Lineage selection and the evolution of multistage carcinogenesis

A wide array of proto-oncogenes and tumour suppressor genes are involved in the prevention of cancer. Each form of cancer requires mutations in a characteristic group of genes, but no single group controls all cancers. This lack of generality shows that the control of cancer is not an ancient, fixed property of cells. By contrast, it supports a dynamic evolutionary model, whereby genetic controls over unregulated cell growth are recruited independently through evolutionary time in different tissues within different taxa. The complexity of this genetic control can be predicted from a population genetic model of lineage selection driven by the detrimental fitness effects of cancer. Cancer occurs because the genetic control of cell growth is vulnerable to somatic mutations (or 'hits'), particularly in large, continuously dividing tissues. Thus, compared to small rodents, humans must have evolved more complex genetic controls over cell growth in at least some of their tissues because of their greater size and longevity; an expectation relevant to the application of mouse data to humans. Similarly, the 'two-hit' model so successfully applied to retinoblastoma, which originates in a small embryonic tissue, is unlikely to be generally applicable to other human cancers; instead, more complex scenarios are expected to dominate, with complexity depending upon a tissue's size and its pattern of proliferation.     

Computer simulations: tools for population and evolutionary genetics

Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions.     

An immune network inspired evolutionary algorithm for the diagnosis of Parkinson's disease

This paper presents a novel evolutionary algorithm inspired by protein/substrate binding exploited in enzyme genetic programming (EGP) and artificial immune networks. The immune network-inspired evolutionary algorithm has been developed in direct response to an application in clinical neurology, the diagnosis of Parkinson's disease. The inspiration for, and implementation of the algorithm is described and its performance to the application area considered.     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

基于全基因组关联的中国常见肿瘤遗传病因学研究

肿瘤是基因-环境交互作用引起的复杂性疾病.在同样的环境暴露下,不同遗传背景的个体发生肿瘤的风险有很大差异.研究肿瘤相关遗传因素对理解肿瘤发生发展乃至诊断治疗都有重要意义.近年来发展的全基因组关联研究(genome-wide association study,GWAS)可在全基因组范围内发现与复杂疾病或表型关联的遗传因素,为复杂疾病遗传学研究提供了强有力的手段.欧美研究者运用全基因组关联研究的方法,对各种常见肿瘤进行了研究,获得了重要成果.2010年以来,中国科学家在国际核心期刊发表了一系列高水平的肿瘤全基因组关联研究成果,在中国常见肿瘤的遗传病因学研究方面取得了重要进展.     

Genetic programming of polynomial harmonic networks using the discrete Fourier transform

This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.     

全基因组关联分析实现水稻粒型自然变异的分子解析

全基因组关联分析(GWAS)近年来被广泛应用于解析生物自然变异的遗传基础。但限于其遗传定位精度, 在水稻(Oryza sativa)遗传学研究中, 该方法尚无法取代传统的图位克隆法在克隆复杂性状调控基因中的作用。近期, 中国科学家在应用GWAS等大数据来克隆控制水稻粒长和粒重等复杂性状的QTL方面取得了新突破。     

Population genomics and the causes of local differentiation

Exactly 50 years ago, a revolution in empirical population genetics began with the introduction of methods for detecting allelic variation using protein electrophoresis (Throckmorton 1962; Hubby 1963; Lewontin & Hubby 1966). These pioneering scientists showed that populations are chock‐full of genetic variation. This variation was a surprise that required a re‐thinking of evolutionary genetic heuristics. Understanding the causes for the maintenance of this variation became and remains a major area of research. In the process of addressing the causes, this same group of scientists documented geographical genetic structure (Prakash et al. 1969), spawning the continued accumulation of what is now a huge case study catalogue of geographical differentiation (e.g. Loveless & Hamrick 1984; Linhart & Grant 1996). Geographical differentiation is clearly quite common. Yet, a truly general understanding of the patterns in and causes of spatial genetic structure across the genome remains elusive. To what extent is spatial structure driven by drift and phylogeography vs. geographical differences in environmental sources of selection? What proportion of the genome participates? A general understanding requires range‐wide data on spatial patterning of variation across the entire genome. In this issue of Molecular Ecology, Lasky et al. (2012) make important strides towards addressing these issues, taking advantage of three contemporary revolutions in evolutionary biology. Two are technological: high‐throughput sequencing and burgeoning computational power. One is cultural: open access to data from the community of scientists and especially data sets that result from large collaborative efforts. Together, these developments may at last put answers within reach.     

Memetic algorithms for the unconstrained binary quadratic programming problem

This paper presents a memetic algorithm, a highly effective evolutionary algorithm incorporating local search for solving the unconstrained binary quadratic programming problem (BQP). To justify the approach, a fitness landscape analysis is conducted experimentally for several instances of the BQP. The results of the analysis show that recombination-based variation operators are well suited for the evolutionary algorithms with local search. Therefore, the proposed approach includes--besides a highly effective randomized k-opt local search--a new variation operator that has been tailored specially for the application in the hybrid evolutionary framework. The operator is called innovative variation and is fundamentally different from traditional crossover operators, since new genetic material is included in the offspring which is not contained in one of the parents. The evolutionary heuristic is tested on 35 publicly available BQP instances, and it is shown experimentally that the algorithm is capable of finding best-known solutions to large BQPs in a short time and with a high frequency. In comparison to other approaches for the BQP, the approach appears to be much more effective, particularly for large instances of 1000 or 2500 binary variables.     

Guidelines: From artificial evolution to computational evolution: a research agenda

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.     

A simple genetic algorithm for multiple sequence alignment

Multiple sequence alignment plays an important role in molecular sequence analysis. An alignment is the arrangement of two (pairwise alignment) or more (multiple alignment) sequences of 'residues' (nucleotides or amino acids) that maximizes the similarities between them. Algorithmically, the problem consists of opening and extending gaps in the sequences to maximize an objective function (measurement of similarity). A simple genetic algorithm was developed and implemented in the software MSA-GA. Genetic algorithms, a class of evolutionary algorithms, are well suited for problems of this nature since residues and gaps are discrete units. An evolutionary algorithm cannot compete in terms of speed with progressive alignment methods but it has the advantage of being able to correct for initially misaligned sequences; which is not possible with the progressive method. This was shown using the BaliBase benchmark, where Clustal-W alignments were used to seed the initial population in MSA-GA, improving outcome. Alignment scoring functions still constitute an open field of research, and it is important to develop methods that simplify the testing of new functions. A general evolutionary framework for testing and implementing different scoring functions was developed. The results show that a simple genetic algorithm is capable of optimizing an alignment without the need of the excessively complex operators used in prior study. The clear distinction between objective function and genetic algorithms used in MSA-GA makes extending and/or replacing objective functions a trivial task.     

Genetic identification of biological species in theSaccharomyces sensu stricto complex

Studies on taxonomic and evolutionary genetics of theSaccharomyces sensu stricto complex are considered in light of the biological species concept. Genetic variability of some physiological properties traditionally used in yeast taxonomy is discussed. Genetic hybridization analysis and molecular karyotyping revealed six biological species in theSaccharomyces sensu stricto complex. DNA-DNA reassociation data are concordant with the data obtained by genetic analysis. A new system for naming the cultivatedSaccharomyces yeast (groups of cultivars) is proposed.This paper is dedicated to Danish scientists Ö Winge and V Jensen in recognition of their contributions to zymology.     

Did natural selection or genetic drift produce the cranial diversification of neotropical monkeys?

A central controversy among biologists is the relative importance of natural selection and genetic drift as creative forces shaping biological diversification (Fisher 1930; Wright 1931). Historically, this controversy has been an effective engine powering several evolutionary research programs during the last century (Provine 1989). While all biologists agree that both processes operate in nature to produce evolutionary change, there is a diversity of opinion about which process dominates at any particular organizational level (from DNA and proteins to complex morphologies). To address this last level, we did a broadscale analysis of cranial diversification among all living New World monkeys. Quantitative genetic models yield specific predictions about the relationship between variation patterns within and between populations that may be used to test the hypothesis that genetic drift is a sufficient explanation for morphological diversification. Diversity at several levels in a hierarchy of taxonomic/phylogenetics relationship was examined from species within genera to families within superfamilies. The major conclusion is that genetic drift can be ruled out as the primary source of evolutionary diversification in cranial morphology among taxa at the level of the genus and above as well as for diversification of most genera. However, drift may account for diversification among species within some Neotropical primate genera, implying that morphological diversification associated with speciation need not be adaptive in some radiations.     

Using creation science to demonstrate evolution: application of a creationist method for visualizing gaps in the fossil record to a phylogenetic study of coelurosaurian dinosaurs

It is important to demonstrate evolutionary principles in such a way that they cannot be countered by creation science. One such way is to use creation science itself to demonstrate evolutionary principles. Some creation scientists use classic multidimensional scaling (CMDS) to quantify and visualize morphological gaps or continuity between taxa, accepting gaps as evidence of independent creation and accepting continuity as evidence of genetic relatedness. Here, I apply CMDS to a phylogenetic analysis of coelurosaurian dinosaurs and show that it reveals morphological continuity between Archaeopteryx, other early birds, and a wide range of nonavian coelurosaurs. Creation scientists who use CMDS must therefore accept that these animals are genetically related. Other uses of CMDS for evolutionary biologists include the identification of taxa with much missing evolutionary history and the tracing of the progressive filling of morphological gaps in the fossil record through successive years of discovery.     

1001 optimal PDB structure alignments: integer programming methods for finding the maximum contact map overlap.

Protein structure comparison is a fundamental problem for structural genomics, with applications to drug design, fold prediction, protein clustering, and evolutionary studies. Despite its importance, there are very few rigorous methods and widely accepted similarity measures known for this problem. In this paper we describe the last few years of developments on the study of an emerging measure, the contact map overlap (CMO), for protein structure comparison. A contact map is a list of pairs of residues which lie in three-dimensional proximity in the protein's native fold. Although this measure is in principle computationally hard to optimize, we show how it can in fact be computed with great accuracy for related proteins by integer linear programming techniques. These methods have the advantage of providing certificates of near-optimality by means of upper bounds to the optimal alignment value. We also illustrate effective heuristics, such as local search and genetic algorithms. We were able to obtain for the first time optimal alignments for large similar proteins (about 1,000 residues and 2,000 contacts) and used the CMO measure to cluster proteins in families. The clusters obtained were compared to SCOP classification in order to validate the measure. Extensive computational experiments showed that alignments which are off by at most 10% from the optimal value can be computed in a short time. Further experiments showed how this measure reacts to the choice of the threshold defining a contact and how to choose this threshold in a sensible way.     

hCDC4 and Genetic Instability in Cancer

About twenty years ago, scientists began to discover that colorectal cancers are caused by the sequential acquisition of genetic alterations in specific genes. To this day, we are still dissecting the genome of colorectal cancers to identify specific “culprit” genes that play a role in tumorigenesis. At the same time, we have more recently begun to turn our attention to the features of cancer cells that distinguish them from normal cells and that may be targeted therapeutically. Aneuploidy is one such hallmark of cancers, but its role in tumorigenesis is heretofore undetermined. Our efforts have focused on elucidating the fundamental mechanisms underlying aneuploidy. The assertion that a genetic basis for aneuploidy would imply its importance in tumorigenesis, and consequently make it a potential therapeutic target, represents the rationale for our pursuit of this line of research. For the last few years, we have been trying to determine whether there is a genetic cause underlying this attribute of cancers. Our recently published work entitled, “Inactivation of hCDC4 can cause chromosomal instability,” attempts to address this issue and raises more questions about the cause, mechanism, timing, and therapeutic potential of genetic instability.1     

Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.     

Developmental quantitative genetic models of evolutionary change

Discussions about evolutionary change in developmental processes or morphological structures are predicated on specific quantitative genetic models whose parameters predict whether evolutionary change can occur, its relative rate and direction, and if correlated change will occur in other related and unrelated structures. The appropriate genetic model should reflect the relevant genetical and developmental biology of the organisms, yet be simple enough in its parameters so that deductions can be made and hypotheses tested. As a consequence, the choice of the most appropriate genetic model for polygenically controlled traits is a complex tissue and the eventual choice of model is often a compromise between completeness of the model and computational expediency. Herein, we discuss several developmental quantitative genetic models for the evolution of development and morphology. The models range from the classical direct effects model to complex epigenetic models. Further, we demonstrate the algebraic equivalency of the Cowley and Atchley epigenetic model and Wagner's developmental mapping model. Finally, we propose a new multivariate model for continuous growth trajectories. The relative efficacy of these various models for understanding evolutionary change in developmental and morphological traits is discussed. © 1994 Wiley-Liss, Inc.     

Ecological and evolutionary consequences of linked life-history stages in the sea

Naturalists and scientists have been captivated by the diversity of marine larval forms since they were discovered following the advent of the microscope. Because they often bear little resemblance to adults, larvae were identified initially as new life forms, classified into different groups based on the similarity of their body plans and given new names that are still with us today. The radically different body plans and lifestyles of marine larvae and adults have led most investigators historically to study the two phases of complex life cycles in isolation. More recently, important ecological insights have sprung from taking a holistic view of marine life cycles. Meanwhile, the evolutionary (phenotypic and genetic) links among life-history phases remain less appreciated. In this review, our objective is to evaluate the evolutionary links within marine life cycles, and explore their ecological and evolutionary consequences. We provide a brief overview of marine life histories, discuss the phenotypic and genetic links between the two phases of the life cycle and pose challenges to advance our understanding of the evolutionary constraints acting on marine life histories.     

CVhaplot: a consensus tool for statistical haplotyping

Haplotypes contain genealogical information and play a prominent part in population genetic and evolutionary studies. However, haplotype inference is a complex statistical problem, showing considerable internal algorithm variability and among-algorithm discordance. Thus, haplotypes inferred by statistical algorithms often contain hidden uncertainties, which may complicate and even mislead downstream analysis. Consensus strategy is one of the effective means to increase the confidence of inferred haplotypes. Here, we present a consensus tool, the CVhaplot package, to automate consensus techniques for haplotype inference. It generates consensus haplotypes from inferrals of competing algorithms to increase the confidence of haplotype inference results, while improving the performance of individual algorithms by considering their internal variability. It can effectively identify uncertain haplotypes potentially associated with inference errors. In addition, this tool allows file format conversion for several popular algorithms and extends the applicability of some algorithms to complex data containing triallelic polymorphic sites. CVhaplot is written in PERL and freely available at http://www.ioz.ac.cn/department/agripest/group/zhangdx/CVhaplot.htm.