Maximum likelihood of phylogenetic networks

Bioinformatics (2006) 22(21), 2604–2611 The authors would like to apologize for errors of graph misplacementin Figures 4–6, and an     

Maximum likelihood of phylogenetic networks

MOTIVATION: Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications for human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative. RESULTS: In this article we provide a new HGT-oriented likelihood framework for many problems that involve phylogeny-based HGT detection and reconstruction. Beside the formulation of various likelihood criteria, we show that most of these problems are NP-hard, and offer heuristics for efficient and accurate reconstruction of HGT under these criteria. We implemented our heuristics and used them to analyze biological as well as synthetic data. In both cases, our criteria and heuristics exhibited very good performance with respect to identifying the correct number of HGT events as well as inferring their correct location on the species tree. AVAILABILITY: Implementation of the criteria as well as heuristics and hardness proofs are available from the authors upon request. Hardness proofs can also be downloaded at http://www.cs.tau.ac.il/~tamirtul/MLNET/Supp-ML.pdf     

Efficient parsimony-based methods for phylogenetic network reconstruction

MOTIVATION: Phylogenies--the evolutionary histories of groups of organisms-play a major role in representing relationships among biological entities. Although many biological processes can be effectively modeled as tree-like relationships, others, such as hybrid speciation and horizontal gene transfer (HGT), result in networks, rather than trees, of relationships. Hybrid speciation is a significant evolutionary mechanism in plants, fish and other groups of species. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Maximum parsimony is one of the most commonly used criteria for phylogenetic tree inference. Roughly speaking, inference based on this criterion seeks the tree that minimizes the amount of evolution. In 1990, Jotun Hein proposed using this criterion for inferring the evolution of sequences subject to recombination. Preliminary results on small synthetic datasets. Nakhleh et al. (2005) demonstrated the criterion's application to phylogenetic network reconstruction in general and HGT detection in particular. However, the naive algorithms used by the authors are inapplicable to large datasets due to their demanding computational requirements. Further, no rigorous theoretical analysis of computing the criterion was given, nor was it tested on biological data. RESULTS: In the present work we prove that the problem of scoring the parsimony of a phylogenetic network is NP-hard and provide an improved fixed parameter tractable algorithm for it. Further, we devise efficient heuristics for parsimony-based reconstruction of phylogenetic networks. We test our methods on both synthetic and biological data (rbcL gene in bacteria) and obtain very promising results.     

The strength of chemical linkage as a criterion for pruning metabolic graphs

MOTIVATION: A metabolic graph represents the connectivity patterns of a metabolic system, and provides a powerful framework within which the organization of metabolic reactions can be analyzed and elucidated. A common practice is to prune (i.e. remove nodes and edges) the metabolic graph prior to any analysis in order to eliminate confounding signals from the representation. Currently, this pruning process is carried out in an ad hoc fashion, resulting in discrepancies and ambiguities across studies. RESULTS: We propose a biochemically informative criterion, the strength of chemical linkage (SCL), for a systematic pruning of metabolic graphs. By analyzing the metabolic graph of Escherichia coli, we show that thresholding SCL is powerful in selecting the conventional pathways' connectivity out of the raw network connectivity when the network is restricted to the reactions collected from these pathways. Further, we argue that the root of ambiguity in pruning metabolic graphs is in the continuity of the amount of chemical content that can be conserved in reaction transformation patterns. Finally, we demonstrate how biochemical pathways can be inferred efficiently if the search procedure is guided by SCL.     

Convergent evolution of modularity in metabolic networks through different community structures

ABSTRACT: BACKGROUND: It has been reported that the modularity of metabolic networks of bacteria is closely relatedto the variability of their living habitats. However, given the dependency of the modularityscore on the community structure, it remains unknown whether organisms achieve certainmodularity via similar or different community structures. RESULTS: In this work, we studied the relationship between similarities in modularity scores andsimilarities in community structures of the metabolic networks of 1021 species. Bothsimilarities are then compared against the genetic distances. We revisited the associationbetween modularity and variability of the microbial living environments and extended theanalysis to other aspects of their life style such as temperature and oxygen requirements. Wealso tested both topological and biological intuition of the community structures identifiedand investigated the extent of their conservation with respect to the taxomony. CONCLUSIONS: We find that similar modularities are realized by different community structures. We findthat such convergent evolution of modularity is closely associated with the number of(distinct) enzymes in the organism's metabolome, a consequence of different life styles ofthe species. We find that the order of modularity is the same as the order of the number ofthe enzymes under the classification based on the temperature preference but not on theoxygen requirement. Besides, inspection of modularity-based communities reveals thatthese communities are graph-theoretically meaningful yet not reflective of specificbiological functions. From an evolutionary perspective, we find that the communitystructures are conserved only at the level of kingdoms. Our results call for moreinvestigation into the interplay between evolution and modularity: how evolution shapesmodularity, and how modularity affects evolution (mainly in terms of fitness andevolvability). Further, our results call for exploring new measures of modularity andnetwork communities that better correspond to functional categorizations.     

Population genomics in bacteria: a case study of Staphylococcus aureus

We analyzed the genome-wide pattern of single nucleotide polymorphisms (SNPs) in a sample with 12 strains of Staphylococcus aureus. Population structure of S. aureus seems to be complex, and the 12 strains were divided into five groups, named A, B, C, D, and E. We conducted a detailed analysis of the topologies of gene genealogies across the genomes and observed a high rate and frequency of tree-shape switching, indicating extensive homologous recombination. Most of the detected recombination occurred in the ancestral population of A, B, and C, whereas there are a number of small regions that exhibit evidence for homologous recombination with a distinct related species. As such regions would contain a number of novel mutations, it is suggested that homologous recombination would play a crucial role to maintain genetic variation within species. In the A-B-C ancestral population, we found multiple lines of evidence that the coalescent pattern is very similar to what is expected in a panmictic population, suggesting that this population is suitable to apply the standard population genetic theories. Our analysis showed that homologous recombination caused a dramatic decay in linkage disequilibrium (LD) and there is almost no LD between SNPs with distance more than 10 kb. Coalescent simulations demonstrated that a high rate of homologous recombination-a relative rate of 0.6 to the mutation rate with an average tract length of about 10 kb-is required to produce patterns similar to those observed in the S. aureus genomes. Our results call for more research into the evolutionary role of homologous recombination in bacterial populations.     

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.     

Phylogenomic assessment of the role of hybridization and introgression in trait evolution

Trait evolution among a set of species—a central theme in evolutionary biology—has long been understood and analyzed with respect to a species tree. However, the field of phylogenomics, which has been propelled by advances in sequencing technologies, has ushered in the era of species/gene tree incongruence and, consequently, a more nuanced understanding of trait evolution. For a trait whose states are incongruent with the branching patterns in the species tree, the same state could have arisen independently in different species (homoplasy) or followed the branching patterns of gene trees, incongruent with the species tree (hemiplasy). Another evolutionary process whose extent and significance are better revealed by phylogenomic studies is gene flow between different species. In this work, we present a phylogenomic method for assessing the role of hybridization and introgression in the evolution of polymorphic or monomorphic binary traits. We apply the method to simulated evolutionary scenarios to demonstrate the interplay between the parameters of the evolutionary history and the role of introgression in a binary trait’s evolution (which we call xenoplasy). Very importantly, we demonstrate, including on a biological data set, that inferring a species tree and using it for trait evolution analysis in the presence of gene flow could lead to misleading hypotheses about trait evolution.     

Colocalization of cellular nanostructure using confocal fluorescence and partial wave spectroscopy

A new multimodal confocal microscope has been developed, which includes a parallel Partial Wave Spectroscopic (PWS) microscopy path. This combination of modalities allows molecular‐specific sensing of nanoscale intracellular structure using fluorescent labels. Combining molecular specificity and sensitivity to nanoscale structure allows localization of nanostructural intracellular changes, which is critical for understanding the mechanisms of diseases such as cancer. To demonstrate the capabilities of this multimodal instrument, we imaged HeLa cells treated with valinomycin, a potassium ionophore that uncouples oxidative phosphorylation. Colocalization of fluorescence images of the nuclei (Hoechst 33342) and mitochondria (anti‐mitochondria conjugated to Alexa Fluor 488) with PWS measurements allowed us to detect a significant decrease in nuclear nanoscale heterogeneity (Σ), while no significant change in Σ was observed at mitochondrial sites. In addition, application of the new multimodal imaging approach was demonstrated on human buccal samples prepared using a cancer screening protocol. These images demonstrate that nanoscale intracellular structure can be studied in healthy and diseased cells at molecular‐specific sites.