Network theory analysis of antibody-antigen reactivity data: the immune trees at birth and adulthood

MOTIVATION: New antigen microarray technology enables parallel recording of antibody reactivities with hundreds of antigens. Such data affords system level analysis of the immune system's organization using methods and approaches from network theory. Here we measured the reactivity of 290 antigens (for both the IgG and IgM isotypes) of 10 healthy mothers and their term newborns. We constructed antigen correlation networks (or immune networks) whose nodes are the antigens and the edges are the antigen-antigen reactivity correlations, and we also computed their corresponding minimum spanning trees (MST)--maximal information reduced sub-graphs. We quantify the network organization (topology) in terms of the network theory divergence rate measure and rank the antigen importance in the full antigen correlation networks by the eigen-value centrality measure. This analysis makes possible the characterization and comparison of the IgG and IgM immune networks at birth (newborns) and adulthood (mothers) in terms of topology and node importance. RESULTS: Comparison of the immune network topology at birth and adulthood revealed partial conservation of the IgG immune network topology, and significant reorganization of the IgM immune networks. Inspection of the antigen importance revealed some dominant (in terms of high centrality) antigens in the IgG and IgM networks at birth, which retain their importance at adulthood.     

Efficient Reconstruction of Heterogeneous Networks from Time Series via Compressed Sensing

Recent years have witnessed a rapid development of network reconstruction approaches, especially for a series of methods based on compressed sensing. Although compressed-sensing based methods require much less data than conventional approaches, the compressed sensing for reconstructing heterogeneous networks has not been fully exploited because of hubs. Hub neighbors require much more data to be inferred than small-degree nodes, inducing a cask effect for the reconstruction of heterogeneous networks. Here, a conflict-based method is proposed to overcome the cast effect to considerably reduce data amounts for achieving accurate reconstruction. Moreover, an element elimination method is presented to use the partially available structural information to reduce data requirements. The integration of both methods can further improve the reconstruction performance than separately using each technique. These methods are validated by exploring two evolutionary games taking place in scale-free networks, where individual information is accessible and an attempt to decode the network structure from measurable data is made. The results demonstrate that for all of the cases, much data are saved compared to that in the absence of these two methods. Due to the prevalence of heterogeneous networks in nature and society and the high cost of data acquisition in large-scale networks, these approaches have wide applications in many fields and are valuable for understanding and controlling the collective dynamics of a variety of heterogeneous networked systems.     

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.     

微生物生态系统代谢网络研究进展

微生物生态系统代谢网络是自然生态系统中微生物遗传分子发挥功能作用的主要形式。本文通过全面介绍微生物生态系统代谢网络的特点及其研究的重要意义,综述了基于共培养技术、免培养高通量技术和计算生物学模拟技术的微生物生态系统代谢网络研究进展和所取得的主要发现,以及近年来快速发展的微生物生态系统代谢网络研究技术和方法体系。在此基础上,提出目前微生物生态系统代谢网络研究中存在的主要问题和重点研究方向,并对其在环境污染治理和资源化利用方面的应用潜力进行了展望。     

Modelling Immune System: Principles, Models,Analysis and Perspectives

The biological immune system is a complex adaptive system.There are lots of benefits for building the model of theimmune system.For biological researchers,they can test some hypotheses about the infection process or simulate the responsesof some drugs.For computer researchers,they can build distributed,robust and fault tolerant networks inspired by the functionsof the immune system.This paper provides a comprehensive survey of the literatures on modelling the immune system.Fromthe methodology perspective,the paper compares and analyzes the existing approaches and models,and also demonstrates thefocusing research effort on the future immune models in the next few years.     

The Plasmodium parasite--a 'new' challenge for insect innate immunity

Though lacking adaptive immunity, insects possess a powerful innate immune system, a genome-encoded defence machinery used to confront infections. Studies in the fruit fly Drosophila melanogaster revealed a remarkable capacity of the innate immune system to differentiate between and subsequently respond to different bacteria and fungi. However, hematophagous compared to non-hematophagous insects encounter additional blood-borne infectious agents, such as parasites and viruses, during their lifetime. Anopheles mosquitoes become infected with the malaria parasite Plasmodium during feeding on infected human hosts and may then transmit the parasite to new hosts during subsequent bites. Whether Anopheles has developed mechanisms to confront these infections is the subject of this review. Initially, we review our current understanding of innate immune reactions and give an overview of the Anopheles immune system as revealed through comparative genomic analyses. Then, we examine and discuss the capacity of mosquitoes to recognize and respond to infections, especially to Plasmodium, and finally, we explore approaches to investigate and potentially utilize the vector immune competence to prevent pathogen transmission. Such approaches constitute a new challenge for insect immunity research, a challenge for global health.     

Growing biological networks: beyond the gene-duplication model

In this paper we propose a generalized growth model for biological interaction networks, including a set of biological features which have been inspired by a long tradition of simulations of immune system and chemical reaction networks. In our models we include characteristics such as the heterogeneity of biological nodes, the existence of natural hubs, the nodes binding by mutual affinity and the significance of type-based networks as compared with instance-based networks. Under these assumptions, we analyse the importance of the nodes concentration with respect to the selection of incoming nodes. We show that networks with fat-tailed degree distribution and highly clustered structure naturally emerge in systems possessing certain properties: new instances need to be produced through an endogenous source and this source needs to provide a positive feedback favouring nodes with high concentration to receive new connections. Furthermore, we show that understanding the concentration dynamics of each node and the consequent correlation between connectivity and concentration is a more adequate way to capture the global properties of type-based biological networks.     

A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions

Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking the multivariate nature of interactions: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable because of the combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm uses interaction delays reconstructed for directed bivariate interactions to tag potentially spurious edges on the basis of their timing signatures in the context of the surrounding network. Such tagged interactions may then be pruned, which produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation in MATLAB to test the algorithm’s performance on simulated networks as well as networks derived from magnetoencephalographic data. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.     

An effective structure learning method for constructing gene networks

MOTIVATION: Bayesian network methods have shown promise in gene regulatory network reconstruction because of their capability of capturing causal relationships between genes and handling data with noises found in biological experiments. The problem of learning network structures, however, is NP hard. Consequently, heuristic methods such as hill climbing are used for structure learning. For networks of a moderate size, hill climbing methods are not computationally efficient. Furthermore, relatively low accuracy of the learned structures may be observed. The purpose of this article is to present a novel structure learning method for gene network discovery. RESULTS: In this paper, we present a novel structure learning method to reconstruct the underlying gene networks from the observational gene expression data. Unlike hill climbing approaches, the proposed method first constructs an undirected network based on mutual information between two nodes and then splits the structure into substructures. The directional orientations for the edges that connect two nodes are then obtained by optimizing a scoring function for each substructure. Our method is evaluated using two benchmark network datasets with known structures. The results show that the proposed method can identify networks that are close to the optimal structures. It outperforms hill climbing methods in terms of both computation time and predicted structure accuracy. We also apply the method to gene expression data measured during the yeast cycle and show the effectiveness of the proposed method for network reconstruction.     

Reconstructible Phylogenetic Networks: Do Not Distinguish the Indistinguishable

Phylogenetic networks represent the evolution of organisms that have undergone reticulate events, such as recombination, hybrid speciation or lateral gene transfer. An important way to interpret a phylogenetic network is in terms of the trees it displays, which represent all the possible histories of the characters carried by the organisms in the network. Interestingly, however, different networks may display exactly the same set of trees, an observation that poses a problem for network reconstruction: from the perspective of many inference methods such networks are indistinguishable. This is true for all methods that evaluate a phylogenetic network solely on the basis of how well the displayed trees fit the available data, including all methods based on input data consisting of clades, triples, quartets, or trees with any number of taxa, and also sequence-based approaches such as popular formalisations of maximum parsimony and maximum likelihood for networks. This identifiability problem is partially solved by accounting for branch lengths, although this merely reduces the frequency of the problem. Here we propose that network inference methods should only attempt to reconstruct what they can uniquely identify. To this end, we introduce a novel definition of what constitutes a uniquely reconstructible network. For any given set of indistinguishable networks, we define a canonical network that, under mild assumptions, is unique and thus representative of the entire set. Given data that underwent reticulate evolution, only the canonical form of the underlying phylogenetic network can be uniquely reconstructed. While on the methodological side this will imply a drastic reduction of the solution space in network inference, for the study of reticulate evolution this is a fundamental limitation that will require an important change of perspective when interpreting phylogenetic networks.     

Killer Artificial Antigen Presenting Cells (KaAPC) for Efficient In Vitro Depletion of Human Antigen-specific T Cells

Current treatment of T cell mediated autoimmune diseases relies mostly on strategies of global immunosuppression, which, in the long term, is accompanied by adverse side effects such as a reduced ability to control infections or malignancies. Therefore, new approaches need to be developed that target only the disease mediating cells and leave the remaining immune system intact. Over the past decade a variety of cell based immunotherapy strategies to modulate T cell mediated immune responses have been developed. Most of these approaches rely on tolerance-inducing antigen presenting cells (APC). However, in addition to being technically difficult and cumbersome, such cell-based approaches are highly sensitive to cytotoxic T cell responses, which limits their therapeutic capacity. Here we present a protocol for the generation of non-cellular killer artificial antigen presenting cells (KaAPC), which allows for the depletion of pathologic T cells while leaving the remaining immune system untouched and functional. KaAPC is an alternative solution to cellular immunotherapy which has potential for treating autoimmune diseases and allograft rejections by regulating undesirable T cell responses in an antigen specific fashion.     

Modeling cell signaling networks

Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.     

The Plasmodium parasite—a ‘new’ challenge for insect innate immunity

Though lacking adaptive immunity, insects possess a powerful innate immune system, a genome-encoded defence machinery used to confront infections. Studies in the fruit fly Drosophila melanogaster revealed a remarkable capacity of the innate immune system to differentiate between and subsequently respond to different bacteria and fungi. However, hematophagous compared to non-hematophagous insects encounter additional blood-borne infectious agents, such as parasites and viruses, during their lifetime. Anopheles mosquitoes become infected with the malaria parasite Plasmodium during feeding on infected human hosts and may then transmit the parasite to new hosts during subsequent bites. Whether Anopheles has developed mechanisms to confront these infections is the subject of this review. Initially, we review our current understanding of innate immune reactions and give an overview of the Anopheles immune system as revealed through comparative genomic analyses. Then, we examine and discuss the capacity of mosquitoes to recognize and respond to infections, especially to Plasmodium, and finally, we explore approaches to investigate and potentially utilize the vector immune competence to prevent pathogen transmission. Such approaches constitute a new challenge for insect immunity research, a challenge for global health.     

Glycomics, glycoproteomics and the immune system

Glycomics and glycoproteomics have become indispensible tools in the study of glycoconjugates. Mass spectrometry based methods are standardly used to study the proteome and/or glycome and these approaches are capable of providing both, qualitative and quantitative information using top down techniques. The human immune system marks a particular area of interest for glycomics and glycoproteomics research since a large number of key proteins in innate and adaptive immunity are glycoproteins. In numerous examples, the crucial influence of glycosylation on critical steps such as receptor interaction and binding has been demonstrated. In this review, we focus on different glycomics and glycoproteomics approaches and their application for studying protein glycosylation in the immune system.     

Network Reconstruction Using Nonparametric Additive ODE Models

Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative estimation.     

Cellular signaling in Abdominal Aortic Aneurysm

Abdominal aortic aneurysms (AAAs) are highly lethal cardiovascular diseases without effective medications. However, the molecular and signaling mechanisms remain unclear. A series of pathological cellular processes have been shown to contribute to AAA formation, including vascular extracellular matrix remodeling, inflammatory and immune responses, oxidative stress, and dysfunction of vascular smooth muscle cells. Each cellular process involves complex cellular signaling, such as NF-κB, MAPK, TGFβ, Notch and inflammasome signaling. In this review, we discuss how cellular signaling networks function in various cellular processes during the pathogenesis and progression of AAA. Understanding the interaction of cellular signaling networks with AAA pathogenesis as well as the crosstalk of different signaling pathways is essential for the development of novel therapeutic approaches to and personalized treatments of AAA diseases.     

Biological networks

Recent advances in high-throughput methods have provided us with a first glimpse of the overall structure of molecular interaction networks in biological systems. Ultimately, we expect that such information will change how we think about biological systems in a fundamental way. Instead of viewing the genetic parts list of an organism as a loose collection of biochemical activities, in the best case, we anticipate discrete networks of function to bridge the gap between genotype and phenotype, and to do so in a more profound way than the current qualitative classification of linked reactions into familiar pathways, such as glycolysis and the MAPK signal transduction cascades. At the present time, however, we are still far from a complete answer to the most basic question: what can we learn about biology by studying networks? Promising steps in this direction have come from such diverse approaches as mathematical analysis of global network structure, partitioning networks into functionally related modules and motifs, and even de novo design of networks. A complete picture will probably require integrating the data obtained from all of these approaches with modeling efforts at many different levels of detail.