Modeling cellular machinery through biological network comparison

Molecular networks represent the backbone of molecular activity within the cell. Recent studies have taken a comparative approach toward interpreting these networks, contrasting networks of different species and molecular types, and under varying conditions. In this review, we survey the field of comparative biological network analysis and describe its applications to elucidate cellular machinery and to predict protein function and interaction. We highlight the open problems in the field as well as propose some initial mathematical formulations for addressing them. Many of the methodological and conceptual advances that were important for sequence comparison will likely also be important at the network level, including improved search algorithms, techniques for multiple alignment, evolutionary models for similarity scoring and better integration with public databases.     

Frontiers and challenges in soil respiration research: from measurements to model-data integration

Soil respiration, the flux of CO₂ from the soil to the atmosphere represents a major flux in the global carbon cycle. Our ability to predict this flux remains limited because of multiple controlling mechanisms that interact over different temporal and spatial scales. However, new advances in measurement and analyses present an opportunity for the scientific community to improve the understanding of the mechanisms that regulate soil respiration. In this paper, we address several recent advancements in soil respiration research from experimental measurements and data analysis to new considerations for model-data integration. We focus on the links between the soil?Cplant-atmosphere continuum at short (i.e., diel) and medium (i.e., seasonal-years) temporal scales. First, we bring attention to the importance of identifying sources of soil CO₂ production and highlight the application of automated soil respiration measurements and isotope approaches. Second, we discuss the need of quality assurance and quality control for applications in time series analysis. Third, we review perspectives about emergent ideas for modeling development and model-data integration for soil respiration research. Finally, we call for stronger interactions between modelers and experimentalists as a way to improve our understanding of soil respiration and overall terrestrial carbon cycling.     

Visual Data Mining of Biological Networks: One Size Does Not Fit All

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.     

Towards precise reconstruction of gene regulatory networks by data integration

Background: More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks. Results: In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences. Conclusions: We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.     

Multiscale kinetic analysis of proteins

The stochasticity of molecular motion results in the existence of multiple kinetically relevant pathways in many biomolecular mechanisms. Because it is highly demanding to characterize them for complex systems, mechanisms are often described with a single-pathway perspective. However, kinetic network analysis and sub-ensemble experimental insight are increasingly demonstrating not only the existence of competing pathways but also the importance of kinetic selection in biology. This review focuses on advances in multiscale kinetic analysis of proteins, which connects molecular level information from simulations to macroscopic data to characterize mechanistic reaction networks and the reactive flux through them. We describe a range of methods used and highlight several examples where kinetic modeling has revealed functional importance of pathway heterogeneity.     

社会网络分析法及其在动物行为学研究中的应用

近年来,社会网络分析法被广泛用于动物行为学研究。它通过量化动物社会关系中的特定属性 (如中心度和中心势),可辨识动物群体中的关键个体及其在群体中的作用,揭示动物社会交往的形成机制,深化人们对动物社会性起源、个体行为与种群动态格局间关系的理解。本文简要介绍了社会网络分析法的发展史和基本概念,然后阐述了如何建立网络及选择常用指标,强调了创建原模型的必要性及途径。最后着重评述了社会网络分析法在动物行为学研究中的应用现状,并提出未来应当关注直接身体接触 (如打斗和理毛) 和非肢体接触 (如声音通讯) 等不同交往形式的变动过程,以此构建社会交往的动态网络。同时也需要加强种间社交网络的研究,以便增进人们对社会行为的生态功能以及合作理论等问题的理解。     

Computational identification of cellular networks and pathways

In this article we highlight recent developments in computational functional genomics to identify networks of functionally related genes and proteins based on diverse sources of genomic data. Our specific focus is on statistical methods to identify genetic networks. We discuss integrated analysis of microarray datasets, methods to combine heterogeneous data sources, the analysis of high-dimensional phenotyping screens and describe efforts to establish a reliable and unbiased gold standard for method comparison and evaluation.     

Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization

We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.     

Inferring microbial interaction networks based on consensus similarity network fusion

With the rapid accumulation of high-throughput metagenomic sequencing data, it is possible to infer microbial species relations in a microbial community systematically. In recent years, some approaches have been proposed for identifying microbial interaction network. These methods often focus on one dataset without considering the advantage of data integration. In this study, we propose to use a similarity network fusion (SNF) method to infer microbial relations. The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process. We also introduce consensus k-nearest neighborhood (Ck-NN) method instead of k-NN in the original SNF (we call the approach CSNF). The final network represents the augmented species relationships with aggregated evidence from various datasets, taking advantage of complementarity in the data. We apply the method on genus profiles derived from three microbiome datasets and we find that CSNF can discover the modular structure of microbial interaction network which cannot be identified by analyzing a single dataset.     

Parallel labeling experiments and metabolic flux analysis: Past,present and future methodologies

Radioactive and stable isotopes have been applied for decades to elucidate metabolic pathways and quantify carbon flow in cellular systems using mass and isotope balancing approaches. Isotope-labeling experiments can be conducted as a single tracer experiment, or as parallel labeling experiments. In the latter case, several experiments are performed under identical conditions except for the choice of substrate labeling. In this review, we highlight robust approaches for probing metabolism and addressing metabolically related questions though parallel labeling experiments. In the first part, we provide a brief historical perspective on parallel labeling experiments, from the early metabolic studies when radioisotopes were predominant to present-day applications based on stable-isotopes. We also elaborate on important technical and theoretical advances that have facilitated the transition from radioisotopes to stable-isotopes. In the second part of the review, we focus on parallel labeling experiments for ¹³C-metabolic flux analysis (¹³C-MFA). Parallel experiments offer several advantages that include: tailoring experiments to resolve specific fluxes with high precision; reducing the length of labeling experiments by introducing multiple entry-points of isotopes; validating biochemical network models; and improving the performance of ¹³C-MFA in systems where the number of measurements is limited. We conclude by discussing some challenges facing the use of parallel labeling experiments for ¹³C-MFA and highlight the need to address issues related to biological variability, data integration, and rational tracer selection.     

Construction and evaluation of a robust trophic network model for the northern Gulf of Mexico ecosystem

A critical issue in understanding trophic connectivity in ecological systems is the lack in quality and quantity of information about feeding habits. In this work, we present a method for integrating a diversity of feeding habits data from published studies to evaluate the impact on indices that describe characteristics of individual taxa and their connectivity. We focus our study on feeding habits of the fishes of the northern Gulf of Mexico and seek to understand the importance of the forage fish Gulf Menhaden (Brevoortia patronus) in predator diets. We created a database of diet studies from the northern Gulf of Mexico that included six diet metrics: frequency of occurrence, wet weight, dry weight, number, volume, index of relative importance, and index of caloric importance. We then used this information to construct a set of traditional networks (all prey and predators were from a single taxonomic level and trophic connections were parameterized with a single diet metric). We also constructed a “robust” network where all taxa were identified to the lowest taxonomic level and trophic connections were parameterized using a resampling approach that included all available information. Linear regression and resampling methods were used to convert data reported in other diet metrics into the frequency of occurrence diet metric. For both traditional and robust networks, we used network indices to describe topological properties. With the robust network, we conducted removal simulations where the forage fish species Gulf Menhaden, and associated Clupeidae representatives, were removed from the network and the feeding effort of the predators was reallocated among their other prey items. We found that network and node-specific indices were sensitive to the choice of taxonomy and diet metric level. In the robust network, predator species with the greatest number of identified prey had the lowest precision in their connections and prey from the Arthropoda phyla had the lowest precision for connections. From the removal and reallocation simulations, we found that Actinopterygii and Arthropoda were the most impacted prey taxa with 1.2% to 4.3% increase in predation and approximately 23 taxa would receive 50% of the reallocated predation. Overall, the resampling methods we present provide a potential means for combining disparate diet data and enables a comprehensive understanding of trophic interactions within an ecosystem.     

Advancing analytical algorithms and pipelines for billions of microbial sequences

The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for the development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit.     

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.     

Cross-cultural,developmental psychology: integrating approaches and key insights

Like psychology more broadly, developmental psychology has long suffered from a narrow focus on children from WEIRD societies—or those that are Western, Educated, Industrialized, Rich, and Democratic. In this review, we discuss how developmental scientists have sought to correct this bias through two complementary approaches: one centered on detailed, ethnographic investigations of child development within populations (increasing the depth of our understanding) and one focused on larger, multi-site studies that test children on standardized tasks across populations (increasing breadth). We review key papers from each of these approaches, describe how they are currently practiced, and discuss their strengths and weaknesses. Next, we highlight exemplary papers from the adult literature that offer useful insights, namely the importance of formal modeling and a greater focus on studying variation at multiple levels of analysis. We end by outlining best practices for future waves of cross-cultural, developmental science. Overall, we argue that a more integrated perspective, combining the strengths of the breadth & depth approaches, can help better elucidate the developmental origins of human behavioral diversity.     

Linking Earth Observation and taxonomic,structural and functional biodiversity: Local to ecosystem perspectives

Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales.This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research.EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models.     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

Building a BRIDGE for the integration of heterogeneous data from functional genomics into a platform for systems biology

The flood of data acquired from the increasing number of publicly available genomes has led to new demands for bioinformatics software. With the growing amount of information resulting from high throughput experiments new questions arise that often focus on the comparison of genes, genomes, and their expression profiles. Inferring new knowledge by combining different kinds of "post-genomics" data obviously necessitates the development of new approaches that allow the integration of variable data sources into a flexible framework. In this paper, we describe our concept for the integration of heterogeneous data into a platform for systems biology. We have implemented a Bioinformatics Resource for the Integration of heterogeneous Data from Genomic Explorations (BRIDGE) and illustrate the usability of our approach as a platform for systems biology for two sample applications.