Processing, analysing and modelling kinetic data in the era of high-throughput single-molecule biophysics

Biomolecular reactions are characterized by a multitude of stochastic, reversible, and interconnected transition paths that involve various intermediates. These reactions result in a complex set of dynamics. The field of single-molecule biophysics has transformed our understanding of biology by exposing the variability in the pathways that occur and highlighting the significance of rare events.

Recent advancements in high-throughput single-molecule biophysics techniques now enable quantitative studies of this variability, including the characterization of even the rarest kinetic events. Researchers have been working on processing, analyzing, and modeling high-throughput single-molecule data, but these methods can be challenging for those without expertise in the field.

In this context, the authors present a comprehensive guide to maximize the insights gained from single-molecule biophysics data using a first-passage time framework and maximum likelihood estimation. The focus is on systems with one or two characteristic timescales, and the authors demonstrate how these systems can be analyzed using a minimal kinetic model, taking into account dependencies on enzyme/substrate concentration, force, and temperature.

The authors introduce a general framework for data-driven modeling of both single- and two-state models, providing concrete examples to illustrate their approach. Furthermore, they offer software tools with graphical user interfaces to facilitate the analysis of raw data, with the aim of empowering experimental single-molecule biophysicists to extract the maximum value from their experiments.