From 335cdf963ef4664ba1b634dac7b1a78ec0261169 Mon Sep 17 00:00:00 2001
From: Lukas Buecherl <60665186+LukasBuecherl@users.noreply.github.com>
Date: Tue, 5 Nov 2024 16:11:09 -0700
Subject: [PATCH] Update index.md

---
 .../buecherl-decoding-genetic-circuit-failures-2024/index.md   | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/content/publication/buecherl-decoding-genetic-circuit-failures-2024/index.md b/content/publication/buecherl-decoding-genetic-circuit-failures-2024/index.md
index 8bf2df75a..9c7aef1ff 100644
--- a/content/publication/buecherl-decoding-genetic-circuit-failures-2024/index.md
+++ b/content/publication/buecherl-decoding-genetic-circuit-failures-2024/index.md
@@ -6,6 +6,5 @@ authors: ["Lukas Buecherl"]
 publication_types: ["7"]
 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."
 featured: false
-publication: ""
-url_pdf: "https://www.proquest.com/docview/3056978435?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses"
+publication: "*ProQuest Dissertations and Theses*"
 ---