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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.


Output Example. Using the experimental dynamics in 1 and the PSM, the timing of each adaptive event in the chemostat is calculated and displayed for the user as shaded time points.
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Figure 3: Output Example. Using the experimental dynamics in 1 and the PSM, the timing of each adaptive event in the chemostat is calculated and displayed for the user as shaded time points.

Mentions: An example of the PSM predictions is shown for a yeast chemostat (Large1-KK-2007) in Figure 1. In this system, three fluorescent strains are competing for access to limited glucose; adaptive events occur as individual acquire mutations that affect the rate of glucose transport into the cell. Upon visual inspection of the raw population data in Figure 1, an experienced VERT user would likely conclude that adaptive events (expansions) occur several times in each subpopulation and that the mutations conferring the greatest fitness advantage occur in the yellow population. Analyzing these population dynamics using the PSM produces the adaptive event predictions shown in Figure 3 as shaded regions within each subpopulation. While the model is very successful at identifying the adaptive expansion regions that would likely be identified during a qualitative analysis in this case, it should be noted that excessive noise in the raw FACS data arising from experimental error or constantly varying selective pressure may render adaptive event identification more error prone. However, this tendency should not be a problem in most situations.


Computational identification of adaptive mutants using the VERT system.

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

Output Example. Using the experimental dynamics in 1 and the PSM, the timing of each adaptive event in the chemostat is calculated and displayed for the user as shaded time points.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Output Example. Using the experimental dynamics in 1 and the PSM, the timing of each adaptive event in the chemostat is calculated and displayed for the user as shaded time points.
Mentions: An example of the PSM predictions is shown for a yeast chemostat (Large1-KK-2007) in Figure 1. In this system, three fluorescent strains are competing for access to limited glucose; adaptive events occur as individual acquire mutations that affect the rate of glucose transport into the cell. Upon visual inspection of the raw population data in Figure 1, an experienced VERT user would likely conclude that adaptive events (expansions) occur several times in each subpopulation and that the mutations conferring the greatest fitness advantage occur in the yellow population. Analyzing these population dynamics using the PSM produces the adaptive event predictions shown in Figure 3 as shaded regions within each subpopulation. While the model is very successful at identifying the adaptive expansion regions that would likely be identified during a qualitative analysis in this case, it should be noted that excessive noise in the raw FACS data arising from experimental error or constantly varying selective pressure may render adaptive event identification more error prone. However, this tendency should not be a problem in most situations.

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.