Mathematical model of LsrR-binding and derepression inEscherichia coliK12

Abstract

Quorum sensing (QS) enables bacterial communication and collective behavior in response to self-secreted signaling molecules. Unlocking its genetic regulation will provide insight towards understanding its influence on pathogenesis, formation of biofilms, and many other phenotypes. There are few datasets available that link QS-mediated gene expression to its regulatory components and even fewer mathematical models that incorporate known mechanistic detail. By integrating these data with annotated sequence information, mathematical inferences can be pieced together that shed light on regulatory structure. A first principles model, developed here for the E. coli QS system, builds on known mechanistic detail and is used to develop a working model of LuxS-regulated (Lsr) activity. That is, our model is meant to discriminate among hypothetical mechanisms governing lsr transcriptional regulation. Our simulations are in qualitative agreement with experimentally observed data. Importantly, our results point to the importance of transcriptional regulator, LsrR, cycling on genetic control. We also found several experimental observations in E. coli and homologous systems that were not explained by current mechanistic understanding. For example, by comparing simulations with reports of the integrating host factor in Aggrigatibacter actinomycetemcomitans, we conclude that additional transcriptional components are likely involved. An iterative process of simulation and experiment, therefore, is needed to inform new experiments and incorporate new model detail, the benefit of which will more rapidly validate mechanistic understanding.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 01, 2017
Source ID
10.1142/s0219720016500396

Entities

People

  • Steven M. Graff
  • William E. Bentley

Organizations

  • Defense Threat Reduction Agency
  • Division of Chemical, Bioengineering, Environmental, and Transport Systems
  • University of Maryland

Tags

Fields of Study

  • Biology

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Immunology and Pathology
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Biotechnology