In Silico Experimental Evolution suggests a complex intertwining of selection, robustness and drift in the evolution of genetic networks complexity
نویسندگان
چکیده
Using the RAevol model we investigate whether the molecular complexity of evolving organisms is linked to the “complexity” of their environment. Here, the complexity is considered as the number of different states environments can have. Results strikingly show that the number of genes acquired by an organism during its evolution does not increase when the number of states of the environment increases but that the connectivity of their genetic regulation network actually does. On the opposite, we show that the mutation rate has an important influence on the gene content. We interpret these results as a complex intertwining of direct selective pressures (the more genes, the better the organisms can be) and robustness and drift thresholds that limit the maximum number of genes at different values depending on the mutation rates.
منابع مشابه
The evolution of epistasis and its links with genetic robustness, complexity and drift in a phenotypic model of adaptation.
The epistatic interactions among mutations have a large effect on the evolution of populations. In this article we provide a formalism under which epistatic interactions among pairs of mutations have a distribution whose mean can be modulated. We find that the mean epistasis is correlated to the effect of mutations or genetic robustness, which suggests that such formalism is in good agreement w...
متن کاملطراحی و آموزش شبکه های عصبی مصنوعی به وسیله استراتژی تکاملی با جمعیت های موازی
Application of artificial neural networks (ANN) in areas such as classification of images and audio signals shows the ability of this artificial intelligence technique for solving practical problems. Construction and training of ANNs is usually a time-consuming and hard process. A suitable neural model must be able to learn the training data and also have the generalization ability. In this pap...
متن کاملA Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network
Abstract Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...
متن کاملMutational Pressure Drives Evolution of Synonymous Codon Usage in Genetically Distinct Oenothera plastomes
Background: Most of the amino acids are encoded by more than one codon, termed as synonymous codons. Synonymous codon usage is not random as it is unique to species. In each amino acid family, some synonymous codons are preferred and this is referred to as synonymous codon usage bias (SCUB). Trends associated with evolution of SCUB and factors influencing its diversification in plastomes of gen...
متن کاملOptimization of Beam Orientation and Weight in Radiotherapy Treatment Planning using a Genetic Algorithm
Introduction: The selection of suitable beam angles and weights in external-beam radiotherapy is at present generally based upon the experience of the planner. Therefore, automated selection of beam angles and weights in forward-planned radiotherapy will be beneficial. Material and Methods: In this work, an efficient method is presented within the MATLAB environment to investigate how to improv...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017