Pattern formation

Last updated
Pattern formation in a computational model of dendrite growth.

The science of pattern formation deals with the visible, (statistically) orderly outcomes of self-organization and the common principles behind similar patterns in nature.

In developmental biology, pattern formation refers to the generation of complex organizations of cell fates in space and time. The role of genes in pattern formation is an aspect of morphogenesis, the creation of diverse anatomies from similar genes, now being explored in the science of evolutionary developmental biology or evo-devo. The mechanisms involved are well seen in the anterior-posterior patterning of embryos from the model organism Drosophila melanogaster (a fruit fly), one of the first organisms to have its morphogenesis studied, and in the eyespots of butterflies, whose development is a variant of the standard (fruit fly) mechanism.

Patterns in nature

Examples of pattern formation can be found in biology, physics, and science, [1] and can readily be simulated with computer graphics, as described in turn below.

Biology

Biological patterns such as animal markings, the segmentation of animals, and phyllotaxis are formed in different ways. [2]

In developmental biology, pattern formation describes the mechanism by which initially equivalent cells in a developing tissue in an embryo assume complex forms and functions. [3] Embryogenesis, such as of the fruit fly Drosophila, involves coordinated control of cell fates. [4] [5] [6] 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 the French flag model in the 1960s. [7] [8] More generally, the morphology of organisms is patterned by the mechanisms of evolutionary developmental biology, such as changing the timing and positioning of specific developmental events in the embryo. [9]

Possible mechanisms of pattern formation in biological systems include the classical reaction–diffusion model proposed by Alan Turing [10] and the more recently found elastic instability mechanism which is thought to be responsible for the fold patterns on the cerebral cortex of higher animals, among other things. [11] [12]

Growth of colonies

Bacterial colonies show a large variety of patterns formed during colony growth. The resulting shapes depend on the growth conditions. In particular, stresses (hardness of the culture medium, lack of nutrients, etc.) enhance the complexity of the resulting patterns. [13] Other organisms such as slime moulds display remarkable patterns caused by the dynamics of chemical signaling. [14] Cellular embodiment (elongation and adhesion) can also have an impact on the developing patterns. [15]

Vegetation patterns

Tiger bush is a vegetation pattern that forms in arid conditions. Tiger Bush Niger Corona 1965-12-31.jpg
Tiger bush is a vegetation pattern that forms in arid conditions.

Vegetation patterns such as tiger bush [16] and fir waves [17] form for different reasons. Tiger bush consists of stripes of bushes on arid slopes in countries such as Niger where plant growth is limited by rainfall. Each roughly horizontal stripe of vegetation absorbs rainwater from the bare zone immediately above it. [16] In contrast, fir waves occur in forests on mountain slopes after wind disturbance, during regeneration. When trees fall, the trees that they had sheltered become exposed and are in turn more likely to be damaged, so gaps tend to expand downwind. Meanwhile, on the windward side, young trees grow, protected by the wind shadow of the remaining tall trees. [17] In flat terrains additional pattern morphologies appear besides stripes - hexagonal gap patterns and hexagonal spot patterns. Pattern formation in this case is driven by positive feedback loops between local vegetation growth and water transport towards the growth location. [18] [19]

Chemistry

Pattern formation has been well studied in chemistry and chemical engineering, including both temperature and concentration patterns. [20] The Brusselator model developed by Ilya Prigogine and collaborators is one such example that exhibits Turing instability. [21] Pattern formation in chemical systems often involve oscillatory chemical kinetics or autocatalytic reactions [22] such as Belousov–Zhabotinsky reaction or Briggs–Rauscher reaction. In industrial applications such as chemical reactors, pattern formation can lead to temperature hot spots which can reduce the yield or create hazardous safety problems such as a thermal runaway. [23] [20] The emergence of pattern formation can be studied by mathematical modeling and simulation of the underlying reaction-diffusion system. [20] [22]

Similarly as in chemical systems, patterns can develop in a weakly ionized plasma of a positive column of a glow discharge. In such cases creation and annihilation of charged particles due to collisions of atoms corresponds to reactions in chemical systems. Corresponding processes are essentially non-linear and lead in a discharge tube to formation of striations with regular or random character. [24] [25]

Physics

When a planar body of fluid under the influence of gravity is heated from below, Rayleigh-Bénard convection can form organized cells in hexagons or other shapes. These patterns form on the surface of the Sun and in the mantle of the Earth as well as during more pedestrian processes. The interaction between rotation, gravity, and convection can cause planetary atmospheres to form patterns, as is seen in Saturn's hexagon and the Great Red Spot and stripes of Jupiter. The same processes cause ordered cloud formations on Earth, such as stripes and rolls.

In the 1980s Lugiato and Lefever developed a model of light propagation in an optical cavity that results in pattern formation by the exploitation of nonlinear effects.

Precipitating and solidifying materials can crystallize into intricate patterns, such as those seen in snowflakes and dendritic crystals.

Mathematics

Sphere packings and coverings. Mathematics underlies the other pattern formation mechanisms listed.

Computer graphics

Pattern resembling a reaction-diffusion model, produced using sharpen and blur Homebrew reaction diffusion example 512iter.jpg
Pattern resembling a reaction–diffusion model, produced using sharpen and blur

Some types of automata have been used to generate organic-looking textures for more realistic shading of 3d objects. [26] [27]

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 the 'KPT reaction' can be achieved with convolution functions in digital image processing, with a little patience, by repeatedly sharpening and blurring an image in a graphics editor. If other filters are used, such as emboss or edge detection, different types of effects can be achieved.

Computers are often used to simulate the biological, physical or chemical processes that lead to pattern formation, and they can display the results in a realistic way. Calculations using models like reaction–diffusion or MClone are based on the actual mathematical equations designed by the scientists to model the studied phenomena.

Related Research Articles

<span class="mw-page-title-main">Chemotaxis</span> Movement of an organism or entity in response to a chemical stimulus

Chemotaxis is the movement of an organism or entity in response to a chemical stimulus. Somatic cells, bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food by swimming toward the highest concentration of food molecules, or to flee from poisons. In multicellular organisms, chemotaxis is critical to early development and development as well as in normal function and health. In addition, it has been recognized that mechanisms that allow chemotaxis in animals can be subverted during cancer metastasis. The aberrant chemotaxis of leukocytes and lymphocytes also contribute to inflammatory diseases such as atherosclerosis, asthma, and arthritis. Sub-cellular components, such as the polarity patch generated by mating yeast, may also display chemotactic behavior.

<i>In vitro</i> Latin term meaning outside a natural biological environment

In vitro studies are performed with microorganisms, cells, or biological molecules outside their normal biological context. Colloquially called "test-tube experiments", these studies in biology and its subdisciplines are traditionally done in labware such as test tubes, flasks, Petri dishes, and microtiter plates. Studies conducted using components of an organism that have been isolated from their usual biological surroundings permit a more detailed or more convenient analysis than can be done with whole organisms; however, results obtained from in vitro experiments may not fully or accurately predict the effects on a whole organism. In contrast to in vitro experiments, in vivo studies are those conducted in living organisms, including humans, known as clinical trials, and whole plants.

Morphogenesis is the biological process that causes a cell, tissue or organism to develop its shape. It is one of three fundamental aspects of developmental biology along with the control of tissue growth and patterning of cellular differentiation.

<span class="mw-page-title-main">Evolutionary developmental biology</span> Comparison of organism developmental processes

Evolutionary developmental biology is a field of biological research that compares the developmental processes of different organisms to infer how developmental processes evolved.

<span class="mw-page-title-main">Gastrulation</span> Stage in embryonic development in which germ layers form

Gastrulation is the stage in the early embryonic development of most animals, during which the blastula, or in mammals the blastocyst is reorganized into a two-layered or three-layered embryo known as the gastrula. Before gastrulation, the embryo is a continuous epithelial sheet of cells; by the end of gastrulation, the embryo has begun differentiation to establish distinct cell lineages, set up the basic axes of the body, and internalized one or more cell types including the prospective gut.

<span class="mw-page-title-main">Mathematical and theoretical biology</span> Branch of biology

Mathematical and theoretical biology, or biomathematics, is a branch of biology which employs theoretical analysis, mathematical models and abstractions of living organisms to investigate the principles that govern the structure, development and behavior of the systems, as opposed to experimental biology which deals with the conduction of experiments to test scientific theories. The field is sometimes called mathematical biology or biomathematics to stress the mathematical side, or theoretical biology to stress the biological side. Theoretical biology focuses more on the development of theoretical principles for biology while mathematical biology focuses on the use of mathematical tools to study biological systems, even though the two terms are sometimes interchanged.

<span class="mw-page-title-main">Morphogen</span> Biological substance that guides development by non-uniform distribution

A morphogen is a substance whose non-uniform distribution governs the pattern of tissue development in the process of morphogenesis or pattern formation, one of the core processes of developmental biology, establishing positions of the various specialized cell types within a tissue. More specifically, a morphogen is a signaling molecule that acts directly on cells to produce specific cellular responses depending on its local concentration.

Stuart Alan Newman is a professor of cell biology and anatomy at New York Medical College in Valhalla, NY, United States. His research centers around three program areas: cellular and molecular mechanisms of vertebrate limb development, physical mechanisms of morphogenesis, and mechanisms of morphological evolution. He also writes about social and cultural aspects of biological research and technology.

<i>Origination of Organismal Form</i>

Origination of Organismal Form: Beyond the Gene in Developmental and Evolutionary Biology is an anthology published in 2003 edited by Gerd B. Müller and Stuart A. Newman. The book is the outcome of the 4th Altenberg Workshop in Theoretical Biology on "Origins of Organismal Form: Beyond the Gene Paradigm", hosted in 1999 at the Konrad Lorenz Institute for Evolution and Cognition Research. It has been cited over 200 times and has a major influence on extended evolutionary synthesis research.

<span class="mw-page-title-main">Patterned vegetation</span>

Patterned vegetation is a vegetation community that exhibits distinctive and repetitive patterns. Examples of patterned vegetation include fir waves, tiger bush, and string bog. The patterns typically arise from an interplay of phenomena that differentially encourage plant growth or mortality. A coherent pattern arises because there is a strong directional component to these phenomena, such as wind in the case of fir waves, or surface runoff in the case of tiger bush. The regular patterning of some types of vegetation is a striking feature of some landscapes. Patterns can include relatively evenly spaced patches, parallel bands or some intermediate between those two. These patterns in the vegetation can appear without any underlying pattern in soil types, and are thus said to “self-organize” rather than be determined by the environment. Several of the mechanisms underlying patterning of vegetation have been known and studied since at least the middle of the 20th century, however, mathematical models replicating them have only been produced much more recently. Self-organization in spatial patterns is often a result of spatially uniform states becoming unstable through the monotonic growth and amplification of nonuniform perturbations. A well known instability of this kind leads to so-called Turing patterns. These patterns occur at many scales of life, from cellular development to pattern formation on animal pelts to sand dunes and patterned landscapes. In their simplest form models that capture Turing instabilities require two interactions at differing scales: local facilitation and more distant competition. For example, when Sato and Iwasa produced a simple model of fir waves in the Japanese Alps, they assumed that trees exposed to cold winds would suffer mortality from frost damage, but upwind trees would protect nearby downwind trees from wind. Banding appears because the protective boundary layer created by the wind-most trees is eventually disrupted by turbulence, exposing more distant downwind trees to freezing damage once again.

<span class="mw-page-title-main">Structuralism (biology)</span> Attempt to explain evolution by forces other than natural selection

Biological or process structuralism is a school of biological thought that objects to an exclusively Darwinian or adaptationist explanation of natural selection such as is described in the 20th century's modern synthesis. It proposes instead that evolution is guided differently, basically by more or less physical forces which shape the development of an animal's body, and sometimes implies that these forces supersede selection altogether.

Decapentaplegic (Dpp) is a key morphogen involved in the development of the fruit fly Drosophila melanogaster and is the first validated secreted morphogen. It is known to be necessary for the correct patterning and development of the early Drosophila embryo and the fifteen imaginal discs, which are tissues that will become limbs and other organs and structures in the adult fly. It has also been suggested that Dpp plays a role in regulating the growth and size of tissues. Flies with mutations in decapentaplegic fail to form these structures correctly, hence the name. Dpp is the Drosophila homolog of the vertebrate bone morphogenetic proteins (BMPs), which are members of the TGF-β superfamily, a class of proteins that are often associated with their own specific signaling pathway. Studies of Dpp in Drosophila have led to greater understanding of the function and importance of their homologs in vertebrates like humans.

<span class="mw-page-title-main">Reaction–diffusion system</span> Type of mathematical model

Reaction–diffusion systems are mathematical models which correspond to several physical phenomena. The most common is the change in space and time of the concentration of one or more chemical substances: local chemical reactions in which the substances are transformed into each other, and diffusion which causes the substances to spread out over a surface in space.

Mathematical Biology is a two-part monograph on mathematical biology first published in 1989 by the applied mathematician James D. Murray. It is considered to be a classic in the field and sweeping in scope.

<span class="mw-page-title-main">The Chemical Basis of Morphogenesis</span> 1952 article written by English mathematician Alan Turing

"The Chemical Basis of Morphogenesis" is an article that the English mathematician Alan Turing wrote in 1952. It describes how patterns in nature, such as stripes and spirals, can arise naturally from a homogeneous, uniform state. The theory, which can be called a reaction–diffusion theory of morphogenesis, has become a basic model in theoretical biology. Such patterns have come to be known as Turing patterns. For example, it has been postulated that the protein VEGFC can form Turing patterns to govern the formation of lymphatic vessels in the zebrafish embryo.

<span class="mw-page-title-main">Cell polarity</span> Polar morphology of a cell, a specific orientation of the cell structure

Cell polarity refers to spatial differences in shape, structure, and function within a cell. Almost all cell types exhibit some form of polarity, which enables them to carry out specialized functions. Classical examples of polarized cells are described below, including epithelial cells with apical-basal polarity, neurons in which signals propagate in one direction from dendrites to axons, and migrating cells. Furthermore, cell polarity is important during many types of asymmetric cell division to set up functional asymmetries between daughter cells.

The following outline is provided as an overview of and topical guide to natural science:

<span class="mw-page-title-main">Turing pattern</span> Concept from evolutionary biology

The Turing pattern is a concept introduced by English mathematician Alan Turing in a 1952 paper titled "The Chemical Basis of Morphogenesis" which describes how patterns in nature, such as stripes and spots, can arise naturally and autonomously from a homogeneous, uniform state. The pattern arises due to Turing instability which in turn arises due to the interplay between differential diffusion of chemical species and chemical reaction. The instability mechanism is unforeseen because a pure diffusion process would be anticipated to have a stabilizing influence on the system.

Virtual Cell (VCell) is an open-source software platform for modeling and simulation of living organisms, primarily cells. It has been designed to be a tool for a wide range of scientists, from experimental cell biologists to theoretical biophysicists.

<span class="mw-page-title-main">Patterns in nature</span> Visible regularity of form found in the natural world

Patterns in nature are visible regularities of form found in the natural world. These patterns recur in different contexts and can sometimes be modelled mathematically. Natural patterns include symmetries, trees, spirals, meanders, waves, foams, tessellations, cracks and stripes. Early Greek philosophers studied pattern, with Plato, Pythagoras and Empedocles attempting to explain order in nature. The modern understanding of visible patterns developed gradually over time.

References

  1. Ball, 2009.
  2. Ball, 2009. Shapes, pp. 231–252.
  3. Ball, 2009. Shapes, pp. 261–290.
  4. Eric C. Lai (March 2004). "Notch signaling: control of cell communication and cell fate". Development. 131 (5): 965–73. doi:10.1242/dev.01074. PMID   14973298. S2CID   6930563.
  5. Melinda J. Tyler; David A. Cameron (2007). "Cellular pattern formation during retinal regeneration: A role for homotypic control of cell fate acquisition". Vision Research. 47 (4): 501–511. doi: 10.1016/j.visres.2006.08.025 . PMID   17034830. S2CID   15998615.
  6. Hans Meinhard (2001-10-26). "Biological pattern formation: How cell[s] talk with each other to achieve reproducible pattern formation". Max-Planck-Institut für Entwicklungsbiologie, Tübingen, Germany.
  7. Wolpert L (October 1969). "Positional information and the spatial pattern of cellular differentiation". J. Theor. Biol. 25 (1): 1–47. Bibcode:1969JThBi..25....1W. doi:10.1016/S0022-5193(69)80016-0. PMID   4390734.
  8. Wolpert, Lewis; et al. (2007). Principles of development (3rd ed.). Oxford [Oxfordshire]: Oxford University Press. ISBN   978-0-19-927536-6.
  9. Hall, B. K. (2003). "Evo-Devo: evolutionary developmental mechanisms". International Journal of Developmental Biology. 47 (7–8): 491–495. PMID   14756324.
  10. S. Kondo, T. Miura, "Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation", Science 24 Sep 2010: Vol. 329, Issue 5999, pp. 1616-1620 DOI: 10.1126/science.1179047
  11. Mercker, M; Brinkmann, F; Marciniak-Czochra, A; Richter, T (4 May 2016). "Beyond Turing: mechanochemical pattern formation in biological tissues". Biology Direct. 11: 22. doi: 10.1186/s13062-016-0124-7 . PMC   4857296 . PMID   27145826.
  12. Tallinen et al. Nature Physics 12, 588–593 (2016) doi:10.1038/nphys3632
  13. Ball, 2009. Branches, pp. 52–59.
  14. Ball, 2009. Shapes, pp. 149–151.
  15. Duran-Nebreda, Salva; Pla, Jordi; Vidiella, Blai; Piñero, Jordi; Conde-Pueyo, Nuria; Solé, Ricard (2021-01-15). "Synthetic Lateral Inhibition in Periodic Pattern Forming Microbial Colonies". ACS Synthetic Biology. 10 (2): 277–285. doi:10.1021/acssynbio.0c00318. ISSN   2161-5063. PMC   8486170 . PMID   33449631.
  16. 1 2 Tongway, D.J., Valentin, C. & Seghieri, J. (2001). Banded vegetation patterning in arid and semiarid environments. New York: Springer-Verlag. ISBN   978-1-4612-6559-7.{{cite book}}: CS1 maint: multiple names: authors list (link)
  17. 1 2 D'Avanzo, C. (22 February 2004). "Fir Waves: Regeneration in New England Conifer Forests". TIEE. Retrieved 26 May 2012.
  18. Meron, E (2019). "Vegetation pattern formation: the mechanisms behind the forms". Physics Today. 72 (11): 30–36. Bibcode:2019PhT....72k..30M. doi:10.1063/PT.3.4340. S2CID   209478350.
  19. Meron, E (2018). "From Patterns to Function in Living Systems: Dryland Ecosystems as a Case Study". Annual Review of Condensed Matter Physics. 9: 79–103. Bibcode:2018ARCMP...9...79M. doi: 10.1146/annurev-conmatphys-033117-053959 .
  20. 1 2 3 Gupta, Ankur; Chakraborty, Saikat (January 2009). "Linear stability analysis of high- and low-dimensional models for describing mixing-limited pattern formation in homogeneous autocatalytic reactors". Chemical Engineering Journal. 145 (3): 399–411. doi:10.1016/j.cej.2008.08.025. ISSN   1385-8947.
  21. Prigogine, I.; Nicolis, G. (1985), Hazewinkel, M.; Jurkovich, R.; Paelinck, J. H. P. (eds.), "Self-Organisation in Nonequilibrium Systems: Towards a Dynamics of Complexity", Bifurcation Analysis: Principles, Applications and Synthesis, Springer Netherlands, pp. 3–12, doi:10.1007/978-94-009-6239-2_1, ISBN   978-94-009-6239-2
  22. 1 2 Gupta, Ankur; Chakraborty, Saikat (2008-01-19). "Dynamic Simulation of Mixing-Limited Pattern Formation in Homogeneous Autocatalytic Reactions". Chemical Product and Process Modeling. 3 (2). doi:10.2202/1934-2659.1135. ISSN   1934-2659. S2CID   95837792.
  23. Marwaha, Bharat; Sundarram, Sandhya; Luss, Dan (September 2004). "Dynamics of Transversal Hot Zones in Shallow Packed-Bed Reactors†". The Journal of Physical Chemistry B. 108 (38): 14470–14476. doi:10.1021/jp049803p. ISSN   1520-6106.
  24. Igor Grabec (1974). "Nonlinear Properties of High Amplitude Ionization Waves". The Physics of Fluids. 17 (10): 1834–1840. Bibcode:1974PhFl...17.1834G. doi: 10.1063/1.1694626 .
  25. Igor Grabec; Simon Mandelj (2001). "Desynchronization of striations in the development of ionization turbulence". Physics Letters A. 287 (1–2): 105–110. Bibcode:2001PhLA..287..105G. doi:10.1016/S0375-9601(01)00406-6.
  26. Greg Turk, Reaction–Diffusion
  27. Andrew Witkin; Michael Kassy (1991). "Reaction-diffusion textures" (PDF). Proceedings of the 18th annual conference on Computer graphics and interactive techniques - SIGGRAPH '91. pp. 299–308. doi:10.1145/122718.122750. ISBN   0-89791-436-8. S2CID   207162368.{{cite book}}: CS1 maint: date and year (link)

Bibliography