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Journal articles on the topic 'Population genetics Mathematics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Morrison, Margaret. "Population Genetics and Population Thinking: Mathematics and the Role of the Individual." Philosophy of Science 71, no. 5 (December 2004): 1189–200. http://dx.doi.org/10.1086/425241.

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2

PROVINE, W. "Population genetics." Bulletin of Mathematical Biology 52, no. 1-2 (1990): 201–7. http://dx.doi.org/10.1016/s0092-8240(05)80009-6.

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3

Kozyrev, S. V. "Learning Theory and Population Genetics." Lobachevskii Journal of Mathematics 43, no. 7 (July 2022): 1655–62. http://dx.doi.org/10.1134/s1995080222100195.

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4

Lambert, Amaury. "Population genetics, ecology and the size of populations." Journal of Mathematical Biology 60, no. 3 (August 6, 2009): 469–72. http://dx.doi.org/10.1007/s00285-009-0286-3.

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5

Provine, William B. "Discussion: Population genetics." Bulletin of Mathematical Biology 52, no. 1-2 (January 1990): 199–207. http://dx.doi.org/10.1007/bf02459573.

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6

Curnow, R. N., A. H. D. Brown, M. T. Clegg, A. L. Kahler, and B. S. Weir. "Plant Population Genetics, Breeding, and Genetic Resources." Biometrics 46, no. 4 (December 1990): 1241. http://dx.doi.org/10.2307/2532478.

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7

Haigh, John. "INTRODUCTION TO THEORETICAL POPULATION GENETICS (Biomathematics 21)." Bulletin of the London Mathematical Society 26, no. 3 (May 1994): 318–20. http://dx.doi.org/10.1112/blms/26.3.318.

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8

L�nger, Helmut. "On an inequality arising from population genetics." Archiv der Mathematik 48, no. 2 (February 1987): 175–77. http://dx.doi.org/10.1007/bf01189288.

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9

KIMMEL, MAREK. "WHY MATHEMATICS IS NEEDED TO UNDERSTAND COMPLEX GENETICS DISEASES." Journal of Biological Systems 10, no. 04 (December 2002): 359–80. http://dx.doi.org/10.1142/s0218339002000688.

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We discuss mathematical approaches to population genetics and evolutionary theory in the context of complex genetic disease. Mechanisms, which we discuss, include gene-environment interaction in lung cancer as well as classical mechanisms of stabilization of genetic disease such as overdominance, antagonistic pleiotropy and recurring mutations. Specific modeling approaches discussed include: (1) Mathematical model of the evolution of disease chromosome applied to mapping of a disease gene. (2) Iterated Galton–Watson branching process applied to modeling of trinucleotide expansion in triplet-repeat diseases. (3) Application of Ewens' sampling formula to analysis of Single Nucleotide Polymorphism haplotypes at disease-related genes. The aim of this paper is not to present an exhaustive review, but rather to advocate mathematical modeling approaches in a field of current interest.

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10

Rabani, Yuval, Yuri Rabinovich, and Alistair Sinclair. "A computational view of population genetics." Random Structures and Algorithms 12, no. 4 (July 1998): 313–34. http://dx.doi.org/10.1002/(sici)1098-2418(199807)12:4<313::aid-rsa1>3.0.co;2-w.

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11

Ethier, S. N., and Thomas G. Kurtz. "Fleming–Viot Processes in Population Genetics." SIAM Journal on Control and Optimization 31, no. 2 (March 1993): 345–86. http://dx.doi.org/10.1137/0331019.

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12

Chalub, Fabio A. C. C., and Max O. Souza. "A non-standard evolution problem arising in population genetics." Communications in Mathematical Sciences 7, no. 2 (2009): 489–502. http://dx.doi.org/10.4310/cms.2009.v7.n2.a11.

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13

Jiang, Yingying, and Wendi Wang. "Bifurcation Analysis in Population Genetics Model with Partial Selfing." Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/164504.

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A new model which allows both the effect of partial selfing selection and an exponential function of the expected payoff is considered. This combines ideas from genetics and evolutionary game theory. The aim of this work is to study the effects of partial selfing selection on the discrete dynamics of population evolution. It is shown that the system undergoes period doubling bifurcation, saddle-node bifurcation, and Neimark-Sacker bifurcation by using center manifold theorem and bifurcation theory. Numerical simulations are presented not only to illustrate our results with the theoretical analysis, but also to exhibit the complex dynamical behaviors, such as the period-3, 6 orbits, cascade of period-doubling bifurcation in period-2, 4, 8, and the chaotic sets. These results reveal richer dynamics of the discrete model compared with the model in Tao et al., 1999. The analysis and results in this paper are interesting in mathematics and biology.

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14

Gallo, Ignacio. "Population Genetics of Gene Function." Bulletin of Mathematical Biology 75, no. 7 (April 24, 2013): 1082–103. http://dx.doi.org/10.1007/s11538-013-9841-6.

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15

Wang, Yantao, and Linlin Su. "A semilinear interface problem arising from population genetics." Journal of Differential Equations 310 (February 2022): 264–301. http://dx.doi.org/10.1016/j.jde.2021.11.017.

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16

Cormack, R. M., D. L. Hartl, and A. G. Clark. "Principles of Population Genetics." Biometrics 46, no. 2 (June 1990): 546. http://dx.doi.org/10.2307/2531471.

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17

Burke, Donald S., Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. "Putting More Genetics into Genetic Algorithms." Evolutionary Computation 6, no. 4 (December 1998): 387–410. http://dx.doi.org/10.1162/evco.1998.6.4.387.

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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.

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18

B�rger, Reinhard. "Perturbations of positive semigroups and applications to population genetics." Mathematische Zeitschrift 197, no. 2 (June 1988): 259–72. http://dx.doi.org/10.1007/bf01215194.

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19

Lin, Guojian, and Rong Yuan. "Travelling waves for the population genetics model with delay." ANZIAM Journal 48, no. 1 (July 2006): 57–71. http://dx.doi.org/10.1017/s1446181100003412.

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AbstractUnder the assumptions that the spatial variable is one dimensional and the distributed delay kernel is the general Gamma distributed delay kernel, when the average delay is small, the existence of travelling wave solutions for the population genetics model with distributed delay is obtained by using the linear chain trick and geometric singular perturbation theory. On the other hand, for the population genetics model with small discrete delay, the existence of travelling wave solutions is obtained by employing a technique which is based on a result concerning the existence of the inertial manifold for small discrete delay equations.

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20

ELETTREBY, M. F. "SATO–CRUTCHFIELD EQUATION FOR POPULATION GENETICS." International Journal of Modern Physics C 16, no. 05 (May 2005): 717–26. http://dx.doi.org/10.1142/s0129183105007431.

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In this paper, we applied the Sato–Crutchfield equation to population genetics. We studied it analytically and numerically. It is applied to some cases in population of one locus with two alleles. It is found that Sato–Crutchfield equation does not affect the stability of the evolutionary equations but the reinforcement of the choice of heterozygote state.

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21

Antao, Tiago, and Ian M. Hastings. "ogaraK: a population genetics simulator for malaria." Bioinformatics 27, no. 9 (March 16, 2011): 1335–36. http://dx.doi.org/10.1093/bioinformatics/btr139.

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22

Gil, Marie-Eve, Francois Hamel, Guillaume Martin, and Lionel Roques. "Mathematical Properties of a Class of Integro-differential Models from Population Genetics." SIAM Journal on Applied Mathematics 77, no. 4 (January 2017): 1536–61. http://dx.doi.org/10.1137/16m1108224.

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23

B�rger, Reinhard. "Mutation-selection models in population genetics and evolutionary game theory." Acta Applicandae Mathematicae 14, no. 1-2 (1989): 75–89. http://dx.doi.org/10.1007/bf00046675.

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24

Gimbernat-Mayol, Julia, Albert Dominguez Mantes, Carlos D. Bustamante, Daniel Mas Montserrat, and Alexander G. Ioannidis. "Archetypal Analysis for population genetics." PLOS Computational Biology 18, no. 8 (August 25, 2022): e1010301. http://dx.doi.org/10.1371/journal.pcbi.1010301.

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The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse, large-scale cohorts. However, existing methods are computationally-intensive, prohibitively so in the case of nationwide biobanks. Here we explore Archetypal Analysis as an efficient, unsupervised approach for identifying genetic clusters and for associating individuals with them. Such unsupervised approaches help avoid conflating socially constructed ethnic labels with genetic clusters by eliminating the need for exogenous training labels. We show that Archetypal Analysis yields similar cluster structure to existing unsupervised methods such as ADMIXTURE and provides interpretative advantages. More importantly, we show that since Archetypal Analysis can be used with lower-dimensional representations of genetic data, significant reductions in computational time and memory requirements are possible. When Archetypal Analysis is run in such a fashion, it takes several orders of magnitude less compute time than the current standard, ADMIXTURE. Finally, we demonstrate uses ranging across datasets from humans to canids.

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25

Jamieson, A., N. Ryman, and F. Utter. "Population Genetics and Fishery Management." Biometrics 45, no. 4 (December 1989): 1343. http://dx.doi.org/10.2307/2531800.

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26

Labra, A., M. Ladra, and U. A. Rozikov. "An evolution algebra in population genetics." Linear Algebra and its Applications 457 (September 2014): 348–62. http://dx.doi.org/10.1016/j.laa.2014.05.036.

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27

Османова, Г. О. "ЛЕВ АНАТОЛЬЕВИЧ ЖИВОТОВСКИЙ (к 80-летию со дня рождения)." Biosfera 14, no. 3 (November 12, 2022): 21. http://dx.doi.org/10.24855/biosfera.v14i3.684.

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November 22, 2022 is the 80th birthday of Lev Anatolyevich Zhivotovsky, an outstanding scientist, expert in population and mathematical biology, Professor, PhD in biology and mathematics, head of a laboratory at Research Institute of General Genetics of the Russia Academy of Sciences, principal researcher at All-Russia Research Institute of Fishery and Oceanography, Honored Scientist of the Russian Federation, laureate of Science and Technology Award of the Russian Federation, of I.I. Schmalhausen Evolutionary Biology Award of the Russian Academy of Sciences, and of The Lancet Award for the Best Article of the Year (2003), and just a wonderful personality. Most of his research focuses on studying natural populations of plants and animals. Jointly with his colleagues, he studied the population structure of oaks in the Caucasus, the genetics of conifers and of agricultural plants (cotton, grapes etc.) and addressed flora restoration in oil production areas in Tyumen Region. Based on these works, patents were obtained for methods of selection and breeding of plant populations. He introduced a new population indicator «efficiency index» and a new classification of normal plant populations («delta-omega»). Based on the concerted application of the efficiency index and the age index suggested by A.A. Uranov, Lev A. developed an ecogeographic approach by proposing the concept of ecogeographic units thereby making a significant contribution to the development of a general theory of species population structure. Lev A. Zhivitovsky has been being a scientific supervisor and participant of many expeditions to the Far East, Siberia and other regions of the Russian Federation. He has authored/coauthored more than 300 research articles and eight monographs, including his fundamental textbook «The Genetics of Natural Populations» (2021).

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28

Peng, B., and M. Kimmel. "simuPOP: a forward-time population genetics simulation environment." Bioinformatics 21, no. 18 (July 14, 2005): 3686–87. http://dx.doi.org/10.1093/bioinformatics/bti584.

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29

Vlad, Marcel Ovidiu. "Separable models for age-structured population genetics." Journal of Mathematical Biology 26, no. 1 (February 1988): 73–92. http://dx.doi.org/10.1007/bf00280174.

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30

Gonshor, Harry. "An Application of Random Walk to a Problem in Population Genetics." American Mathematical Monthly 94, no. 7 (August 1987): 668. http://dx.doi.org/10.2307/2322223.

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31

CHOI, WON. "ON THE LIMITING DIFFUSION OF SPECIAL DIPLOID MODEL IN POPULATION GENETICS." Bulletin of the Korean Mathematical Society 42, no. 2 (May 1, 2005): 397–404. http://dx.doi.org/10.4134/bkms.2005.42.2.397.

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32

LI, F., K. NAKASHIMA, and W. M. NI. "Non-local effects in an integro-PDE model from population genetics." European Journal of Applied Mathematics 28, no. 1 (November 20, 2015): 1–41. http://dx.doi.org/10.1017/s0956792515000601.

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In this paper, we study the following non-local problem:\begin{equation*} \begin{cases} \displaystyle u_t=d{1\over\rho}\nabla\cdot(\rho V\nabla u)+b(\bar{u}-u)+ g(x) u^2(1-u) &\displaystyle \quad \textrm{in} \; \Omega\times (0,\infty),\\[3pt] \displaystyle 0\leq u\leq 1 & \quad\displaystyle \textrm{in}\ \Omega\times (0,\infty),\\[3pt] \displaystyle \nu \cdot V\nabla u=0 &\displaystyle \quad \textrm{on} \; \partial\Omega\times (0,\infty).\vspace*{-2pt} \end{cases} \end{equation*}This model, proposed by T. Nagylaki, describes the evolution of two alleles under the joint action of selection, migration, andpartial panmixia – a non-local term, for thecomplete dominancecase, whereg(x) is assumed to change sign at least once to reflect the diversity of the environment. First, properties for general non-local problems are studied. Then, existence of non-trivial steady states, in terms of the diffusion coefficientdand the partial panmixia rateb, is obtained under different signs of the integral ∫Ωg(x)dx. Furthermore, stability and instability properties for non-trivial steady states, as well as the trivial steady statesu≡ 0 andu≡ 1 are investigated. Our results illustrate how the non-local term – namely, the partial panmixia – helps the migration in this model.

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33

Grant, W. Stewart, Einar Árnason, and Bjarki Eldon. "New DNA coalescent models and old population genetics software†." ICES Journal of Marine Science 73, no. 9 (May 18, 2016): 2178–80. http://dx.doi.org/10.1093/icesjms/fsw076.

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Abstract The analyses of often large amounts of field and laboratory data depend on computer programs to generate descriptive statistics and to test hypotheses. The algorithms in these programs are often complex and can be understood only with advanced training in mathematics and programming, topics that are beyond the capabilities of most fisheries biologists and empirical population geneticists. The backward looking Kingman coalescent model, based on the classic forward-looking Wright–Fisher model of genetic change, is used in many genetics software programs to generate null distributions against which to test hypotheses. An article in this issue by Niwa et al. shows that the assumption of bifurcations at nodes in the Kingman coalescent model is inappropriate for highly fecund Japanese sardines, which have type III life histories. Species with this life history pattern are better modelled with multiple mergers at the nodes of a coalescent gene genealogy. However, only a few software programs allow analysis with multiple-merger coalescent models. This parameter misspecification produces demographic reconstructions that reach too far into the past and greatly overestimates genetically effective population sizes (the number of individuals actually contributing to the next generation). The results of Niwa et al. underline the need to understand the assumptions and model parameters in the software programs used to analyse DNA sequences.

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34

Smith, Robert E., Stephanie Forrest, and Alan S. Perelson. "Searching for Diverse, Cooperative Populations with Genetic Algorithms." Evolutionary Computation 1, no. 2 (June 1993): 127–49. http://dx.doi.org/10.1162/evco.1993.1.2.127.

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In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an ‘optimized’ solution. The majority of GA analysis has focused on these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified genetics-based machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit technique for multimodal GA optimization. Further analysis shows how the GA population can adapt to express various degrees of generalization. The results show how GAs can automatically and simultaneously discover effective groups of cooperative computational structures.

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35

Anderson, C. N. K., U. Ramakrishnan, Y. L. Chan, and E. A. Hadly. "Serial SimCoal: A population genetics model for data from multiple populations and points in time." Bioinformatics 21, no. 8 (November 25, 2004): 1733–34. http://dx.doi.org/10.1093/bioinformatics/bti154.

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36

Wu, Yufeng. "Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach." Bioinformatics 36, Supplement_1 (July 1, 2020): i326—i334. http://dx.doi.org/10.1093/bioinformatics/btaa465.

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Abstract Motivation Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data. Results In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests. Availability and implementation The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix. Supplementary information Supplementary data are available at Bioinformatics online.

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37

BURIE, J. B., R. DJIDJOU-DEMASSE, and A. DUCROT. "Asymptotic and transient behaviour for a nonlocal problem arising in population genetics." European Journal of Applied Mathematics 31, no. 1 (September 18, 2018): 84–110. http://dx.doi.org/10.1017/s0956792518000487.

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This work is devoted to the study of an integro-differential system of equations modelling the genetic adaptation of a pathogen by taking into account both mutation and selection processes. First, we study the asymptotic behaviour of the system and prove that it eventually converges to a stationary state. Next, we more closely investigate the behaviour of the system in the presence of multiple EAs. Under suitable assumptions and based on a small mutation variance asymptotic, we describe the existence of a long transient regime during which the pathogen population remains far from its asymptotic behaviour and highly concentrated around some phenotypic value that is different from the one described by its asymptotic behaviour. In that setting, the time needed for the system to reach its large time configuration is very long and multiple evolutionary attractors may act as a barrier of evolution that can be very long to bypass.

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38

SHIGA, Tokuzo. "A certain class of infinite dimensional diffusion processes arising in population genetics." Journal of the Mathematical Society of Japan 39, no. 1 (January 1987): 17–25. http://dx.doi.org/10.2969/jmsj/03910017.

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39

Iwasa, Yoh, Franziska Michor, Natalia L. Komarova, and Martin A. Nowak. "Population genetics of tumor suppressor genes." Journal of Theoretical Biology 233, no. 1 (March 2005): 15–23. http://dx.doi.org/10.1016/j.jtbi.2004.09.001.

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40

Welch, John J., and David Waxman. "The nk model and population genetics." Journal of Theoretical Biology 234, no. 3 (June 2005): 329–40. http://dx.doi.org/10.1016/j.jtbi.2004.11.027.

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41

FRIEDEN, B. R., A. PLASTINO, and B. H. SOFFER. "Population Genetics from an Information Perspective." Journal of Theoretical Biology 208, no. 1 (January 2001): 49–64. http://dx.doi.org/10.1006/jtbi.2000.2199.

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42

Lui, Roger. "A Nonlinear Integral Operator Arising from a Model in Population Genetics IV. Clines." SIAM Journal on Mathematical Analysis 17, no. 1 (January 1986): 152–68. http://dx.doi.org/10.1137/0517015.

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43

Brown, K. J., and A. Tertikas. "On the Bifurcation of Radially Symmetric Steady-State Solutions Arising in Population Genetics." SIAM Journal on Mathematical Analysis 22, no. 2 (March 1991): 400–413. http://dx.doi.org/10.1137/0522026.

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44

Song, Y. S., R. Lyngso, and J. Hein. "Counting All Possible Ancestral Configurations of Sample Sequences in Population Genetics." IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 3 (July 2006): 239–51. http://dx.doi.org/10.1109/tcbb.2006.31.

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45

Wakano, Joe Yuichiro, Tadahisa Funaki, and Satoshi Yokoyama. "Derivation of replicator–mutator equations from a model in population genetics." Japan Journal of Industrial and Applied Mathematics 34, no. 2 (June 19, 2017): 473–88. http://dx.doi.org/10.1007/s13160-017-0249-9.

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46

Madeira, Gustavo Ferron. "Existence and regularity for a nonlinear boundary flow problem of population genetics." Nonlinear Analysis: Theory, Methods & Applications 70, no. 2 (January 2009): 974–81. http://dx.doi.org/10.1016/j.na.2008.01.025.

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47

Prince, Thomas, and Neville Weber. "Fixation in Conditional Branching Process Models in Population Genetics." Journal of Applied Probability 44, no. 4 (December 2007): 1103–10. http://dx.doi.org/10.1239/jap/1197908828.

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An alternative version of the necessary and sufficient condition for almost sure fixation in the conditional branching process model is derived. This formulation provides an insight into why the examples considered in Buckley and Seneta (1983) all have the same condition for fixation.

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48

Guillot, Gilles. "On the inference of spatial structure from population genetics data." Bioinformatics 25, no. 14 (July 15, 2009): 1796–801. http://dx.doi.org/10.1093/bioinformatics/btp267.

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49

Mugo, Jacquiline W., Ephifania Geza, Joel Defo, Samar S. M. Elsheikh, Gaston K. Mazandu, Nicola J. Mulder, and Emile R. Chimusa. "A multi-scenario genome-wide medical population genetics simulation framework." Bioinformatics 33, no. 19 (June 24, 2017): 2995–3002. http://dx.doi.org/10.1093/bioinformatics/btx369.

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50

Maciejewski, Wes. "Resistance and relatedness on an evolutionary graph." Journal of The Royal Society Interface 9, no. 68 (August 17, 2011): 511–17. http://dx.doi.org/10.1098/rsif.2011.0429.

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When investigating evolution in structured populations, it is often convenient to consider the population as an evolutionary graph—individuals as nodes, and whom they may act with as edges. There has, in recent years, been a surge of interest in evolutionary graphs, especially in the study of the evolution of social behaviours. An inclusive fitness framework is best suited for this type of study. A central requirement for an inclusive fitness analysis is an expression for the genetic similarity between individuals residing on the graph. This has been a major hindrance for work in this area as highly technical mathematics are often required. Here, I derive a result that links genetic relatedness between haploid individuals on an evolutionary graph to the resistance between vertices on a corresponding electrical network. An example that demonstrates the potential computational advantage of this result over contemporary approaches is provided. This result offers more, however, to the study of population genetics than strictly computationally efficient methods. By establishing a link between gene transfer and electric circuit theory, conceptualizations of the latter can enhance understanding of the former.

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