Fitting in the Age of Single-Molecule Experiments: A Guide to Maximum-Likelihood Estimation and Its Advantages
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Biological function often springs from the intricate synchronization of individual proteins, rather than from bulk interactions. High-throughput single-molecule techniques now allow us to move beyond bulk rates to record distributions of reaction times. Such distributions can greatly help mechanistic modeling efforts, as they often contain signatures of the underlying reaction path. With a tentative model at hand, correctly judging its predictive power is predicated on correctly estimating its parameters from the available data. For complex models, such parameter estimation can be far from trivial, and the choice of method can significantly influence the result. We here provide a self-contained introduction to maximum-likelihood estimation aimed at single-molecule experimenters. By considering relevant examples, we explain how to use maximum-likelihood estimation and we compare its performance to that of popular least-squares methods. Considering single-molecule data, we argue that maximum-likelihood estimation is generally the superior choice and conclude with a discussion of how to estimate the spread in parameter estimates through bootstrapping.
KeywordsMaximum likelihood Least squares Fitting FRET Single molecule High throughput Exponential distribution Bootstrapping
We thank Tao Ju (Thijs) Cui, Misha Klein, and Olivera Rakic for careful reading of the manuscript and thoughtful feedback. B. Eslami-Mosallam acknowledges financial support through the research program Crowd management: the physics of genome processing in complex environments, which is financed by the Netherlands Organisation for Scientific Research. I. Katechis acknowledges financial support from the Netherlands Organisation for Scientific Research, as part of the Frontiers in Nanoscience program.
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