title: Universality in stochastic exponential growth.
abstract: Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth.
authors: James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, USA.
authors: Srividya
authors: S
authors: Iyer-Biswas
authors: Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
authors: Gavin E
authors: GE
authors: Crooks
authors: James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, USA.
authors: Norbert F
authors: NF
authors: Scherer
authors: James Franck Institute and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, USA.
authors: Aaron R
authors: AR
authors: Dinner
mesh_terms: D048788:Growth and Development; D008954:Models, Biological; D013269:Stochastic Processes