(622g) Fluorescence Microscopy –Based Inverse Cell Population Balance Modeling

Isogenic cell populations are typically heterogeneous since their intracellular content is unevenly distributed amongst the cells of the population, thus resulting in a distribution of phenotypes. Therefore, in order to accurately describe cell population dynamics, one needs to take into account cell population heterogeneity. Such a requirement is satisfied by a large class of mathematical models, known as Cell Population Balance models (CPBs). Despite their generality and the recent progress in their numerical simulation, the use of CPB models for predictive and design purposes has been hindered by the difficulty in determining the single-cell level information that is required for their application.

Motivated by this challenge, we developed an integrated experimental and theoretical methodology to determine the so-called intrinsic physiological state functions, which are necessary to accurately predict the dynamics of cell populations through simulation of CPB models. The framework is based on fluorescence microscopy and digital image processing, which offer unique advantages over more high-throughput experimental techniques such as flow cytometry. We developed specific criteria and a set of algorithms to post-process the images acquired with the fluorescence microscope in order to quantify the distributions of the overall cell population as well as those of dividing and newborn subpopulations. The intrinsic physiological state functions were subsequently determined through the application of the Collins-Richmond inverse cell population balance modeling approach. As a model system we chose an E. coli cell population that carries the IPTG-inducible genetic toggle network, a well-known artificial gene regulatory network equipped with a gfmut3 gene functioning as a reporter of intracellular expression levels. The distributions of the overall population obtained with fluorescence microscopy were found to be in excellent agreement with those obtained through flow cytometry measurements. Moreover, the framework was applied to study in detail the effect of IPTG concentration on the single-cell reaction rate and division frequency. Thus, the results we will present serve as an example showing the rich capabilities of this framework in investigating the mechanisms leading to cell population heterogeneity as well as the profound implications of the latter on the dynamics of cell populations.