(embryology) The embryogenic process in which the spatial differentiation of cells is specified in a structure that initially is largely homogeneous.
| Sci-Tech Dictionary: pattern formation |
(embryology) The embryogenic process in which the spatial differentiation of cells is specified in a structure that initially is largely homogeneous.
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| Sci-Tech Encyclopedia: Pattern formation |
The mechanisms that ensure that particular cell types differentiate in the correct location within the embryo and that the layers of cells bend and grow in the correct relative positions. Pattern formation is one of four processes that underlie development, the others being growth, cell diversification, and morphogenesis. See also Animal growth; Animal morphogenesis; Cell differentiation; Plant growth; Plant morphogenesis.
Pattern formation is the creation of a predictable arrangement of cell types in space during embryonic development. The types of patterns of cell types found in animals and plants can be conveniently described as simple or complex. Simple patterns involve the spatial arrangement of identical or equivalent structures such as bristles on the leg of a fly, hairs on a person's head, or leaves on a plant. Such equivalent patterns are thought to be produced by mechanisms that are the same or very similar in the fly and the plant. Complex patterns are those that are made up of parts that are not equivalent to one another. In the vertebrate limb, for example, the structure of the arm is different at each level, with one bone (humerus) in the upper arm, two bones (radius and ulna) in the lower arm, and a complex set of bones making up the wrist and the hand. How are such nonequivalent parts patterned during development? The theoretical framework that allows a basis for understanding how such patterns arise is called positional information. Two stages exist in the positional information framework. First, a cell must become aware of its position within a developing group, or field, of cells. This specification of cellular position requires a mechanism by which each cell within a field can obtain a unique value or address. The second component is the interpretation of the positional address by a cell to manifest a particular cell type by the expression of a particular set of genes. See also Developmental biology; Embryonic differentiation.
| Wikipedia: Pattern formation |
The science of pattern formation deals with the visible, (statistically) orderly outcomes of self-organisation and the common principles behind similar patterns.
In developmental biology, pattern formation refers to the generation of complex organizations of cell fates in space and time. Pattern formation is controlled by genes. The role of genes in pattern formation is best understood in the anterior-posterior patterning of embryos from the model organism Drosophila melanogaster (fruit fly).
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Animal markings, segmentation of animals, phyllotaxis, neuronal activation patterns like tonotopy, predator-prey equations' trajectories.
In developmental biology, pattern formation describes the mechanism by which initially equivalent cells in a developing tissue assume complex forms and functions by coordinated cell fate control[1][2]. Pattern formation is genetically controlled, and often involves each cell in a field sensing and responding to its position along a morphogen gradient, followed by short distance cell-to-cell communication through cell signaling pathways to refine the initial pattern. In this context, a field of cells is the group of cells whose fates are affected by responding to the same set positional information cues. This conceptual model was first described as French flag model in the 1960s.
One of the best understood examples of pattern formation is the patterning along the future head to tail (antero-posterior) axis of the fruit fly Drosophila melanogaster. The development of Drosophila is particularly well studied, and it is representative of a major class of animals, the insects or insecta. Other multicellular organisms sometimes use similar mechanisms for axis formation, although the relative importance of signal transfer between the earliest cells of many developing organisms is greater than in the example described here.
Bacterial colonies show a large variety of beautiful patterns formed during colony growth. Experiments show that the resulting shapes depend on the growth conditions. In particular stresses (hardness of the culture medium, lack of nutrients, etc) seem to enhance the complexity of the resulting patterns.
see reaction-diffusion systems and Turing Patterns
Sphere packings and coverings.
Bénard cells, Laser, cloud formations in stripes or rolls. Ripples in icicles. Washboard patterns on dirtroads. Dendrites in solidification, liquid crystals, the structure of foams [3].
Some types of automata have been used to generate organic-looking textures for more realistic shading of 3d objects [4][5].
A popular photoshop plugin, KPT 6, included a filter called 'KPT reaction'. Reaction produced reaction-diffusion style patterns based on the supplied seed image.
A similar effect to 'kpt reaction' can be achieved, with a little patience, by repeatedly sharpening and then blurring an image in many graphics applications. If other filters are used, such as emboss or edge detection, different types of effects can be achieved.
In addition, computers are often used to simulate the biological, physical or chemical processes -described above- that lead to pattern formation, and they are then able to display the results in a realistic way (applications of virtual reality for Science). Calculations are based on the actual mathematical equations designed by the scientists to model the studied phenomena.
The analysis of pattern-forming systems often consists of finding a PDE model of the system (the Swift-Hohenberg equation is one such model) of the form

where F is generically a nonlinear differential operator, and postulating solutions of the form

where the zj are complex amplitudes associated to different modes in the solution and the
are the wave-vectors associated to a lattice, e.g. a square or hexagonal lattice in two dimensions. There is in general no rigorous justification for this restriction to a lattice.
Symmetry considerations can now be taken into account, and evolution equations obtained for the complex amplitudes governing the solution. This reduction puts the problem into the form of a system of first-order ODEs, which can be analysed using standard methods (see dynamical systems). The same formalism can also be used to analyse bifurcations in pattern-forming systems, for example to analyse the formation of convection rolls in a Rayleigh-Bénard experiment as the temperature is increased.
Such analysis predicts many of the quantitative features of such experiments - for example, the ODE reduction predicts hysteresis in convection experiments as patterns of rolls and hexagons compete for stability. The same hysteresis has been observed experimentally.
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