Poster Session II: BIOLOGICAL PHYSICS (DBIO)
Model-free inference of population growth rates from time-series data
Poster 260The population growth rate is a measure of fitness for microbial populations. Growth rate differences as small as 10^-5 are enough to cause bacteria with higher growth rates to dominate the population. Traditionally, the population growth rate has been measured using bulk assays, which are not sensitive enough to detect such small differences in growth rates and lack a connection to single-cell traits. Recently, it has been discovered that the population growth rate can be accurately inferred from single-cell lineage statistics measured using a microfluidic device called a mother machine. Single-lineage methods are advantageous not only because they can accurately infer the population growth rate, but also because they do not assume a model of the organism's biology or cell-cycle dynamics, such as generation times. In this poster, I demonstrate how systematic errors arise due to finite data. After eliminating these errors, I show that multiple single-lineage methods agree and are accurate enough to detect small but biologically relevant differences in growth rates and to infer models of cell-size regulation. This new framework provides a quantitative understanding of growth rate estimators, clarifies the conditions under which they can be effectively applied to finite data, and introduces model-free approaches for studying the connections between physiology and cell growth.