Mathematical models and modular composition rules for synthetic genetic circuits
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One major challenge in synthetic biology is how to design genetic circuits with predictable behaviors in various biological contexts. There are two limitations to addressing this challenge in mammalian cells. First, models that can predict circuit behaviors accurately in bacteria cells cannot be directly translated to mammalian cells. Second, upon interconnection, the behavior of a module, the building block of a circuit, may be different from its behavior in a standalone setting. In this thesis, I present a bottom-up modeling framework that can be used to predict circuit behaviors in transiently transfected mammalian cells (TTMC). The first part of the framework is based on a novel bin-dependent ODE model that can describe the behavior of modules in TTMC accurately. The second part of the framework rests upon a method of modular composition that allows model-based design of circuits. The efficacies of the bin-dependent model and the method of modular composition are validated via experimental data. The effects of retroactivity, a loading effect that arises from modular composition, on circuit behaviors are also investigated.
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