Dynamics of the emergence of genetic resistance to biocides among asexual and sexual organisms
Dynamics of the emergence of genetic resistance to biocides among asexual and sexual organisms
复制标题
DOI：
10.1006/jtbi.1997.0472
复制
发表时间：
1997-10-07
影响因子：
2
通讯作者：
Haile, D
中科院分区：
文献类型：
--
作者：
Jaffe, K;Issa, S;Haile, D
一个随机的，基于代理的，进化算法，建模交配，繁殖，遗传变异，表型表达和选择被用来研究影响多基因系统的动态相互作用。结果表明，强大的不可逆的限制影响的演变，抗药性的杀虫剂。抗性基因在无性生物中的进化与有性生物相比是不同的，以应对各种模式的杀生物剂应用。无性种群（病毒和细菌）不太可能对多种农药产生遗传抗性，或者如果农药的使用剂量很低，而有性种群（例如昆虫）更有可能对农药产生抗性，如果对农药的敏感性与配偶选择有关。与抗性产生无关的基因的适应性变化将影响抗性进化的动态。增加杀虫剂的数量会降低无性生物对其中任何一种产生抗药性的可能性，但在有性生物中则要少得多。在有性和无性生物体中，与同时施用混合物相比，毒素的顺序施用在减缓抗性出现方面的效率略低。只针对害虫的一种性别会加速抗药性的发展。研究结果是一致的，大多数已发表的分析模型，但更接近已知的实验结果，表明非线性，基于代理的仿真模型更强大的解释复杂的过程。(C)出版社：Academic Press Limited。
A stochastic, agent based, evolutionary algorithm, modeling mating, reproduction, genetic variation, phenotypic expression and selection was used to study the dynamic interactions affecting a multiple-gene system. The results suggest that strong irreversible constraints affect the evolution of resistance to biocides. Resistant genes evolve differently in asexual organisms compared with sexual ones in response to various patterns of biocide applications. Asexual populations (viruses and bacteria) are less likely to develop genetic resistance in response to multiple pesticides or if pesticides are used at low doses, whereas sexual populations (insects for example) are more likely to become resistant to pesticides if susceptibility to the pesticide relates to mate selection. The adaptation of genes not related to the emergence of resistance will affect the dynamics of the evolution of resistance. Increasing the number of pesticides reduces the probability of developing resistance to any of them in asexual organisms but much less so in sexual organisms. Sequential applications of toxins, were slightly less efficient in slowing emergence of resistance compared with simultaneous application of a mix in both sexual and asexual organisms. Targeting only one sex of the pest speeds the development of resistance. The findings are consistent to most of the published analytical models but are closer to known experimental results, showing that nonlinear, agent based simulation models are more powerful in explaining complex processes. (C) 1997 Academic Press Limited.