ArticlePloS one2010
A Boolean model of the Pseudomonas syringae hrp regulon predicts a tightly regulated system.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Data-driven learning of Boolean networks and functions by optimal causation entropy principle.Patterns (New York, N.Y.) · 2022Article
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
- Regulation of theMicroorganisms · 2021Review
- A revised model for the role of GacS/GacA in regulating type III secretion by Pseudomonas syringae pv. tomato DC3000.Molecular plant pathology · 2020Article
- A model of the onset of the senescence associated secretory phenotype after DNA damage induced senescence.PLoS computational biology · 2017Article
- The hrp pathogenicity island of Pseudomonas syringae pv. tomato DC3000 is induced by plant phenolic acids.Journal of microbiology (Seoul, Korea) · 2015Article
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- A boolean model of the cardiac gene regulatory network determining first and second heart field identity.PloS one · 2012Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Type III secretion system (TTSS) is a protein secretion machinery used by certain gram-negative bacterial pathogens of plants and animals to deliver effector molecules to the host and is at the core of the ability to cause disease. Extensive molecular and biochemical study has revealed the components and their interactions within this system but reductive approaches do not consider the dynamical properties of the system as a whole. In order to gain a better understanding of these dynamical behaviours and to create a basis for the refinement of the experimentally derived knowledge we created a Boolean model of the regulatory interactions within the hrp regulon of Pseudomonas syringae pathovar tomato strain DC3000 Pseudomonas syringae. We compared simulations of the model with experimental data and found them to be largely in accordance, though the hrpV node shows some differences in state changes to that expected. Our simulations also revealed interesting dynamical properties not previously predicted. The model predicts that the hrp regulon is a biologically stable two-state system, with each of the stable states being strongly attractive, a feature indicative of selection for a tightly regulated and responsive system. The model predicts that the state of the GacS/GacA node confers control, a prediction that is consistent with experimental observations that the protein has a role as master regulator. Simulated gene "knock out" experiments with the model predict that HrpL is a central information processing point within the network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.