Department of Chemical Engineering, Texas A&M University, College Station, TX, USA.
J Biol Eng. 2012 Apr 2;6(1):3. doi: 10.1186/1754-1611-6-3.
Evolutionary dynamics of microbial organisms can now be visualized using the Visualizing Evolution in Real Time (VERT) system, in which several isogenic strains expressing different fluorescent proteins compete during adaptive evolution and are tracked using fluorescent cell sorting to construct a population history over time. Mutations conferring enhanced growth rates can be detected by observing changes in the fluorescent population proportions.
Using data obtained from several VERT experiments, we construct a hidden Markov-derived model to detect these adaptive events in VERT experiments without external intervention beyond initial training. Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events. A method to determine the optimal time point to isolate adaptive mutants is also introduced.
The developed model offers a new way to monitor adaptive evolution experiments without the need for external intervention, thereby simplifying adaptive evolution efforts relying on population tracking. Future efforts to construct a fully automated system to isolate adaptive mutants may find the algorithm a useful tool.
现在可以使用实时进化可视化 (VERT) 系统来可视化微生物的进化动态，在该系统中，几个表达不同荧光蛋白的同基因株系在适应性进化过程中竞争，并使用荧光细胞分选进行跟踪，以随时间构建种群历史。通过观察荧光群体比例的变化，可以检测到赋予增强生长速率的突变。
使用从几个 VERT 实验中获得的数据，我们构建了一个隐马尔可夫衍生模型，无需在初始训练之外进行任何外部干预即可在 VERT 实验中检测到这些适应性事件。对注释数据的分析表明，当检测到适应性事件时，该模型在 85%-93%的数据点上与人工注释达成共识。还介绍了一种确定分离适应性突变体的最佳时间点的方法。
开发的模型提供了一种无需外部干预即可监测适应性进化实验的新方法，从而简化了依赖于群体跟踪的适应性进化工作。未来构建全自动系统来分离适应性突变体的努力可能会发现该算法是一个有用的工具。
