Gary B. Fogel

Prokaryotic Genome Size and SSU rDNA Copy Number: Estimation of Microbial Relative Abundance from a Mixed Population.

A bstractDetermination of the relative abundance of a specific prokaryote in an environmental sample is of major interest in applied and environmental microbiology. Relative abundance can be calculated using knowledge of SSU rDNA copy number, amount of SSU rDNA in the sample, and a weighted average estimate of the genome sizes for organisms in the original sample. By surveying the literature, we provide estimates of genome size and SSU rDNA copy number for 303 and 101 prokaryotes, respectively. This compilation can be used to make reasonable estimates for a wide range of organisms in the calculation of relative abundance. A statistical analysis suggests that no correlation exists between genome size and SSU rDNA copy number. A phylogenetic analysis is used to offer insights into the evolution of both genome size and SSU rDNA copy number.

On the instability of evolutionary stable strategies in small populations

Evolutionary stable strategies (ESSs) are often used to explain the behaviors of individuals and species. The analysis of ESSs determines which, if any, combinations of behaviors cannot be invaded by alternative strategies. Two assumptions required to generate an ESS (i.e. an infinite population and payoffs described only on the average) do not hold under natural conditions. Previous experiments have indicated that under more realistic conditions of finite populations and stochastic payoffs, populations may evolve in trajectories that are unrelated to an ESS, even in very simple evolutionary games. The simulations are extended here to small populations with varying levels of selection pressure and mixing levels. The results suggest that ESSs may not provide a good explanation of the behavior of small populations even at relatively low levels of selection pressure and even under persistent mixing. The implications of these results are discussed briefly in light of previous literature which claimed that ESSs generated suitable explanations of real-world data.

ON THE INSTABILITY OF EVOLUTIONARY STABLE STRATEGIES

Evolutionary stable strategies (ESSs) are often used to explain the behaviors of individuals and species. The analysis of ESSs determines which, if any, combinations of behaviors cannot be invaded by alternative strategies. However, two of the assumptions required to generate ESSs, an infinite population and payoffs described only on the average, are not particularly realistic in natural situations. Previous experiments have indicated that under more natural conditions of finite populations and stochastic payoffs, populations may evolve in trajectories that are unrelated to an ESS, even in very simple evolutionary games. Those earlier simulations are extended here under a variety of conditions. The results suggest that ESSs may not provide a good explanation of a finite populations behavior even when the conditions correspond closely with the infinite population model. The implications of these results are discussed briefly in light of previous literature claiming that ESSs generated suitable explanations of real-world data.

CONTINUOUS EVOLUTIONARY PROGRAMMING: ANALYSIS AND EXPERIMENTS

Evolutionary programming is a method for simulating evolution that emphasizes the behavioral rather than the genetic relationship of parents and their offspring. In a typical evolutionary program, every parent simultaneously generates a number of offspring, which are all subsequently placed in competition. Evolution can be abstracted as a more continuous process by generating only a single offspring from one parent and then immediately placing it in competition with all existing solutions. Some theoretical observations are made with respect to this new model. The results of empirical trials on a test landscape with multiple local minima indicate that the standard method of reproduction and selection may be more appropriate for practical optimization problems.

Identification of Coding Regions in DNA Sequences Using Evolved Neural Networks

Publisher Summary Artificial neural networks (ANNs) are computer algorithms based loosely on modeling the neuronal structure of natural organisms. They are stimulus-response transfer functions that accept some input and yield some output. This chapter focuses on the use of evolutionary computation (EC) for developing non-rule-based ANNs for the task of discriminating functional elements associated with coding nucleotides from noncoding sequences of DNA. Success in this regard will provide a platform to assist the molecular biologist to identify regions of importance in previously unannotated DNA sequences. Optimizing ANNs through simulated evolution not only offers a superior search for appropriate network parameters, but the evolution can also be used to adjust the networks topology simultaneously. This chapter introduces the reader to ANNs, identifies methods to evolve ANNs for pattern recognition, and then shows examples of this technique applied to gene detection.

Reconstruction of DNA Sequence Information from a Simulated DNA Chip Using Evolutionary Programming

DNA sequencing methods are the subject of continued interest in molecular biology for use in a wide variety of applications. Sequencing DNA by hybridization on a “DNA chip” has been estimated to increase the rate of DNA sequencing by as much as one-million fold. In this process, the sequence of a target molecule is reconstructed by the complementary binding of a pool of random probe molecules. For each target, an appropriate probe length must be used to unambiguously determine the sequence of a given target sequence of length N. Using evolutionary programming, we have simulated the binding of probes of length four nucleotides to a series of target lengths to determine most optimal target length that can be unambiguously reconstructed. Evolutionary programming is demonstrated to be well suited to sequence reconstruction problems and could also be extended for gene expression monitoring with DNA chip technology.

The Application of Evolutionary Computation to Selected Problems in Molecular Biology

Molecular biologists are currently faced with an array of computationally complex optimization problems. Over the past six years, evolutionary computation has been demonstrated to be useful for some of these problems, in particular RNA and protein structure prediction. This survey will focus on applications of evolutionary programming and genetic algorithms. Future applications of evolutionary computation in the medical and molecular sciences are suggested. The problems faced in computer-aided molecular design represent a challenging new testing ground for algorithms incorporating the evolutionary process.

Expression of Tetrahymena histone H4 in yeast

Histone H4 is one of the most conserved proteins known. The very low rate of nonsynonymous substitution in H4 suggests that it fulfills an essential function in virtually all eukaryotes. While the majority of histone H4 sequences differ only slightly from the general consensus H4 sequence, yeast and Tetrahymena sequences diverge substantially from both the consensus and from each other. This study demonstrates that despite this divergence, when Saccharomyces cerevisiae cells are forced to use the Tetrahymena thermophila histone H4 protein, they are viable although they have a reduced growth rate, are temperature-sensitive relative to wild-type, have a lengthened G2 phase, and show a dramatic repression of mating. An amino acid replacement at position 33 in the protein improves the growth rate of these cells growing at temperatures above 28 degrees C. This replacement changes a proline to a serine and is a further divergence from both the Tetrahymena thermophila and Saccharomyces cerevisiae histone H4 sequences. Thus, the replacement and expression of a non wild-type histone H4 in yeast offers measurable effects on cell growth, identifying amino acids required for optimal yeast functioning.