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A Low Dimensional Approximation For Competence In Bacillus Subtilis.

An Nguyen, Adam Prugel-Bennett, Srinandan Dasmahapatra

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 6, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study develops low-dimensional models to understand noise-induced excitable dynamics in Bacillus subtilis, focusing on the ComK protein. The models accurately capture the time-course of protein expression, improving analysis of bacterial competence.

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    Area of Science:

    • Systems Biology
    • Biophysics
    • Computational Biology

    Background:

    • High-dimensional stochastic systems, like those described by the chemical master equation (CME), are complex to simulate.
    • Understanding bacterial competence in Bacillus subtilis involves complex regulatory networks, such as those driven by the ComK protein.

    Purpose of the Study:

    • To develop accurate low-dimensional models for noise-induced excitable dynamics in Bacillus subtilis.
    • To improve the analysis of bacterial competence by accurately capturing the time-course of ComK protein expression.

    Main Methods:

    • Application of a modified iterative functional procedure to create a two-dimensional model involving ComK and ComS proteins.
    • Introduction of a tunable multiplicative noise term in a two-dimensional Langevin model to describe bimodal probability distributions.
    • Utilizing Gillespie simulations of the chemical master equation (CME) for analysis.

    Main Results:

    • A two-dimensional model was developed that faithfully approximates the time-course of protein expression trajectories.
    • The adiabatic approximation was found to be inaccurate for competence duration, necessitating a modified approach.
    • The Langevin model with multiplicative noise successfully described the bimodal probability distribution.

    Conclusions:

    • Low-dimensional modeling provides a tractable approach for analyzing complex biological systems.
    • The developed models offer improved accuracy in simulating bacterial competence dynamics.
    • This work facilitates a deeper understanding of noise-induced regulatory mechanisms in bacteria.