Houra Merrikh | Searcharxiv

arXiv2022

The high-resolution in vivo measurement of replication fork velocity and pausing by lag-time analysis

An important step towards understanding the mechanistic basis of the central dogma is the quantitative characterization of the dynamics of nucleic-acid-bound molecular motors in the context of the living cell, where a crowded cytoplasm as well as competing and potentially antagonistic processes may significantly affect their rapidity and reliability. To capture these dynamics, we develop a novel method, lag-time analysis, for measuring in vivo dynamics. The approach uses exponential growth as the stopwatch to resolve dynamics in an asynchronous culture and therefore circumvents the difficulties and potential artifacts associated with synchronization or fluorescent labeling. Although lag-time analysis has the potential to be widely applicable to the quantitative analysis of in vivo dynamics, we focus on an important application: characterizing replication dynamics. To benchmark the approach, we analyze replication dynamics in three different species and a collection of mutants. We provide the first quantitative locus-specific measurements of fork velocity, in units of kb per second, as well as replisome-pause durations, some with the precision of seconds. The measured fork velocity is observed to be both locus and time dependent, even in wild-type cells. In addition to quantitatively characterizing known phenomena, we detect brief, locus-specific pauses at rDNA in wild-type cells for the first time. We also observe temporal fork velocity oscillations in three highly-divergent bacterial species. Lag-time analysis not only has great potential to offer new insights into replication, as demonstrated in the paper, but also has potential to provide quantitative insights into other important processes.

physics.bio-ph↗

arXiv2022

Characterizing stochastic cell cycle dynamics in exponential growth

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.

physics.bio-ph↗