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The physics of bacterial decision making.

Ben-Jacob E, Lu M, Schultz D, Onuchic JN - Front Cell Infect Microbiol (2014)

Bottom Line: In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise.We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles.We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Biological Physics, Rice University Houston, TX, USA ; Department of Biosciences, Rice University Houston, TX, USA ; School of Physics and Astronomy and The Sagol School of Neuroscience, Tel-Aviv University Tel-Aviv, Israel.

ABSTRACT
The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

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Examples of simple functional gene circuits. (A) Examples of two-gene circuits. Top is a classical toggle switch comprised of two mutually inhibiting genes described in details in the next section. Bottom is a classical flip-flop element comprised of two genes that are mutually inhibiting in one direction and activating in the other. (B) Example of a self-activating timer. Time is measured by the level of the phosphorylated protein A*, which is accumulated by external signal that phosphorylates A. In typical timers the gene A is self-activated by A*. (C) Example of an inhibition gated switch. Gene B inhibits the self-activating gene C from making transition into high expression (high level state). When the level of protein A increases it inhibits the inhibition of C by B, thus permits a stochastic transition into a state of high C. (D) Example of an oscillator (termed in systems biology as a repressilator) comprised of an inhibition loop among three genes. The origin of the oscillation is as follows: when the level of A increases, it inhibits B. As a result, the level of C increases leading to a decrease in the level of A and so on.
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Figure 2: Examples of simple functional gene circuits. (A) Examples of two-gene circuits. Top is a classical toggle switch comprised of two mutually inhibiting genes described in details in the next section. Bottom is a classical flip-flop element comprised of two genes that are mutually inhibiting in one direction and activating in the other. (B) Example of a self-activating timer. Time is measured by the level of the phosphorylated protein A*, which is accumulated by external signal that phosphorylates A. In typical timers the gene A is self-activated by A*. (C) Example of an inhibition gated switch. Gene B inhibits the self-activating gene C from making transition into high expression (high level state). When the level of protein A increases it inhibits the inhibition of C by B, thus permits a stochastic transition into a state of high C. (D) Example of an oscillator (termed in systems biology as a repressilator) comprised of an inhibition loop among three genes. The origin of the oscillation is as follows: when the level of A increases, it inhibits B. As a result, the level of C increases leading to a decrease in the level of A and so on.

Mentions: In Figure 2 we show examples of functional gene circuits (regulatory motifs) comprised of only a few interacting genes: the toggle switch and the flip-flop circuits (Figure 2A), the self-activating timer (Figure 2B), a gated switch (Figure 2C) and an oscillator (Figure 2D).


The physics of bacterial decision making.

Ben-Jacob E, Lu M, Schultz D, Onuchic JN - Front Cell Infect Microbiol (2014)

Examples of simple functional gene circuits. (A) Examples of two-gene circuits. Top is a classical toggle switch comprised of two mutually inhibiting genes described in details in the next section. Bottom is a classical flip-flop element comprised of two genes that are mutually inhibiting in one direction and activating in the other. (B) Example of a self-activating timer. Time is measured by the level of the phosphorylated protein A*, which is accumulated by external signal that phosphorylates A. In typical timers the gene A is self-activated by A*. (C) Example of an inhibition gated switch. Gene B inhibits the self-activating gene C from making transition into high expression (high level state). When the level of protein A increases it inhibits the inhibition of C by B, thus permits a stochastic transition into a state of high C. (D) Example of an oscillator (termed in systems biology as a repressilator) comprised of an inhibition loop among three genes. The origin of the oscillation is as follows: when the level of A increases, it inhibits B. As a result, the level of C increases leading to a decrease in the level of A and so on.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214203&req=5

Figure 2: Examples of simple functional gene circuits. (A) Examples of two-gene circuits. Top is a classical toggle switch comprised of two mutually inhibiting genes described in details in the next section. Bottom is a classical flip-flop element comprised of two genes that are mutually inhibiting in one direction and activating in the other. (B) Example of a self-activating timer. Time is measured by the level of the phosphorylated protein A*, which is accumulated by external signal that phosphorylates A. In typical timers the gene A is self-activated by A*. (C) Example of an inhibition gated switch. Gene B inhibits the self-activating gene C from making transition into high expression (high level state). When the level of protein A increases it inhibits the inhibition of C by B, thus permits a stochastic transition into a state of high C. (D) Example of an oscillator (termed in systems biology as a repressilator) comprised of an inhibition loop among three genes. The origin of the oscillation is as follows: when the level of A increases, it inhibits B. As a result, the level of C increases leading to a decrease in the level of A and so on.
Mentions: In Figure 2 we show examples of functional gene circuits (regulatory motifs) comprised of only a few interacting genes: the toggle switch and the flip-flop circuits (Figure 2A), the self-activating timer (Figure 2B), a gated switch (Figure 2C) and an oscillator (Figure 2D).

Bottom Line: In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise.We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles.We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

View Article: PubMed Central - PubMed

Affiliation: Center for Theoretical Biological Physics, Rice University Houston, TX, USA ; Department of Biosciences, Rice University Houston, TX, USA ; School of Physics and Astronomy and The Sagol School of Neuroscience, Tel-Aviv University Tel-Aviv, Israel.

ABSTRACT
The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

Show MeSH