Fluctuation analysis: can estimates be trusted?

Area of Science:

• Microbiology
• Evolutionary Biology
• Biostatistics

Background:

• Classical fluctuation analysis relies on the exponential distribution of single-cell division times.
• This assumption can lead to significant bias in estimating relative fitness.
• Existing models do not account for diverse cell division time distributions.

Purpose of the Study:

• To extend fluctuation analysis to accommodate any cell division time distribution.
• To develop a generalized Luria-Delbrück distribution for more accurate estimations.
• To improve the estimation of mutation rates and relative fitness in microbial populations.

Main Methods:

• Developed a generalized Luria-Delbrück distribution accommodating arbitrary division time distributions.
• Utilized empirical probability generating function techniques for precise parameter estimation.
• Investigated the impact of division time distribution assumptions on estimation accuracy.

Main Results:

• The generalized model accurately estimates mutation rates and relative fitness across various division time distributions.
• Empirical probability generating functions provide reliable estimates for mean mutations and relative fitness.
• Using constant division times offers more robust results when distribution is unknown.

Conclusions:

• The extended fluctuation analysis model overcomes limitations of the classical Luria-Delbrück distribution.
• Accurate estimation of mutation rates and relative fitness is achievable without assuming exponential division times.
• The generalized model enhances the reliability of evolutionary and microbial studies.