Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Transcription factor modularity in a gene-centered C. elegans core neuronal protein–DNA interaction network

Abstract: Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms Such networks are comprised of protein–DNA interactions between transcription factors (TFs) and their target genes An important question pertains to how the architecture of such networks relates to network functionality Here, we show that a Caenorhabditis elegans core neuronal protein–DNA interaction network is organized into two TF modules These modules contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules Each module relates to different functional aspects of the network One module contains TFs involved in reproduction and target genes that are expressed in neurons as well as in other tissues The second module is enriched for paired homeodomain TFs and connects to target genes that are often exclusively neuronal We find that paired homeodomain TFs are specifically expressed in C elegans and mouse neurons, indicating that the neuronal function of paired homeodomains is evolutionarily conserved Taken together, we show that a core neuronal C elegans protein–DNA interaction network possesses TF modules that relate to different functional aspects of the complete network

Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data

Abstract: Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein–protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and analysing their structural properties could provide insights into biological function and disease. We have already demonstrated, by using a sensitive graph theoretic method for comparing topologies of node neighbourhoods called ‘graphlet degree signatures’, that proteins with similar surroundings in PPI networks tend to perform the same functions. Here, we explore whether the involvement of genes in cancer suggests the similarity of their topological ‘signatures’ as well. By applying a series of clustering methods to proteins' topological signature similarities, we demonstrate that the obtained clusters are significantly enriched with cancer genes. We apply this methodology to identify novel cancer gene candidates, validating 80 per cent of our predictions in the literature. We also validate predictions biologically by identifying cancer-related negative regulators of melanogenesis identified in our siRNA screen. This is encouraging, since we have done this solely from PPI network topology. We provide clear evidence that PPI network structure around cancer genes is different from the structure around non-cancer genes. Understanding the underlying principles of this phenomenon is an open question, with a potential for increasing our understanding of complex diseases.

Drug target prioritization by perturbed gene expression and network information.

Abstract: Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

Characterization of microRNA-regulated protein-protein interaction network.

Abstract: We have performed topological analysis to elucidate the global correlation between microRNA (miRNA) regulation and protein-protein interaction network in human. The analysis showed that target genes of individual miRNA tend to be hubs and bottlenecks in the network. While proteins directly regulated by miRNA might not form a network module themselves, the miRNA-target genes and their interacting neighbors jointly showed significantly higher modularity. Our findings shed light on how miRNA may regulate the protein interaction network.

The ecology of collective behavior.

Abstract: Similar patterns of interaction, such as network motifs and feedback loops, are used in many natural collective processes, probably because they have evolved independently under similar pressures. Here I consider how three environmental constraints may shape the evolution of collective behavior: the patchiness of resources, the operating costs of maintaining the interaction network that produces collective behavior, and the threat of rupture of the network. The ants are a large and successful taxon that have evolved in very diverse environments. Examples from ants provide a starting point for examining more generally the fit between the particular pattern of interaction that regulates activity, and the environment in which it functions.