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Title: Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information

Abstract

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 regulome is 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 analyzePseudomonas 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 theory to portrayP. fluorescensas an informatics system. This approach enables us to exploit the well-established metrics associated with information theory to model the sulfur regulome. Our computational modeling analysis results in the accurate prediction of gene expression patterns in response to the specific sulfurmore » nutrient environments and provides insights into the molecular mechanisms ofPseudomonassensory capabilities and gene regulatory networks. In addition, modeling the bacterial regulome using the tools of information theory is a powerful and generalizable approach that will have multiple future applications to other bacterial regulomes. IMPORTANCEBacteria sense and respond to 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 theory provides 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 regulomes and our ability to engineer bacterial sensory and regulatory networks.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science - Office of Biological and Environmental Research; USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Bioenergy Technologies Office (BETO)
OSTI Identifier:
1460957
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
mSystems
Additional Journal Information:
Journal Volume: 3; Journal Issue: 3; Journal ID: ISSN 2379-5077
Publisher:
American Society for Microbiology
Country of Publication:
United States
Language:
English

Citation Formats

Larsen, Peter E., Zerbs, Sarah, Laible, Philip D., Collart, Frank R., Korajczyk, Peter, Dai, Yang, Noirot, Philippe, and Baranzini, Sergio. Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information. United States: N. p., 2018. Web. doi:10.1128/mSystems.00189-17.
Larsen, Peter E., Zerbs, Sarah, Laible, Philip D., Collart, Frank R., Korajczyk, Peter, Dai, Yang, Noirot, Philippe, & Baranzini, Sergio. Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information. United States. doi:10.1128/mSystems.00189-17.
Larsen, Peter E., Zerbs, Sarah, Laible, Philip D., Collart, Frank R., Korajczyk, Peter, Dai, Yang, Noirot, Philippe, and Baranzini, Sergio. Tue . "Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information". United States. doi:10.1128/mSystems.00189-17.
@article{osti_1460957,
title = {Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information},
author = {Larsen, Peter E. and Zerbs, Sarah and Laible, Philip D. and Collart, Frank R. and Korajczyk, Peter and Dai, Yang and Noirot, Philippe and Baranzini, Sergio},
abstractNote = {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 regulome is 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 analyzePseudomonas 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 theory to portrayP. fluorescensas an informatics system. This approach enables us to exploit the well-established metrics associated with information theory to 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 ofPseudomonassensory capabilities and gene regulatory networks. In addition, modeling the bacterial regulome using the tools of information theory is a powerful and generalizable approach that will have multiple future applications to other bacterial regulomes. IMPORTANCEBacteria sense and respond to 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 theory provides 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 regulomes and our ability to engineer bacterial sensory and regulatory networks.},
doi = {10.1128/mSystems.00189-17},
journal = {mSystems},
issn = {2379-5077},
number = 3,
volume = 3,
place = {United States},
year = {2018},
month = {6}
}

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