The Adaptive Evolution Database (TAED)

Background  

Developing an understanding of the molecular basis for the divergence of species lies at the heart of biology. The Adaptive Evolution Database (TAED) serves as a starting point to link events that occur at the same time in the evolutionary history (tree of life) of species, based upon coding sequence evolution analyzed with the Master Catalog. The Master Catalog is a collection of evolutionary models, including multiple sequence alignments, phylogenetic trees, and reconstructed ancestral sequences, for all independently evolving protein sequence modules encoded by genes in GenBank [1].     

Machine learning methods for metabolic pathway prediction

Background  

A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism.     

Metabolic innovations towards the human lineage

Background  

We describe a function-driven approach to the analysis of metabolism which takes into account the phylogenetic origin of biochemical reactions to reveal subtle lineage-specific metabolic innovations, undetectable by more traditional methods based on sequence comparison. The origins of reactions and thus entire pathways are inferred using a simple taxonomic classification scheme that describes the evolutionary course of events towards the lineage of interest. We investigate the evolutionary history of the human metabolic network extracted from a metabolic database, construct a network of interconnected pathways and classify this network according to the taxonomic categories representing eukaryotes, metazoa and vertebrates.     

The function of communities in protein interaction networks at multiple scales

Background  

If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network.     

Accelerating the reconstruction of genome-scale metabolic networks

Background  

The genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.     

Composite functional module inference: detecting cooperation between transcriptional regulation and protein interaction by mantel test

Background  

Functional modules are basic units of cell function, and exploring them is important for understanding the organization, regulation and execution of cell processes. Functional modules in single biological networks (e.g., the protein-protein interaction network), have been the focus of recent studies. Functional modules in the integrated network are composite functional modules, which imply the complex relationships involving multiple biological interaction types, and detect them will help us understand the complexity of cell processes.     

Genes encoding hub and bottleneck enzymes of the Arabidopsis metabolic network preferentially retain homeologs through whole genome duplication

Background

Whole genome duplication (WGD) occurs widely in angiosperm evolution. It raises the intriguing question of how interacting networks of genes cope with this dramatic evolutionary event.

Results

In study of the Arabidopsis metabolic network, we assigned each enzyme (node) with topological centralities (in-degree, out-degree and between-ness) to measure quantitatively their centralities in the network. The Arabidopsis metabolic network is highly modular and separated into 11 interconnected modules, which correspond well to the functional metabolic pathways. The enzymes with higher in-out degree and between-ness (defined as hub and bottleneck enzymes, respectively) tend to be more conserved and preferentially retain homeologs after WGD. Moreover, the simultaneous retention of homeologs encoding enzymes which catalyze consecutive steps in a pathway is highly favored and easily achieved, and enzyme-enzyme interactions contribute to the retention of one-third of WGD enzymes.

Conclusions

Our analyses indicate that the hub and bottleneck enzymes of metabolic network obtain great benefits from WGD, and this event grants clear evolutionary advantages in adaptation to different environments.     

Automated group assignment in large phylogenetic trees using GRUNT: GRouping,Ungrouping, Naming Tool

Background  

Accurate taxonomy is best maintained if species are arranged as hierarchical groups in phylogenetic trees. This is especially important as trees grow larger as a consequence of a rapidly expanding sequence database. Hierarchical group names are typically manually assigned in trees, an approach that becomes unfeasible for very large topologies.     

Low degree metabolites explain essential reactions and enhance modularity in biological networks

Background  

Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints.     

Seed selection strategy in global network alignment without destroying the entire structures of functional modules

Background

Network alignment is one of the most common biological network comparison methods. Aligning protein-protein interaction (PPI) networks of different species is of great important to detect evolutionary conserved pathways or protein complexes across species through the identification of conserved interactions, and to improve our insight into biological systems. Global network alignment (GNA) problem is NP-complete, for which only heuristic methods have been proposed so far. Generally, the current GNA methods fall into global heuristic seed-and-extend approaches. These methods can not get the best overall consistent alignment between networks for the opinionated local seed. Furthermore These methods are lost in maximizing the number of aligned edges between two networks without considering the original structures of functional modules.

Methods

We present a novel seed selection strategy for global network alignment by constructing the pairs of hub nodes of networks to be aligned into multiple seeds. Beginning from every hub seed and using the membership similarity of nodes to quantify to what extent the nodes can participate in functional modules associated with current seed topologically we align the networks by modules. By this way we can maintain the functional modules are not damaged during the heuristic alignment process. And our method is efficient in resolving the fatal problem of most conventional algorithms that the initialization selected seeds have a direct influence on the alignment result. The similarity measures between network nodes (e.g., proteins) include sequence similarity, centrality similarity, and dynamic membership similarity and our algorithm can be called Multiple Hubs-based Alignment (MHA).

Results

When applying our seed selection strategy to several pairs of real PPI networks, it is observed that our method is working to strike a balance, extending the conserved interactions while maintaining the functional modules unchanged. In the case study, we assess the effectiveness of MHA on the alignment of the yeast and fly PPI networks. Our method outperforms state-of-the-art algorithms at detecting conserved functional modules and retrieves in particular 86% more conserved interactions than IsoRank.

Conclusions

We believe that our seed selection strategy will lead us to obtain more topologically and biologically similar alignment result. And it can be used as the reference and complement of other heuristic methods to seek more meaningful alignment results.

     

IdentiCS – Identification of coding sequence and <Emphasis Type="Italic">in silico</Emphasis> reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

Background  

A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence.     

Decomposing complex reaction networks using random sampling,principal component analysis and basis rotation

Background  

Metabolism and its regulation constitute a large fraction of the molecular activity within cells. The control of cellular metabolic state is mediated by numerous molecular mechanisms, which in effect position the metabolic network flux state at specific locations within a mathematically-definable steady-state flux space. Post-translational regulation constitutes a large class of these mechanisms, and decades of research indicate that achieving a network flux state through post-translational metabolic regulation is both a complex and complicated regulatory problem. No analysis method for the objective, top-down assessment of such regulation problems in large biochemical networks has been presented and demonstrated.     

Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

Background  

To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees.     

Integrated network reconstruction,visualization and analysis using YANAsquare

Background  

Modeling of metabolic networks includes tasks such as network assembly, network overview, calculation of metabolic fluxes and testing the robustness of the network.     

Modular co-evolution of metabolic networks

Background  

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.     

Bioinformatic identification of novel regulatory DNA sequence motifs in <Emphasis Type="Italic">Streptomyces coelicolor</Emphasis>

Background  

Streptomyces coelicolor is a bacterium with a vast repertoire of metabolic functions and complex systems of cellular development. Its genome sequence is rich in genes that encode regulatory proteins to control these processes in response to its changing environment. We wished to apply a recently published bioinformatic method for identifying novel regulatory sequence signals to gain new insights into regulation in S. coelicolor.