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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.


Markov model decision tree. Decoding of the hidden Markov states for each labeled subpopulation occurs as follows. (1) the set of emission symbols Ok for a subpopulation is generated from the statistical classifier for all n measurements. (2) The forward Viterbi decoder generates the most likely set of hidden states by choosing the path of maximum likelihood through the system trellis (green lines) based upon the known Markov parameters and Ok. (3) The output set Xk is assembled from these predictions for all observations.
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Figure 2: Markov model decision tree. Decoding of the hidden Markov states for each labeled subpopulation occurs as follows. (1) the set of emission symbols Ok for a subpopulation is generated from the statistical classifier for all n measurements. (2) The forward Viterbi decoder generates the most likely set of hidden states by choosing the path of maximum likelihood through the system trellis (green lines) based upon the known Markov parameters and Ok. (3) The output set Xk is assembled from these predictions for all observations.

Mentions: where l denotes the previous hidden state and m the alternative state (e.g. A → A or N). This process is shown graphically in Figure 2. Given that all populations are not expanding immediately after chemostat inoculation, it assumed that all populations are in state N at i = 0. In addition, the final adaptive state predictions are translated back one time point (i.e. i → i -1) based on empirical observation that doing so improved model accuracy. Model validation was accomplished by comparing the predicted hidden state sequences to human annotation of the 19 chemostats and then computing the number of true positives (Amod = Aann), true negatives (Nmod = Nann), false positives (Amod = Nann), and false negatives (Nmod = Aann) within the computational predictions. Despite the use of true and false designations, the human annotations may not always be accurate representations of the true state of each chemostat population. These error rates can be more accurately interpreted as representing the difference between PSM and human annotations.


Computational identification of adaptive mutants using the VERT system.

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

Markov model decision tree. Decoding of the hidden Markov states for each labeled subpopulation occurs as follows. (1) the set of emission symbols Ok for a subpopulation is generated from the statistical classifier for all n measurements. (2) The forward Viterbi decoder generates the most likely set of hidden states by choosing the path of maximum likelihood through the system trellis (green lines) based upon the known Markov parameters and Ok. (3) The output set Xk is assembled from these predictions for all observations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Markov model decision tree. Decoding of the hidden Markov states for each labeled subpopulation occurs as follows. (1) the set of emission symbols Ok for a subpopulation is generated from the statistical classifier for all n measurements. (2) The forward Viterbi decoder generates the most likely set of hidden states by choosing the path of maximum likelihood through the system trellis (green lines) based upon the known Markov parameters and Ok. (3) The output set Xk is assembled from these predictions for all observations.
Mentions: where l denotes the previous hidden state and m the alternative state (e.g. A → A or N). This process is shown graphically in Figure 2. Given that all populations are not expanding immediately after chemostat inoculation, it assumed that all populations are in state N at i = 0. In addition, the final adaptive state predictions are translated back one time point (i.e. i → i -1) based on empirical observation that doing so improved model accuracy. Model validation was accomplished by comparing the predicted hidden state sequences to human annotation of the 19 chemostats and then computing the number of true positives (Amod = Aann), true negatives (Nmod = Nann), false positives (Amod = Nann), and false negatives (Nmod = Aann) within the computational predictions. Despite the use of true and false designations, the human annotations may not always be accurate representations of the true state of each chemostat population. These error rates can be more accurately interpreted as representing the difference between PSM and human annotations.

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.