Metal hyperaccumulation and hypertolerance: a model for plant evolutionary genomics

In the course of evolution, plants adapted to widely differing metal availabilities in soils and therefore represent an important source of natural variation of metal homeostasis networks. Research on plant metal homeostasis can thus provide insights into the functioning, regulation and adaptation of biological networks. Here, we describe major recent breakthroughs in the understanding of the genetic and molecular basis of metal hyperaccumulation and associated hypertolerance, a naturally selected complex trait which represents an extreme adaptation of the metal homeostasis network. Investigations in this field reveal further the molecular alterations underlying the evolution of natural phenotypic diversity and provide a highly relevant framework for comparative genomics.     

Application of neural networks using the volume learning algorithm for the study of structure-activity relationship in chemical compounds

A volume learning algorithm for artificial neural networks was developed to quantitatively describe the three-dimensional structure-activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped to reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.     

Application of Neural Networks Using the Volume Learning Algorithm for Quantitative Study of the Three-Dimensional Structure–Activity Relationships of Chemical Compounds

A volume learning algorithm for artificial neural networks was developed to quantitatively describe three-dimensional structure–activity relationships using as an example N-benzylpiperidine derivatives. The new algorithm combines two types of neural networks, the Kohonen and the feed-forward artificial neural networks, which are used to analyze the input grid data generated by the comparative molecular field approach. Selection of the most informative parameters using the algorithm helped reveal the most important spatial properties of the molecules, which affect their biological activities. Cluster regions determined using the new algorithm adequately predicted the activity of molecules from a control data set.     

Reverse Engineering of Gene Regulatory Networks: A Comparative Study

Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.     

Alignment of molecular networks by integer quadratic programming

MOTIVATION: With more and more data on molecular networks (e.g. protein interaction networks, gene regulatory networks and metabolic networks) available, the discovery of conserved patterns or signaling pathways by comparing various kinds of networks among different species or within a species becomes an increasingly important problem. However, most of the conventional approaches either restrict comparative analysis to special structures, such as pathways, or adopt heuristic algorithms due to computational burden. RESULTS: In this article, to find the conserved substructures, we develop an efficient algorithm for aligning molecular networks based on both molecule similarity and architecture similarity, by using integer quadratic programming (IQP). Such an IQP can be relaxed into the corresponding quadratic programming (QP) which almost always ensures an integer solution, thereby making molecular network alignment tractable without any approximation. The proposed framework is very flexible and can be applied to many kinds of molecular networks including weighted and unweighted, directed and undirected networks with or without loops. AVAILABILITY: Matlab code and data are available from http://zhangroup.aporc.org/bioinfo/MNAligner or http://intelligent.eic.osaka-sandai.ac.jp/chenen/software/MNAligner, or upon request from authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.     

B-cell ligand processing pathways detected by large-scale comparative analysis

The initiation of B-cell ligand recognition is a critical step for the generation of an immune response against foreign bodies.We sought to identify the biochemical pathways involved in the B-cell ligand recognition cascade and sets of ligands that trigger similar immunological responses.We utilized several comparative approaches to analyze the gene coexpression networks generated from a set of microarray experiments spanning 33 different ligands.First,we compared the degree distributions of the generated networks.Second,we utilized a pairwise network alignment algorithm,BiNA,to align the networks based on the hubs in the networks.Third,we aligned the networks based on a set of K_EGG pathways.We summarized our results by constructing a consensus hierarchy of pathways that are involved in B cell ligand recognition.The resulting pathways were further validated through literature for their common physiological responses.Collectively,the results based on our comparative analyses of degree distributions,alignment of hubs,and alignment based on KEGG pathways provide a basis for molecular characterization of the immune response states of B-cells and demonstrate the power of comparative approaches(e.g.,gene coexpression network alignment algorithms) in elucidating biochemical pathways involved in complex signaling events in cells.     

Exploring genetic interactions and networks with yeast

The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.     

Phylogenetic analysis of modularity in protein interaction networks

Background  

In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.     

动态分子网络的构建与分析

分子网络研究是从全局角度揭示生物系统的结构和功能的重要手段,现有的网络分析大部分是基于静态网络.实际上,在不同的环境条件、组织类型和疾病状态以及生长和分化的过程中,分子网络时刻都在发生变化.经过研究人员的努力,人们已经提出了一些可用于分析分子网络动态的生物信息学方法,如节点的动态性分类、动态蛋白质复合物的预测、条件特异子网的构建以及网络动态行为的模拟等.本文综述了动态分子网络的构建与分析方法.可以预见,动态网络分析将成为未来网络研究的标准模式.     

Gene Duplication and Phenotypic Changes in the Evolution of Mammalian Metabolic Networks

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines.     

A perspective of metabolic engineering strategies: moving up the systems hierarchy

Metabolic engineering has been established as an important field in biotechnology. It involves the analysis, design, and alteration of the stoichiometric network using sophisticated mathematical and molecular biology techniques. It allows for improvement of pathway kinetics by removing flux bottlenecks, balancing precursors, and recycling cofactors used to increase product formation. The next step in the systems hierarchy is the constructive manipulation of regulatory networks. As our understanding of regulation continues to expand rapidly, engineering of intracellular regulation will become an integral aspect of metabolic engineering.     

MrBac: a web server for draft metabolic network reconstructions for bacteria

Genome-scale metabolic network reconstruction can be used for simulating cellular behaviors by simultaneously monitoring thousands of biochemical reactions, and is therefore important for systems biology studies in microbes. However, the labor-intensive and time-consuming reconstruction process has hindered the progress of this important field. Here we present a web server, MrBac (Metabolic network Reconstructions for Bacteria), to streamline the network reconstruction process for draft genome-scale metabolic networks and to provide annotation information from multiple databases for further curation of the draft reconstructions. MrBac integrates comparative genomics, retrieval of genome annotations, and generation of standard systems biology file format ready for network analyses. We also used MrBac to automatically generate a draft metabolic model of Salmonella enteric serovar Typhimurium LT2. The high similarity between this automatic model and the experimentally validated models further supports the usefulness and accuracy of MrBac. The high efficiency and accuracy of MrBac may accelerate the advances of systems biology studies on microbiology. MrBac is freely available at http://sb.nhri.org.tw/MrBac.     

Short time-series microarray analysis: Methods and challenges

The rapid accumulation of various network-related data from multiple species and conditions (e.g. disease versus normal) provides unprecedented opportunities to study the function and evolution of biological systems. Comparison of biomolecular networks between species or conditions is a promising approach to understanding the essential mechanisms used by living organisms. Computationally, the basic goal of this network comparison or 'querying' is to uncover identical or similar subnetworks by mapping the queried network (e.g. a pathway or functional module) to another network or network database. Such comparative analysis may reveal biologically or clinically important pathways or regulatory networks. In particular, we argue that user-friendly tools for network querying will greatly enhance our ability to study the fundamental properties of biomolecular networks at a system-wide level.     

Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks

The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology—random networks of Erdős-Rényi type and networks with highly interconnected hubs—we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Structure prediction and network analysis of chitinases from the Cape sundew,Drosera capensis

BackgroundCarnivorous plants possess diverse sets of enzymes with novel functionalities applicable to biotechnology, proteomics, and bioanalytical research. Chitinases constitute an important class of such enzymes, with future applications including human-safe antifungal agents and pesticides. Here, we compare chitinases from the genome of the carnivorous plant Drosera capensis to those from related carnivorous plants and model organisms.MethodsUsing comparative modeling, in silico maturation, and molecular dynamics simulation, we produce models of the mature enzymes in aqueous solution. We utilize network analytic techniques to identify similarities and differences in chitinase topology.ResultsHere, we report molecular models and functional predictions from protein structure networks for eleven new chitinases from D. capensis, including a novel class IV chitinase with two active domains. This architecture has previously been observed in microorganisms but not in plants. We use a combination of comparative and de novo structure prediction followed by molecular dynamics simulation to produce models of the mature forms of these proteins in aqueous solution. Protein structure network analysis of these and other plant chitinases reveal characteristic features of the two major chitinase families.General significanceThis work demonstrates how computational techniques can facilitate quickly moving from raw sequence data to refined structural models and comparative analysis, and to select promising candidates for subsequent biochemical characterization. This capability is increasingly important given the large and growing body of data from high-throughput genome sequencing, which makes experimental characterization of every target impractical.