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The importance of structured noise in the generation of self-organizing tissue patterns through contact-mediated cell-cell signalling.

Cohen M, Baum B, Miodownik M - J R Soc Interface (2010)

Bottom Line: Here, we develop a general model of protrusion-based patterning to analyse the role of noise in this process.By analysing the effects of introducing thresholds required for signal detection in this model of lateral inhibition, our study shows how filopodia-mediated cell-cell communication can generate complex patterns of spots and stripes, which, in the presence of signalling noise, align themselves across a patterning field.Thus, intermittent protrusion-based signalling has the potential to yield robust self-organizing tissue-wide patterns without the need to invoke diffusion-mediated signalling.

View Article: PubMed Central - PubMed

Affiliation: CoMPLEX, University College London, London, UK. m.cohen@ucl.ac.uk

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An enhanced model of lateral inhibition incorporating signalling noise and different inhibitory thresholds. (a) The rule table determines the probability that a selected cell in an asynchronous cellular automaton will actively express an inhibitory signal. The threshold (T) is the minimum number of active signalling cells (n) required to inhibit an inactive cell. Temporal noise (Nt) is the probability that a cell will stop signalling even without an inhibitory signal at the required threshold. Spatial noise(Ns) is the probability that an inactive cell will signal even when it is in contact with the threshold number of active signalling cells. This probability reduces as the number of active neighbours increases over the threshold. (b) A schematic of pattern shifting owing to temporal signalling noise. A cell's inhibitory signal (dark grey) oscillates over time. Its signal effectively ceases such that at subsequent time steps neighbouring cells that were previously inhibited may become active. (c) A schematic of pattern shifting owing to spatial signalling noise. A signal ‘connection’ is broken and an inactive cell (white) no longer receives an inhibitory signal and becomes active. At subsequent time steps when the connection is re-established, the cells compete and a stable configuration is re-established. (d) A representation of spatial noise in which a minimum threshold of two active cells is required to inhibit a third cell. As in (c) the pattern may shift as a result of the signal noise.
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RSIF20100488F3: An enhanced model of lateral inhibition incorporating signalling noise and different inhibitory thresholds. (a) The rule table determines the probability that a selected cell in an asynchronous cellular automaton will actively express an inhibitory signal. The threshold (T) is the minimum number of active signalling cells (n) required to inhibit an inactive cell. Temporal noise (Nt) is the probability that a cell will stop signalling even without an inhibitory signal at the required threshold. Spatial noise(Ns) is the probability that an inactive cell will signal even when it is in contact with the threshold number of active signalling cells. This probability reduces as the number of active neighbours increases over the threshold. (b) A schematic of pattern shifting owing to temporal signalling noise. A cell's inhibitory signal (dark grey) oscillates over time. Its signal effectively ceases such that at subsequent time steps neighbouring cells that were previously inhibited may become active. (c) A schematic of pattern shifting owing to spatial signalling noise. A signal ‘connection’ is broken and an inactive cell (white) no longer receives an inhibitory signal and becomes active. At subsequent time steps when the connection is re-established, the cells compete and a stable configuration is re-established. (d) A representation of spatial noise in which a minimum threshold of two active cells is required to inhibit a third cell. As in (c) the pattern may shift as a result of the signal noise.

Mentions: Having previously suggested that signalling noise could be involved in this process of refinement, we next used the CA model of lateral inhibition to consider the effect of signalling noise on the long-term development of patterns by introducing non-binary cell update probabilities into the model. These generalized rules are defined in figure 3a. Two types of ‘structured’ noise were considered in turn, as follows.Figure 3.


The importance of structured noise in the generation of self-organizing tissue patterns through contact-mediated cell-cell signalling.

Cohen M, Baum B, Miodownik M - J R Soc Interface (2010)

An enhanced model of lateral inhibition incorporating signalling noise and different inhibitory thresholds. (a) The rule table determines the probability that a selected cell in an asynchronous cellular automaton will actively express an inhibitory signal. The threshold (T) is the minimum number of active signalling cells (n) required to inhibit an inactive cell. Temporal noise (Nt) is the probability that a cell will stop signalling even without an inhibitory signal at the required threshold. Spatial noise(Ns) is the probability that an inactive cell will signal even when it is in contact with the threshold number of active signalling cells. This probability reduces as the number of active neighbours increases over the threshold. (b) A schematic of pattern shifting owing to temporal signalling noise. A cell's inhibitory signal (dark grey) oscillates over time. Its signal effectively ceases such that at subsequent time steps neighbouring cells that were previously inhibited may become active. (c) A schematic of pattern shifting owing to spatial signalling noise. A signal ‘connection’ is broken and an inactive cell (white) no longer receives an inhibitory signal and becomes active. At subsequent time steps when the connection is re-established, the cells compete and a stable configuration is re-established. (d) A representation of spatial noise in which a minimum threshold of two active cells is required to inhibit a third cell. As in (c) the pattern may shift as a result of the signal noise.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3104346&req=5

RSIF20100488F3: An enhanced model of lateral inhibition incorporating signalling noise and different inhibitory thresholds. (a) The rule table determines the probability that a selected cell in an asynchronous cellular automaton will actively express an inhibitory signal. The threshold (T) is the minimum number of active signalling cells (n) required to inhibit an inactive cell. Temporal noise (Nt) is the probability that a cell will stop signalling even without an inhibitory signal at the required threshold. Spatial noise(Ns) is the probability that an inactive cell will signal even when it is in contact with the threshold number of active signalling cells. This probability reduces as the number of active neighbours increases over the threshold. (b) A schematic of pattern shifting owing to temporal signalling noise. A cell's inhibitory signal (dark grey) oscillates over time. Its signal effectively ceases such that at subsequent time steps neighbouring cells that were previously inhibited may become active. (c) A schematic of pattern shifting owing to spatial signalling noise. A signal ‘connection’ is broken and an inactive cell (white) no longer receives an inhibitory signal and becomes active. At subsequent time steps when the connection is re-established, the cells compete and a stable configuration is re-established. (d) A representation of spatial noise in which a minimum threshold of two active cells is required to inhibit a third cell. As in (c) the pattern may shift as a result of the signal noise.
Mentions: Having previously suggested that signalling noise could be involved in this process of refinement, we next used the CA model of lateral inhibition to consider the effect of signalling noise on the long-term development of patterns by introducing non-binary cell update probabilities into the model. These generalized rules are defined in figure 3a. Two types of ‘structured’ noise were considered in turn, as follows.Figure 3.

Bottom Line: Here, we develop a general model of protrusion-based patterning to analyse the role of noise in this process.By analysing the effects of introducing thresholds required for signal detection in this model of lateral inhibition, our study shows how filopodia-mediated cell-cell communication can generate complex patterns of spots and stripes, which, in the presence of signalling noise, align themselves across a patterning field.Thus, intermittent protrusion-based signalling has the potential to yield robust self-organizing tissue-wide patterns without the need to invoke diffusion-mediated signalling.

View Article: PubMed Central - PubMed

Affiliation: CoMPLEX, University College London, London, UK. m.cohen@ucl.ac.uk

Show MeSH
Related in: MedlinePlus