Search results for "Network motif"
showing 6 items of 6 documents
Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs
2019
The genome and interactome of Saccharomyces cerevisiae have been characterized extensively over the course of the past few decades. However, despite many insights gained over the years, both functional studies and evolutionary analyses continue to reveal many complexities and confounding factors in the construction of reliable transcriptional regulatory network models. We present here a graph-based technique for comparing transcriptional regulatory networks based on network motif similarity for gene pairs. We construct interaction graphs for duplicated transcription factor pairs traceable to the ancestral whole-genome duplication as well as other paralogues in Saccharomyces cerevisiae. We c…
Network motif-based analysis of regulatory patterns in paralogous gene pairs
2020
Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes at the whole genome level, and in particular, to assess similarity of regulatory interactions for homologous gene pairs either from the same or from different species. We present here a new technique for analyzing the regulatory interaction neighborhoods of paralogous gene pairs. Our central focu…
New Trends in Graph Mining
2010
Searching for repeated features characterizing biological data is fundamental in computational biology. When biological networks are under analysis, the presence of repeated modules across the same network (or several distinct ones) is shown to be very relevant. Indeed, several studies prove that biological networks can be often understood in terms of coalitions of basic repeated building blocks, often referred to as network motifs.This work provides a review of the main techniques proposed for motif extraction from biological networks. In particular, main intrinsic difficulties related to the problem are pointed out, along with solutions proposed in the literature to overcome them. Open ch…
Searching for repetitions in biological networks: methods, resources and tools
2013
We present here a compact overview of the data, models and methods proposed for the analysis of biological networks based on the search for significant repetitions. In particular, we concentrate on three problems widely studied in the literature: ‘network alignment’, ‘network querying’ and ‘network motif extraction’. We provide (i) details of the experimental techniques used to obtain the main types of interaction data, (ii) descriptions of the models and approaches introduced to solve such problems and (iii) pointers to both the available databases and software tools. The intent is to lay out a useful roadmap for identifying suitable strategies to analyse cellular data, possibly based on t…
Algorithms for Graph and Network Analysis: Clustering and Search of Motifs in Graphs
2019
In this article we deal with problems that involve the analysis of topology in graphs modeling biological networks. In particular, we consider two important problems: (i) Network clustering, aiming at finding compact subgraphs inside the input graph in order to isolate molecular complexes, and (ii) searching for motifs, i.e., sub-structures repeated in the input network and presenting high significance (e.g., in terms of their frequency). We provide a compact overview of the main techniques proposed in the literature to solve these problems.
Analysis of the Structure and Dynamics of European Flight Networks
2022
We analyze structure and dynamics of flight networks of 50 airlines active in the European airspace in 2017. Our analysis shows that the concentration of the degree of nodes of different flight networks of airlines is markedly heterogeneous among airlines reflecting heterogeneity of the airline business models. We obtain an unsupervised classification of airlines by performing a hierarchical clustering that uses a correlation coefficient computed between the average occurrence profiles of 4-motifs of airline networks as similarity measure. The hierarchical tree is highly informative with respect to properties of the different airlines (for example, the number of main hubs, airline participa…