Modelling microbial adaptation

模拟微生物适应

• 批准号: 238389-2011
• 负责人: Wahl, Lindi
• 金额: $ 3.35万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667945
• 关键词: Modelling; microbial; adaptation

Bacteria, viruses and other microbes adapt much more rapidly than humans. I use mathematical models to predict how quickly this microbial evolution unfolds. To do this, we need to predict which types of mutations might be beneficial to the organism, and how large their effects might be; this is called the "distribution of selective effects". Since many mutations, even if they are beneficial, are eventually lost from an evolving population, we would also like to predict whether new beneficial mutations will successfully spread through the microbial population, the "fixation probability". Finally, we would like to understand the "adaptive walk", that is, the process through which several beneficial mutations are acquired in sequence as the microbial population adapts to its environment.****Sophisticated mathematical treatments of each of these questions have been previously described in the literature. In particular, the fixation probability was first estimated, mathematically, nearly 90 years ago by some of the "founding fathers" of population genetics, but has never been tested. Although experimental techniques which could test these concepts have finally become available, recent work has demonstrated that our mathematical predictions must be precisely tailored to the organism and to the experimental protocol. Thus, quantitative experimental tests of these foundational quantities will only be possible after new mathematical predictions, specific for microbial populations, are developed. This is my research program.****The significance of understanding microbial adaptation is clear. The techniques developed here will be critical in predicting the rate of loss of microbial biodiversity, and the rate at which the microbiome will adapt to climate change. From a public health perspective, this work will also allow us to better predict the rate at which new pathogenic microbes will emerge, and the emergence of drug-resistance.****************

细菌，病毒和其他微生物比人类更快地适应。 我使用数学模型来预测这种微生物进化的速度。 为此，我们需要预测哪种类型的突变可能对生物体有益，以及它们的影响可能有多大。这称为“选择性效果的分布”。 由于许多突变（即使是有益的突变）最终会因不断发展的人群而损失，因此我们还想预测新的有益突变是否会成功地通过微生物种群成功传播，即“固定概率”。 最后，我们想理解“自适应步行”，即，随着微生物种群适应其环境，获得了几个有益突变的过程。****先前在文献中已经描述了这些问题的复杂数学处理。 特别是，在数学上，固定概率首先是在大约90年前由人口遗传学的一些“开国元勋”估算出来的，但从未进行过测试。 尽管可以测试这些概念的实验技术最终已获得，但最近的工作表明，我们的数学预测必须精确地针对有机体和实验方案量身定制。 因此，只有在开发了针对微生物种群的新数学预测之后，才有可能对这些基础数量进行定量实验测试。 这是我的研究计划。****了解微生物适应的意义很明显。 这里开发的技术对于预测微生物生物多样性的损失率以及微生物组适应气候变化的速率至关重要。 从公共卫生的角度来看，这项工作还将使我们能够更好地预测新的病原微生物出现的速度以及抗药性的出现。****************