scholarly journals Identifying Representative Network Motifs for Inferring Higher-order Structure of Biological Networks

2018 ◽  
Author(s):  
Wang Tao ◽  
Yadong Wang ◽  
Jiajie Peng ◽  
Chen Jin
Keyword(s):  
Biological Networks ◽  
Motif Discovery ◽  
Network Motif ◽  
Low Frequency ◽  
Organizational Structures ◽  
Higher Order ◽  
Order Structure ◽  
Network Motifs ◽  
Scalable Algorithm ◽  
Significant Patterns

AbstractNetwork motifs are recurring significant patterns of inter-connections, which are recognized as fundamental units to study the higher-order organizations of networks. However, the principle of selecting representative network motifs for local motif based clustering remains largely unexplored. We present a scalable algorithm called FSM for network motif discovery. FSM accelerates the motif discovery process by effectively reducing the number of times to do subgraph isomorphism labeling. Multiple heuristic optimizations for subgraph enumeration and subgraph classification are also adopted in FSM to further improve its performance. Experimental results show that FSM is more efficient than the compared models on computational efficiency and memory usage. Furthermore, our experiments indicate that large and frequent network motifs may be more appropriate to be selected as the representative network motifs for discovering higher-order organizational structures in biological networks than small or low-frequency network motifs.

scholarly journals MODA: An efficient algorithm for network motif discovery in biological networks

2009 ◽  
Vol 84 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Saeed Omidi ◽  
Falk Schreiber ◽  
Ali Masoudi-Nejad
Keyword(s):  
Biological Networks ◽  
Efficient Algorithm ◽  
Motif Discovery ◽  
Network Motif

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.

NMDB: NETWORK MOTIF DATABASE ENVISAGED AND EXPLICATED FROM HUMAN DISEASE SPECIFIC PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 89-100 ◽  
Author(s):  
ABHAY PRATAP ◽  
SETU TALIYAN ◽  
TIRATHA RAJ SINGH
Keyword(s):  
Biological Networks ◽  
Human Disease ◽  
Biological Network ◽  
Metabolic Diseases ◽  
Network Motif ◽  
Network Motifs ◽  
Efficient Detection ◽  
Single Input ◽  
Motif Database ◽  
Disease Specific

The study of network motifs for large number of networks can aid us to resolve the functions of complex biological networks. In biology, network motifs that reappear within a network more often than expected in random networks include negative autoregulation, positive autoregulation, single-input modules, feedforward loops, dense overlapping regulons and feedback loops. These network motifs have their different dynamical functions. In this study, our main objective is to examine the enrichment of network motifs in different biological networks of human disease specific pathways. We characterize biological network motifs as biologically significant sub-graphs. We used computational and statistical criteria for efficient detection of biological network motifs, and introduced several estimation measures. Pathways of cardiovascular, cancer, infectious, repair, endocrine and metabolic diseases, were used for identifying and interlinking the relation between nodes. 3–8 sub-graph size network motifs were generated. Network Motif Database was then developed using PHP and MySQL. Results showed that there is an abundance of autoregulation, feedforward loops, single-input modules, dense overlapping regulons and other putative regulatory motifs in all the diseases included in this study. It is believed that the database will assist molecular and system biologists, biotechnologists, and other scientific community to encounter biologically meaningful information. Network Motif Database is freely available for academic and research purpose at: http://www.bioinfoindia.org/nmdb .

scholarly journals Application of dynamic expansion tree for finding large network motifs in biological networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6917 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Interaction Network ◽  
Motif Finding ◽  
Network Motifs ◽  
Large Network ◽  
Scalable Network

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

Motif discovery in biological network using expansion tree

2018 ◽  
Vol 16 (06) ◽  
pp. 1850024 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Complex Networks ◽  
Protein Interaction ◽  
Motif Discovery ◽  
Statistical Significance ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Building Blocks ◽  
Network Motifs ◽  
Topological Features ◽  
Scalable Network

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

scholarly journals Hydrogen bonding investigation of poly(3-hydroxybutyrate) / glycol chitosan blends studied by infrared and terahertz spectroscopies

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Dian Marlina ◽  
Harumi Sato
Keyword(s):  
Hydrogen Bonding ◽  
Crystalline Structure ◽  
Low Frequency ◽  
Helical Structure ◽  
Higher Order ◽  
Order Structure ◽  
Glycol Chitosan ◽  
Structure Deformation ◽  
Structure And Bonding ◽  
Chitosan Blends

Poly(3-hydroxybutyrate) (PHB)/glycol chitosan (GC) polymer blend was developed as one of the new biopolymer materials. Effects of different PHB / GC concentrations were analysed as a function of the blend compositions by using Fourier transform infrared (FTIR) and terahertz (THz) spectroscopies to investigate the changes in the higher-order structure and bonding of hydrogen. The higher-order structure and hydrogen bonding monitored in this study include the crystalline structure and (C=O…H-C) hydrogen bonding of PHB. The FTIR and THz spectra showed that PHB's higher-order structure transforms into the less-order structure by adding GC without altering the crystalline structure and PHB's intramolecular (C = O ... H-C) hydrogen bonding with increasing GC concentration. Because of the addition of GC, the intensity ratio of THz bands figure out the crystalline dynamics of PHB, the helical structure deformation occurs first followed by the weakening of intramolecular (C = O ... H-C) hydrogen bonding within PHB-PHB molecules. Keywords: Chitosan, higher-order structure, hydrogen bonding, low-frequency vibrational spectroscopy

FSM: Fast and scalable network motif discovery for exploring higher-order network organizations

Methods ◽  
2020 ◽  
Vol 173 ◽  
pp. 83-93 ◽  
Author(s):  
Tao Wang ◽  
Jiajie Peng ◽  
Qidi Peng ◽  
Yadong Wang ◽  
Jin Chen
Keyword(s):  
Motif Discovery ◽  
Network Motif ◽  
Higher Order ◽  
Network Organizations ◽  
Scalable Network

scholarly journals Nonlinear delay differential equations and their application to modeling biological network motifs

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David S. Glass ◽  
Xiaofan Jin ◽  
Ingmar H. Riedel-Kruse
Keyword(s):  
Differential Equation ◽  
Biological Networks ◽  
Network Motif ◽  
Network Motifs ◽  
Ode Models ◽  
Regulatory Systems ◽  
Nonlinear Delay Differential Equations ◽  
Cell Signaling Networks ◽  
Analytical Relations ◽  
Delay Differential

AbstractBiological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models, both analytically and numerically. We find many broadly applicable results, including parameter reduction versus canonical ordinary differential equation (ODE) models, analytical relations for converting between ODE and DDE models, criteria for when delays may be ignored, a complete phase space for autoregulation, universal behaviors of feedforward loops, a unified Hill-function logic framework, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.

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