Parameterization of a Stochastic Model of Gene Expression from Adaptive Imaging Cytometry Data

Supplementary material to a manuscript under review. The manuscript can be downloaded from the link below. Abstract Recent decades have yielded an understanding of cellular behavior as arising from a complicated soup of noisy molecular interactions. Systems biologists use this perspective to build models to explain cellular behavior, while synthetic biologists attempt to forward engineer novel function in this complex environment. It is well known that some cellular behaviors can be explained only when the stochasticity of the underlying molecular dynamics are considered; however, models that capture this molecular noise are exceedingly difficult to construct. First, current datasets typically do not capture time courses of individual cells and rely on fluorescent reporters that do not detail the dynamics of underlying components, such as mRNA. Second, matching stochastic models to single-cell data is far more difficult than matching deterministic models to population averages. In this work, we addressed both of these concerns by using a novel instrument based on time-lapse microscopy and by applying a distribution-based method to assess the match between model and data. We demonstrate our model’s ability to match our experimental data in detail, and then use the model to successfully predict the behavior of a modified system. Our approach should lead to more predictive models of simple genetic systems in both systems and synthetic biology.