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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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Simulating lateral inhibition patterning. (a) A schematic of the lateral inhibition patterning. Initially homogeneous cells (light grey) compete to express an inhibitory signal. Eventually a single cell becomes active (dark grey) and strongly inhibits the expression of the signal in its contacting neighbours. (b) The outcome of lateral inhibition signalling expressed as a probabilistic rule set. The signalling probability determines whether a single cell in a field will express an inhibitory signal based on the total number of its active signalling neighbours (n). (c) An asynchronous cellular automata simulation of lateral inhibition. Cells in the 8 × 8 hexagonally packed array are sequentially selected at random and updated according to the rule table in (b). The outcome is a notably disordered packing of active cells (dark grey) expressing the inhibitory signal.
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RSIF20100488F2: Simulating lateral inhibition patterning. (a) A schematic of the lateral inhibition patterning. Initially homogeneous cells (light grey) compete to express an inhibitory signal. Eventually a single cell becomes active (dark grey) and strongly inhibits the expression of the signal in its contacting neighbours. (b) The outcome of lateral inhibition signalling expressed as a probabilistic rule set. The signalling probability determines whether a single cell in a field will express an inhibitory signal based on the total number of its active signalling neighbours (n). (c) An asynchronous cellular automata simulation of lateral inhibition. Cells in the 8 × 8 hexagonally packed array are sequentially selected at random and updated according to the rule table in (b). The outcome is a notably disordered packing of active cells (dark grey) expressing the inhibitory signal.

Mentions: Lateral inhibition patterns arise as a homogeneous group of cells compete to express an inhibitory signal. The end result of this signalling process is cells that either express an inhibitory signal or are inhibited from doing so by signalling cells with which they are in contact (figure 2a). In this way, an array of cells can be described as a two-state system, in which cells are either active (expressing inhibitory signals) or inactive (inhibited). This binary state system lends itself to analysis as a two-state CA. Within this formalism, the transition probabilities for lateral inhibition can be easily captured using a simple rule-based logic: a cell with an active neighbour has zero probability of being active, while a cell with no active neighbours has a probability of being active of 1. This is represented in figure 2b,c, and constitutes a discrete version of the continuum models of lateral inhibition, which rely on threshold concentrations of Notch and Delta determined through coupled differential equations to determine cell state [2,21].Figure 2.


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)

Simulating lateral inhibition patterning. (a) A schematic of the lateral inhibition patterning. Initially homogeneous cells (light grey) compete to express an inhibitory signal. Eventually a single cell becomes active (dark grey) and strongly inhibits the expression of the signal in its contacting neighbours. (b) The outcome of lateral inhibition signalling expressed as a probabilistic rule set. The signalling probability determines whether a single cell in a field will express an inhibitory signal based on the total number of its active signalling neighbours (n). (c) An asynchronous cellular automata simulation of lateral inhibition. Cells in the 8 × 8 hexagonally packed array are sequentially selected at random and updated according to the rule table in (b). The outcome is a notably disordered packing of active cells (dark grey) expressing the inhibitory signal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3104346&req=5

RSIF20100488F2: Simulating lateral inhibition patterning. (a) A schematic of the lateral inhibition patterning. Initially homogeneous cells (light grey) compete to express an inhibitory signal. Eventually a single cell becomes active (dark grey) and strongly inhibits the expression of the signal in its contacting neighbours. (b) The outcome of lateral inhibition signalling expressed as a probabilistic rule set. The signalling probability determines whether a single cell in a field will express an inhibitory signal based on the total number of its active signalling neighbours (n). (c) An asynchronous cellular automata simulation of lateral inhibition. Cells in the 8 × 8 hexagonally packed array are sequentially selected at random and updated according to the rule table in (b). The outcome is a notably disordered packing of active cells (dark grey) expressing the inhibitory signal.
Mentions: Lateral inhibition patterns arise as a homogeneous group of cells compete to express an inhibitory signal. The end result of this signalling process is cells that either express an inhibitory signal or are inhibited from doing so by signalling cells with which they are in contact (figure 2a). In this way, an array of cells can be described as a two-state system, in which cells are either active (expressing inhibitory signals) or inactive (inhibited). This binary state system lends itself to analysis as a two-state CA. Within this formalism, the transition probabilities for lateral inhibition can be easily captured using a simple rule-based logic: a cell with an active neighbour has zero probability of being active, while a cell with no active neighbours has a probability of being active of 1. This is represented in figure 2b,c, and constitutes a discrete version of the continuum models of lateral inhibition, which rely on threshold concentrations of Notch and Delta determined through coupled differential equations to determine cell state [2,21].Figure 2.

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