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Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains
Published on: January 18, 2014
Molecular level stochastic model for competence cycles in Bacillus subtilis
Daniel Schultz1, Eshel Ben Jacob, José N Onuchic
1Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093-0374, USA. schultz@ucsd.edu
Summary
Stochasticity drives genetic circuit transitions in Bacillus subtilis, with noise from protein-DNA interactions playing a key role. This nonadiabatic mechanism better explains experimental data than conventional models.
Area of Science:
- Microbiology and Systems Biology
- Investigating gene regulatory networks and cellular decision-making.
Background:
- Bacillus subtilis transitions into and out of competence are critical for genetic transformation.
- Previous models suggested bistability was necessary for these transitions.
- Recent experiments indicate a single stable state and an excitable unstable state suffice.
Purpose of the Study:
- To investigate the role of stochasticity and noise in Bacillus subtilis competence transitions.
- To explore noise generated by protein binding/unbinding to DNA as a key factor.
Main Methods:
- Development of a theoretical model incorporating nonadiabatic noise from protein-DNA interactions.
- Comparison of model predictions with experimental data.
Main Results:
- Bistability is not required for competence transitions; a single stable and an excitable unstable state are sufficient.
- A model including nonadiabatic noise shows better agreement with experimental observations.
- Noise from discrete events like synthesis and degradation, and protein-DNA binding/unbinding, are significant.
Conclusions:
- Stochasticity, particularly from nonadiabatic protein-DNA interactions, is crucial for Bacillus subtilis competence.
- This nonconventional noise source may be vital for various biological functions.
- The findings challenge previous assumptions about the necessity of bistability in genetic circuits.
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