Nature, Nurture, and Noise: Developmental Instability, Fluctuating Asymmetry, and the Causes of Phenotypic Variation

Phenotypic variation arises from genetic and environmental variation, as well as random aspects of development. The genetic (nature) and environmental (nurture) components of this variation have been appreciated since at least 1900. The random developmental component (noise) has taken longer for quantitative geneticists to appreciate. Here, I sketch the historical development of the concepts of random developmental noise and developmental instability, and its quantification via fluctuating asymmetry. The unsung pioneers in this story are Hugo DeVries (fluctuating variation, 1909), C. H. Danforth (random variation between monozygotic twins, 1919), and Sewall Wright (random developmental variation in piebald guinea pigs, 1920). The first pioneering study of fluctuating asymmetry, by Sumner and Huestis in 1921, is seldom mentioned, possibly because it failed to connect the observed random asymmetry with random developmental variation. This early work was then synthesized by Boris Astaurov in 1930 and Wilhelm Ludwig in 1932, and then popularized by Drosophila geneticists beginning with Kenneth Mather in 1953. Population phenogeneticists are still trying to understand the origins and behavior of random developmental variation. Some of the developmental noise represents true stochastic behavior of molecules and cells, while some represents deterministic chaos, nonlinear feedback, and symmetry breaking.

Regresi Logistik Ordinal untuk Mengetahui Faktor-Faktor yang Mempengaruhi Motivasi Menabung Mahasiswa

Analisis jalur (path analysis) dikembangkan oleh Sewall Wright pada tahun 1934 yang bertujuan untuk menerangkan akibat langsung dan tidak langsung dari seperangkat variabel penyebab (variabel eksogen) terhadap seperangkat variabel akibat (variabel endogen). Analisis jalur dalam penelitian ini digunakan sebagai konseptul untuk memperkuat teoritis, sedangkan metode analisis menggunakan regresi logistik ordinal. Data primer digunakan dalam penelitian ini. Jumlah populasi sebanyak 421 mahasiswa program studi Pendidikan Agama Islam Universitas KH A Wahab Hasbullah. Teknik sampling yang digunakan yaitu simple random sampling, dan dari hasil perhitungan didapatkan 75 sampel yang diambil dari populasi Metode analisis yang digunakan pada penelitian ini menggunakan analisis jalur (path analysis). Tujuan penelitian ini untuk mengetahui hubungan struktural antara variabel eksogen (motivasi orang tua, motivasi dosen, kebutuhan pribadi mahasiswa dan program menabung dikampus) terhadap variabel endogen (pemahaman mahasiswa uang dan menabung dan motivasi menabung). Hasil penelitian menunjukkan bahwa variabel Y2 (minat mahasiswa menabung) mendapat pengaruh langsung maupun tidak langsung dari variabel X1 (orang tua) dan X2 (dosen). Sedangkan variabel X3 (kebutuhan mahasiswa) dan X4 (program menabung kampus) hanya memberikan pengaruh langsung

Why Edgar Anderson Visited Math Departments

Edgar Anderson of the Missouri Botanical Garden had long and rich collaborations with such mathematicians and mathematically inclined biologists as R. A. Fisher, Sewall Wright, and John Tukey. It was Anderson’s Iris data that Fisher used to develop his linear discriminant function to capture multiple variations. A sabbatical with Wright in 1933 helped hone Anderson’s mathematical skills while helping him understand what mathematics could and could not do. He and Tukey shared an interest in conveying data graphically. This long-standing commitment to applying mathematics to natural history problems informed his scientific career as he sought to capture the variations he recognized in the natural populations. He used graphical tools to examine hybridization as an evolutionary mechanism and to use the taxonomic data from these variations to study the underlying genetic forces at work in evolution. In important synthesis articles in the mid-1950s, he summarized his conclusions about applied mathematics and natural history. They were not mere technical tools, but reflected a commitment to observation and pattern recognition as the basis of his science. Understanding these views more fully deepens an appreciation of this constantly independent-minded contributor to evolutionary theory in the twentieth century.

Landscapes

This chapter begins by discussing fitness landscape, an idea first introduced by evolutionary geneticist Sewall Wright and later extended by several other authors. The fitness landscape is defined in terms of some particular traits that are implicit in the virus particle phenotype and are usually described in terms of replication rate or infectivity. The landscape appears in most textbook plots as a multi-peaked surface. Local maxima represent optimal fitness values, which can be reached through mutation from a subset of lower-fitness neighbors. Given an initial condition defined by a quasi-species distribution localized somewhere in the sequence space, the population will evolve by exploring nearest positions through mutation. The remainder of the chapter deals with symmetric competition, epistasis in RNA viruses, experimental virus landscapes, the survival of the flattest effect, and virus robustness.