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Journal Articles mSystems Year : 2018

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

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1
Peter Larsen
  • Function : Author
Sarah Zerbs
  • Function : Author
Philip Laible
  • Function : Author
Frank Collart
  • Function : Author
Peter Korajczyk
  • Function : Author
Yang Dai
  • Function : Author
Philippe Noirot

Abstract

Bacteria 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.
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hal-03760720 , version 1 (25-08-2022)

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Peter Larsen, Sarah Zerbs, Philip Laible, Frank Collart, Peter Korajczyk, et al.. Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information. mSystems, 2018, 3 (3), ⟨10.1128/mSystems.00189-17⟩. ⟨hal-03760720⟩

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