Significant Network Motifs Research Articles

Subjectivity Detection in Nuclear Energy Tweets

The subjectivity detection is an important binary classification task that aims at distinguishing natural language texts as opinionated (positive or negative) and non-opinionated (neutral). In this paper, we develop and apply recent subjectivity detection techniques to determine subjective and objective tweets towards the hot topic of nuclear energy. This will further help us to detect the presence or absence of social media bias towards Nuclear Energy. In particular, significant network motifs of words and concepts were learned in dynamic Gaussian Bayesian networks, while using Twitter as a source of information. We use reinforcement learning to update each weight based on a probabilistic reward function over all the weights and, hence, to regularize the sentence model. The proposed framework opens new avenues in helping government agencies manage online public opinion to decide and act according to the need of the hour.

Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

BackgroundA huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner.ResultsWe propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant network motifs, further validated by expert knowledge and functional enrichment analyses.ConclusionsWe have developed a novel network-based computational approach to investigate the heterogeneous drug-gene-disease network extracted from Semantic MEDLINE. We demonstrate the power of this approach by prioritizing candidate disease genes, inferring potential disease relationships, and proposing novel drug targets, within the context of the entire knowledge. The results indicate that such approach will facilitate the formulization of novel research hypotheses, which is critical for translational medicine research and personalized medicine.

Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns.

Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug–disease–gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

An approach for dynamical network reconstruction of simple network motifs

BackgroundOne of the most important projects in the post-genome-era is the systemic identification of biological network. The almost of studies for network identification focused on the improvement of computational efficiency in large-scale network inference of complex system with cyclic relations and few attempted have been done for answering practical problem occurred in real biological systems. In this study, we focused to evaluate inferring performance of our previously proposed method for inferring biological network on simple network motifs.ResultsWe evaluated the network inferring accuracy and efficiency of our previously proposed network inferring algorithm, by using 6 kinds of repeated appearance of highly significant network motifs in the regulatory network of E. coli proposed by Shen-Orr et al and Herrgård et al, and 2 kinds of network motif in S. cerevisiae proposed by Lee et. al. As a result, our method could reconstruct about 40% of interactions in network motif from time-series data set. Moreover the introduction of time-series data of one-factor disrupted model could remarkably improved the performance of network inference.ConclusionsThe results of network inference examination of E. coli network motif shows that our network inferring algorithm was able to apply to typical topology of biological network. A continuous examination of inferring well established network motif in biology would strengthen the applicability of our algorithm to the realistic biological network.

Dynamic network rewiring determines temporal regulatory functions in Drosophilamelanogaster development processes

The identification of network motifs has been widely considered as a significant step towards uncovering the design principles of biomolecular regulatory networks. To date, time-invariant networks have been considered. However, such approaches cannot be used to reveal time-specific biological traits due to the dynamic nature of biological systems, and hence may not be applicable to development, where temporal regulation of gene expression is an indispensable characteristic. We propose a concept of a "temporal sequence of network motifs", a sequence of network motifs in active sub-networks constructed over time, and investigate significant network motifs in the active temporal sub-networks of Drosophila melanogaster. Based on this concept, we find a temporal sequence of network motifs which changes according to developmental stages and thereby cannot be identified from the whole static network. Moreover, we show that the temporal sequence of network motifs corresponding to each developmental stage can be used to describe pivotal developmental events.

Cross-species common regulatory network inference without requirement for prior gene affiliation

Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity. We present an algorithm merging microarray datasets on the basis of co-expression alone, without any requirement for orthology information to affiliate genes. Combining existing methods such as co-inertia analysis, back-transformation, Hungarian matching and majority voting in an iterative non-greedy hill-climbing approach, it affiliates arrays and genes at the same time, maximizing the co-structure between the datasets. To introduce the method, we demonstrate its performance on two closely and two distantly related datasets of different experimental context and produced on different platforms. Each pair stems from two different species. The resulting cross-species dynamic Bayesian gene networks improve on the networks inferred from each dataset alone by yielding more significant network motifs, as well as more of the interactions already recorded in KEGG and other databases. Also, it is shown that our algorithm converges on the optimal number of nodes for network inference. Being readily extendable to more than two datasets, it provides the opportunity to infer extensive gene regulatory networks. Source code (MATLAB and R) freely available for download at http://www.mchips.org/supplements/moghaddasi_source.tgz.

Mining Bridge and Brick Motifs From Complex Biological Networks for Functionally and Statistically Significant Discovery

A major task for postgenomic systems biology researchers is to systematically catalogue molecules and their interactions within living cells. Advancements in complex-network theory are being made toward uncovering organizing principles that govern cell formation and evolution, but we lack understanding of how molecules and their interactions determine how complex systems function. Molecular bridge motifs include isolated motifs that neither interact nor overlap with others, whereas brick motifs act as network foundations that play a central role in defining global topological organization. To emphasize their structural organizing and evolutionary characteristics, we define bridge motifs as consisting of weak links only and brick motifs as consisting of strong links only, then propose a method for performing two tasks simultaneously, which are as follows: 1) detecting global statistical features and local connection structures in biological networks and 2) locating functionally and statistically significant network motifs. To further understand the role of biological networks in system contexts, we examine functional and topological differences between bridge and brick motifs for predicting biological network behaviors and functions. After observing brick motif similarities between E. coli and S. cerevisiae, we note that bridge motifs differentiate C. elegans from Drosophila and sea urchin in three types of networks. Similarities (differences) in bridge and brick motifs imply similar (different) key circuit elements in the three organisms. We suggest that motif-content analyses can provide researchers with global and local data for real biological networks and assist in the search for either isolated or functionally and topologically overlapping motifs when investigating and comparing biological system functions and behaviors.

Robustness and modular design of the Drosophila segment polarity network

Biomolecular networks have to perform their functions robustly. A robust function may have preferences in the topological structures of the underlying network. We carried out an exhaustive computational analysis on network topologies in relation to a patterning function in Drosophila embryogenesis. We found that whereas the vast majority of topologies can either not perform the required function or only do so very fragilely, a small fraction of topologies emerges as particularly robust for the function. The topology adopted by Drosophila, that of the segment polarity network, is a top ranking one among all topologies with no direct autoregulation. Furthermore, we found that all robust topologies are modular—each being a combination of three kinds of modules. These modules can be traced back to three subfunctions of the patterning function, and their combinations provide a combinatorial variability for the robust topologies. Our results suggest that the requirement of functional robustness drastically reduces the choices of viable topology to a limited set of modular combinations among which nature optimizes its choice under evolutionary and other biological constraints.

Environmental selection of the feed-forward loop circuit in gene-regulation networks

Gene-regulation networks contain recurring elementary circuits termed network motifs. It is of interest to understand under which environmental conditions each motif might be selected. To address this, we study one of the most significant network motifs, a three-gene circuit called the coherent feed-forward loop (FFL). The FFL has been demonstrated theoretically and experimentally to perform a basic information-processing function: it shows a delay following ON steps of an input inducer, but not after OFF steps. Here, we ask under what environmental conditions might the FFL be selected over simpler gene circuits, based on this function. We employ a theoretical cost–benefit analysis for the selection of gene circuits in a given environment. We find conditions that the environment must satisfy in order for the FFL to be selected over simpler circuits: the FFL is selected in environments where the distribution of the input pulse duration is sufficiently broad and contains both long and short pulses. Optimal values of the biochemical parameters of the FFL circuit are determined as a function of the environment such that the delay in the FFL blocks deleterious short pulses of induction. This approach can be generally used to study the evolutionary selection of other network motifs.

The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks

Recent analysis of the structure of transcription regulation networks revealed several “network motifs”: regulatory circuit patterns that occur much more frequently than in randomized networks. It is important to understand whether these network motifs have specific functions. One of the most significant network motifs is the coherent feedforward loop, in which transcription factor X regulates transcription factor Y, and both jointly regulate gene Z. On the basis of mathematical modeling and simulations, it was suggested that the coherent feedforward loop could serve as a sign-sensitive delay element: a circuit that responds rapidly to step-like stimuli in one direction (e.g. ON to OFF), and at a delay to steps in the opposite direction (OFF to ON). Is this function actually carried out by feedforward loops in living cells? Here, we address this experimentally, using a system with feedforward loop connectivity, the l-arabinose utilization system of Escherichiacoli. We measured responses to step-like cAMP stimuli at high temporal resolution and accuracy by means of green fluorescent protein reporters. We show that the arabinose system displays sign-sensitive delay kinetics. This type of kinetics is important for making decisions based on noisy inputs by filtering out fluctuations in input stimuli, yet allowing rapid response. This information-processing function may be performed by the feedforward loop regulation modules that are found in diverse systems from bacteria to humans.