Information Encoding and Decoding in Bacteria
Bacteria are found throughout the environment, from the air to the soil, but more importantly, they reside within the human body. Crucial to their survival in each of these environments is the constant interplay between these organisms and their surroundings. Inadvertently, the ways in which these stimuli are processed can have a profound impact on human health. With potentially negative or positive consequences, it becomes critical to understand how microorganisms encode and decode signals.
Understanding bacterial signal processing is crucial to tackling the treatment of infectious diseases, especially with the rise of antibiotic resistant organisms. Antibiotic resistance has become a global health issue as bacteria have developed or acquired genes that confer resistance to all antibiotics currently in use today. This has serious implications for the future treatment of infectious diseases, potentially limiting options to those from a pre-antibiotic era. However, as with other external factors, antibiotics are just another signal that bacteria need to decode and encode a response to. As such, it is of utmost importance to better understand how bacteria process stimuli.
In my dissertation, I analyzed the ways in which bacteria both encode and decode information. In particular, I focused on how information is processed from signals with a temporal domain. To start, I developed a computational framework to understand how organisms decode signals, specifically oscillatory signals. With this model, I examined the capability of an incoherent feedforward loop motif to exhibit temporal adaptation, in which a network becomes desensitized to sustained stimuli. I discovered that this property is crucial for networks to distinguish signals of varying temporal dynamics.
In terms of information encoding, I utilized the complexity of this process to predict bacterial characteristics of interest. The fundamental premise behind this work is to increase the information content of phenotypes for the prediction of bacterial characteristics. Specifically, I used the temporal domain of growth for the prediction of genetic identity and traits of interest. I demonstrated that temporal growth dynamics under standardized conditions can differentiate among hundreds of strains, even strains of the same species. While growth dynamics could, with high accuracy, differentiate between unique strains, it was insufficient to quantify how genetically different these strains were. This absence highlighted the challenges in using genomics to infer phenotypes and vice versa. Bypassing this complexity, I showed that growth dynamics alone could robustly predict antibiotic responses. Together, my findings demonstrate the ability to develop applications that take advantage of the complexity of bacterial information encoding.
This work highlights the importance of understanding how bacteria decode signals with temporal dynamics. Additionally, I demonstrated one application for utilizing bacterial signal encoding, the prediction of bacterial characteristics.
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