Population Design for Synthetic Gene Circuits

Abstract

Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

Joint Distribution of Protein Concentration and Cell Volume Coupled by Feedback in Dilution

“…Such feedback coupling is often manifested as a result of the burden placed by the protein on the cells' expression/metabolic machinery [16,37,41,51]. In the feedback scenario, it is important to distinguish between the single cell (genealogy) framework and the population framework [19,23,50]. For a single cell line case, the model is a bivariate Markov process, the variables being the protein concentration and cell volume.…”