Stochastic chemical kinetics of cell fate decision systems: from single cells to populations and back

This dataset has been furnished in support of the article "Stochastic chemical kinetics of cell fate decision systems: from single cells to populations and back" (2022). All the data used in the article is contained within Publication_data.zip.

Stochastic chemical kinetics is the most widely used formalism for studying stochasticity of natural or synthetic chemical react networks inside single cells. Experimental studies of reaction networks are generally performed with cells that either are, or have been, part of a growing population, yet the population context is rarely taken into account when models are developed. Models that neglect the population context lose their validity wheneverthe studied system influences traits of cells that can be selected in the population, a property that naturally arises in the complex interplay between single-cell and population dynamics of cell fate decision systems. Here, on the basis of the chemical master equation, we show how statistics of cell fate decision systems can be tracked within growing populations and how this allows one to derive consistent population dynamics models from mechanistic descriptions of single-cell processes. We use this approach to classify cell fate decision systems into two types that lead to different characteristic phases in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events inside single cells to growth of cell populations in dynamically changing environments.

The data is divided in two folders, "cytometry data" and "growth rates" for each of the 4 experiments in duplicates in the form of matlab files and csv files respectively. The key that maps reactors to experiments with details about media switching times is provided in the parent folder titled "Key.xlsx".