Sewall Green Wright was an American geneticist known for his influential work on evolutionary theory and for his work on path analysis. He was a founder of population genetics alongside Ronald Fisher and J. B. S. Haldane, a major step in the development of the modern synthesis combining genetics with evolution, he discovered methods of computing it in pedigree animals. He extended this work to populations, computing the amount of inbreeding between members of populations as a result of random genetic drift, along with Fisher he pioneered methods for computing the distribution of gene frequencies among populations as a result of the interaction of natural selection, mutation and genetic drift. Wright made major contributions to mammalian and biochemical genetics. Sewall Wright was born in Melrose, Massachusetts to Philip Green Wright and Elizabeth Quincy Sewall Wright, his parents were first cousins, an interesting fact in light of Wright's research on inbreeding. The family moved three years after Philip accepted a teaching job at Lombard College, a Universalist college in Galesburg, Illinois.
As a child, Wright helped his father and brother print and publish an early book of poems by his father's student Carl Sandburg. He was the oldest of three gifted brothers—the others being the aeronautical engineer Theodore Paul Wright and the political scientist Quincy Wright. From an early age Wright had a talent for mathematics and biology. Wright attended Galesburg High School and graduated in 1906, he enrolled in Lombard College where his father taught, to study mathematics. He was influenced by Professor Wilhelmine Entemann Key, one of the first women to receive a Ph. D. in biology. Wright received his Ph. D. from Harvard University, where he worked at the Bussey Institute with the pioneering mammalian geneticist William Ernest Castle investigating the inheritance of coat colors in mammals. He worked for the U. S. Department of Agriculture until 1925, when he joined the Department of Zoology at the University of Chicago, he remained there until his retirement in 1955, when he moved to the University of Wisconsin–Madison.
He received many honors in his long career, including the National Medal of Science, the Balzan Prize, the Darwin Medal of the Royal Society. He was a member of the National Academy of Sciences and a Foreign Member of the Royal Society; the American Mathematical Society selected him as the Josiah Willards Gibbs lecturer for 1941. For his work on genetics of evolutionary processes, Wright was awarded the Daniel Giraud Elliot Medal from the National Academy of Sciences in 1945. Wright married Louise Lane Williams in 1921, they had three children: Richard and Elizabeth. Sewall Wright worshipped as a Unitarian, his papers on inbreeding, mating systems, genetic drift make him a principal founder of theoretical population genetics, along with R. A. Fisher and J. B. S. Haldane, their theoretical work is the origin of the modern evolutionary neodarwinian synthesis. Wright was the inventor/discoverer of the inbreeding coefficient and F-statistics, standard tools in population genetics, he was the chief developer of the mathematical theory of genetic drift, sometimes known as the Sewall Wright effect, cumulative stochastic changes in gene frequencies that arise from random births and Mendelian segregations in reproduction.
In this work he introduced the concept of effective population size. Wright was convinced that the interaction of genetic drift and the other evolutionary forces was important in the process of adaptation, he described the relationship between genotype or phenotype and fitness as fitness surfaces or evolutionary landscapes. On these landscapes mean population fitness was the height, plotted against horizontal axes representing the allele frequencies or the average phenotypes of the population. Natural selection would lead to a population climbing the nearest peak, while genetic drift would cause random wandering. Wright's explanation for stasis was. In order to evolve to another, higher peak, the species would first have to pass through a valley of maladaptive intermediate stages; this could happen by genetic drift. If a species was divided into small populations, some could find higher peaks. If there was some gene flow between the populations, these adaptations could spread to the rest of the species.
This was Wright's shifting balance theory of evolution. There has been much skepticism among evolutionary biologists as to whether these rather delicate conditions hold in natural populations. Wright had a long-standing and bitter debate about this with R. A. Fisher, who felt that most populations in nature were too large for these effects of genetic drift to be important. Wright's statistical method of path analysis, which he invented in 1921 and, one of the first methods using a graphical model, is still used in social science, he was a hugely influential reviewer of manuscripts, as one of the most frequent reviewers for Genetics. Such was his reputation that he was credited with reviews that he did not write. Wright influenced Jay Lush, the most influential figure in introducing quantitative genetics into animal and plant breeding. From 1915 to 1925 Wright was employed by the Animal Husbandry Division of the U. S. Bureau of Animal Husbandry, his main project was to investigate the inbreeding that had occurred in the artificial selection that resulted in the leading breeds of livestock used in American beef production.
He performed experiments with 80,000 guinea pigs in the study of physiological genetics. Further more he an
In biology, a population is all the organisms of the same group or species, which live in a particular geographical area, have the capability of interbreeding. The area of a sexual population is the area where inter-breeding is possible between any pair within the area, where the probability of interbreeding is greater than the probability of cross-breeding with individuals from other areas. In sociology, population refers to a collection of humans. Demography is a social science. Population in simpler terms is the number of people in a city or town, country or world. In population genetics a sex population is a set of organisms in which any pair of members can breed together; this means that they can exchange gametes to produce normally-fertile offspring, such a breeding group is known therefore as a Gamo deme. This implies that all members belong to the same species. If the Gamo deme is large, all gene alleles are uniformly distributed by the gametes within it, the Gamo deme is said to be panmictic.
Under this state, allele frequencies can be converted to genotype frequencies by expanding an appropriate quadratic equation, as shown by Sir Ronald Fisher in his establishment of quantitative genetics. This occurs in Nature: localization of gamete exchange – through dispersal limitations, preferential mating, cataclysm, or other cause – may lead to small actual Gamo demes which exchange gametes reasonably uniformly within themselves but are separated from their neighboring Gamo demes. However, there may be low frequencies of exchange with these neighbors; this may be viewed as the breaking up of a large sexual population into smaller overlapping sexual populations. This failure of panmixia leads to two important changes in overall population structure: the component Gamo demos vary in their allele frequencies when compared with each other and with the theoretical panmictic original; the overall rise in homozygosity is quantified by the inbreeding coefficient. Note that all homozygotes are increased in frequency – both the deleterious and the desirable.
The mean phenotype of the Gamo demes collection is lower than that of the panmictic original –, known as inbreeding depression. It is most important to note, that some dispersion lines will be superior to the panmictic original, while some will be about the same, some will be inferior; the probabilities of each can be estimated from those binomial equations. In plant and animal breeding, procedures have been developed which deliberately utilize the effects of dispersion, it can be shown that dispersion-assisted selection leads to the greatest genetic advance, is much more powerful than selection acting without attendant dispersion. This is so for both autogamous Gamo demes. In ecology, the population of a certain species in a certain area can be estimated using the Lincoln Index. According to the United States Census Bureau the world's population was about 7.55 billion in 2019 and that the 7 billion number was surpassed on 12 March 2012. According to a separate estimate by the United Nations, Earth’s population exceeded seven billion in October 2011, a milestone that offers unprecedented challenges and opportunities to all of humanity, according to UNFPA, the United Nations Population Fund.
According to papers published by the United States Census Bureau, the world population hit 6.5 billion on 24 February 2006. The United Nations Population Fund designated 12 October 1999 as the approximate day on which world population reached 6 billion; this was about 12 years after world population reached 5 billion in 1987, 6 years after world population reached 5.5 billion in 1993. The population of countries such as Nigeria, is not known to the nearest million, so there is a considerable margin of error in such estimates. Researcher Carl Haub calculated that a total of over 100 billion people have been born in the last 2000 years. Population growth increased as the Industrial Revolution gathered pace from 1700 onwards; the last 50 years have seen a yet more rapid increase in the rate of population growth due to medical advances and substantial increases in agricultural productivity beginning in the 1960s, made by the Green Revolution. In 2017 the United Nations Population Division projected that the world's population will reach about 9.8 billion in 2050 and 11.2 billion in 2100.
In the future, the world's population is expected to peak, after which it will decline due to economic reasons, health concerns, land exhaustion and environmental hazards. According to one report, it is likely that the world's population will stop growing before the end of the 21st century. Further, there is some likelihood that population will decline before 2100. Population has declined in the last decade or two in Eastern Europe, the Baltics and in the Commonwealth of Independent States; the population pattern of less-developed regions of the world in recent years has been marked by increasing birth rates. These followed an earlier sharp reduction in death rates; this transition from high birth and death rates to low birth
Genetic diversity is the total number of genetic characteristics in the genetic makeup of a species. It is distinguished from genetic variability, which describes the tendency of genetic characteristics to vary. Genetic diversity serves as a way for populations to adapt to changing environments. With more variation, it is more that some individuals in a population will possess variations of alleles that are suited for the environment; those individuals are more to survive to produce offspring bearing that allele. The population will continue for more generations because of the success of these individuals; the academic field of population genetics includes several hypotheses and theories regarding genetic diversity. The neutral theory of evolution proposes that diversity is the result of the accumulation of neutral substitutions. Diversifying selection is the hypothesis that two subpopulations of a species live in different environments that select for different alleles at a particular locus; this may occur, for instance, if a species has a large range relative to the mobility of individuals within it.
Frequency-dependent selection is the hypothesis that as alleles become more common, they become more vulnerable. This occurs in host–pathogen interactions, where a high frequency of a defensive allele among the host means that it is more that a pathogen will spread if it is able to overcome that allele. A study conducted by the National Science Foundation in 2007 found that genetic diversity and biodiversity are dependent upon each other — i.e. that diversity within a species is necessary to maintain diversity among species, vice versa. According to the lead researcher in the study, Dr. Richard Lankau, "If any one type is removed from the system, the cycle can break down, the community becomes dominated by a single species." Genotypic and phenotypic diversity have been found in all species at the protein, DNA, organismal levels. The interdependence between genetic and species diversity is delicate. Changes in species diversity lead to changes in the environment, leading to adaptation of the remaining species.
Changes in genetic diversity, such as in loss of species, leads to a loss of biological diversity. Loss of genetic diversity in domestic animal populations has been studied and attributed to the extension of markets and economic globalization. Variation in the populations gene pool allows natural selection to act upon traits that allow the population to adapt to changing environments. Selection for or against a trait can occur with changing environment – resulting in an increase in genetic diversity or a decrease in genetic diversity. Hence, genetic diversity plays an important role in the adaptability of a species; the capability of the population to adapt to the changing environment will depend on the presence of the necessary genetic diversity The more genetic diversity a population has, the more likelihood the population will be able to adapt and survive. Conversely, the vulnerability of a population to changes, such as climate change or novel diseases will increase with reduction in genetic diversity.
For example, the inability of koalas to adapt to fight Chlamydia and the koala retrovirus has been linked to the koala’s low genetic diversity. This low genetic diversity has geneticists concerned for the koalas ability to adapt to climate change and human-induced environmental changes in the future. Large populations are more to maintain genetic material and thus have higher genetic diversity. Small populations are more to experience the loss of diversity over time by random chance, called genetic drift; when an allele drifts to fixation, the other allele at the same locus is lost, resulting in a loss in genetic diversity. In small population sizes, inbreeding, or mating between individuals with similar genetic makeup, is more to occur, thus perpetuating more common alleles to the point of fixation, thus decreasing genetic diversity. Concerns about genetic diversity are therefore important with large mammals due to their small population size and high levels of human-caused population effects.
A genetic bottleneck can occur when a population goes through a period of low number of individuals, resulting in a rapid decrease in genetic diversity. With an increase in population size, the genetic diversity continues to be low if the entire species began with a small population, since beneficial mutations are rare, the gene pool is limited by the small starting population; this is an important consideration in the area of conservation genetics, when working toward a rescued population or species, genetically-healthy. Random mutations generate genetic variation. A mutation will increase genetic diversity in the short term, as a new gene is introduced to the gene pool. However, the persistence of this gene is dependent of selection. Most new mutations either have a neutral or negative effect on fitness, while some have a positive effect. A beneficial mutation is more to persist and thus have a long-term positive effect on genetic diversity. Mutation rates differ across the genome, larger populations have greater mutation rates.
In smaller populations a mutation is less to persist because it is more to be eliminated by drift. Gene flow by migration, is the movement of genetic material. Gene flow can introduce novel alleles to a population; these alleles
Population genetics is a subfield of genetics that deals with genetic differences within and between populations, is a part of evolutionary biology. Studies in this branch of biology examine such phenomena as adaptation and population structure. Population genetics was a vital ingredient in the emergence of the modern evolutionary synthesis, its primary founders were Sewall Wright, J. B. S. Haldane and Ronald Fisher, who laid the foundations for the related discipline of quantitative genetics. Traditionally a mathematical discipline, modern population genetics encompasses theoretical and field work. Population genetic models are used both for statistical inference from DNA sequence data and for proof/disproof of concept. What sets population genetics apart today from newer, more phenotypic approaches to modelling evolution, such as evolutionary game theory and adaptive dynamics, is its emphasis on genetic phenomena as dominance and the degree to which genetic recombination breaks up linkage disequilibrium.
This makes it appropriate for comparison to population genomics data. Population genetics began as a reconciliation of Mendelian biostatistics models. Natural selection will only cause evolution. Before the discovery of Mendelian genetics, one common hypothesis was blending inheritance, but with blending inheritance, genetic variance would be lost, making evolution by natural or sexual selection implausible. The Hardy–Weinberg principle provides the solution to how variation is maintained in a population with Mendelian inheritance. According to this principle, the frequencies of alleles will remain constant in the absence of selection, mutation and genetic drift; the next key step was the work of statistician Ronald Fisher. In a series of papers starting in 1918 and culminating in his 1930 book The Genetical Theory of Natural Selection, Fisher showed that the continuous variation measured by the biometricians could be produced by the combined action of many discrete genes, that natural selection could change allele frequencies in a population, resulting in evolution.
In a series of papers beginning in 1924, another British geneticist, J. B. S. Haldane, worked out the mathematics of allele frequency change at a single gene locus under a broad range of conditions. Haldane applied statistical analysis to real-world examples of natural selection, such as peppered moth evolution and industrial melanism, showed that selection coefficients could be larger than Fisher assumed, leading to more rapid adaptive evolution as a camouflage strategy following increased pollution; the American biologist Sewall Wright, who had a background in animal breeding experiments, focused on combinations of interacting genes, the effects of inbreeding on small isolated populations that exhibited genetic drift. In 1932 Wright introduced the concept of an adaptive landscape and argued that genetic drift and inbreeding could drive a small, isolated sub-population away from an adaptive peak, allowing natural selection to drive it towards different adaptive peaks; the work of Fisher and Wright founded the discipline of population genetics.
This integrated natural selection with Mendelian genetics, the critical first step in developing a unified theory of how evolution worked. John Maynard Smith was Haldane's pupil, whilst W. D. Hamilton was influenced by the writings of Fisher; the American George R. Price worked with both Maynard Smith. American Richard Lewontin and Japanese Motoo Kimura were influenced by Wright; the mathematics of population genetics were developed as the beginning of the modern synthesis. Authors such as Beatty have asserted that population genetics defines the core of the modern synthesis. For the first few decades of the 20th century, most field naturalists continued to believe that Lamarckism and orthogenesis provided the best explanation for the complexity they observed in the living world. During the modern synthesis, these ideas were purged, only evolutionary causes that could be expressed in the mathematical framework of population genetics were retained. Consensus was reached as to which evolutionary factors might influence evolution, but not as to the relative importance of the various factors.
Theodosius Dobzhansky, a postdoctoral worker in T. H. Morgan's lab, had been influenced by the work on genetic diversity by Russian geneticists such as Sergei Chetverikov, he helped to bridge the divide between the foundations of microevolution developed by the population geneticists and the patterns of macroevolution observed by field biologists, with his 1937 book Genetics and the Origin of Species. Dobzhansky examined the genetic diversity of wild populations and showed that, contrary to the assumptions of the population geneticists, these populations had large amounts of genetic diversity, with marked differences between sub-populations; the book took the mathematical work of the population geneticists and put it into a more accessible form. Many more biologists were influenced by population genetics via Dobzhansky than were able to read the mathematical works in the original. In Great Britain E. B. Ford, the pioneer of ecological genetics, continued throughout the 1930s and 1940s to empirically demonstrate the power of selection due to ecological factors including the ability to maintain genetic diversity through genetic polymorphisms such as human blood types.
Ford's work, in collaboration with Fisher, contributed to a shift in emphasis during the course of the modern synthesis towards natural selection as the dominant force. The original, modern synthesis view of population genetics assumes that mutations provi
A simulation is an approximate imitation of the operation of a process or system. This model is a well-defined description of the simulated subject, represents its key characteristics, such as its behaviour and abstract or physical properties; the model represents the system itself. Simulation is used in many contexts, such as simulation of technology for performance optimization, safety engineering, training and video games. Computer experiments are used to study simulation models. Simulation is used with scientific modelling of natural systems or human systems to gain insight into their functioning, as in economics. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Simulation is used when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may not exist. Key issues in simulation include the acquisition of valid source information about the relevant selection of key characteristics and behaviours, the use of simplifying approximations and assumptions within the simulation, fidelity and validity of the simulation outcomes.
Procedures and protocols for model verification and validation are an ongoing field of academic study, refinement and development in simulations technology or practice in the field of computer simulation. Simulations used in different fields developed independently, but 20th-century studies of systems theory and cybernetics combined with spreading use of computers across all those fields have led to some unification and a more systematic view of the concept. Physical simulation refers to simulation in which physical objects are substituted for the real thing; these physical objects are chosen because they are smaller or cheaper than the actual object or system. Interactive simulation is a special kind of physical simulation referred to as a human in the loop simulation, in which physical simulations include human operators, such as in a flight simulator, sailing simulator, or a driving simulator. Continuous simulation is a simulation where time evolves continuously based on numerical integration of Differential Equations.
Discrete Event Simulation is a simulation where time evolves along events that represent critical moments, while the values of the variables are not relevant between two of them or result trivial to be computed in case of necessityStochastic Simulation is a simulation where some variable or process is regulated by stochastic factors and estimated based on Monte Carlo techniques using pseudo-random numbers, so replicated runs from same boundary conditions are expected to produce different results within a specific confidence band Deterministic Simulation is a simulation where the variable is regulated by deterministic algorithms, so replicated runs from same boundary conditions produce always identical results. Hybrid Simulation corresponds to a mix between Continuous and Discrete Event Simulation and results in integrating numerically the differential equations between two sequential events to reduce the number of discontinuities Stand Alone Simulation is a Simulation running on a single workstation by itself.
Distributed Simulation is operating over distributed computers in order to guarantee access from/to different resources. Modeling & Simulation as a Service where Simulation is accessed as a Service over the web. Modeling, interoperable Simulation and Serious Games where Serious Games Approaches are integrated with Interoperable Simulation. Simulation Fidelity is used to describe the accuracy of a simulation and how it imitates the real-life counterpart. Fidelity is broadly classified as 1 of 3 categories: low and high. Specific descriptions of fidelity levels are subject to interpretation but the following generalization can be made: Low – the minimum simulation required for a system to respond to accept inputs and provide outputs Medium – responds automatically to stimuli, with limited accuracy High – nearly indistinguishable or as close as possible to the real systemHuman in the loop simulations can include a computer simulation as a so-called synthetic environment. Simulation in failure analysis refers to simulation in which we create environment/conditions to identify the cause of equipment failure.
This was the fastest method to identify the failure cause. A computer simulation is an attempt to model a real-life or hypothetical situation on a computer so that it can be studied to see how the system works. By changing variables in the simulation, predictions may be made about the behaviour of the system, it is a tool to investigate the behaviour of the system under study. Computer simulation has become a useful part of modeling many natural systems in physics and biology, human systems in economics and social science as well as in engineering to gain insight into the operation of those systems
Sir Ronald Aylmer Fisher was a British statistician and geneticist. For his work in statistics, he has been described as "a genius who single-handedly created the foundations for modern statistical science" and "the single most important figure in 20th century statistics". In genetics, his work used mathematics to combine natural selection. For his contributions to biology, Fisher has been called "the greatest of Darwin’s successors". From 1919 onward, he worked at the Rothamsted Experimental Station for 14 years, he established his reputation there in the following years as a biostatistician. He is known as one of the three principal founders of population genetics, he outlined Fisher's principle, the Fisherian runaway and sexy son hypothesis theories of sexual selection. His contributions to statistics include the maximum likelihood, fiducial inference, the derivation of various sampling distributions, founding principles of the design of experiments, much more. Fisher held strong views on race.
Throughout his life, he was a prominent supporter of eugenics, an interest which led to his work on statistics and genetics. Notably, he was a dissenting voice in UNESCO's statement The Race Question, insisting on racial differences. Fisher was born in East Finchley in London, into a middle-class household, he was one of twins, with the other twin being still-born and grew up the youngest, with three sisters and one brother. From 1896 until 1904 they lived at Inverforth House in London, where English Heritage installed a blue plaque in 2002, before moving to Streatham, his mother, died from acute peritonitis when he was 14, his father lost his business 18 months later. Lifelong poor eyesight caused his rejection by the British Army for World War I, but developed his ability to visualize problems in geometrical terms, not in writing mathematical solutions, or proofs, he entered Harrow School won the school's Neeld Medal in mathematics. In 1909, he won a scholarship to study Mathematics at Cambridge.
In 1912, he gained a First in Astronomy. In 1915 he published a paper The evolution of sexual preference on sexual mate choice. During 1913–1919, Fisher worked for six years as a statistician in the City of London and taught physics and maths at a sequence of public schools, at the Thames Nautical Training College, at Bradfield College. There he settled with Eileen Guinness, with whom he had two sons and six daughters. In 1918 he published "The Correlation Between Relatives on the Supposition of Mendelian Inheritance", in which he introduced the term variance and proposed its formal analysis, he put forward a genetics conceptual model showing that continuous variation amongst phenotypic traits measured by biostatisticians could be produced by the combined action of many discrete genes and thus be the result of Mendelian inheritance. This was the first step towards establishing population genetics and quantitative genetics, which demonstrated that natural selection could change allele frequencies in a population, resulting in reconciling its discontinuous nature with gradual evolution.
Joan Box, Fisher's biographer and daughter says that Fisher had resolved this problem in 1911. In 1919, he began working at the Rothamsted Experimental Station for 14 years, where he analysed its immense data from crop experiments since the 1840s, developed the analysis of variance. In 1919, he was offered a position at the Galton Laboratory in University College London led by Karl Pearson, but instead accepted a temporary job at Rothamsted in Harpenden to investigate the possibility of analysing the vast amount of crop data accumulated since 1842 from the "Classical Field Experiments", he analysed the data recorded over many years and in 1921, published Studies in Crop Variation, his first application of the analysis of variance ANOVA. In 1928, Joseph Oscar Irwin began a three-year stint at Rothamsted and became one of the first people to master Fisher's innovations. Between 1912 and 1922 Fisher recommended and vastly popularized Maximum likelihood. Fisher's 1924 article On a distribution yielding the error functions of several well known statistics presented Pearson's chi-squared test and William Gosset's Student's t-distribution in the same framework as the Gaussian distribution and is where he developed Fisher's z-distribution a new statistical method used decades as the F distribution.
He pioneered the principles of the design of experiments and the statistics of small samples and the analysis of real data. In 1925 he published Statistical Methods for Research Workers, one of the 20th century's most influential books on statistical methods. Fisher's method is a technique for data fusion or "meta-analysis"; this book popularized the p-value, plays a central role in his approach. Fisher proposes the level p=0.05, or a 1 in 20 chance of being exceeded by chance, as a limit for statistical significance, applies this to a normal distribution, thus yielding the rule of two standard deviations for statistical significance. The 1.96, the approximate value of the 97.5 percentile point of the normal distribution used in probability and statistics originated in this book. "The value for which P=.05, or 1 in 20, is 1.96 or nearly 2