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• W3201416848 abstract "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." @default.
• W3201416848 created "2021-09-27" @default.
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• W3201416848 date "2021-01-01" @default.
• W3201416848 modified "2023-10-01" @default.
• W3201416848 title "Population Design for Synthetic Gene Circuits" @default.
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• W3201416848 doi "https://doi.org/10.1007/978-3-030-85633-5_11" @default.
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