A mathematical or computational model in evolutionary biologyshould necessary combine several comparatively fast processes,which actually drive natural selection and evolution, with a veryslow process of evolution. As a result, several very differenttime scales are simultaneously present in the model; this makesits analytical study an extremely difficult task. However, thesignificant difference of the time scales implies the existence ofa possibility of the model order reduction through a process oftime separation. In this paper we conduct the procedure of modelorder reduction for a reasonably simple model of RNA virusevolution reducing the original system of three integro-partialderivative equations to a single equation. Computations confirmthat there is a good fit between the results for the original andreduced models.
Keywords:
- viral dynamics,
- phenotype space,
- basicreproduction number.,
- Nowak--May model,
- singularly perturbed system,
- Darwinianfitness,
- variant space,
- HIV,
- slow-fast dynamics,
- viral evolution,
- integro--differential equations
Citation: Andrei Korobeinikov, Aleksei Archibasov, Vladimir Sobolev. Order reduction for an RNA virus evolution model[J]. Mathematical Biosciences and Engineering, 2015, 12(5): 1007-1016. doi: 10.3934/mbe.2015.12.1007
Abstract
A mathematical or computational model in evolutionary biologyshould necessary combine several comparatively fast processes,which actually drive natural selection and evolution, with a veryslow process of evolution. As a result, several very differenttime scales are simultaneously present in the model; this makesits analytical study an extremely difficult task. However, thesignificant difference of the time scales implies the existence ofa possibility of the model order reduction through a process oftime separation. In this paper we conduct the procedure of modelorder reduction for a reasonably simple model of RNA virusevolution reducing the original system of three integro-partialderivative equations to a single equation. Computations confirmthat there is a good fit between the results for the original andreduced models.
References
[1]
|
Applicable Analysis, 91 (2012), 1265-1277.
|
[2]
|
Applicable Analysis, 52 (1994), 143-154.
|
[3]
|
Phil. Trans. R. Soc. B, 352 (1997), 11-20.
|
[4]
|
IEEE Trans. Aut. Control, 32 (1987), 260-263.
|
[5]
|
Bull. Math. Biol., 66 (2004), 879-883.
|
[6]
|
Math. Med. Biol., 26 (2009), 225-239.
|
[7]
|
Math. Med. Biol., 26 (2009), 309-321.
|
[8]
|
Math. Biosci. Eng., 11 (2014), 919-927.
|
[9]
|
Applicable Analysis, 89 (2010), 1271-1292.
|
[10]
|
SIAM, Philadelphia, 2005.
|
[11]
|
Computational Mathematics and Mathematical Physics, 47 (2007), 629-637.
|
[12]
|
Oxford University Press, New York, 2000.
|
[13]
|
J. Theor. Biol., 168 (1994), 291-308.
|
[14]
|
J. Mol. Evol, 51 (2000), 245-255.
|
[15]
|
J. Theor. Biol., 203 (2000), 285-301.
|
[16]
|
Phys. Rev. Lett., 76 (1996), 4440-4443.
|
[17]
|
Math. Med. Biol., 30 (2013), 65-72.
|
[18]
|
SIAM, Philadelphia, 1995.
|
[19]
|
TRENDS in Immunology, 23 (2002), 194-200.
|