-implicit tau-leaping method with automatic tau selection, The Journal of Chemical Physics, 126 (2007), 224101.
[10]	N. M. Dixit and A. S. Perelson, Complex patterns of viral load decay under antiretroviral therapy: Influence of pharmacokinetics and intracellular delay, J. of Theoretical Biology, 226 (2004), 95-109.
[11]	D. T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, The Journal of Computational Physics, 22 (1976), 403-434.doi: 10.1016/0021-9991(76)90041-3.
[12]	D. T. Gillespie, Approximate accelerated stochastic simulation of chemically reacting systems, The Journal of Chemical Physics, 115 (2001), 1716-1733.doi: 10.1063/1.1378322.
[13]	D. T. Gillespie and L. R. Petzold, Improved leap-size selection for accelerated stochastic simulation, The Journal of Chemical Physics, 119 (2003), 8229-8234.doi: 10.1063/1.1613254.
[14]	D. T. Gillespie and L. R. Petzold, Stochastic simulation of chemical kinetics, Annual Review of Physical Chemistry, 58 (2007), 25-55.
[15]	G. M. Kepler, H. T. Banks, M. Davidian and E. S. Rosenberg, A model for HCMV infection in immunosuppressed patients, Mathematical and Computer Modelling, 49 (2009), 1653-1663.doi: 10.1016/j.mcm.2008.06.003.
[16]	T. G. Kurtz, Solutions of ordinary differential equations as limits of pure jump Markov processes, J. Appl. Prob., 7 (1970), 49-58.
[17]	T. G. Kurtz, Limit theorems for sequences of pure jump Markov processes approximating ordinary differential processes, J. Appl. Prob., 8 (1971), 344-356.
[18]	H. H. McAdams and A. Arkin, Stochastic mechanisms in gene expression, PNAS, 94 (1997), 814-819.doi: 10.1073/pnas.94.3.814.
[19]	A. R. Ortiz, H. T. Banks, C. Castillo-Chavez, G. Chowell and X. Wang, A deterministic methodology for estimation of parameters in dynamic Markov chain models, Journal of Biological Systems, 19 (2011), 71-100.doi: 10.1142/S0218339011003798.
[20]	J. Pahle, Biochemical simulations: Stochastic, approximate stochastic and hybrid approaches, Brief Bioinform, 10 (2009), 53-64.doi: 10.1093/bib/bbn050.
[21]	A. S. Perelson, P. Essunger, Y. Z. Cao, M. Vesanen, A. Hurley, K. Saksela, M. Markowitz and D. D. Ho, Decaycharacteristics of HIV-1-infected compartments during combination therapy, Nature, 387 (1997), 187-191.
[22]	J. E. Pearson, P. Krapivsky and A. S. Perelson, Stochastic theory of early viral infection: Continuous versus burst production of virions, PLoS Comput. Biol., 7 (2011), e1001058, 17 pp.
[23]	A. S. Perelson and P. W. Nelson, Mathematical analysis of HIV-1 dynamics in vivo, SIAM Review, 41 (1999), 3-44.
[24]	M. Rathinam, L. R. Petzold, Y. Cao and D. T. Gillespie, Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method, The Journal of Chemical Physics, 119 (2003), 12784-12794.doi: 10.1063/1.1627296.
[25]	E. Renshaw, "Modelling Biological Populations in Space and Time," Cambridge Studies in Mathematical Biology, 11, Cambridge Univ. Press, Cambridge, 1991.
[26]	D. J. Wilkinson, Stochastic modelling for quantitative description of heterogeneous biological systems, Nature Reviews Genetics, 10 (2009), 122-133.doi: 10.1038/nrg2509.