Development of antibiotic regimens using graph based evolutionary algorithms

This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems.     

On the use of multi-objective evolutionary algorithms for survival analysis

This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approximate lifetime/failure time distributions. These models can be used to estimate the time that an event takes to happen to an object. To use of multi-objective evolutionary algorithms for survival analysis has several advantages. They can cope with feature interactions, noisy data, and are capable of optimising several objectives. This is important, as model extraction is a multi-objective problem. It has at least two objectives, which are the extraction of accurate and simple models. Accurate models are required to achieve good predictions. Simple models are important to prevent overfitting, improve the transparency of the models, and to save computational resources. Although there is a plethora of evolutionary approaches to extract models for classification and regression, the presented approach is one of the first applied to survival analysis. The approach is evaluated on several artificial datasets and one medical dataset. It is shown that the approach is capable of producing accurate models, even for problems that violate some of the assumptions made by classical approaches.     

Inference of Euler angles for single-particle analysis by means of evolutionary algorithms

Single-particle analysis is one of the methods for structural studies of protein and macromolecules; it requires advanced image analysis of electron micrographics. Reconstructing three-dimensional (3D) structure from microscope images is not an easy analysis because of the low image resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified, and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures was automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying genetic algorithms. We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.     

Genome-wide population genetic analysis identifies evolutionary forces establishing continuous population divergence

Elucidating the mechanism shaping the spatial variations of traits has long been a central concern of evolutionary biologists. Geographic clines of allele/morph frequencies along environmental gradients are suggested to be established and maintained by the balancing of two opposing evolutionary forces, namely selection that generates spatial differentiation in morph frequencies, and selection and/or stochastic factors that lead to the coexistence of multiple morphs within a population. Thus, testing for both selection and stochastic factors is necessary for a comprehensive understanding of the mechanism underlying clinal variation in morph/allele frequency in natural populations. Here, I identified the evolutionary forces responsible for clinal variation of color morph frequency in Ischnura senegalensis by comparing the population divergence of putatively neutral loci generated by high-throughput next-generation sequencing (F _STn) with that of the putative color locus (F _STc). No strong correlation was observed between F _STn and F _STc, suggesting that stochastic factors contribute less to color-locus population divergence. F _STc was less than F _STn between populations exposed to similar environmental conditions, but greater than F _STn between populations exposed to different environmental conditions, suggesting that both balancing selection and divergent selection act on the color locus. Therefore, two antagonistic selection factors rather than stochastic and historical factors contribute to establishing the clinal variation of morph frequency in I. senegalensis.     

Molecular evolutionary analysis based on the amino acid sequence of catalase

Heme-containing catalase sequences from 20 different organisms representing prokaryotes, fungi, animals, and plants have been compiled for phylogenetic reconstruction. Phylogenies based on distance and parsimony analysis show that fungal and animal catalases can be derived from one ancestor, whereas bacterial catalases fail to form a monophyletic group. Plant catalases appear to form a second class of catalases that arose independently from a possible prokaryotic ancestor.Correspondence to: P.C. Loewen     

Efficient algorithms for protein sequence design and the analysis of certain evolutionary fitness landscapes.

Protein sequence design is a natural inverse problem to protein structure prediction: given a target structure in three dimensions, we wish to design an amino acid sequence that is likely fold to it. A model of Sun, Brem, Chan, and Dill casts this problem as an optimization on a space of sequences of hydrophobic (H) and polar (P) monomers; the goal is to find a sequence that achieves a dense hydrophobic core with few solvent-exposed hydrophobic residues. Sun et al. developed a heuristic method to search the space of sequences, without a guarantee of optimality or near-optimality; Hart subsequently raised the computational tractability of constructing an optimal sequence in this model as an open question. Here we resolve this question by providing an efficient algorithm to construct optimal sequences; our algorithm has a polynomial running time, and performs very efficiently in practice. We illustrate the implementation of our method on structures drawn from the Protein Data Bank. We also consider extensions of the model to larger amino acid alphabets, as a way to overcome the limitations of the binary H/P alphabet. We show that for a natural class of arbitrarily large alphabets, it remains possible to design optimal sequences efficiently. Finally, we analyze some of the consequences of this sequence design model for the study of evolutionary fitness landscapes. A given target structure may have many sequences that are optimal in the model of Sun et al.; following a notion raised by the work of J. Maynard Smith, we can ask whether these optimal sequences are "connected" by successive point mutations. We provide a polynomial-time algorithm to decide this connectedness property, relative to a given target structure. We develop the algorithm by first solving an analogous problem expressed in terms of submodular functions, a fundamental object of study in combinatorial optimization.     

Reliable classification of two-class cancer data using evolutionary algorithms

In the area of bioinformatics, the identification of gene subsets responsible for classifying available disease samples to two or more of its variants is an important task. Such problems have been solved in the past by means of unsupervised learning methods (hierarchical clustering, self-organizing maps, k-mean clustering, etc.) and supervised learning methods (weighted voting approach, k-nearest neighbor method, support vector machine method, etc.). Such problems can also be posed as optimization problems of minimizing gene subset size to achieve reliable and accurate classification. The main difficulties in solving the resulting optimization problem are the availability of only a few samples compared to the number of genes in the samples and the exorbitantly large search space of solutions. Although there exist a few applications of evolutionary algorithms (EAs) for this task, here we treat the problem as a multiobjective optimization problem of minimizing the gene subset size and minimizing the number of misclassified samples. Moreover, for a more reliable classification, we consider multiple training sets in evaluating a classifier. Contrary to the past studies, the use of a multiobjective EA (NSGA-II) has enabled us to discover a smaller gene subset size (such as four or five) to correctly classify 100% or near 100% samples for three cancer samples (Leukemia, Lymphoma, and Colon). We have also extended the NSGA-II to obtain multiple non-dominated solutions discovering as much as 352 different three-gene combinations providing a 100% correct classification to the Leukemia data. In order to have further confidence in the identification task, we have also introduced a prediction strength threshold for determining a sample's belonging to one class or the other. All simulation results show consistent gene subset identifications on three disease samples and exhibit the flexibilities and efficacies in using a multiobjective EA for the gene subset identification task.     

Currencies for foraging based on energetic gain

We carry out a theoretical investigation of the behavior of a foraging animal that maximizes either the net amount of energy obtained (self-feeding) or the amount of energy delivered to another animal such as its young or to a store (provisioning). Using an novel graphical approach, we derive general results concerning the effects of constraints on the amount of energy the animal can spend or acquire. In the context of an animal that is provisioning, that is, both feeding itself and delivering energy to a given location, we establish a general relationship between the best foraging option when feeding itself and the best option to use when delivering energy. Our results extend and unify previous results in this area.     

Cytological, genetic and evolutionary functions of chiasmata based on chiasma graph analysis.

The nature of the chiasma as a cytological parameter for analysing cross-over was reexamined quantitatively by an improved chiasma graph method. It was reconfirmed in Mus platythrix (n =13) that interstitial chiasmata at diakinesis are distributed randomly and almost uniformly along bivalents except for the centromere and telomere regions. The size of these chiasma blank regions was consistently 0.8% of the total length of haploid autosomes in all chromosomes. There was a minimum value of chiasma interference distance between two adjacent chiasmata, which was constantly 1.8% in all chromosomes. The chiasma frequency at diakinesis was 20.1+/-2. 0 by the conventional method including terminal chiasmata. However, the primed in situ labeling technique revealed that terminal chiasmata were mostly telomere-telomere associations. From these data and also from recent molecular data we concluded that the terminal chiasma is cytologically functional for ensuring the normal disjunction of bivalents at anaphase I, but genetically non-functional for shuffling genes. The chiasma frequency excluding terminal chiasmata was 14.6+/-1.8. Reexamination of the chiasma frequency of 106 animal species revealed that the chiasma frequency increased linearly in proportion to the haploid chromosome number in spite of remarkable difference in their genome size. The increase in chiasma frequency would be evolution-adaptive, because gene shuffling is expected to be accelerated in species with high chromosome numbers.     

Comparison of evolutionary algorithms in gene regulatory network model inference

Background  

The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.     

The evolutionary gain of spliceosomal introns: sequence and phase preferences

Theories regarding the evolution of spliceosomal introns differ in the extent to which the distribution of introns reflects either a formative role in the evolution of protein-coding genes or the adventitious gain of genetic elements. Here, systematic methods are used to assess the causes of the present-day distribution of introns in 10 families of eukaryotic protein-coding genes comprising 1,868 introns in 488 distinct alignment positions. The history of intron evolution inferred using a probabilistic model that allows ancestral inheritance of introns, gain of introns, and loss of introns reveals that the vast majority of introns in these eukaryotic gene families were not inherited from the most recent common ancestral genes, but were gained subsequently. Furthermore, among inferred events of intron gain that meet strict criteria of reliability, the distribution of sites of gain with respect to reading-frame phase shows a 5:3:2 ratio of phases 0, 1 and 2, respectively, and exhibits a nucleotide preference for MAG GT (positions -3 to +2 relative to the site of gain). The nucleotide preferences of intron gain may prove to be the ultimate cause for the phase bias. The phase bias of intron gain is sufficient to account quantitatively for the well-known 5:3:2 bias in phase frequencies among extant introns, a conclusion that holds even when taxonomic heterogeneity in phase patterns is considered. Thus, intron gain accounts for the vast majority of extant introns and for the bias toward phase 0 introns that previously was interpreted as evidence for ancient formative introns.     

Optimal algorithms for the interval location problem with range constraints on length and average

Let A be a sequence of n real numbers, L(1) and L(2) be two integers such that L(1) < or = L(2) , and R(1) and R(2) be two real numbers such that R(1) < or = R(2). An interval of A is feasible if its length is between L(1) and L(2) and its average is between R(1) and R(2). In this paper, we study the following problems: finding all feasible intervals of A, counting all feasible intervals of A, finding a maximum cardinality set of non-overlapping feasible intervals of A, locating a longest feasible interval of A, and locating a shortest feasible interval of A. The problems are motivated from the problem of locating CpG islands in biomolecular sequences. In this paper, we firstly show that all the problems have Omega (n log n)-time lower bound in the comparison model. Then, we use geometric approaches to design optimal algorithms for the problems. All the presented algorithms run in an on-line manner and use O(n) space.     

利用生物信息学方法对人类(Homo sapiens)肌肉增强因子2(MEF2)的特性比较及进化分析

肌肉增强因子2(Myocyte enhancer factor 2,MEF2)是MADS(MCM1,agamous,deficiens和serum response factor)家族成员之一,在动物发育过程中起到重要的调节作用。为了进一步了解其调控的复杂性,本文根据NCBI中已有的人类MEF2相关数据,应用ExPASy在线序列分析工具、CBS在线分析服务器软件、Conserved Domain Database(CDD)数据库、SABLE在线分析软件等对人类MEF2蛋白的不同亚型序列进行比较分析,同时,根据相关序列的比对结果构建系统进化树进行分析。结果表明,MEF2在人体内以多种蛋白形式存在,其理化性质存在一定差别,可能的翻译后糖基化修饰多为O型糖基化且均存在较多磷酸化位点。人类各MEF2蛋白具明显MADS结构域,多数具有MEF2结构域和HJURP_C结构域。各MEF2蛋白二级结构均包括了螺旋、折叠和无规则卷曲等多种形式,其三级结构模式相似。系统进化树显示MEF2B蛋白与其他蛋白有着较大的序列差异及较远进化关系,可能较为原始。     

Quantitative metagenomic analyses based on average genome size normalization

Over the past quarter-century, microbiologists have used DNA sequence information to aid in the characterization of microbial communities. During the last decade, this has expanded from single genes to microbial community genomics, or metagenomics, in which the gene content of an environment can provide not just a census of the community members but direct information on metabolic capabilities and potential interactions among community members. Here we introduce a method for the quantitative characterization and comparison of microbial communities based on the normalization of metagenomic data by estimating average genome sizes. This normalization can relieve comparative biases introduced by differences in community structure, number of sequencing reads, and sequencing read lengths between different metagenomes. We demonstrate the utility of this approach by comparing metagenomes from two different marine sources using both conventional small-subunit (SSU) rRNA gene analyses and our quantitative method to calculate the proportion of genomes in each sample that are capable of a particular metabolic trait. With both environments, to determine what proportion of each community they make up and how differences in environment affect their abundances, we characterize three different types of autotrophic organisms: aerobic, photosynthetic carbon fixers (the Cyanobacteria); anaerobic, photosynthetic carbon fixers (the Chlorobi); and anaerobic, nonphotosynthetic carbon fixers (the Desulfobacteraceae). These analyses demonstrate how genome proportionality compares to SSU rRNA gene relative abundance and how factors such as average genome size and SSU rRNA gene copy number affect sampling probability and therefore both types of community analysis.     

Standard error of immunological dating of evolutionary time

Summary The empirical variance of the immunological distance as measured by microcomplement fixation with albumin is determined. The variance obtained is at least two times larger than the mean when the mean is small and the ratio of the variance to the mean increases with increasing mean. Thus, the immunological dating of evolutionary time has a large standard error. It is shown that in bird lysozymes the relationship between immunological distance (y) and the number of amino acid substitutions per 100 sites (x) is given byy = 4.2x approximately.     

Rates of evolution on the time scale of the evolutionary process

A generational time scale, involving change from one generation to the next, is the time scale of evolution by natural selection. Microevolutionary and macroevolutionary patterns reflect this process on longer time scales. Rates of evolution are most efficiently expressed in haldane units, H, in standard deviations per generation, indexed by the log of the time interval. Rates from replicated selection experiments and simulations have rate-interval [RI] and log rate-log interval [LRI] scaling relations enabling directional, stationary, and random time series to be distinguished. Empirical microevolutionary and macroevolutionary data exhibit stationary scaling, but point to generational rates of evolution (H ₀) conservatively on the order of 0.2 standard deviations per generation on the time scale of the evolutionary process. This paradox of long-term stationary scaling and short-term high rates of change can be explained by considering the shape of an heuristic time-form evolutionary lattice. Cenozoic mammals occupy a lattice that is about four orders of magnitude longer in time than it has ever been wide in form. The evolutionary process is dynamic but operates within relatively narrow morphological constraints compared to the time available for change.