An Improved Method for Completely Uncertain Biological Network Alignment

With the continuous development of biological experiment technology, more and more data related to uncertain biological networks needs to be analyzed. However, most of current alignment methods are designed for the deterministic biological network. Only a few can solve the probabilistic network alignment problem. However, these approaches only use the part of probabilistic data in the original networks allowing only one of the two networks to be probabilistic. To overcome the weakness of current approaches, an improved method called completely probabilistic biological network comparison alignment (C_PBNA) is proposed in this paper. This new method is designed for complete probabilistic biological network alignment based on probabilistic biological network alignment (PBNA) in order to take full advantage of the uncertain information of biological network. The degree of consistency (agreement) indicates that C_PBNA can find the results neglected by PBNA algorithm. Furthermore, the GO consistency (GOC) and global network alignment score (GNAS) have been selected as evaluation criteria, and all of them proved that C_PBNA can obtain more biologically significant results than those of PBNA algorithm.

Download Full-text

PetriScape - A plugin for discrete Petri net simulations in Cytoscape

Journal of Integrative Bioinformatics ◽

10.1515/jib-2016-284 ◽

2016 ◽

Vol 13 (1) ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Diogo Almeida ◽

Vasco Azevedo ◽

Artur Silva ◽

Jan Baumbach

Keyword(s):

Petri Nets ◽

Petri Net ◽

Biological Networks ◽

Computational Analysis ◽

Biological Network ◽

Global Network ◽

Data Sets ◽

Biological Network Analysis ◽

Network Properties ◽

Sbml Format

SummarySystems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

Download Full-text

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

Current Bioinformatics ◽

10.2174/1574893612666171020103747 ◽

2018 ◽

Vol 14 (1) ◽

pp. 4-10

Author(s):

Fang Jing ◽

Shao-Wu Zhang ◽

Shihua Zhang

Keyword(s):

Gene Ontology ◽

Topological Structure ◽

Metabolic Networks ◽

Biological Network ◽

Genomic Sequence ◽

Network Alignment ◽

Future Directions ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

Download Full-text

Data-driven biological network alignment that uses topological, sequence, and functional information

BMC Bioinformatics ◽

10.1186/s12859-021-03971-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shawn Gu ◽

Tijana Milenković

Keyword(s):

Prediction Accuracy ◽

Biological Network ◽

Network Alignment ◽

Data Driven ◽

Sequence Information ◽

Functional Prediction ◽

Research Knowledge ◽

Topological Information ◽

Functional Knowledge ◽

Related Proteins

Abstract Background Network alignment (NA) can transfer functional knowledge between species’ conserved biological network regions. Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions’ functional relatedness. However, we recently found that functionally unrelated proteins are as topologically similar as functionally related proteins. So, we redefined NA as a data-driven method called TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to their functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, TARA yielded higher protein functional prediction accuracy than existing NA methods, even those that used both topological and sequence information. Results Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods. Conclusions As such, combining research knowledge from different domains is promising. Overall, improvements in protein functional prediction have biomedical implications, for example allowing researchers to better understand how cancer progresses or how humans age.

Download Full-text

Stochastic block coordinate Frank-Wolfe algorithm for large-scale biological network alignment

EURASIP Journal on Bioinformatics and Systems Biology ◽

10.1186/s13637-016-0041-1 ◽

2016 ◽

Vol 2016 (1) ◽

Cited By ~ 2

Author(s):

Yijie Wang ◽

Xiaoning Qian

Keyword(s):

Large Scale ◽

Biological Network ◽

Network Alignment

Download Full-text

“Master-Slave” Biological Network Alignment

Bioinformatics Research and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-13078-6_24 ◽

2010 ◽

pp. 215-229 ◽

Cited By ~ 4

Author(s):

Nicola Ferraro ◽

Luigi Palopoli ◽

Simona Panni ◽

Simona E. Rombo

Keyword(s):

Biological Network ◽

Network Alignment

Download Full-text

Optimal Network Alignment with Graphlet Degree Vectors

Cancer Informatics ◽

10.4137/cin.s4744 ◽

2010 ◽

Vol 9 ◽

pp. CIN.S4744 ◽

Cited By ~ 95

Author(s):

Tijana Milenković ◽

Weng Leong Ng ◽

Wayne Hayes ◽

NatašA PržUlj

Keyword(s):

Cost Function ◽

Network Topology ◽

Biological Networks ◽

Phylogenetic Trees ◽

Biological Function ◽

Network Alignment ◽

New Method ◽

Biological Information ◽

Global Alignment ◽

Sequence Alignments

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

Download Full-text

Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm

BMC Bioinformatics ◽

10.1186/s12859-019-3101-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Reagon Karki ◽

Alpha Tom Kodamullil ◽

Charles Tapley Hoyt ◽

Martin Hofmann-Apitius

Keyword(s):

Biological Networks ◽

Molecular Mechanisms ◽

Biological Network ◽

Expression Patterns ◽

Cell Types ◽

Brain Regions ◽

Heat Diffusion ◽

Disease Etiology ◽

The Individual ◽

Biological Entities

Abstract Background Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. Results Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. Conclusions This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.

Download Full-text

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Bioinformatics ◽

10.1093/bioinformatics/btaa962 ◽

2020 ◽

Author(s):

Hyun-Myung Woo ◽

Byung-Jun Yoon

Keyword(s):

Biological Network ◽

Protein Complexes ◽

Ground Truth ◽

Network Alignment ◽

Supplementary Information ◽

Alignment Algorithm ◽

Computationally Efficient ◽

Topological Similarity ◽

Alignment Algorithms ◽

Multiple Network

Abstract Motivation Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results In this article, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks. Availability and implementation Matlab implementation is freely available at https://github.com/bjyoontamu/MONACO. Supplementary information Supplementary data are available at Bioinformatics online.

Download Full-text