Limits...
Computational identification of adaptive mutants using the VERT system.

Winkler J, Kao KC - J Biol Eng (2012)

Bottom Line: Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Texas A&M University, College Station, TX, USA. kao.katy@mail.che.tamu.edu.

ABSTRACT

Unlabelled: :

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Data example. Population dynamics from a yeast population (KK-Large1-2007) selected for growth in glucose limited media.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3351376&req=5

Figure 1: Data example. Population dynamics from a yeast population (KK-Large1-2007) selected for growth in glucose limited media.

Mentions: We seek to analyze the population dynamics that arise during a chemostat evolution experiment. In this type of system, a continuous, constant volume, bioreactor is inoculated with several isogenic microbial populations, each marked with a different fluorescent protein (or equivalent unique label), and evolved for hundreds of generations in the presence of the desired selective pressure. Adaptive mutants from each labeled subpopulation that arise during the course of the evolution experiment trigger an observable increase in the size of the labeled subpopulation, as shown in Figure 1. FACS devices are typically used to track the proportion of each fluorescent strain in the evolving population over time in a series a discrete measurements (typically 1 measurement/day); obtaining continuous data is usually not possible due to experimental and technical limitations. In this case we utilize population dynamics data obtained from evolving yeast and Escherichia coli that express several fluorescent proteins.


Computational identification of adaptive mutants using the VERT system.

Winkler J, Kao KC - J Biol Eng (2012)

Data example. Population dynamics from a yeast population (KK-Large1-2007) selected for growth in glucose limited media.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3351376&req=5

Figure 1: Data example. Population dynamics from a yeast population (KK-Large1-2007) selected for growth in glucose limited media.
Mentions: We seek to analyze the population dynamics that arise during a chemostat evolution experiment. In this type of system, a continuous, constant volume, bioreactor is inoculated with several isogenic microbial populations, each marked with a different fluorescent protein (or equivalent unique label), and evolved for hundreds of generations in the presence of the desired selective pressure. Adaptive mutants from each labeled subpopulation that arise during the course of the evolution experiment trigger an observable increase in the size of the labeled subpopulation, as shown in Figure 1. FACS devices are typically used to track the proportion of each fluorescent strain in the evolving population over time in a series a discrete measurements (typically 1 measurement/day); obtaining continuous data is usually not possible due to experimental and technical limitations. In this case we utilize population dynamics data obtained from evolving yeast and Escherichia coli that express several fluorescent proteins.

Bottom Line: Analysis of annotated data revealed that the model achieves consensus with human annotation for 85-93% of the data points when detecting adaptive events.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Texas A&M University, College Station, TX, USA. kao.katy@mail.che.tamu.edu.

ABSTRACT

Unlabelled: :

Background: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.