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Journal articles on the topic 'Protein and gene networks'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Akker, Erik van den, Bas Verbruggen, Bas Heijmans, Marian Beekman, Joost Kok, Eline Slagboom, and Marcel Reinders. "Integrating Protein-Protein Interaction Networks with Gene- Gene Co-Expression Networks improves Gene Signatures for Classifying Breast Cancer Metastasis." Journal of Integrative Bioinformatics 8, no. 2 (June 1, 2011): 222–38. http://dx.doi.org/10.1515/jib-2011-188.

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Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.
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2

D’Arcy, Cian, Olivia Bass, Philipp Junk, Thomas Sevrin, Giorgio Oliviero, Kieran Wynne, Melinda Halasz, and Christina Kiel. "Disease–Gene Networks of Skin Pigmentation Disorders and Reconstruction of Protein–Protein Interaction Networks." Bioengineering 10, no. 1 (December 21, 2022): 13. http://dx.doi.org/10.3390/bioengineering10010013.

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Melanin, a light and free radical absorbing pigment, is produced in melanocyte cells that are found in skin, but also in hair follicles, eyes, the inner ear, heart, brain and other organs. Melanin synthesis is the result of a complex network of signaling and metabolic reactions. It therefore comes as no surprise that mutations in many of the genes involved are associated with various types of pigmentation diseases and phenotypes (‘pigmentation genes’). Here, we used bioinformatics tools to first reconstruct gene-disease/phenotype associations for all pigmentation genes. Next, we reconstructed protein–protein interaction (PPI) networks centered around pigmentation gene products (‘pigmentation proteins’) and supplemented the PPI networks with protein expression information obtained by mass spectrometry in a panel of melanoma cell lines (both pigment producing and non-pigment producing cells). The analysis provides a systems network representation of all genes/ proteins centered around pigmentation and melanin biosynthesis pathways (‘pigmentation network map’). Our work will enable the pigmentation research community to experimentally test new hypothesis arising from the pigmentation network map and to identify new targets for drug discovery.
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3

Amoutzias, Gregory D., David L. Robertson, and Erich Bornberg-Bauer. "The Evolution of Protein Interaction Networks in Regulatory Proteins." Comparative and Functional Genomics 5, no. 1 (2004): 79–84. http://dx.doi.org/10.1002/cfg.365.

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Interactions between proteins are essential for intracellular communication. They form complex networks which have become an important source for functional analysis of proteins. Combining phylogenies with network analysis, we investigate the evolutionary history of interaction networks from the bHLH, NR and bZIP transcription-factor families. The bHLH and NR networks show a hub-like structure with varying γ values. Mutation and gene duplication play an important role in adding and removing interactions. We conclude that in several of the protein families that we have studied, networks have primarily arisen by the development of heterodimerizing transcription factors, from an ancestral gene which interacts with any of the newly emerging proteins but also homodimerizes.
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4

Aloraini, Adel, and Karim M. ElSawy. "Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study." Cancer Informatics 17 (January 1, 2018): 117693511875535. http://dx.doi.org/10.1177/1176935118755354.

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Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks revealed that the resistant network is highly connected with 2 large domains of connectivity: one in which the RAF1 and MAP2K2 genes form hubs of connectivity and another in which the RAS gene is highly connected. On the contrary, the sensitive network is highly disrupted with a lower degree of connectivity. We investigated the interactions of the neoadjuvant docetaxel drug with the protein chains encoded by gene-gene interactions that underlie the disruption of the sensitive network topology using protein-protein and drug-protein docking techniques. We found that the sensitive network is likely to be disrupted by interaction of the neoadjuvant docetaxel drug with the DAXX and FGR1 proteins, which is consistent with the observed accumulation of cytoplasmic DAXX and overexpression of FGR1 precursors in cancer cell lines. This indicates that the DAXX and FGR1 proteins could be potential targets for the neoadjuvant docetaxel drug. The work, therefore, provides a new route for understanding the effect of the drug mode of action from the viewpoint of the change in the topology of gene-gene regulatory networks and provides a new avenue for bridging the gap between gene-gene interactions and protein-protein interactions which could have deep implications on mainstream drug development protocols.
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5

Wang, Li Qin, Gui Qiang Chen, and Hong Hai Zhao. "Construction of Regulatory Boolean Networks Based on Expression Profiles Data Noise." Advanced Materials Research 588-589 (November 2012): 2046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.2046.

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After while the “Human Genome Project” proposes, the people complete measures the foreword plan after the multi-gene genome team, also starts to change to these genes and their reciprocity function understanding research. Varieties of gene regulation Boolean networks algorithms have been proposed of the gene expression profiles, however, the problem of noise could always be found in creating a Boolean network. Due to gene expression data are always noisy. In this paper, it show that after the Boolean networks logic function are learned from noisy data, some noise in the Boolean function could be restructure Karnaugh Maps. It could find logic relationships between protein and protein and restructure protein logic networks. It find logic relationship among proteins as well as COGs (clusters of orthogous groups) and build the logic network of protein.
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6

Chatzaroulas, Evangelos, Vytenis Sliogeris, Pedro Victori, Francesca M. Buffa, Sotiris Moschoyiannis, and Roman Bauer. "A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer." Symmetry 14, no. 5 (May 16, 2022): 1009. http://dx.doi.org/10.3390/sym14051009.

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Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network’s functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network’s structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.
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7

Lundström, Jesper, Johan Björkegren, and Jesper Tegnér. "Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae." Bioinformatics and Biology Insights 2 (January 2008): BBI.S853. http://dx.doi.org/10.4137/bbi.s853.

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Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.
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Wahab Khattak, Fazal, Yousef Salamah Alhwaiti, Amjad Ali, Mohammad Faisal, and Muhammad Hameed Siddiqi. "Protein-Protein Interaction Analysis through Network Topology (Oral Cancer)." Journal of Healthcare Engineering 2021 (January 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/6623904.

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Oral cancer is a complex disorder. Its creation and spreading are due to the interaction of several proteins and genes in different biological thoroughfares. To study biological pathways, many high-yield methods have been used. Efforts to merge several data found at separate levels related to biological thoroughfares and interlinkage networks remain elusive. In our research work, we have proposed a technique known as protein-protein interaction network for analysis and exploring the genes involved in oral cancer disorders. The previous studies have not fully analyzed the proteins or genes involved in oral cancer. Our proposed technique is fully interactive and analyzes the data of oral cancer disorder more accurately and efficiently. The methods used here enabled us to observe the wide network consists of one mighty network comprising of 208 nodes 1572 edges which connect these nodes and various detached small networks. In our study, TP53 is a gene that occupied an important position in the network. TP53 has a 113-degree value and 0.03881821 BC value, indicating that TP53 is centrally localized in the network and is a significant bottleneck protein in the oral cancer protein-protein interaction network. These findings suggested that the pathogenesis of oral cancer variation was organized by means of an integrated PPI network, which is centered on TP53. Furthermore, our identification shows that TP53 is the key role-playing protein in the oral cancer network, and its significance in the cellular networks in the body is determined as well. As TP53 (tumor protein 53) is a vital player in the cell division process, the cells may not grow or divide disorderly; it fulfills the function of at least one of the gene groups in oral cancer. However, the latter progression in the area is any measure; the intention of developing these networks is to transfigure sketch of core disease development, prognosis, and treatment.
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9

Nguyen, Cao D., Katheleen J. Gardiner, and Krzysztof J. Cios. "Protein annotation from protein interaction networks and Gene Ontology." Journal of Biomedical Informatics 44, no. 5 (October 2011): 824–29. http://dx.doi.org/10.1016/j.jbi.2011.04.010.

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10

Kim, Yoonbee, Jong-Hoon Park, and Young-Rae Cho. "Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks." International Journal of Molecular Sciences 23, no. 13 (July 3, 2022): 7411. http://dx.doi.org/10.3390/ijms23137411.

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Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.
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11

IMOTO, SEIYA, TOMOYUKI HIGUCHI, TAKAO GOTO, KOUSUKE TASHIRO, SATORU KUHARA, and SATORU MIYANO. "COMBINING MICROARRAYS AND BIOLOGICAL KNOWLEDGE FOR ESTIMATING GENE NETWORKS VIA BAYESIAN NETWORKS." Journal of Bioinformatics and Computational Biology 02, no. 01 (March 2004): 77–98. http://dx.doi.org/10.1142/s021972000400048x.

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We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.
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12

Pastor-Satorras, Romualdo, Eric Smith, and Ricard V. Solé. "Evolving protein interaction networks through gene duplication." Journal of Theoretical Biology 222, no. 2 (May 2003): 199–210. http://dx.doi.org/10.1016/s0022-5193(03)00028-6.

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13

Zhang, Fengyu, Wei Peng, Yunfei Yang, Wei Dai, and Junrong Song. "A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein–Protein Interactive Networks." Genes 10, no. 1 (January 8, 2019): 31. http://dx.doi.org/10.3390/genes10010031.

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Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.
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14

Borotkanics, Robert, and Harold Lehmann. "Network motifs that recur across species, including gene regulatory and protein–protein interaction networks." Archives of Toxicology 89, no. 4 (May 22, 2014): 489–99. http://dx.doi.org/10.1007/s00204-014-1274-y.

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15

Bull, Larry. "A Simple Computational Cell: Coupling Boolean Gene and Protein Networks." Artificial Life 18, no. 2 (April 2012): 223–36. http://dx.doi.org/10.1162/artl_a_00060.

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This article presents an abstract, tunable model containing two of the principal information-processing features of cells and explores its use with simulated evolution. The random Boolean model of genetic regulatory networks is extended to include a protein interaction network. The underlying behavior of the resulting two coupled dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes.
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Weighill, Deborah, Marouen Ben Guebila, Kimberly Glass, John Quackenbush, and John Platig. "Predicting genotype-specific gene regulatory networks." Genome Research 32, no. 3 (February 22, 2022): 524–33. http://dx.doi.org/10.1101/gr.275107.120.

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Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.
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Li, Cheng-Wei, and Bor-Sen Chen. "Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses." Comparative and Functional Genomics 2010 (2010): 1–20. http://dx.doi.org/10.1155/2010/408705.

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Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways.
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18

Lappe, M., and L. Holm. "Algorithms for protein interaction networks." Biochemical Society Transactions 33, no. 3 (June 1, 2005): 530–34. http://dx.doi.org/10.1042/bst0330530.

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The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.
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Gassmann, Walter, and Saikat Bhattacharjee. "Effector-Triggered Immunity Signaling: From Gene-for-Gene Pathways to Protein-Protein Interaction Networks." Molecular Plant-Microbe Interactions® 25, no. 7 (July 2012): 862–68. http://dx.doi.org/10.1094/mpmi-01-12-0024-ia.

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In its simplicity and testability, Flor's gene-for-gene hypothesis has been a powerful driver in plant immunity research for decades. Once the molecular underpinnings of gene-for-gene resistance had come into sharper focus, there was a reassessment of Flor's hypothesis and a name change to effector-triggered immunity. As implied by the name change and exemplified by pioneering studies, plant immunity is increasingly described in terms of protein rather than genetic interactions. This progress leads to a reinterpretation of old concepts of pathogen recognition and resistance signaling and, of course, opens up new questions. Here, we provide a brief historical overview of resistance gene function and how a new focus on protein interactions can lead to a deeper understanding of the logic of plant innate immunity signaling.
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Amiri Dashatan, Nasrin, Mostafa Rezaie Tavirani, Hakimeh Zali, Mehdi Koushki, and Nayebali Ahmadi. "Prediction of Leishmania Major Key Proteins via Topological Analysis of Protein-Protein Interaction Network." Galen Medical Journal 7 (June 12, 2018): e1129. http://dx.doi.org/10.31661/gmj.v7i0.1129.

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Background: Although leishmaniasis is regarded as a public health problem, no effective vaccine or decisive treatment has been introduced for this disease. Therefore, representing novel therapeutic proteins is essential. Protein-protein Interaction network analysis is a suitable tool to discover novel drug targets for leishmania major. To this aim, gene and protein expression data is used for instructing protein network and the key proteins are highlighted.Materials and Methods: In this computational and bioinformatics study, the protein/gene expression data related to leishmania major were studied, and 252 candidate proteins were extracted. Then, the protein networks of these proteins were explored and visualized by using String database and Cytoscape software. Finally, clustering and gene ontology were performed by MCODE and PANTHER databases, respectively.Results: Based on gene ontology analysis, most of the leishmania major proteins were located in cell compartments and membrane. Catalytic activity and binding were regarded as the relevant molecular functions and metabolic and cellular processes were the significant biological process. In this network analysis, UB-EP52, EF-2, chaperonin, Hsp70.4, Hsp60, tubulin alpha and beta chain, and ENOL and LACK were introduced as hub-bottleneck proteins. Based on clustering analysis, Lmjf.32.3270, ENOL and Lmjf.13.0290 were determined as seed proteins in each cluster.Conclusion: The results indicated that hub proteins play a significant role in pathogenesis and life cycle of leishmania major. Further studies of hubs will provide a better understanding of leishmaniasis mechanisms. Finally, these key hub proteins could be a suitable and helpful potential for drug targets and treating leishmaniasis by considering their validation. [GMJ.2018;7:e1129]
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Thuy, Bui Phuong, and Trinh Xuan Hoang. "Evolution of Protein-protein Interaction Networks in Duplication-Divergence Model." Communications in Physics 22, no. 1 (May 7, 2012): 7–14. http://dx.doi.org/10.15625/0868-3166/22/1/629.

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Protein interacts with one another resulting in complex functions in living organisms. Like many other real-world networks, the networks of protein-protein interactions possess a certain degree of ordering, such as the scale-free property. The latter means that the probability $P$ to find a protein that interacts with $k$ other proteins follows a power law, $P(k) \sim k^{-\gamma}$. Protein interaction networks (PINs) have been studied by using a stochastic model, the duplication-divergence model, which is based on mechanisms of gene duplication and divergence during evolution. In this work, we show that this model can be used to fit experimental data on the PIN of yeast Saccharomyces cerevisae at two different time instances simultaneously. Our study shows that the evolution of PIN given by model is consistent with growing experimental data over time, and that the scale-free property of protein interaction network is robust against random deletion of interactions.
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Li, Min, Weijie Chen, Jianxin Wang, Fang-Xiang Wu, and Yi Pan. "Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/375262.

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Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data ofSaccharomyces cerevisiaeand the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.
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Liu, Wuyi, and Deyu Chen. "Phylogeny, Functional Annotation, and Protein Interaction Network Analyses of theXenopus tropicalisBasic Helix-Loop-Helix Transcription Factors." BioMed Research International 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/145037.

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The previous survey identified 70 basic helix-loop-helix (bHLH) proteins, but it was proved to be incomplete, and the functional information and regulatory networks of frog bHLH transcription factors were not fully known. Therefore, we conducted an updated genome-wide survey in theXenopus tropicalisgenome project databases and identified 105 bHLH sequences. Among the retrieved 105 sequences, phylogenetic analyses revealed that 103 bHLH proteins belonged to 43 families or subfamilies with 46, 26, 11, 3, 15, and 4 members in the corresponding supergroups. Next, gene ontology (GO) enrichment analyses showed 65 significant GO annotations of biological processes and molecular functions and KEGG pathways counted in frequency. To explore the functional pathways, regulatory gene networks, and/or related gene groups coding forXenopus tropicalisbHLH proteins, the identified bHLH genes were put into the databases KOBAS and STRING to get the signaling information of pathways and protein interaction networks according to available public databases and known protein interactions. From the genome annotation and pathway analysis using KOBAS, we identified 16 pathways in theXenopus tropicalisgenome. From the STRING interaction analysis, 68 hub proteins were identified, and many hub proteins created a tight network or a functional module within the protein families.
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Cao, Renzhi, and Jianlin Cheng. "Integrated protein function prediction by mining function associations, sequences, and protein–protein and gene–gene interaction networks." Methods 93 (January 2016): 84–91. http://dx.doi.org/10.1016/j.ymeth.2015.09.011.

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25

Saric, J., L. J. Jensen, R. Ouzounova, I. Rojas, and P. Bork. "Extraction of regulatory gene/protein networks from Medline." Bioinformatics 22, no. 6 (July 26, 2005): 645–50. http://dx.doi.org/10.1093/bioinformatics/bti597.

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Alexiou, Athanasios, Stylianos Chatzichronis, Asma Perveen, Abdul Hafeez, and Ghulam Md Ashraf. "Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions." Current Topics in Medicinal Chemistry 19, no. 6 (May 2, 2019): 413–25. http://dx.doi.org/10.2174/1568026619666190311125256.

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Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI models are presented and discussed in this review study such as: Graphs representation, Boolean Networks, Generalized Logical Networks, Bayesian Networks, Relevance Networks, Graphical Gaussian models, Weight Matrices, Reverse Engineering Approach, Evolutionary Algorithms, Forward Modeling Approach, Deterministic models, Static models, Hybrid models, Stochastic models, Petri Nets, BioAmbients calculus and Differential Equations.Results:GRN and PPI methods have been already applied in various clinical processes with potential positive results, establishing promising diagnostic tools.Conclusion:In literature many stochastic algorithms are focused in the simulation, analysis and visualization of the various biological networks and their dynamics interactions, which are referred and described in depth in this review paper.
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Makrodimitris, Stavros, Marcel Reinders, and Roeland van Ham. "A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins." PLOS ONE 15, no. 11 (November 25, 2020): e0242723. http://dx.doi.org/10.1371/journal.pone.0242723.

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Physical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extent these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.
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Kiss-Toth, E., D. H. Wyllie, K. Holland, L. Marsden, V. Jozsa, K. M. Oxley, T. Polgar, E. E. Qwarnstrom, and S. K. Dower. "Functional mapping of Toll/interleukin-1 signalling networks by expression cloning." Biochemical Society Transactions 33, no. 6 (October 26, 2005): 1405–6. http://dx.doi.org/10.1042/bst0331405.

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Multiple cellular proteins have been identified as participating in Toll/interleukin-1 receptor-mediated inflammatory gene expression. The continuing isolation of novel components, based on sequence similarities, protein–protein interactions and protein purification, suggests that many elements of this signalling network remain to be identified. We report here the development of a high-throughput functional screening platform and its application for the identification of components of inflammatory signalling networks. Our results enable us to estimate that 100–150 gene products are involved in controlling the transcription of the human interleukin 8 gene. The approach, which is simple and robust, constitutes a general method for mapping signal transduction systems and for rapid isolation of a large number of signalling components based on the control of pathways leading to regulation of gene expression.
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Gómez, Antonio, Juan Cedano, Isaac Amela, Antoni Planas, Jaume Piñol, and Enrique Querol. "Gene Ontology Function prediction in Mollicutes using Protein-Protein Association Networks." BMC Systems Biology 5, no. 1 (2011): 49. http://dx.doi.org/10.1186/1752-0509-5-49.

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Guney, Emre, and Baldo Oliva. "Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization." PLoS ONE 7, no. 9 (September 21, 2012): e43557. http://dx.doi.org/10.1371/journal.pone.0043557.

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Zhang, Wei, Jia Xu, Yuanyuan Li, and Xiufen Zou. "Integrating network topology, gene expression data and GO annotation information for protein complex prediction." Journal of Bioinformatics and Computational Biology 17, no. 01 (February 2019): 1950001. http://dx.doi.org/10.1142/s021972001950001x.

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The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict protein complexes based on protein–protein interaction (PPI) networks in recent years. However, because the high throughput data obtained from experimental biotechnology are incomplete, and usually contain a large number of spurious interactions, most of the network-based protein complex identification methods are sensitive to the reliability of the PPI network. In this paper, we propose a new method, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks. To demonstrate the performance of the IPC-RPIN method, we evaluated the IPC-RPIN on three PPI networks of Saccharomycescerevisiae and compared it with four state-of-the-art methods. The simulation results show that the IPC-RPIN achieved a better result than the other methods on most of the measurements and is able to discover small protein complexes which have traditionally been neglected.
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Milenković, Tijana, Vesna Memišević, Anand K. Ganesan, and Nataša Pržulj. "Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data." Journal of The Royal Society Interface 7, no. 44 (July 22, 2009): 423–37. http://dx.doi.org/10.1098/rsif.2009.0192.

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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.
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FUJITA, ANDRÉ, JOÃO RICARDO SATO, HUMBERTO MIGUEL GARAY-MALPARTIDA, MARI CLEIDE SOGAYAR, CARLOS EDUARDO FERREIRA, and SATORU MIYANO. "MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 06, no. 05 (October 2008): 961–79. http://dx.doi.org/10.1142/s0219720008003746.

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In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-κB, and c-Myc) for HeLa cells.
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Zhang, Jinxiong, Cheng Zhong, Hai Xiang Lin, and Mian Wang. "Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks." BioMed Research International 2019 (August 21, 2019): 1–17. http://dx.doi.org/10.1155/2019/3726721.

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Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.
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Zamani, Zahra, Amirhossein Hajihosseini, and Ali Masoudi-Nejad. "Computational Methodologies for Analyzing, Modeling and Controlling Gene Regulatory Networks." Biomedical Engineering and Computational Biology 2 (January 2010): BECB.S5594. http://dx.doi.org/10.4137/becb.s5594.

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Molecular biology focuses on genes and their interactions at the transcription, regulation and protein level. Finding genes that cause certain behaviors can make therapeutic interventions more effective. Although biological tools can extract the genes and perform some analyses, without the help of computational methods, deep insight of the genetic function and its effects will not occur. On the other hand, complex systems can be modeled by networks, introducing the main data as nodes and the links in-between as the transactions occurring within the network. Gene regulatory networks are examples that are modeled and analyzed in order to gain insight of their exact functions. Since a cell's specific functionality is greatly determined by the genes it expresses, translation or the act of converting mRNA to proteins is highly regulated by the control network that directs cellular activities. This paper briefly reviews the most important computational methods for analyzing, modeling and controlling the gene regulatory networks.
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Di Filippo, Marzia, Chiara Damiani, and Dario Pescini. "GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction." PLOS Computational Biology 17, no. 11 (November 8, 2021): e1009550. http://dx.doi.org/10.1371/journal.pcbi.1009550.

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Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.
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Yamaguchi, Eimi, Tatsuya Akutsu, and Jose C. Nacher. "Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles." International Journal of Molecular Sciences 22, no. 18 (September 13, 2021): 9891. http://dx.doi.org/10.3390/ijms22189891.

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Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20–99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates.
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Kohutyuk, Oksana, Fadi Towfic, M. Heather West Greenlee, and Vasant Honavar. "BioNetwork Bench: Database and Software for Storage, Query, and Analysis of Gene and Protein Networks." Bioinformatics and Biology Insights 6 (January 2012): BBI.S9728. http://dx.doi.org/10.4137/bbi.s9728.

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Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/ Background The emergence of high-throughput technologies has allowed many biological investigators to collect a great deal of information about the behavior of genes and gene products over time or during a particular disease state. Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from such high-throughput analyses. There are a growing number of public databases, as well as tools for visualization and analysis of networks. However, such databases and tools have yet to be widely utilized by bench scientists, as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by biological scientists with limited computational expertise. Results We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. BioNetwork Bench currently supports a broad class of gene and protein network models (eg, weighted and un-weighted, undirected graphs, multi-graphs). It enables biologists to analyze public as well as private gene expression, macromolecular interaction and annotation data; interactively query gene expression datasets; integrate data from multiple networks; query multiple networks for interactions of interest; store and selectively share the data as well as results of analyses. BioNetwork Bench is implemented as a plug-in for, and hence is fully interoperable with, Cytoscape, a popular open-source software suite for visualizing macromolecular interaction networks. Finally, we describe an application of BioNetwork Bench to the problem of assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. Conclusions BioNetwork Bench provides a suite of open source software for construction, querying, and selective sharing of gene and protein networks. Although initially aimed at a community of biologists interested in retinal development, the tool can be adapted easily to work with other biological systems simply by populating the associated database with the relevant datasets.
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LEIER, ANDRÉ, P. DWIGHT KUO, and WOLFGANG BANZHAF. "ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE." Advances in Complex Systems 10, no. 02 (June 2007): 155–72. http://dx.doi.org/10.1142/s0219525907000994.

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Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.
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Cai, Chunhui, Lujia Chen, Xia Jiang, and Xinghua Lu. "Modeling Signal Transduction from Protein Phosphorylation to Gene Expression." Cancer Informatics 13s1 (January 2014): CIN.S13883. http://dx.doi.org/10.4137/cin.s13883.

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Background Signaling networks are of great importance for us to understand the cell's regulatory mechanism. The rise of large-scale genomic and proteomic data, and prior biological knowledge has paved the way for the reconstruction and discovery of novel signaling pathways in a data-driven manner. In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways. Method We proposed a novel Bayesian Network (BN) framework to integrate transcriptomic data with proteomic data reflecting protein phosphorylation states for the purpose of identifying the pathways transmitting the signal of diverse stimuli in rat and human cells. We represented the proteins and genes as nodes in a BN in which edges reflect the regulatory relationship between signaling proteins. We designed an efficient inference algorithm that incorporated the prior knowledge of pathways and searched for a network structure in a data-driven manner. Results We applied our method to infer rat and human specific networks given gene expression and proteomic datasets. We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data. Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli. Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task. The capability of inferring signaling pathways in a data-driven fashion may contribute to cancer research by identifying distinct aberrations in signaling pathways underlying heterogeneous cancers subtypes.
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Liu, Bernard A., and Piers D. Nash. "Evolution of SH2 domains and phosphotyrosine signalling networks." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1602 (September 19, 2012): 2556–73. http://dx.doi.org/10.1098/rstb.2012.0107.

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Src homology 2 (SH2) domains mediate selective protein–protein interactions with tyrosine phosphorylated proteins, and in doing so define specificity of phosphotyrosine (pTyr) signalling networks. SH2 domains and protein-tyrosine phosphatases expand alongside protein-tyrosine kinases (PTKs) to coordinate cellular and organismal complexity in the evolution of the unikont branch of the eukaryotes. Examination of conserved families of PTKs and SH2 domain proteins provides fiduciary marks that trace the evolutionary landscape for the development of complex cellular systems in the proto-metazoan and metazoan lineages. The evolutionary provenance of conserved SH2 and PTK families reveals the mechanisms by which diversity is achieved through adaptations in tissue-specific gene transcription, altered ligand binding, insertions of linear motifs and the gain or loss of domains following gene duplication. We discuss mechanisms by which pTyr-mediated signalling networks evolve through the development of novel and expanded families of SH2 domain proteins and the elaboration of connections between pTyr-signalling proteins. These changes underlie the variety of general and specific signalling networks that give rise to tissue-specific functions and increasingly complex developmental programmes. Examination of SH2 domains from an evolutionary perspective provides insight into the process by which evolutionary expansion and modification of molecular protein interaction domain proteins permits the development of novel protein-interaction networks and accommodates adaptation of signalling networks.
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LUO, JIAWEI, and NAN ZHANG. "PREDICTION OF ESSENTIAL PROTEINS BASED ON EDGE CLUSTERING COEFFICIENT AND GENE ONTOLOGY INFORMATION." Journal of Biological Systems 22, no. 03 (August 28, 2014): 339–51. http://dx.doi.org/10.1142/s0218339014500119.

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Essential proteins are important for the survival and development of organisms. Lots of centrality algorithms based on network topology have been proposed to detect essential proteins and achieve good results. However, most of them only focus on the network topology, but ignore the false positive (FP) interactions in protein–protein interaction (PPI) network. In this paper, gene ontology (GO) information is proposed to measure the reliability of the edges in PPI network and we propose a novel algorithm for identifying essential proteins, named EGC algorithm. EGC algorithm integrates topology character of PPI network and GO information. To validate the performance of EGC algorithm, we use EGC and other nine methods (DC, BC, CC, SC, EC, LAC, NC, PEC and CoEWC) to identify the essential proteins in the two different yeast PPI networks: DIP and MIPS. The results show that EGC is better than the other nine methods, which means adding GO information can help in predicting essential proteins.
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JAROMERSKA, SLAVKA, PETR PRAUS, and YOUNG-RAE CHO. "DISTANCE-WISE PATHWAY DISCOVERY FROM PROTEIN–PROTEIN INTERACTION NETWORKS WEIGHTED BY SEMANTIC SIMILARITY." Journal of Bioinformatics and Computational Biology 12, no. 01 (January 28, 2014): 1450004. http://dx.doi.org/10.1142/s0219720014500048.

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Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein–protein interaction (PPI) networks. However, predicting directed pathways from the undirected genome-wide PPI networks has been challenging. We present a novel computational algorithm to efficiently predict signaling pathways from PPI networks given a starting protein and an ending protein. Our approach integrates topological analysis of PPI networks and semantic analysis of PPIs using Gene Ontology data. An advanced semantic similarity measure is used for weighting each interacting protein pair. Our distance-wise algorithm iteratively selects an adjacent protein from a PPI network to build a pathway based on a distance condition. On each iteration, the strength of a hypothetical path passing through a candidate edge is estimated by a local heuristic. We evaluate the performance by comparing the resultant paths to known signaling pathways on yeast. The results show that our approach has higher accuracy and efficiency than previous methods.
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Kim, Chan Yeong, Seungbyn Baek, Junha Cha, Sunmo Yang, Eiru Kim, Edward M. Marcotte, Traver Hart, and Insuk Lee. "HumanNet v3: an improved database of human gene networks for disease research." Nucleic Acids Research 50, no. D1 (November 8, 2021): D632—D639. http://dx.doi.org/10.1093/nar/gkab1048.

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Abstract Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein–protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene–phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein–protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.
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Hanna, Eileen Marie, Nazar Zaki, and Amr Amin. "Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters." PLOS ONE 10, no. 12 (December 7, 2015): e0144163. http://dx.doi.org/10.1371/journal.pone.0144163.

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Noor, Amina, Erchin Serpedin, Mohamed Nounou, Hazem Nounou, Nady Mohamed, and Lotfi Chouchane. "An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks." Advances in Bioinformatics 2013 (February 21, 2013): 1–12. http://dx.doi.org/10.1155/2013/953814.

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The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
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Lei, Xiujuan, Siguo Wang, and Fang-Xiang Wu. "Identification of Essential Proteins Based on Improved HITS Algorithm." Genes 10, no. 2 (February 25, 2019): 177. http://dx.doi.org/10.3390/genes10020177.

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Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques.
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Woycinck Kowalski, Thayne, Larissa Brussa Reis, Tiago Finger Andreis, Patricia Ashton-Prolla, and Clévia Rosset. "Systems Biology Approaches Reveal Potential Phenotype-Modifier Genes in Neurofibromatosis Type 1." Cancers 12, no. 9 (August 26, 2020): 2416. http://dx.doi.org/10.3390/cancers12092416.

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Neurofibromatosis type (NF1) is a syndrome characterized by varied symptoms, ranging from mild to more aggressive phenotypes. The variation is not explained only by genetic and epigenetic changes in the NF1 gene and the concept of phenotype-modifier genes in extensively discussed in an attempt to explain this variability. Many datasets and tools are already available to explore the relationship between genetic variation and disease, including systems biology and expression data. To suggest potential NF1 modifier genes, we selected proteins related to NF1 phenotype and NF1 gene ontologies. Protein–protein interaction (PPI) networks were assembled, and network statistics were obtained by using forward and reverse genetics strategies. We also evaluated the heterogeneous networks comprising the phenotype ontologies selected, gene expression data, and the PPI network. Finally, the hypothesized phenotype-modifier genes were verified by a random-walk mathematical model. The network statistics analyses combined with the forward and reverse genetics strategies, and the assembly of heterogeneous networks, resulted in ten potential phenotype-modifier genes: AKT1, BRAF, EGFR, LIMK1, PAK1, PTEN, RAF1, SDC2, SMARCA4, and VCP. Mathematical models using the random-walk approach suggested SDC2 and VCP as the main candidate genes for phenotype-modifiers.
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Guo, A. Y., J. Sun, B. P. Riley, D. L. Thiselton, K. S. Kendler, and Z. Zhao. "The dystrobrevin-binding protein 1 gene: features and networks." Molecular Psychiatry 14, no. 1 (July 29, 2008): 18–29. http://dx.doi.org/10.1038/mp.2008.88.

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Coulomb, Stéphane, Michel Bauer, Denis Bernard, and Marie-Claude Marsolier-Kergoat. "Gene essentiality and the topology of protein interaction networks." Proceedings of the Royal Society B: Biological Sciences 272, no. 1573 (July 14, 2005): 1721–25. http://dx.doi.org/10.1098/rspb.2005.3128.

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