Computational methods for identifying the critical nodes in biological networks

2019 ◽  
Vol 21 (2) ◽  
pp. 486-497 ◽  
Author(s):  
Xiangrong Liu ◽  
Zengyan Hong ◽  
Juan Liu ◽  
Yuan Lin ◽  
Alfonso Rodríguez-Patón ◽  
...  
Keyword(s):  
Computational Methods ◽  
Biological Networks ◽  
Biological Network ◽  
Low Cost ◽  
Experimental Methods ◽  
Computing Methods ◽  
Critical Nodes

Abstract A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network.

scholarly journals Drug Repurposing Using Biological Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1057
Author(s):  
Francisco Javier Somolinos ◽  
Carlos León ◽  
Sara Guerrero-Aspizua
Keyword(s):  
Computational Methods ◽  
Biological Networks ◽  
High Performance ◽  
Biological Network ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Network Models ◽  
Biomedical Data ◽  
Multiple Targets ◽  
Medical Indication

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

Comparison and Analysis of Computational Methods for Identifying N6 - methyladenosine Sites in Saccharomyces Cerevisiae

2020 ◽  
Vol 26 ◽  
Author(s):  
Pengmian Feng ◽  
Lijing Feng ◽  
Chaohui Tang
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Computational Methods ◽  
Computational Analysis ◽  
Computational Prediction ◽  
Experimental Methods ◽  
Biological Processes ◽  
Biological Functions ◽  
Precise Location ◽  
Future Directions ◽  
The Past

Background and Purpose: N 6 -methyladenosine (m6A) plays critical roles in a broad set of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As good complements to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. Methods: In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to give a comprehensive review and comparison on existing methods. Results: Since researches on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progresses on computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites were presented. Conclusion: Taken together, we anticipate that this review will provide important guides for computational analysis of m 6A modifications.

scholarly journals A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites

2020 ◽  
Vol 21 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Jianwei Li ◽  
Yan Huang ◽  
Yuan Zhou
Keyword(s):  
Computational Model ◽  
Computational Methods ◽  
Experimental Methods ◽  
Rna Metabolism ◽  
Vital Role ◽  
Accurate Localization ◽  
Growing Body ◽  
Tissue Cells

RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

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

2019 ◽  
Vol 20 (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.

scholarly journals An Improved Method for Completely Uncertain Biological Network Alignment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Shen ◽  
Muwei Zhao ◽  
Wei Zhong ◽  
Jieyue He
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Evaluation Criteria ◽  
Network Alignment ◽  
Global Network ◽  
Uncertain Information ◽  
Improved Method ◽  
Network Comparison ◽  
Probabilistic Network ◽  
Alignment Problem

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.

An algebraic approach to parameter optimization in biomolecular bistable systems

2016 ◽  
Author(s):  
Vahid Mardanlou ◽  
Elisa Franco
Keyword(s):  
Parameter Optimization ◽  
Biological Networks ◽  
Optimization Problem ◽  
Biological Network ◽  
Algebraic Approach ◽  
Reaction Rates ◽  
Toggle Switch ◽  
Switching Speed ◽  
Bistable Systems ◽  
Total Concentrations

AbstractIn a synthetic biological network it may often be desirable to maximize or minimize parameters such as reaction rates, fluxes and total concentrations of reagents, while preserving a given dynamic behavior. We consider the problem of parameter optimization in biomolecular bistable circuits. We show that, under some assumptions often satisfied by bistable biological networks, it is possible to derive algebraic conditions on the parameters that determine when bistability occurs. These (global) algebraic conditions can be included as nonlinear constraints in a parameter optimization problem. We derive bistability conditions using Sturm's theorem for Gardner and Collins toggle switch. Then we optimize its nominal parameters to improve switching speed and robustness to a subset of uncertain parameters.

Design of a post-disaster shelter through soft computing

2019 ◽  
Vol 17 (2) ◽  
pp. 185-205 ◽  
Author(s):  
Füsun Cemre Karaoğlan ◽  
Sema Alaçam
Keyword(s):  
Soft Computing ◽  
Architectural Design ◽  
Low Cost ◽  
Computing Methods ◽  
Modular System ◽  
Design Problems ◽  
Novel Approach ◽  
Fuzzy Neural ◽  
Experiential Quality ◽  
Post Disaster

Temporary shelters become a more critical subject of architectural design as the increasing number of natural disasters taking place each year result in a larger number of people in need of urgent sheltering. Therefore, this project focuses on designing a temporary living space that can respond to the needs of different post-disaster scenarios and form a modular system through differentiation of units. When designing temporary shelters, it is a necessity to deal with the provision of materials, low-cost production and the time limit in the emergency as well as the needs of the users and the experiential quality of the space. Although computational approaches might lead to much more efficient and resilient design solutions, they have been utilized in very few examples. For that reason and due to their suitability to work with architectural design problems, soft computing methods shape the core of the methodology of the study. Initially, a digital model is generated through a set of rules that define a growth algorithm. Then, Multi-Objective Genetic Algorithms alter this growth algorithm while evaluating different configurations through the objective functions constructed within a Fuzzy Neural Tree. The struggle to represent design goals in the form of Fuzzy Neural Tree holds potential for the further use of it for architectural design problems centred on resilience. Resilience in this context is defined as a measure of how agile a design is when dealing with a major sheltering need in a post-disaster environment. Different from the previous studies, this article aims to focus on the design of a temporary shelter that can respond to different user types and disaster scenarios through mass customization, using Fuzzy Neural Tree as a novel approach. While serving as a temporary space, the design outcomes are expected to create a more neighbourhood-like pattern with a stronger sense of community for the users compared to the previous examples.

HiSCF: leveraging higher-order structures for clustering analysis in biological networks

2020 ◽  
Author(s):  
Lun Hu ◽  
Jun Zhang ◽  
Xiangyu Pan ◽  
Hong Yan ◽  
Zhu-Hong You
Keyword(s):  
Biological Networks ◽  
Clustering Analysis ◽  
Biological Network ◽  
Higher Order ◽  
Supplementary Information ◽  
Network Motifs ◽  
Functional Modules ◽  
Connectivity Patterns ◽  
Biological Entities ◽  
Insight Into

Abstract Motivation Clustering analysis in a biological network is to group biological entities into functional modules, thus providing valuable insight into the understanding of complex biological systems. Existing clustering techniques make use of lower-order connectivity patterns at the level of individual biological entities and their connections, but few of them can take into account of higher-order connectivity patterns at the level of small network motifs. Results Here, we present a novel clustering framework, namely HiSCF, to identify functional modules based on the higher-order structure information available in a biological network. Taking advantage of higher-order Markov stochastic process, HiSCF is able to perform the clustering analysis by exploiting a variety of network motifs. When compared with several state-of-the-art clustering models, HiSCF yields the best performance for two practical clustering applications, i.e. protein complex identification and gene co-expression module detection, in terms of accuracy. The promising performance of HiSCF demonstrates that the consideration of higher-order network motifs gains new insight into the analysis of biological networks, such as the identification of overlapping protein complexes and the inference of new signaling pathways, and also reveals the rich higher-order organizational structures presented in biological networks. Availability and implementation HiSCF is available at https://github.com/allenv5/HiSCF. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Biological network analysis with deep learning

2020 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O’Bray ◽  
Karsten Borgwardt
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Biological Networks ◽  
Protein Function ◽  
Regulatory Networks ◽  
Biological Network ◽  
Gene Interaction ◽  
Disease Diagnosis ◽  
Interaction Prediction ◽  
Graph Neural Networks

Abstract Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

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