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Biological applications of the theory of birth-and-death processes

Open access
Novozhilov, Artem S., Karev, Georgy P., Koonin, Eugene V.·2006·Briefings in Bioinformatics

Abstract

In this review, we discuss applications of the theory of birth-and-death processes to problems in biology, primarily, those of evolutionary genomics. The mathematical principles of the theory of these processes are briefly described. Birth-and-death processes, with some straightforward additions such as innovation, are a simple, natural and formal framework for modeling a vast variety of biological processes such as population dynamics, speciation, genome evolution, including growth of paralogous gene families and horizontal gene transfer and somatic evolution of cancers. We further describe how empirical data, e.g. distributions of paralogous gene family size, can be used to choose the model that best reflects the actual course of evolution among different versions of birth-death-and-innovation models. We conclude that birth-and-death processes, thanks to their mathematical transparency, flexibility and relevance to fundamental biological processes, are going to be an indispensable mathematical tool for the burgeoning field of systems biology.

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Metadata

Authors
Novozhilov, A. S., Karev, G. P., & Koonin, E. V.
Venue
Briefings in Bioinformatics
Published
2006-03-01
Volume
7
Issue
1
Pages
70-85
ISSN
1467-5463 (ISSN-L)
OpenAlex
W2123786618
Cited by
174
References
5
FWCI
1.06
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Biological applications of the theory of birth-and-death processes. https://app.cochat.ai/papers/10.1093/bib/bbk006
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Novozhilov, A. S., Karev, G. P., & Koonin, E. V. (2006). Biological applications of the theory of birth-and-death processes. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbk006

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.