Finding Network Motifs Research Articles

Discovery of network motifs based on induced subgraphs using a dynamic expansion tree

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.

Large-scale network motif analysis using compression

We introduce a new method for finding network motifs. Subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model. To compute this expectation, a full or approximate count of the occurrences of a motif is normally repeated on as many as 1000 random graphs sampled from the null model; a prohibitively expensive step. We use ideas from the minimum description length literature to define a new measure of motif relevance. With our method, samples from the null model are not required. Instead we compute the probability of the data under the null model and compare this to the probability under a specially designed alternative model. With this new relevance test, we can search for motifs by random sampling, rather than requiring an accurate count of all instances of a motif. This allows motif analysis to scale to networks with billions of links.

Identification of large disjoint motifs in biological networks.

BackgroundBiological networks provide great potential to understand how cells function. Network motifs, frequent topological patterns, are key structures through which biological networks operate. Finding motifs in biological networks remains to be computationally challenging task as the size of the motif and the underlying network grow. Often, different copies of a given motif topology in a network share nodes or edges. Counting such overlapping copies introduces significant problems in motif identification.ResultsIn this paper, we develop a scalable algorithm for finding network motifs. Unlike most of the existing studies, our algorithm counts independent copies of each motif topology. We introduce a set of small patterns and prove that we can construct any larger pattern by joining those patterns iteratively. By iteratively joining already identified motifs with those patterns, our algorithm avoids (i) constructing topologies which do not exist in the target network (ii) repeatedly counting the frequency of the motifs generated in subsequent iterations. Our experiments on real and synthetic networks demonstrate that our method is significantly faster and more accurate than the existing methods including SUBDUE and FSG.ConclusionsWe conclude that our method for finding network motifs is scalable and computationally feasible for large motif sizes and a broad range of networks with different sizes and densities. We proved that any motif with four or more edges can be constructed as a join of the small patterns.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1271-7) contains supplementary material, which is available to authorized users.

Suffix Graph - An Efficient Approach for Network Motif Mining

Network motif is a pattern of inter-connections occurring in complex network in numbers that are significantly higher than those in similar randomized network. The basic premise of finding network motifs lie in the ability to compute the frequency of the subgraphs. In order to discover network motif, one has to compute a subgraph census on the original network that calculates the frequency of all the subgraphs of certain type. Then there is a need to compute the frequency of a set of subgraphs on the randomized similar network. The bottleneck of the entire motif discovery process is therefore to compute the subgraph frequencies and this is the core computational problem. The proposed work is to present the Suffix-Graph, a data structure that store graphs efficiently and to design an algorithm to retrieve subgraph efficiently that detects network motifs and apply them to transcriptional interactions in Escherichia coli.

A comparative study on network motif discovery algorithms

Subgraphs that occur in complex networks with significantly higher frequency than those in randomised networks are called network motifs. Such subgraphs often play important roles in the functioning of those networks. Finding network motifs is a computationally challenging problem. The main difficulties arise from the fact that real networks are large and the size of the search space grows exponentially with increasing network and motif size. Numerous methods have been developed to overcome these challenges. This paper provides a comparative study of the key network motif discovery algorithms in the literature and presents their algorithmic details on an example network.

An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks

Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.e., the attractors of the system. This is a problem for large networks, because the state space of the system increases exponentially with network size. In this work, we present a novel network reduction approach that is based on finding network motifs that stabilize in a fixed state. Notably, we use a topological criterion to identify these motifs. Specifically, we find certain types of strongly connected components in a suitably expanded representation of the network. To test our method, we apply it to a dynamic network model for a type of cytotoxic T cell cancer and to an ensemble of random Boolean networks of size up to 200. Our results show that our method goes beyond reducing the network and in most cases can actually predict the dynamical repertoire of the nodes (fixed states or oscillations) in the attractors of the system.

Biological network motif detection and evaluation

BackgroundMolecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.ResultsWe define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well.ConclusionWe provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

Kavosh: a new algorithm for finding network motifs

BackgroundComplex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.ResultsWe present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.ConclusionThe efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: .