Abstract :
This article provides a tremendous opportunity for the use of biological sequence information as "molecular fossils" of information that could be compared between extant organisms to determine their evolution. Seemingly all that was required was additional sequence information and better computers and algorithms for their interpretation. Assessing the reliability of phylogenies involves difficult statistical and computational problems, including the NP-complete problems of sequence alignment and discovering the best phylogenetic tree that fits the data. Given modern databases filled with sequence information, interest has turned from one of generating sequence to rapid interpretation and discovery of "true" phytogenies for their application in not only the resolution of the history of life but also for epidemiology as it relates to human disease. Three developments have been essential in this progression: 1) the development of criteria and algorithms for discriminating among potential phylogenies, 2) increased computational power over time, and 3) the rapid increase in sequence data availability. An assortment of algorithms has been offered to solve the phylogenetic reconstruction problem, some using evolutionary algorithms.
Keywords :
biology computing; diseases; evolution (biological); evolutionary computation; molecular biophysics; NP-complete problems; biological sequence information; epidemiology; evolutionary algorithms; human disease; molecular fossils; phylogenetic tree; phylogenies reliability; sequence alignment; Biology computing; Databases; Diseases; Evolution (biology); Evolutionary computation; History; Humans; NP-complete problem; Organisms; Phylogeny;