Naturally-Inspired Circuits for Microbial Composition Control and Biosensing
Year: 2025
Type: dissertation
Abstract: When considering the design of gene circuits, there are many possible sources of inspiration. Many early synthetic gene circuits used nature as an inspiration, seeking to recreate biological behaviors with non-native components. As the field grew, alternative approaches sourcing designs from adjacent engineering fields and computational approaches emerged and grew in prominence. Despite this shift, there remains a great deal of naturally-inspired circuits that provide useful functions for biotechnology. Indeed nature has often been uniquely capable of exploiting typically undesirable phenomena, e.g. noise to create biologically useful function. This thesis presents two projects directly inspired by natural systems. Each project aims to replicate a behavior or circuit topology found in nature, leveraging its unique dynamics to address key challenges in biotechnology. Chapters 2 and 3 will cover the development of a circuit emulating the microbial behavior of phase variation, whereby individual cells reversibly and stochastically transition between distinct phenotypes. We recreate this behavior using serine recombinases and demonstrate how it can enable stable, bulk control of phenotype composition—a task of great relevance to biotechnology. Chapter 4 lays the groundwork for applying the biologically-relevant feed-forward loop topology to the problem of spurious biosensor activation. We realize this topology in a modular manner using small transcription activating RNAs (STARs) and provide a preliminary characterization of its dynamical properties. Finally, we discuss alternative implementations that may provide more directly applicable properties than the current STAR implementation
Source: Caltech Library
DOI: 10.7907/d42b-jh46
Author: Kratz, Matthieu Francois
Institution: California Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7110927980
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