Swimming bacteria are ubiquitous in aqueous environments ranging from oceans to fluidic environments within a living host. Furthermore, engineered bacteria are being increasingly utilized for a host of applications including environmental bioremediation, biosensing, and for the treatment of diseases. Often driven by chemotaxis (i.e. biased migration in response to gradients of chemical effectors) and quorum sensing (i.e. number density dependent regulation of gene expression), bacterial population dynamics and emergent behavior play a key role in regulating their own life and their impact on their immediate environment. Computational models that accurately and robustly describe bacterial population behavior and response to environmental stimuli are crucial to both understanding the dynamics of microbial communities and efficiently utilizing engineered microbes in practice. Many existing computational frameworks are finely-detailed at the cellular level, leading to extended computational time requirements, or are strictly population scale models, which do not permit population heterogeneities or spatiotemporal variability in the environment. To bridge this gap, we have created and experimentally validated a scalable, computationally-efficient, agent-based model of bacterial chemotaxis and quorum sensing (QS) which robustly simulates the stochastic behavior of each cell across a wide range of bacterial populations, ranging from a few to several hundred cells. We quantitatively and accurately capture emergent behavior in both isogenic QS populations and the altered QS response in a mixed QS and quorum quenching (QQ) microbial community. Finally, we show that the model can be used to predictively design synthetic genetic components towards programmed microbial behavior. / Master of Science / Bacteria are an integral part of life and possess a host of characteristics that make them a powerful tool with which to confront many modern day problems. Using genetic engineering and the burgeoning field of synthetic biology, these single-celled organisms can be manipulated to perform many useful tasks such as detecting oil spills or other environmental pollutants, producing pharmaceuticals such as insulin, and even invading and killing cancer cells. Accurate computational simulations of microbial behavior will aid in the efficient design of such synthetic bacteria-based systems and reduce dependency on the current time-intensive “guess and check” paradigm. Towards this goal, we have built a comprehensive computer simulation of bacterial swimming behavior, response to chemo-effector concentration gradients called chemotaxis, a form of microbial communication called quorum sensing (QS), and a form of communication disruption called quorum quenching (QQ). Not only do we demonstrate an unprecedented level of accuracy in predicting experimental results, but we also couple the simulation with synthetic biology to precisely tune bacteria QS behavior, neither of which have previously been reported in literature. The overarching outcome of this thesis is a tool that could be used to improve the design process of useful bacteria-based systems in diverse areas of biotechnology.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78072 |
| Date | 03 February 2017 |
| Creators | Leaman, Eric Joshua |
| Contributors | Mechanical Engineering, Behkam, Bahareh, Paul, Mark R., Senger, Ryan S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis, Text |
| Format | application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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