Fast maximum likelihood estimation of mutation rates using a birth-death process.

Area of Science:

• Microbiology
• Genetics
• Computational Biology

Background:

• Fluctuation analysis, introduced by Luria and Delbrück, is crucial for inferring spontaneous mutation rates in cell cultures.
• Maximum likelihood estimators (MLEs) are preferred for mutation rate estimation due to their consistency and low mean squared error.
• Conventional MLEs face computational challenges with large datasets due to the recursive nature of mutant-count distributions.

Purpose of the Study:

• To develop a computationally efficient maximum likelihood estimator for mutation rates.
• To address the limitations of existing methods in handling large experimental datasets.
• To introduce a novel approach based on a birth-death process model.

Main Methods:

• Proposed a fast maximum likelihood estimator (MLE-BD) based on a birth-death process model.
• Assumed non-differential growth for the cell populations.
• Evaluated performance through simulation studies comparing MLE-BD with conventional MLEs.

Main Results:

• MLE-BD demonstrated substantial improvements in computational speed compared to conventional MLEs.
• The new estimator is applicable to experiments with arbitrarily large numbers of mutants.
• MLE-BD maintained good accuracy for point estimation of mutation rates.

Conclusions:

• MLE-BD offers a significant advancement in the computational efficiency of mutation rate estimation.
• This method enhances the practical application of fluctuation analysis, especially for large-scale experiments.
• The birth-death process model provides a robust framework for accurate and fast mutation rate inference.