Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information
2018
ABSTRACT Bacteria are not simply passive consumers of nutrients or merely steady-state systems. Rather, bacteria are active participants in their environments, collecting information from their surroundings and processing and using that information to adapt their behavior and optimize survival. The bacterial
regulomeis the set of physical interactions that link environmental information to the expression of genes by way of networks of sensors, transporters, signal cascades, and transcription factors. As bacteria cannot have one dedicated sensor and regulatory response system for every possible condition that they may encounter, the sensor systems must respond to a variety of overlapping stimuli and collate multiple forms of information to make “decisions” about the most appropriate response to a specific set of environmental conditions. Here, we analyze
Pseudomonas fluorescenstranscriptional responses to multiple sulfur nutrient sources to generate a predictive, computational model of the sulfur
regulome. To model the
regulome, we utilize a transmitter-channel-receiver scheme of information transfer and utilize principles from
information theoryto portray P. fluorescens as an informatics system. This approach enables us to exploit the well-established metrics associated with
information theoryto model the sulfur
regulome. Our computational modeling analysis results in the accurate prediction of gene expression patterns in response to the specific sulfur nutrient environments and provides insights into the molecular mechanisms of Pseudomonas sensory capabilities and
gene regulatory networks. In addition, modeling the bacterial
regulomeusing the tools of
information theoryis a powerful and generalizable approach that will have multiple future applications to other bacterial
regulomes. IMPORTANCE Bacteria
senseand
respondto their environments using a sophisticated array of sensors and regulatory networks to optimize their fitness and survival in a constantly changing environment. Understanding how these regulatory and sensory networks work will provide the capacity to predict bacterial behaviors and, potentially, to manipulate their interactions with an environment or host. Leveraging the
information theoryprovides useful quantitative metrics for modeling the information processing capacity of bacterial regulatory networks. As our model accurately predicted gene expression profiles in a bacterial model system, we posit that the
information theory-based approaches will be important to enhance our understanding of a wide variety of bacterial
regulomesand our ability to engineer bacterial sensory and regulatory networks.
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