Decoding Genetic Circuit Failures: Analyzing Static and Dynamic Failures in Genetic Circuitry
Abstract
Synthetic biology resides at the nexus of engineering and biology, employing diverse ap- proaches to engineer biological systems. These systems can be as simple as DNA sequences, bio- chemical reactions, or more abstracted through control theory or digital logic, among other ways. Similar to other engineering disciplines, for real-world applications, the designed systems must ex- hibit robustness and adaptability to environmental changes beyond controlled laboratory settings. This dissertation focuses on genetic constructs viewed specifically as digital logic genetic circuits, examining their implementation and failure behavior. It aims to elucidate and analyze various failure modes and proposes analytical methods to enhance genetic circuit robustness. This work defines genetic circuit failure, where deviations from expected output are deemed as unexpected and faulty. Such deviations may stem from failures at the cellular level or from flaws in the circuit’s logic implementation or Boolean function. Subsequently, this dissertation develops computational methods to predict circuit behavior, employing diverse analysis techniques such as ordinary differ- ential equation analysis, stochastic simulation algorithms, and stochastic model verification. These methodologies enable the prediction of the likelihood of failure occurrence. Furthermore, this dis- sertation compares different computational modeling techniques to assess the effort required for genetic circuit analysis. Finally, experimental validation is provided for a predicted circuit failure, demonstrating the practical application of the proposed methodologies.
