### Abstract

Evolutionary and population dynamics are rarely linked together, although the two are intimately connected. Here we attempt to outline these connections on the example of the human immunodeficiency virus (HIV), which has been studied intensively in both aspects, and focus on those aspects for which our understanding has been promoted substantially by the use of mathematical techniques. Special emphasis will be given to the effects of drug treatment, both on HIV population dynamics and on the evolution to drug resistance. Although much of this chapter has an explicit mathematical basis, we have attempted to make the content accessible to the non-mathematical reader. Population dynamics has important effects on the nature and rate of evolution. While in infinite populations, the fixation and accumulation of mutations is governed by the rates of mutation and selection only, in finite popula-tions stochastic effects also play a role. The strength of these effects in turn depends on the size and structure of a populationâ which belong to the realm of population dynamics. Furthermore, the clock of evolution ticks in generations and its calibration to â real time â requires an estimate of the generation time, which again population dynamics can provide. We start by an introduction to the within-host population dynamics of HIV infection, describing virus and cell populations and their interactions. This section builds upon an established mathematical framework, which will be fleshed out by the interpretation of the model results. The mathematical framework is essen-tial for tackling a system of such vast complexity: it allows us to extract presumed key processes with an unambiguous definition of assumptions, and thereby to test hypotheses and to interpret experimental observations within a given hypothesis. We describe the steady state that characterises asymptomatic infection, and discuss the factors that set the steady-state virus load, which is an important predictor of disease progression. We present models of drug treatment on short and long time-scales, and show how population dynamics provided important insight.

Original language | English |
---|---|

Title of host publication | Origin and Evolution of Viruses |

Publisher | Elsevier Ltd |

Pages | 279-301 |

Number of pages | 23 |

ISBN (Print) | 9780123741530 |

DOIs | |

Publication status | Published - 2008 |

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### ASJC Scopus subject areas

- Immunology and Microbiology(all)

### Cite this

*Origin and Evolution of Viruses*(pp. 279-301). Elsevier Ltd. https://doi.org/10.1016/B978-0-12-374153-0.00014-X

**Intra-Host Dynamics and Evolution of HIV Infection.** / Müller, V.; Bonhoeffer, Sebastian.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Origin and Evolution of Viruses.*Elsevier Ltd, pp. 279-301. https://doi.org/10.1016/B978-0-12-374153-0.00014-X

}

TY - CHAP

T1 - Intra-Host Dynamics and Evolution of HIV Infection

AU - Müller, V.

AU - Bonhoeffer, Sebastian

PY - 2008

Y1 - 2008

N2 - Evolutionary and population dynamics are rarely linked together, although the two are intimately connected. Here we attempt to outline these connections on the example of the human immunodeficiency virus (HIV), which has been studied intensively in both aspects, and focus on those aspects for which our understanding has been promoted substantially by the use of mathematical techniques. Special emphasis will be given to the effects of drug treatment, both on HIV population dynamics and on the evolution to drug resistance. Although much of this chapter has an explicit mathematical basis, we have attempted to make the content accessible to the non-mathematical reader. Population dynamics has important effects on the nature and rate of evolution. While in infinite populations, the fixation and accumulation of mutations is governed by the rates of mutation and selection only, in finite popula-tions stochastic effects also play a role. The strength of these effects in turn depends on the size and structure of a populationâ which belong to the realm of population dynamics. Furthermore, the clock of evolution ticks in generations and its calibration to â real time â requires an estimate of the generation time, which again population dynamics can provide. We start by an introduction to the within-host population dynamics of HIV infection, describing virus and cell populations and their interactions. This section builds upon an established mathematical framework, which will be fleshed out by the interpretation of the model results. The mathematical framework is essen-tial for tackling a system of such vast complexity: it allows us to extract presumed key processes with an unambiguous definition of assumptions, and thereby to test hypotheses and to interpret experimental observations within a given hypothesis. We describe the steady state that characterises asymptomatic infection, and discuss the factors that set the steady-state virus load, which is an important predictor of disease progression. We present models of drug treatment on short and long time-scales, and show how population dynamics provided important insight.

AB - Evolutionary and population dynamics are rarely linked together, although the two are intimately connected. Here we attempt to outline these connections on the example of the human immunodeficiency virus (HIV), which has been studied intensively in both aspects, and focus on those aspects for which our understanding has been promoted substantially by the use of mathematical techniques. Special emphasis will be given to the effects of drug treatment, both on HIV population dynamics and on the evolution to drug resistance. Although much of this chapter has an explicit mathematical basis, we have attempted to make the content accessible to the non-mathematical reader. Population dynamics has important effects on the nature and rate of evolution. While in infinite populations, the fixation and accumulation of mutations is governed by the rates of mutation and selection only, in finite popula-tions stochastic effects also play a role. The strength of these effects in turn depends on the size and structure of a populationâ which belong to the realm of population dynamics. Furthermore, the clock of evolution ticks in generations and its calibration to â real time â requires an estimate of the generation time, which again population dynamics can provide. We start by an introduction to the within-host population dynamics of HIV infection, describing virus and cell populations and their interactions. This section builds upon an established mathematical framework, which will be fleshed out by the interpretation of the model results. The mathematical framework is essen-tial for tackling a system of such vast complexity: it allows us to extract presumed key processes with an unambiguous definition of assumptions, and thereby to test hypotheses and to interpret experimental observations within a given hypothesis. We describe the steady state that characterises asymptomatic infection, and discuss the factors that set the steady-state virus load, which is an important predictor of disease progression. We present models of drug treatment on short and long time-scales, and show how population dynamics provided important insight.

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UR - http://www.scopus.com/inward/citedby.url?scp=84884754100&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-374153-0.00014-X

DO - 10.1016/B978-0-12-374153-0.00014-X

M3 - Chapter

SN - 9780123741530

SP - 279

EP - 301

BT - Origin and Evolution of Viruses

PB - Elsevier Ltd

ER -