30 research outputs found
Detecting Network Communities: An Application to Phylogenetic Analysis
This paper proposes a new method to identify communities in generally weighted
complex networks and apply it to phylogenetic analysis. In this case, weights
correspond to the similarity indexes among protein sequences, which can be used
for network construction so that the network structure can be analyzed to
recover phylogenetically useful information from its properties. The analyses
discussed here are mainly based on the modular character of protein similarity
networks, explored through the Newman-Girvan algorithm, with the help of the
neighborhood matrix . The most relevant
networks are found when the network topology changes abruptly revealing distinct
modules related to the sets of organisms to which the proteins belong. Sound
biological information can be retrieved by the computational routines used in
the network approach, without using biological assumptions other than those
incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases,
also some bacterial classes corresponded totally (100%) or to a great
extent (>70%) to the modules. We checked for internal consistency in
the obtained results, and we scored close to 84% of matches for community
pertinence when comparisons between the results were performed. To illustrate
how to use the network-based method, we employed data for enzymes involved in
the chitin metabolic pathway that are present in more than 100 organisms from an
original data set containing 1,695 organisms, downloaded from GenBank on May 19,
2007. A preliminary comparison between the outcomes of the network-based method
and the results of methods based on Bayesian, distance, likelihood, and
parsimony criteria suggests that the former is as reliable as these commonly
used methods. We conclude that the network-based method can be used as a
powerful tool for retrieving modularity information from weighted networks,
which is useful for phylogenetic analysis
Loss of Genetic Redundancy in Reductive Genome Evolution
Biological systems evolved to be functionally robust in uncertain environments, but also highly adaptable. Such robustness is partly achieved by genetic redundancy, where the failure of a specific component through mutation or environmental challenge can be compensated by duplicate components capable of performing, to a limited extent, the same function. Highly variable environments require very robust systems. Conversely, predictable environments should not place a high selective value on robustness. Here we test this hypothesis by investigating the evolutionary dynamics of genetic redundancy in extremely reduced genomes, found mostly in intracellular parasites and endosymbionts. By combining data analysis with simulations of genome evolution we show that in the extensive gene loss suffered by reduced genomes there is a selective drive to keep the diversity of protein families while sacrificing paralogy. We show that this is not a by-product of the known drivers of genome reduction and that there is very limited convergence to a common core of families, indicating that the repertoire of protein families in reduced genomes is the result of historical contingency and niche-specific adaptations. We propose that our observations reflect a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment
Statistical Mechanics of Horizontal Gene Transfer in Evolutionary Ecology
The biological world, especially its majority microbial component, is
strongly interacting and may be dominated by collective effects. In this
review, we provide a brief introduction for statistical physicists of the way
in which living cells communicate genetically through transferred genes, as
well as the ways in which they can reorganize their genomes in response to
environmental pressure. We discuss how genome evolution can be thought of as
related to the physical phenomenon of annealing, and describe the sense in
which genomes can be said to exhibit an analogue of information entropy. As a
direct application of these ideas, we analyze the variation with ocean depth of
transposons in marine microbial genomes, predicting trends that are consistent
with recent observations using metagenomic surveys.Comment: Accepted by Journal of Statistical Physic