scholarly journals NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yang Cao ◽  
Wenjian Xu ◽  
Chao Niu ◽  
Xiaochen Bo ◽  
Fei Li
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
R Package ◽  
System Level ◽  
Application Development ◽  
Mathematical Concepts ◽  
Computational Framework ◽  
Knowledge Based ◽  
Different Types ◽  
Gene Regulatory

Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied “basic networks,” measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette.

scholarly journals Boolean factor graph model for biological systems: the yeast cell-cycle network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephen Kotiang ◽  
Ali Eslami
Keyword(s):  
Cell Cycle ◽  
Yeast Cell ◽  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Error Propagation ◽  
Regulatory Networks ◽  
Biological Systems ◽  
Yeast Cell Cycle ◽  
Computational Framework ◽  
Gene Regulatory

Abstract Background The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. Results This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. Conclusion Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

scholarly journals Fibration symmetries uncover the building blocks of biological networks

2020 ◽  
Vol 117 (15) ◽  
pp. 8306-8314 ◽  
Author(s):  
Flaviano Morone ◽  
Ian Leifer ◽  
Hernán A. Makse
Keyword(s):  
Gene Regulatory Networks ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Biological Network ◽  
Building Blocks ◽  
Branching Ratios ◽  
Graph Representation ◽  
Systems Science ◽  
Gene Regulatory ◽  
Fibonacci Sequences

A major ambition of systems science is to uncover the building blocks of any biological network to decipher how cellular function emerges from their interactions. Here, we introduce a graph representation of the information flow in these networks as a set of input trees, one for each node, which contains all pathways along which information can be transmitted in the network. In this representation, we find remarkable symmetries in the input trees that deconstruct the network into functional building blocks called fibers. Nodes in a fiber have isomorphic input trees and thus process equivalent dynamics and synchronize their activity. Each fiber can then be collapsed into a single representative base node through an information-preserving transformation called “symmetry fibration,” introduced by Grothendieck in the context of algebraic geometry. We exemplify the symmetry fibrations in gene regulatory networks and then show that they universally apply across species and domains from biology to social and infrastructure networks. The building blocks are classified into topological classes of input trees characterized by integer branching ratios and fractal golden ratios of Fibonacci sequences representing cycles of information. Thus, symmetry fibrations describe how complex networks are built from the bottom up to process information through the synchronization of their constitutive building blocks.

scholarly journals Robustness and lethality in multilayer biological molecular networks

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xueming Liu ◽  
Enrico Maiorino ◽  
Arda Halu ◽  
Kimberly Glass ◽  
Rashmi B. Prasad ◽  
...  
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Metabolic Diseases ◽  
Biological Systems ◽  
Interaction Network ◽  
Molecular Networks ◽  
Cancer Genes ◽  
Protein Protein Interaction ◽  
Gene Regulatory ◽  
Comprehensive Framework

AbstractRobustness is a prominent feature of most biological systems. Most previous related studies have been focused on homogeneous molecular networks. Here we propose a comprehensive framework for understanding how the interactions between genes, proteins and metabolites contribute to the determinants of robustness in a heterogeneous biological network. We integrate heterogeneous sources of data to construct a multilayer interaction network composed of a gene regulatory layer, a protein–protein interaction layer, and a metabolic layer. We design a simulated perturbation process to characterize the contribution of each gene to the overall system’s robustness, and find that influential genes are enriched in essential and cancer genes. We show that the proposed mechanism predicts a higher vulnerability of the metabolic layer to perturbations applied to genes associated with metabolic diseases. Furthermore, we find that the real network is comparably or more robust than expected in multiple random realizations. Finally, we analytically derive the expected robustness of multilayer biological networks starting from the degree distributions within and between layers. These results provide insights into the non-trivial dynamics occurring in the cell after a genetic perturbation is applied, confirming the importance of including the coupling between different layers of interaction in models of complex biological systems.

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

The Relationships Between Data, Information and Knowledge Based on a Preliminary Study of Engineering Designers

1999 ◽  
Author(s):  
Saeema Ahmed ◽  
Luciënne Blessing ◽  
Ken Wallace
Keyword(s):  
Aerospace Industry ◽  
Initial Study ◽  
Knowledge Based ◽  
Different Types ◽  
Preliminary Study ◽  
Initial Results

Abstract The aerospace industry, along with other industries, has acknowledged the need to bridge the experience gap between novice and experienced designers [Moore, 1997]. The research aims to identify the support a novice designer requires to gain experience faster. The focus of this paper is to present some initial results of a study of novice and experienced designers. This initial study highlighted the difficulty in establishing consistent and precise usage for the terms data, information and knowledge. It is concluded that data, information and knowledge are relative concepts that cannot be defined in absolute terms. As relative concepts, these help differentiate experts and novices, and different types of novices. The relationships between data, information and knowledge are examined with the aim of prompting a discussion.

scholarly journals mmnet: An R Package for Metagenomics Systems Biology Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Yang Cao ◽  
Xiaofei Zheng ◽  
Fei Li ◽  
Xiaochen Bo
Keyword(s):  
Metabolic Network ◽  
Human Microbiome ◽  
R Package ◽  
Network Reconstruction ◽  
Community Level ◽  
System Level ◽  
Metagenomic Data ◽  
Pure Species ◽  
Integrated Network ◽  
Metabolic Network Reconstruction

The human microbiome plays important roles in human health and disease. Previous microbiome studies focused mainly on single pure species function and overlooked the interactions in the complex communities on system-level. A metagenomic approach introduced recently integrates metagenomic data with community-level metabolic network modeling, but no comprehensive tool was available for such kind of approaches. To facilitate these kinds of studies, we developed an R package,mmnet, to implement community-level metabolic network reconstruction. The package also implements a set of functions for automatic analysis pipeline construction including functional annotation of metagenomic reads, abundance estimation of enzymatic genes, community-level metabolic network reconstruction, and integrated network analysis. The result can be represented in an intuitive way and sent to Cytoscape for further exploration. The package has substantial potentials in metagenomic studies that focus on identifying system-level variations of human microbiome associated with disease.

scholarly journals Why People Who Know Less Think They Know about COVID-19: Evidence from US and Singapore

2021 ◽  
pp. 107769902110494
Author(s):  
Sangwon Lee ◽  
Masahiro Yamamoto ◽  
Edson C. Tandoc
Keyword(s):  
Social Media ◽  
The United States ◽  
Factual Knowledge ◽  
Interaction Patterns ◽  
Traditional Media ◽  
Subjective Knowledge ◽  
Types Of Knowledge ◽  
Knowledge Based ◽  
Different Types ◽  
Main Effects

This study explores the effects of traditional media and social media on different types of knowledge about COVID-19. We also explore how surveillance motivation moderates the relationship between media use and different types of knowledge. Based on cross-national data from Singapore and the United States, we find that news seeking via social media is negatively related to factual knowledge and positively related to subjective knowledge and knowledge miscalibration. News seeking via traditional media is not significantly related to factual knowledge. Although the main effects are highly consistent across the two countries, we find some different interaction patterns across these countries.

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