Starburst radio galaxies: general properties, evolutionary histories and triggering

In this paper we discuss the results of a programme of spectral synthesis modelling of a sample of starburst radio galaxies in the context of scenarios for the triggering of the activity and the evolution of the host galaxies. The starburst radio galaxies -- comprising ~15 - 25% of all powerful extragalactic radio sources -- frequently show disturbed morphologies at optical wavelengths, and unusual radio structures, although their stellar masses are typical of radio galaxies as a class. In terms of the characteristic ages of their young stellar populations (YSP), the objects can be divided into two groups: those with YSP ages t_ysp < 0.1 Gyr, in which the radio source has been triggered quasi-simultaneously with the main starburst episode, and those with older YSP in which the radio source has been triggered or re-triggered a significant period after the starburst episode. Combining the information on the YSP with that on the optical morphologies of the host galaxies, we deduce that the majority of the starburst radio galaxies have been triggered in galaxy mergers in which at least one of the galaxies is gas rich. However, the triggering (or re-triggering) of the radio jets can occur immediately before, around, or a significant period after the final coalescence of the merging nuclei, reflecting the complex gas infall histories of the merger events. Overall, our results provide further evidence that powerful radio jet activity can be triggered via a variety of mechanisms, including different evolutionary stages of major galaxy mergers; clearly radio-loud AGN activity is not solely associated with a particular stage of a unique type of gas accretion event.Comment: 16 pages, 3 Figures, accepted for publication in MNRA

Inferring evolutionary histories of pathway regulation from transcriptional profiling data

One of the outstanding challenges in comparative genomics is to interpret the evolutionary importance of regulatory variation between species. Rigorous molecular evolution-based methods to infer evidence for natural selection from expression data are at a premium in the field, and to date, phylogenetic approaches have not been well-suited to address the question in the small sets of taxa profiled in standard surveys of gene expression. We have developed a strategy to infer evolutionary histories from expression profiles by analyzing suites of genes of common function. In a manner conceptually similar to molecular evolution models in which the evolutionary rates of DNA sequence at multiple loci follow a gamma distribution, we modeled expression of the genes of an \emph{a priori}-defined pathway with rates drawn from an inverse gamma distribution. We then developed a fitting strategy to infer the parameters of this distribution from expression measurements, and to identify gene groups whose expression patterns were consistent with evolutionary constraint or rapid evolution in particular species. Simulations confirmed the power and accuracy of our inference method. As an experimental testbed for our approach, we generated and analyzed transcriptional profiles of four \emph{Saccharomyces} yeasts. The results revealed pathways with signatures of constrained and accelerated regulatory evolution in individual yeasts and across the phylogeny, highlighting the prevalence of pathway-level expression change during the divergence of yeast species. We anticipate that our pathway-based phylogenetic approach will be of broad utility in the search to understand the evolutionary relevance of regulatory change.Comment: 30 pages, 12 figures, 2 tables, contact authors for supplementary table

How much information is needed to infer reticulate evolutionary histories?

Phylogenetic networks are a generalization of evolutionary trees and are an important tool for analyzing reticulate evolutionary histories. Recently, there has been great interest in developing new methods to construct rooted phylogenetic networks, that is, networks whose internal vertices correspond to hypothetical ancestors, whose leaves correspond to sampled taxa, and in which vertices with more than one parent correspond to taxa formed by reticulate evolutionary events such as recombination or hybridization. Several methods for constructing evolutionary trees use the strategy of building up a tree from simpler building blocks (such as triplets or clusters), and so it is natural to look for ways to construct networks from smaller networks. In this article, we shall demonstrate a fundamental issue with this approach. Namely, we show that even if we are given all of the subnetworks induced on all proper subsets of the leaves of some rooted phylogenetic network, we still do not have all of the information required to completely determine that network. This implies that even if all of the building blocks for some reticulate evolutionary history were to be taken as the input for any given network building method, the method might still output an incorrect history. We also discuss some potential consequences of this result for constructing phylogenetic networks

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.Comment: 28 pages, 15 figures. arXiv admin note: text overlap with arXiv:1103.434

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes

The Formation and Evolution of Massive Star Clusters: Historical Overview

Some factors connecting the evolutionary histories of galaxies with the characteristics of their cluster systems are reviewed. Unanswered questions include: How is one to understand the observation that some globular cluster systems have disk kinematics whereas others do not? Why do some galaxies have cluster systems with unimodal metallicity distributions, whereas others have bimodal metallicity distributions? What caused the average ellipticity of individual clusters to differ from galaxy to galaxy?Comment: To be published in ASP Conference Serie