Molecular level stochastic model for competence cycles in Bacillus subtilis.

Molecular level stochastic model for competence cycles in Bacillus subtilis. Research Abstract Details 

Research Abstract Table of Contents

Jump to the:

Molecular level stochastic model for competence cycles in Bacillus subtilis. Abstract Text:

Daniel Schultz,Eshel Ben Jacob, Onuchic,Peter G Wolynes,Daniel Schultz,Eshel Ben Jacob, Onuchic,Peter G Wolynes,

The role of stochasticity and noise in controlling genetic circuits is investigated in the context of transitions into and from competence in Bacillus subtilis. Recent experiments have demonstrated that bistability is not necessary for this function, but that the existence of one stable fixed point (vegetation) and an excitable unstable one (competence) is sufficient. Stochasticity therefore plays a crucial role in this excitation. Noise can be generated by discrete events such as RNA and protein synthesis and their degradation. We consider an alternative noise source connected with the protein binding/unbinding to the DNA. A theoretical model that includes this "nonadiabatic" mechanism appears to produce a better agreement with experiments than models where only the adiabatic limit is considered, suggesting that this nonconventional stochasticity source may be important for biological functions.

Molecular level stochastic model for competence cycles in Bacillus subtilis. Publishing Authors By Initials

D Schultz,E Ben Jacob,JN Onuchic,PG Wolynes,D Schultz,E Ben Jacob,JN Onuchic,PG Wolynes,

For similar abstracts research abstracts see: abstracts research

PUBMED ID PMID:

MEDLINE DATE:

Molecular level stochastic model for competence cycles in Bacillus subtilis. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Proceedings of the National Academy of Sciences of

VOLUME: 104

Page Numbers: 17582-7

Journal Abbreviation: Proc. Natl. Acad. Sci. U.S.A.

ISSN: 0027-8424

DAY: 25

MONTH: 10

YEAR: 2007

Molecular level stochastic model for competence cycles in Bacillus subtilis. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 7505876

Molecular level stochastic model for competence cycles in Bacillus subtilis. Keywords Mesh Terms:

KEYWORDS:

MESH TERMS:

Chemical & Substance for Abstract: Molecular level stochastic model for competence cycles in Bacillus subtilis. Information

Substance Name:

Registry Number:

Grant and Affiliation Information for Molecular level stochastic model for competence cycles in Bacillus subtilis.

AFFILIATION: Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093-0374, USA. schultz@ucsd.edu

Country: United States

AGENCY:

GRANT:

ACRONYM:

MEDLINETA: Proc Natl Acad Sci U S A

REFSOURCE:

DATABASENAME:

ACCESSION NUMBER:

Number Hits: 0

Molecular level stochastic model for competence cycles in Bacillus subtilis Related Publications

• Conformational Switching upon Phosphorylation: A Predictive Framework Based on Energy Landscape Principles.
• Consequences of localized frustration for the folding mechanism of the IM7 protein.
• Establishing the entatic state in folding metallated Pseudomonas aeruginosa azurin.
• Fly-casting in protein-DNA binding: frustration between protein folding and electrostatics facilitates target recognition.
• Fragilities of liquids predicted from the random first order transition theory of glasses.
• Induced fit, folding, and recognition of the NF-kappaB-nuclear localization signals by IkappaBalpha and IkappaBbeta.
• Intermolecular forces and the glass transition.
• Localizing frustration in native proteins and protein assemblies.
• Molecular level stochastic model for competence cycles in Bacillus subtilis.
• Origins of barriers and barrierless folding in BBL.
• Protein structure prediction using basin-hopping.
• Quantizing Ulam's control conjecture.
• Statistics of cellular signal transduction as a race to the nucleus by multiple random walkers in compartment/phosphorylation space.
• The capillarity picture and the kinetics of one-dimensional protein folding.
• The energy landscapes of repeat-containing proteins: topology, cooperativity, and the folding funnels of one-dimensional architectures.
• The quest to understand protein folding.
• The vibrational energy flow transition in organic molecules: theory meets experiment.
• Thermodynamic-kinetic correlations in supercooled liquids: a critical survey of experimental data and predictions of the random first-order transition theory of glasses.