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Sample records for cancer signaling networks

  1. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    as well as cancer resistance to treatment, represent not only a huge challenge, but also one with potentially extreme benefit for our understanding of the disease and for patients. This thesis summarizes my efforts during the last years in contributing positively to overcome this challenge. This thesis...... is divided into six parts. Starting with a brief introduction to the history and some basic concepts of cancer, signaling networks and human protein kinases (part I), we quickly move on to describing existing methods to analyze cancer signaling networks, including methods proposed by us, as well as three...

  2. Modeling Signaling Networks to Advance New Cancer Therapies.

    Science.gov (United States)

    Saez-Rodriguez, Julio; MacNamara, Aidan; Cook, Simon

    2015-01-01

    Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

  3. Databases and tools for constructing signal transduction networks in cancer.

    Science.gov (United States)

    Nam, Seungyoon

    2017-01-01

    Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, highthroughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. [BMB Reports 2017; 50(1): 12-19].

  4. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Science.gov (United States)

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  5. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.

    2015-01-01

    Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified......-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks....

  6. TROVE: A User-friendly Tool for Visualizing and Analyzing Cancer Hallmarks in Signaling Networks.

    Science.gov (United States)

    Chua, Huey Eng; Bhowmick, Sourav S; Zheng, Jie

    2017-09-22

    Cancer hallmarks, a concept that seeks to explain the complexity of cancer initiation and development, provide a new perspective of studying cancer signaling which could lead to a greater understanding of this complex disease. However, to the best of our knowledge, there is currently a lack of tools that support such hallmark-based study of the cancer signaling network, thereby impeding the gain of knowledge in this area. We present TROVE, a user-friendly software that facilitates hallmark annotation, visualization and analysis in cancer signaling networks. In particular, TROVE facilitates hallmark analysis specific to particular cancer types. Available under the Eclipse Public License from: https://sites.google.com/site/cosbyntu/softwares/trove and https://github.com/trove2017/Trove. hechua@ntu.edu.sg or assourav@ntu.edu.sg.

  7. Identification of critical regulatory genes in cancer signaling network using controllability analysis

    Science.gov (United States)

    Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

    2017-05-01

    Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

  8. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  9. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

    Science.gov (United States)

    Kuperstein, I; Bonnet, E; Nguyen, H-A; Cohen, D; Viara, E; Grieco, L; Fourquet, S; Calzone, L; Russo, C; Kondratova, M; Dutreix, M; Barillot, E; Zinovyev, A

    2015-07-20

    Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses

  10. MicroRNAs as Regulator of Signaling Networks in Metastatic Colon Cancer

    Science.gov (United States)

    Wang, Jian; Du, Yong; Liu, Xiaoming; Cho, William C.; Yang, Yinxue

    2015-01-01

    MicroRNAs (miRNAs) are a class of small, noncoding RNA molecules capable of regulating gene expression translationally and/or transcriptionally. A large number of evidence have demonstrated that miRNAs have a functional role in both physiological and pathological processes by regulating the expression of their target genes. Recently, the functionalities of miRNAs in the initiation, progression, angiogenesis, metastasis, and chemoresistance of tumors have gained increasing attentions. Particularly, the alteration of miRNA profiles has been correlated with the transformation and metastasis of various cancers, including colon cancer. This paper reports the latest findings on miRNAs involved in different signaling networks leading to colon cancer metastasis, mainly focusing on miRNA profiling and their roles in PTEN/PI3K, EGFR, TGFβ, and p53 signaling pathways of metastatic colon cancer. The potential of miRNAs used as biomarkers in the diagnosis, prognosis, and therapeutic targets in colon cancer is also discussed. PMID:26064956

  11. KRAS, YAP, and obesity in pancreatic cancer: a signaling network with multiple loops.

    Science.gov (United States)

    Eibl, Guido; Rozengurt, Enrique

    2017-10-24

    Pancreatic ductal adenocarcinoma (PDAC) continues to be a lethal disease with no efficacious treatment modalities. The incidence of PDAC is expected to increase, at least partially because of the obesity epidemic. Increased efforts to prevent or intercept this disease are clearly needed. Mutations in KRAS are initiating events in pancreatic carcinogenesis supported by genetically engineered mouse models of the disease. However, oncogenic KRAS is not entirely sufficient for the development of fully invasive PDAC. Additional genetic mutations and/or environmental, nutritional, and metabolic stressors, e.g. inflammation and obesity, are required for efficient PDAC formation with activation of KRAS downstream effectors. Multiple factors "upstream" of KRAS associated with obesity, including insulin resistance, inflammation, changes in gut microbiota and GI peptides, can enhance/modulate downstream signals. Multiple signaling networks and feedback loops "downstream" of KRAS have been described that respond to obesogenic diets. We propose that KRAS mutations potentiate a signaling network that is promoted by environmental factors. Specifically, we envisage that KRAS mutations increase the intensity and duration of the growth-promoting signaling network. As the transcriptional activator YAP plays a critical role in the network, we conclude that the rationale for targeting the network (at different points), e.g. with FDA approved drugs such as statins and metformin, is therefore compelling. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

    Science.gov (United States)

    Azim, Shafquat; Zubair, Haseeb; Srivastava, Sanjeev K; Bhardwaj, Arun; Zubair, Asif; Ahmad, Aamir; Singh, Seema; Khushman, Moh'd; Singh, Ajay P

    2016-06-29

    We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer.

  13. Bladder Cancer Advocacy Network

    Science.gov (United States)

    ... Grants Bladder Cancer Think Tank Bladder Cancer Research Network Bladder Cancer Genomics Consortium Get Involved Ways to ... us? Who we are The Bladder Cancer Advocacy Network (BCAN) is a community of patients, caregivers, survivors, ...

  14. Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks

    Directory of Open Access Journals (Sweden)

    Shengda Lin

    2016-01-01

    Full Text Available The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002. This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA, the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.

  15. Rare copy number variants observed in hereditary breast cancer cases disrupt genes in estrogen signaling and TP53 tumor suppression network.

    Directory of Open Access Journals (Sweden)

    Katri Pylkäs

    Full Text Available Breast cancer is the most common cancer in women in developed countries, and the contribution of genetic susceptibility to breast cancer development has been well-recognized. However, a great proportion of these hereditary predisposing factors still remain unidentified. To examine the contribution of rare copy number variants (CNVs in breast cancer predisposition, high-resolution genome-wide scans were performed on genomic DNA of 103 BRCA1, BRCA2, and PALB2 mutation negative familial breast cancer cases and 128 geographically matched healthy female controls; for replication an independent cohort of 75 similarly mutation negative young breast cancer patients was used. All observed rare variants were confirmed by independent methods. The studied breast cancer cases showed a consistent increase in the frequency of rare CNVs when compared to controls. Furthermore, the biological networks of the disrupted genes differed between the two groups. In familial cases the observed mutations disrupted genes, which were significantly overrepresented in cellular functions related to maintenance of genomic integrity, including DNA double-strand break repair (P = 0.0211. Biological network analysis in the two independent breast cancer cohorts showed that the disrupted genes were closely related to estrogen signaling and TP53 centered tumor suppressor network. These results suggest that rare CNVs represent an alternative source of genetic variation influencing hereditary risk for breast cancer.

  16. Rare copy number variants observed in hereditary breast cancer cases disrupt genes in estrogen signaling and TP53 tumor suppression network.

    Science.gov (United States)

    Pylkäs, Katri; Vuorela, Mikko; Otsukka, Meeri; Kallioniemi, Anne; Jukkola-Vuorinen, Arja; Winqvist, Robert

    2012-01-01

    Breast cancer is the most common cancer in women in developed countries, and the contribution of genetic susceptibility to breast cancer development has been well-recognized. However, a great proportion of these hereditary predisposing factors still remain unidentified. To examine the contribution of rare copy number variants (CNVs) in breast cancer predisposition, high-resolution genome-wide scans were performed on genomic DNA of 103 BRCA1, BRCA2, and PALB2 mutation negative familial breast cancer cases and 128 geographically matched healthy female controls; for replication an independent cohort of 75 similarly mutation negative young breast cancer patients was used. All observed rare variants were confirmed by independent methods. The studied breast cancer cases showed a consistent increase in the frequency of rare CNVs when compared to controls. Furthermore, the biological networks of the disrupted genes differed between the two groups. In familial cases the observed mutations disrupted genes, which were significantly overrepresented in cellular functions related to maintenance of genomic integrity, including DNA double-strand break repair (P = 0.0211). Biological network analysis in the two independent breast cancer cohorts showed that the disrupted genes were closely related to estrogen signaling and TP53 centered tumor suppressor network. These results suggest that rare CNVs represent an alternative source of genetic variation influencing hereditary risk for breast cancer.

  17. ErbB2-Driven Breast Cancer Cell Invasion Depends on a Complex Signaling Network Activating Myeloid Zinc Finger-1-Dependent Cathepsin B Expression

    DEFF Research Database (Denmark)

    Rafn, Bo; Nielsen, Christian Thomas Friberg; Andersen, Sofie Hagel

    2012-01-01

    signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling...... as effectors of ErbB2-induced invasion in vitro. We identify Cdc42-binding protein kinase beta, extracellular regulated kinase 2, p21-activated protein kinase 4, and protein kinase C alpha as essential mediators of ErbB2-induced cysteine cathepsin expression and breast cancer cell invasiveness. The identified...

  18. Global characterization of signalling networks associated with tamoxifen resistance in breast cancer

    DEFF Research Database (Denmark)

    Browne, Brigid C.; Hochgräfe, Falko; Wu, Jianmin

    2013-01-01

    was perturbed in TamR cells, together with pathways enriched for proteins associated with growth factor, cell–cell and cell matrix‐initiated signalling. Consistent with known roles for Ras/MAPK and PI3‐kinase signalling in tamoxifen resistance, tyrosine‐phosphorylated MAPK1, SHC1 and PIK3R2 were elevated in Tam......R cells. Phosphorylation of the tyrosine kinase Yes and expression of the actin‐binding protein myristoylated alanine‐rich C‐kinase substrate (MARCKS) were increased two‐ and eightfold in TamR cells respectively, and these proteins were selected for further analysis. Knockdown of either protein in Tam...

  19. Modulation of insulin-like growth factor-1 receptor and its signaling network for the treatment of cancer: Current status and future perspectives

    Directory of Open Access Journals (Sweden)

    Meizhong Jin

    2013-04-01

    Full Text Available Based on over three decades of preclinical data, insulin-like growth factor-1 receptor (IGF-1R signaling has gained recognition as a promoter of tumorogenesis, driving cell survival and proliferation in multiple human cancers. As a result, IGF-1R has been pursued as a target for cancer treatment. Early pioneering efforts targeting IGF- 1R focused on highly selective monoclonal antibodies, with multiple agents advancing to clinical trials. However, despite some initial promising results, recent clinical disclosures have been less encouraging. Moreover, recent studies have revealed that IGF-1R participates in a dynamic and complex signaling network, interacting with additional targets and pathways thereof through various crosstalk and compensatory signaling mechanisms. Such mechanisms of bypass signaling help to shed some light on the decreased effectiveness of selective IGF-1R targeted therapies (e.g. monoclonal antibodies and suggest that targeting multiple nodes within this signaling network might be necessary to produce a more effective therapeutic response. Additionally, such finding have led to the development of small molecule IGF-1R inhibitors which also coinhibit additional targets such as IR and EGFR. Such findings have helped to guide the rationale design of numerous drug combinations which are currently being evaluated in clinical trials.

  20. Modulation of insulin-like growth factor-1 receptor and its signaling network for the treatment of cancer: current status and future perspectives

    Directory of Open Access Journals (Sweden)

    Meizhong Jin

    2013-04-01

    Full Text Available Based on over three decades of pre-clinical data, insulin-like growth factor-1 receptor (IGF-1R signaling has gained recognition as a promoter of tumorogenesis, driving cell survival and proliferation in multiple human cancers. As a result, IGF-1R has been pursued as a target for cancer treatment. Early pioneering efforts targeting IGF-1R focused on highly selective monoclonal antibodies, with multiple agents advancing to clinical trials. However, despite some initial promising results, recent clinical disclosures have been less encouraging. Moreover, recent studies have revealed that IGF-1R participates in a dynamic and complex signaling network, interacting with additional targets and pathways thereof through various crosstalk and compensatory signaling mechanisms. Such mechanisms of bypass signaling help to shed some light on the decreased effectiveness of selective IGF- 1R targeted therapies (e.g. monoclonal antibodies and suggest that targeting multiple nodes within this signaling network might be necessary to produce a more effective therapeutic response. Additionally, such findings have led to the development of small molecule IGF-1R inhibitors which also co-inhibit additional targets such as insulin receptor and epidermal growth factor receptor. Such findings have helped to guide the design rationale of numerous drug combinations that are currently being evaluated in clinical trials.

  1. Deep Proteomics of Breast Cancer Cells Reveals that Metformin Rewires Signaling Networks Away from a Pro-growth State.

    Science.gov (United States)

    Sacco, Francesca; Silvestri, Alessandra; Posca, Daniela; Pirrò, Stefano; Gherardini, Pier Federico; Castagnoli, Luisa; Mann, Matthias; Cesareni, Gianni

    2016-03-23

    Metformin is the most frequently prescribed drug for type 2 diabetes. In addition to its hypoglycemic effects, metformin also lowers cancer incidence. This anti-cancer activity is incompletely understood. Here, we profiled the metformin-dependent changes in the proteome and phosphoproteome of breast cancer cells using high-resolution mass spectrometry. In total, we quantified changes of 7,875 proteins and 15,813 phosphosites after metformin changes. To interpret these datasets, we developed a generally applicable strategy that overlays metformin-dependent changes in the proteome and phosphoproteome onto a literature-derived network. This approach suggested that metformin treatment makes cancer cells more sensitive to apoptotic stimuli and less sensitive to pro-growth stimuli. These hypotheses were tested in vivo; as a proof-of-principle, we demonstrated that metformin inhibits the p70S6K-rpS6 axis in a PP2A-phosphatase dependent manner. In conclusion, analysis of deep proteomics reveals both detailed and global mechanisms that contribute to the anti-cancer activity of metformin. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Environmental Stress Affects the Activity of Metabolic and Growth Factor Signaling Networks and Induces Autophagy Markers in MCF7 Breast Cancer Cells*

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    Casado, Pedro; Bilanges, Benoit; Rajeeve, Vinothini; Vanhaesebroeck, Bart; Cutillas, Pedro R.

    2014-01-01

    Phosphoproteomic techniques are contributing to our understanding of how signaling pathways interact and regulate biological processes. This technology is also being used to characterize how signaling networks are remodeled during disease progression and to identify biomarkers of signaling pathway activity and of responses to cancer therapy. A potential caveat in these studies is that phosphorylation is a very dynamic modification that can substantially change during the course of an experiment or the retrieval and processing of cellular samples. Here, we investigated how exposure of cells to ambient conditions modulates phosphorylation and signaling pathway activity in the MCF7 breast cancer cell line. About 1.5% of 3,500 sites measured showed a significant change in phosphorylation extent upon exposure of cells to ambient conditions for 15 min. The effects of this perturbation in modifying phosphorylation patterns did not involve random changes due to stochastic activation of kinases and phosphatases. Instead, exposure of cells to ambient conditions elicited an environmental stress reaction that involved a coordinated response to a metabolic stress situation, which included: (1) the activation of AMPK; (2) the inhibition of PI3K, AKT, and ERK; (3) an increase in markers of protein synthesis inhibition at the level of translation elongation; and (4) an increase in autophagy markers. We also observed that maintaining cells in ice modified but did not completely abolish this metabolic stress response. In summary, exposure of cells to ambient conditions affects the activity of signaling networks previously implicated in metabolic and growth factor signaling. Mass spectrometry data have been deposited to the ProteomeXchange with identifier PXD000472. PMID:24425749

  3. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  4. Navigating cancer network attractors for tumor-specific therapy

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin; Erler, Janine Terra

    2012-01-01

    Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach...... these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper...... understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....

  5. Introduction: Cancer Gene Networks.

    Science.gov (United States)

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  6. Network Topologies Decoding Cervical Cancer.

    Directory of Open Access Journals (Sweden)

    Sarika Jalan

    Full Text Available According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

  7. Simulated evolution of signal transduction networks.

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    Mohammad Mobashir

    Full Text Available Signal transduction is the process of routing information inside cells when receiving stimuli from their environment that modulate the behavior and function. In such biological processes, the receptors, after receiving the corresponding signals, activate a number of biomolecules which eventually transduce the signal to the nucleus. The main objective of our work is to develop a theoretical approach which will help to better understand the behavior of signal transduction networks due to changes in kinetic parameters and network topology. By using an evolutionary algorithm, we designed a mathematical model which performs basic signaling tasks similar to the signaling process of living cells. We use a simple dynamical model of signaling networks of interacting proteins and their complexes. We study the evolution of signaling networks described by mass-action kinetics. The fitness of the networks is determined by the number of signals detected out of a series of signals with varying strength. The mutations include changes in the reaction rate and network topology. We found that stronger interactions and addition of new nodes lead to improved evolved responses. The strength of the signal does not play any role in determining the response type. This model will help to understand the dynamic behavior of the proteins involved in signaling pathways. It will also help to understand the robustness of the kinetics of the output response upon changes in the rate of reactions and the topology of the network.

  8. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2015-12-01

    AD_________________ Award Number: W81XWH-10-2-0056 TITLE: Prostate Cancer Pathology Resource Network PRINCIPAL INVESTIGATOR: Bruce J. Trock, Ph.D... Pathology Resource Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-10-2-0056 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Bruce J. Trock, Ph.D. Betty...The Prostate Cancer Pathology Resource Network (which has since been renamed the Prostate Cancer Biorepository Network or PCBN) is a collaboration

  9. Conflicting Signals for Cancer Treatment.

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    Sujobert, Pierre; Trautmann, Alain

    2016-12-01

    Next-generation sequencing technologies have provided us with a precise description of the mutational burden of cancers, making it possible to identify targetable oncogene addictions. However, the emergence of resistant clones is an inevitable limitation of therapies targeting these addictions. Alternative approaches to cancer treatment are therefore required. We propose here a novel approach, based on the notion of conflicting signals and on a phenotypic description of cancer cells. "Phenotype" is an inherently complex notion that we describe in the conceptual framework of the epigenetic landscape, with a view to bridging the gap between theory and practice at the patient's bedside. By passing from theory to the description of several examples, we will illustrate how this approach can facilitate data analysis and the design of new strategies for cancer treatment. Cancer Res; 76(23); 6768-73. ©2016 AACR. ©2016 American Association for Cancer Research.

  10. Signal propagation in cortical networks: a digital signal processing approach.

    Science.gov (United States)

    Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

    2009-01-01

    This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

  11. Signaling networks associated with AKT activation in non-small cell lung cancer (NSCLC: new insights on the role of phosphatydil-inositol-3 kinase.

    Directory of Open Access Journals (Sweden)

    Marianna Scrima

    Full Text Available Aberrant activation of PI3K/AKT signalling represents one of the most common molecular alterations in lung cancer, though the relative contribution of the single components of the cascade to the NSCLC development is still poorly defined. In this manuscript we have investigated the relationship between expression and genetic alterations of the components of the PI3K/AKT pathway [KRAS, the catalytic subunit of PI3K (p110α, PTEN, AKT1 and AKT2] and the activation of AKT in 107 surgically resected NSCLCs and have analyzed the existing relationships with clinico-pathologic features. Expression analysis was performed by immunohistochemistry on Tissue Micro Arrays (TMA; mutation analysis was performed by DNA sequencing; copy number variation was determined by FISH. We report that activation of PI3K/AKT pathway in Italian NSCLC patients is associated with high grade (G3-G4 compared with G1-G2; n = 83; p<0.05 and more advanced disease (TNM stage III vs. stages I and II; n = 26; p<0.05. In addition, we found that PTEN loss (41/104, 39% and the overexpression of p110α (27/92, 29% represent the most frequent aberration observed in NSCLCs. Less frequent molecular lesions comprised the overexpression of AKT2 (18/83, 22% or AKT1 (17/96, 18%, and KRAS mutation (7/63, 11%. Our results indicate that, among all genes, only p110α overexpression was significantly associated to AKT activation in NSCLCs (p = 0.02. Manipulation of p110α expression in lung cancer cells carrying an active PI3K allele (NCI-H460 efficiently reduced proliferation of NSCLC cells in vitro and tumour growth in vivo. Finally, RNA profiling of lung epithelial cells (BEAS-2B expressing a mutant allele of PIK3 (E545K identified a network of transcription factors such as MYC, FOS and HMGA1, not previously recognised to be associated with aberrant PI3K signalling in lung cancer.

  12. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  13. Community detection by signaling on complex networks.

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; Di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  14. Reconstruction of periodic signals using neural networks

    Directory of Open Access Journals (Sweden)

    José Danilo Rairán Antolines

    2014-01-01

    Full Text Available In this paper, we reconstruct a periodic signal by using two neural networks. The first network is trained to approximate the period of a signal, and the second network estimates the corresponding coefficients of the signal's Fourier expansion. The reconstruction strategy consists in minimizing the mean-square error via backpro-pagation algorithms over a single neuron with a sine transfer function. Additionally, this paper presents mathematical proof about the quality of the approximation as well as a first modification of the algorithm, which requires less data to reach the same estimation; thus making the algorithm suitable for real-time implementations.

  15. Deciphering the signaling networks underlying simvastatin-induced apoptosis in human cancer cells: evidence for non-canonical activation of RhoA and Rac1 GTPases

    Science.gov (United States)

    Zhu, Y; Casey, P J; Kumar, A P; Pervaiz, S

    2013-01-01

    Although statins are known to inhibit proliferation and induce death in a number of cancer cell types, the mechanisms through which downregulation of the mevalonate (MVA) pathway activates death signaling remain poorly understood. Here we set out to unravel the signaling networks downstream of the MVA pathway that mediate the death-inducing activity of simvastatin. Consistent with previous reports, exogenously added geranylgeranylpyrophosphate, but not farnesylpyrophosphate, prevented simvastatin's growth-inhibitory effect, thereby suggesting the involvement of geranylgeranylated proteins such as Rho GTPases in the anticancer activity of simvastatin. Indeed, simvastatin treatment led to increased levels of unprenylated Ras homolog gene family, member A (RhoA), Ras-related C3 botulinum toxin substrate 1 (Rac1) and cell division cycle 42 (Cdc42). Intriguingly, instead of inhibiting the functions of Rho GTPases as was expected with loss of prenylation, simvastatin caused a paradoxical increase in the GTP-bound forms of RhoA, Rac1 and Cdc42. Furthermore, simvastatin disrupted the binding of Rho GTPases with the cytosolic inhibitor Rho GDIα, which provides a potential mechanism for GTP loading of the cytosolic Rho GTPases. We also show that the unprenylated RhoA- and Rac1-GTP retained at least part of their functional activities, as evidenced by the increase in intracellular superoxide production and JNK activation in response to simvastatin. Notably, blocking superoxide production attenuated JNK activation as well as cell death induced by simvastatin. Finally, we provide evidence for the involvement of the B-cell lymphoma protein 2 family, Bcl-2-interacting mediator (Bim), in a JNK-dependent manner, in the apoptosis-inducing activity of simvastatin. Taken together, our data highlight the critical role of non-canonical regulation of Rho GTPases and involvement of downstream superoxide-mediated activation of JNK pathway in the anticancer activity of simvastatin, which

  16. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  17. Evaluation of phosphopeptide enrichment strategies for quantitative TMT analysis of complex network dynamics in cancer-associated cell signalling

    Directory of Open Access Journals (Sweden)

    Benedetta Lombardi

    2015-03-01

    Full Text Available Defining alterations in signalling pathways in normal and malignant cells is becoming a major field in proteomics. A number of different approaches have been established to isolate, identify and quantify phosphorylated proteins and peptides. In the current report, a comparison between SCX prefractionation versus an antibody based approach, both coupled to TiO2 enrichment and applied to TMT labelled cellular lysates, is described. The antibody strategy was more complete for enriching phosphopeptides and allowed the identification of a large set of proteins known to be phosphorylated (715 protein groups with a minimum number of not previously known phosphorylated proteins (2.

  18. Prostate Cancer Biorepository Network (PCBN)

    Science.gov (United States)

    2017-10-01

    Biorepository at the University of Washington joined the Prostate Cancer Pathology Resource Network (PCBN) September 30th 2014. The purpose of this...Cancer Biorepository at the University of Washington joined the Prostate Cancer Pathology Resource Network (PCBN) September 30th 2014. The UW Prostate...outcomes for the project include tissue acquisition, PDX development, and TMA construction , and specimen distribution are already discussed under

  19. Signal processing devices and networks

    Science.gov (United States)

    Graveline, S. W.

    1985-02-01

    According to an axiom employed with respect to electronic warfare (EW) behavior, system effectiveness increases directly with the amount of information recovered from an intercepted signal. The evolution in EW signal processing capability has proceeded accordingly. After an initiation of EW systems as broadband receivers, the most significant advance was related to the development of digital instantaneous frequency measurement (DIFM) devices. The use of such devices provides significant improvements regarding signal identification and RF measurement to within a few MHz. An even more accurate processing device, the digital RF memory (DRFM), allows frequency characterization to within a few Hz. This invention was made in response to the need to process coherent pulse signals. Attention is given to the generic EW system, the modern EW system, and the generic receiver function for a modern EW system showing typical output signals.

  20. Reaction network analysis in biochemical signaling pathways

    OpenAIRE

    Martinez-Forero, I. (Iván); Pelaez, A. (Antonio); Villoslada, P. (Pablo)

    2010-01-01

    The aim of this thesis is to improve the understanding of signaling pathways through a theoretical study of chemical reaction networks. The equilibirum solution to the equations derived from chemical networks will be analytically resolved using tools from algebraic geometry. The chapters are organized as follows: 1. An introduction to chemical dynamics in biological systems with a special emphasis on steady state analysis 2. Complete description of the chemical reaction network theor...

  1. Wnt Signaling and Colorectal Cancer.

    Science.gov (United States)

    Schatoff, Emma M; Leach, Benjamin I; Dow, Lukas E

    2017-04-01

    The WNT signaling pathway is a critical mediator of tissue homeostasis and repair, and frequently co-opted during tumor development. Almost all colorectal cancers (CRC) demonstrate hyperactivation of the WNT pathway, which in many cases is believed to be the initiating and driving event. In this short review, we provide a focused overview of recent developments in our understanding of the WNT pathway in CRC, describe new research tools that are enabling a deeper understanding of WNT biology, and outline ongoing efforts to target this pathway therapeutically.

  2. Cancer, signal transduction and nanotechnology.

    Science.gov (United States)

    Sengupta, Poulomi; Basu, Sudipta; Sengupta, Shiladitya

    2011-05-01

    Understanding the mechanisms underlying different cellular signaling pathways implicated in the pathogenesis of cancer are leading to the identification of novel drug targets as well as novel drug candidates. Multiple targeted therapeutics that modulate aberrant molecular pathways have already reached the clinic. However, targeted therapeutics can exert mechanism-driven side effects as a result of the implication of the molecular target in normal physiological functions besides tumorigenesis. We hypothesize that targeted therapeutics can be optimized by merging them with nanotechnology, which offers the potential for preferential targeting to the tumor, resulting in increased intratumoral concentrations of the active agent with reduced distribution to other parts of the body. This review will address some of the emerging concepts that integrate these two disciplines to engineer novel nanovectors that target different signaling pathways.

  3. SiGNet: A signaling network data simulator to enable signaling network inference.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Coker

    Full Text Available Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks: a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

  4. Signaling pathway networks mined from human pituitary adenoma proteomics data

    Directory of Open Access Journals (Sweden)

    Zhan Xianquan

    2010-04-01

    Full Text Available Abstract Background We obtained a series of pituitary adenoma proteomic expression data, including protein-mapping data (111 proteins, comparative proteomic data (56 differentially expressed proteins, and nitroproteomic data (17 nitroproteins. There is a pressing need to clarify the significant signaling pathway networks that derive from those proteins in order to clarify and to better understand the molecular basis of pituitary adenoma pathogenesis and to discover biomarkers. Here, we describe the significant signaling pathway networks that were mined from human pituitary adenoma proteomic data with the Ingenuity pathway analysis system. Methods The Ingenuity pathway analysis system was used to analyze signal pathway networks and canonical pathways from protein-mapping data, comparative proteomic data, adenoma nitroproteomic data, and control nitroproteomic data. A Fisher's exact test was used to test the statistical significance with a significance level of 0.05. Statistical significant results were rationalized within the pituitary adenoma biological system with literature-based bioinformatics analyses. Results For the protein-mapping data, the top pathway networks were related to cancer, cell death, and lipid metabolism; the top canonical toxicity pathways included acute-phase response, oxidative-stress response, oxidative stress, and cell-cycle G2/M transition regulation. For the comparative proteomic data, top pathway networks were related to cancer, endocrine system development and function, and lipid metabolism; the top canonical toxicity pathways included mitochondrial dysfunction, oxidative phosphorylation, oxidative-stress response, and ERK/MAPK signaling. The nitroproteomic data from a pituitary adenoma were related to cancer, cell death, lipid metabolism, and reproductive system disease, and the top canonical toxicity pathways mainly related to p38 MAPK signaling and cell-cycle G2/M transition regulation. Nitroproteins from a

  5. Decoding network dynamics in cancer

    DEFF Research Database (Denmark)

    Linding, Rune

    2014-01-01

    Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accur...... of disease mutations on cellular signaling networks [Creixell et al. Nature Biotechnology 2012, Erler & Linding Cell 2012, Horn et al. Nature Methods 2014]....

  6. AKT as Locus of Hydrogen Bond Network in Cancer.

    Science.gov (United States)

    Radisavljevic, Ziv

    2018-01-01

    Generation and maintenance of a cancer complexity and robustness are impossible without hydrogen element. It is essential element for the cancer signaling through the AKT locus. Hyperactivated AKT locus by a positive feedback loops from the cancer hypoxic microenvironment generates a hydrogen bond network. Such network initiates protein-protein interaction at the AKT active site and at the same time stabilizes signal propagation. A hydrogen bond network conforms an entropy/enthalpy energetic process used for the interconversion of the AKT protein in metastasis formation and maintenance. Targeting the AKT locus by the redox balance change or hydrogen balance change or proton beam radiation disrupts a hydrogen bond network leading to the disappearance of a cancer complexity and robustness causing failure of the complex energy system in solid cancers and hematological malignancy. J. Cell. Biochem. 119: 130-133, 2018. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  7. Identifying Driver Nodes in the Human Signaling Network Using Structural Controllability Analysis.

    Science.gov (United States)

    Liu, Xueming; Pan, Linqiang

    2015-01-01

    Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.

  8. Auxiliary and autonomous proteoglycan signaling networks.

    Science.gov (United States)

    Elfenbein, Arye; Simons, Michael

    2010-01-01

    signal transduction, and present unique challenges to the study of their indispensable roles within cell signaling networks. Copyright (c) 2010 Elsevier Inc. All rights reserved.

  9. Increased entropy of signal transduction in the cancer metastasis phenotype

    Science.gov (United States)

    2010-01-01

    Background The statistical study of biological networks has led to important novel biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Results Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis and provide examples of de-novo discoveries of gene modules with known roles in apoptosis, immune-mediated tumour suppression, cell-cycle and tumour invasion. Importantly, we also identify a novel gene module within the insulin growth factor signalling pathway, alteration of which may predispose the tumour to

  10. Increased entropy of signal transduction in the cancer metastasis phenotype

    Directory of Open Access Journals (Sweden)

    Teschendorff Andrew E

    2010-07-01

    Full Text Available Abstract Background The statistical study of biological networks has led to important novel biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Results Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis and provide examples of de-novo discoveries of gene modules with known roles in apoptosis, immune-mediated tumour suppression, cell-cycle and tumour invasion. Importantly, we also identify a novel gene module within the insulin growth factor signalling pathway, alteration of which may

  11. National Comprehensive Cancer Network

    Science.gov (United States)

    ... Washington, DC Policy Summit: Redefining Quality Measurement in Oncology - Article Coming Soon! Policy Summit: Ensuring Patient Access and Safety in Cancer Care - Article Coming Soon! Patient Advocacy ...

  12. Signaling, Gene Regulation and Cancer | Center for Cancer Research

    Science.gov (United States)

    Although there have been tremendous progress in cancer research and treatment, the mortality caused by this disease is still very high. Cancer is the leading cause of death worldwide and second leading cause of death in the United States of America. Signaling, Gene Regulation and Cancer covers topics including the role of various signaling pathways in development, regulation of cell fate, tumor angiogenesis, duodenal neoplasias, breast, colorectal and prostate cancer, cancer development and progression, microRNA in cancer and epigenetic regulation of cancer.

  13. Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases.

    Science.gov (United States)

    Zhao, Hong; Jin, Guangxu; Cui, Kemi; Ren, Ding; Liu, Timothy; Chen, Peikai; Wong, Solomon; Li, Fuhai; Fan, Yubo; Rodriguez, Angel; Chang, Jenny; Wong, Stephen T C

    2013-10-15

    A new type of signaling network element, called cancer signaling bridges (CSB), has been shown to have the potential for systematic and fast-tracked drug repositioning. On the basis of CSBs, we developed a computational model to derive specific downstream signaling pathways that reveal previously unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available patient gene expression data. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed specific CSBs for each metastasis that satisfy (i) CSB proteins are activated by the maximal number of enriched signaling pathways specific to a given metastasis, and (ii) CSB proteins are involved in the most differential expressed coding genes specific to each breast cancer metastasis. The identified signaling networks for the three types of breast cancer metastases contain 31, 15, and 18 proteins and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases. We conducted both in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Of special note, we found that the Food and Drug Administration-approved drugs, sunitinib and dasatinib, prohibit brain metastases derived from breast cancer, addressing one particularly challenging aspect of this disease. ©2013 AACR.

  14. Wnt Signaling in Prostate Cancer Bone Metastases

    Science.gov (United States)

    2016-11-01

    Pten, Estrous Cycle MCB - 11 THE EFFECT OF HDACI (AR-42) ON CANINE PROSTATE CANCER METASTASIS. S. Elshafae1, N. Kohart1, L... canine prostate cancer overexpressing Dkk-1 was used in this study to investigate how enhanced Wnt/JNK signaling could alter tumor growth, metastasis and...metastatic phenotype of prostate cancer. Ace-1-Dkk-1, a canine prostate cancer overexpressing human Dkk-1, previously developed in our lab was used in

  15. Altered calcium signaling in cancer cells.

    Science.gov (United States)

    Stewart, Teneale A; Yapa, Kunsala T D S; Monteith, Gregory R

    2015-10-01

    It is the nature of the calcium signal, as determined by the coordinated activity of a suite of calcium channels, pumps, exchangers and binding proteins that ultimately guides a cell's fate. Deregulation of the calcium signal is often deleterious and has been linked to each of the 'cancer hallmarks'. Despite this, we do not yet have a full understanding of the remodeling of the calcium signal associated with cancer. Such an understanding could aid in guiding the development of therapies specifically targeting altered calcium signaling in cancer cells during tumorigenic progression. Findings from some of the studies that have assessed the remodeling of the calcium signal associated with tumorigenesis and/or processes important in invasion and metastasis are presented in this review. The potential of new methodologies is also discussed. This article is part of a Special Issue entitled: Membrane channels and transporters in cancers. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Quantitative phosphoproteomics to characterize signaling networks

    DEFF Research Database (Denmark)

    Rigbolt, Kristoffer T G; Blagoev, Blagoy

    2012-01-01

    Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform for analy......Reversible protein phosphorylation is involved in the regulation of most, if not all, major cellular processes via dynamic signal transduction pathways. During the last decade quantitative phosphoproteomics have evolved from a highly specialized area to a powerful and versatile platform...... for analyzing protein phosphorylation at a system-wide scale and has become the intuitive strategy for comprehensive characterization of signaling networks. Contemporary phosphoproteomics use highly optimized procedures for sample preparation, mass spectrometry and data analysis algorithms to identify...... and quantify thousands of phosphorylations, thus providing extensive overviews of the cellular signaling networks. As a result of these developments quantitative phosphoproteomics have been applied to study processes as diverse as immunology, stem cell biology and DNA damage. Here we review the developments...

  17. Hedgehog signaling and therapeutics in pancreatic cancer.

    LENUS (Irish Health Repository)

    Kelleher, Fergal C

    2012-02-01

    OBJECTIVE: To conduct a systematic review of the role that the hedgehog signaling pathway has in pancreatic cancer tumorigenesis. METHOD: PubMed search (2000-2010) and literature based references. RESULTS: Firstly, in 2009 a genetic analysis of pancreatic cancers found that a core set of 12 cellular signaling pathways including hedgehog were genetically altered in 67-100% of cases. Secondly, in vitro and in vivo studies of treatment with cyclopamine (a naturally occurring antagonist of the hedgehog signaling pathway component; Smoothened) has shown that inhibition of hedgehog can abrogate pancreatic cancer metastasis. Thirdly, experimental evidence has demonstrated that sonic hedgehog (Shh) is correlated with desmoplasia in pancreatic cancer. This is important because targeting the Shh pathway potentially may facilitate chemotherapeutic drug delivery as pancreatic cancers tend to have a dense fibrotic stroma that extrinsically compresses the tumor vasculature leading to a hypoperfusing intratumoral circulation. It is probable that patients with locally advanced pancreatic cancer will derive the greatest benefit from treatment with Smoothened antagonists. Fourthly, it has been found that ligand dependent activation by hedgehog occurs in the tumor stromal microenvironment in pancreatic cancer, a paracrine effect on tumorigenesis. Finally, in pancreatic cancer, cells with the CD44+CD24+ESA+ immunophenotype select a population enriched for cancer initiating stem cells. Shh is increased 46-fold in CD44+CD24+ESA+ cells compared with normal pancreatic epithelial cells. Medications that destruct pancreatic cancer initiating stem cells are a potentially novel strategy in cancer treatment. CONCLUSIONS: Aberrant hedgehog signaling occurs in pancreatic cancer tumorigenesis and therapeutics that target the transmembrane receptor Smoothened abrogate hedgehog signaling and may improve the outcomes of patients with pancreatic cancer.

  18. Prostate Cancer Biorepository Network

    Science.gov (United States)

    2016-10-01

    in a systematic, reproducible fashion using optimized and standardized protocols. The PCBN is funded as a consortium of participating network sites...biospecimens obtained in a systematic, reproducible fashion using optimized and standardized protocols. The PCBN is funded as a consortium of... questionnaires , and surveys, etc. Nothing to report Organization Name: The Brooklyn Hospital Location of Organization: Brooklyn, New York Partner’s

  19. Acoustic signal propagation characterization of conduit networks

    Science.gov (United States)

    Khan, Muhammad Safeer

    Analysis of acoustic signal propagation in conduit networks has been an important area of research in acoustics. One major aspect of analyzing conduit networks as acoustic channels is that a propagating signal suffers frequency dependent attenuation due to thermo-viscous boundary layer effects and the presence of impedance mismatches such as side branches. The signal attenuation due to side branches is strongly influenced by their numbers and dimensions such as diameter and length. Newly developed applications for condition based monitoring of underground conduit networks involve measurement of acoustic signal attenuation through tests in the field. In many cases the exact installation layout of the field measurement location may not be accessible or actual installation may differ from the documented layout. The lack of exact knowledge of numbers and lengths of side branches, therefore, introduces uncertainty in the measurements of attenuation and contributes to the random variable error between measured results and those predicted from theoretical models. There are other random processes in and around conduit networks in the field that also affect the propagation of an acoustic signal. These random processes include but are not limited to the presence of strong temperature and humidity gradients within the conduits, blockages of variable sizes and types, effects of aging such as cracks, bends, sags and holes, ambient noise variations and presence of variable layer of water. It is reasonable to consider that the random processes contributing to the error in the measured attenuation are independent and arbitrarily distributed. The error, contributed by a large number of independent sources of arbitrary probability distributions, is best described by an approximately normal probability distribution in accordance with the central limit theorem. Using an analytical approach to model the attenuating effect of each of the random variable sources can be very complex and

  20. Notch signaling in embryology and cancer

    National Research Council Canada - National Science Library

    Reichrath, J; Reichrath, Sandra

    2012-01-01

    "The goal of this volume is to comprehensively cover a highly readable overview on our present knowledge of the role of Notch signalling for embryology and cancer, with a focus on new findings in molecular biology...

  1. Wnt5a Signaling in Cancer

    Directory of Open Access Journals (Sweden)

    Marwa S. Asem

    2016-08-01

    Full Text Available Wnt5a is involved in activating several non-canonical WNT signaling pathways, through binding to different members of the Frizzled- and Ror-family receptors. Wnt5a signaling is critical for regulating normal developmental processes, including proliferation, differentiation, migration, adhesion and polarity. However, the aberrant activation or inhibition of Wnt5a signaling is emerging as an important event in cancer progression, exerting both oncogenic and tumor suppressive effects. Recent studies show the involvement of Wnt5a in regulating cancer cell invasion, metastasis, metabolism and inflammation. In this article, we review findings regarding the molecular mechanisms and roles of Wnt5a signaling in various cancer types, and highlight Wnt5a in ovarian cancer.

  2. Prostate Cancer Biorepository Network

    Science.gov (United States)

    2015-10-01

    We have the cooperation of palliative care physicians who have assisted in communicating initially with patients. The candidacy of this patient...and disbursement to investigators. The NYU network site procures specimens from more than 3 facilities, from primary localized as well as metastatic...Significant changes in use or care of human subjects, vertebrate animals, biohazards, and/or select agents Significant changes in use or care of human

  3. Qualitative networks: a symbolic approach to analyze biological signaling networks

    Directory of Open Access Journals (Sweden)

    Henzinger Thomas A

    2007-01-01

    Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

  4. The glucose signaling network in yeast

    Science.gov (United States)

    Kim, Jeong-Ho; Roy, Adhiraj; Jouandot, David; Cho, Kyu Hong

    2013-01-01

    Background Most cells possess a sophisticated mechanism for sensing glucose and responsing to it appropriately. Glucose sensing and signaling in the budding yeast Saccharomyces cerevisiae represents an important paradigm for understanding how extracellular signals lead to changes in the gene expression program in eukaryotes. Scope of review This review focuses on the yeast glucose sensing and signaling pathways that operate in a highly regulated and cooperative manner to bring about glucose-induction of HXT gene expression. Major conclusions The yeast cells possess a family of glucose transporters (HXTs), with different kinetic properties. They employ three major glucose signaling pathways— Rgt2/Snf3, AMPK, and cAMP-PKA—to express only those transporters best suited for the amounts of glucose available. We discuss the current understanding of how these pathways are integrated into a regulatory network to ensure efficient uptake and utilization of glucose. General significance Elucidating the role of multiple glucose signals and pathways involved in glucose uptake and metabolism in yeast may reveal the molecular basis of glucose homeostasis in humans, especially under pathological conditions, such as hyperglycemia in diabetics and the elevated rate of glycolysis observed in many solid tumors. PMID:23911748

  5. Stochastic delay accelerates signaling in gene networks.

    Science.gov (United States)

    Josić, Krešimir; López, José Manuel; Ott, William; Shiau, LieJune; Bennett, Matthew R

    2011-11-01

    The creation of protein from DNA is a dynamic process consisting of numerous reactions, such as transcription, translation and protein folding. Each of these reactions is further comprised of hundreds or thousands of sub-steps that must be completed before a protein is fully mature. Consequently, the time it takes to create a single protein depends on the number of steps in the reaction chain and the nature of each step. One way to account for these reactions in models of gene regulatory networks is to incorporate dynamical delay. However, the stochastic nature of the reactions necessary to produce protein leads to a waiting time that is randomly distributed. Here, we use queueing theory to examine the effects of such distributed delay on the propagation of information through transcriptionally regulated genetic networks. In an analytically tractable model we find that increasing the randomness in protein production delay can increase signaling speed in transcriptional networks. The effect is confirmed in stochastic simulations, and we demonstrate its impact in several common transcriptional motifs. In particular, we show that in feedforward loops signaling time and magnitude are significantly affected by distributed delay. In addition, delay has previously been shown to cause stable oscillations in circuits with negative feedback. We show that the period and the amplitude of the oscillations monotonically decrease as the variability of the delay time increases.

  6. Perturbation Biology: Inferring Signaling Networks in Cellular Systems

    Science.gov (United States)

    Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

    2013-01-01

    We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

  7. Muscarinic Receptor Signaling in Colon Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Rosenvinge, Erik C. von, E-mail: evonrose@medicine.umaryland.edu; Raufman, Jean-Pierre [University of Maryland School of Medicine, Division of Gastroenterology & Hepatology, 22 S. Greene Street, N3W62, Baltimore, MD 21201 (United States); Department of Veterans Affairs, VA Maryland Health Care System, 10 North Greene Street, Baltimore, MD 21201 (United States)

    2011-03-02

    According to the adenoma-carcinoma sequence, colon cancer results from accumulating somatic gene mutations; environmental growth factors accelerate and augment this process. For example, diets rich in meat and fat increase fecal bile acids and colon cancer risk. In rodent cancer models, increased fecal bile acids promote colon dysplasia. Conversely, in rodents and in persons with inflammatory bowel disease, low-dose ursodeoxycholic acid treatment alters fecal bile acid composition and attenuates colon neoplasia. In the course of elucidating the mechanism underlying these actions, we discovered that bile acids interact functionally with intestinal muscarinic receptors. The present communication reviews muscarinic receptor expression in normal and neoplastic colon epithelium, the role of autocrine signaling following synthesis and release of acetylcholine from colon cancer cells, post-muscarinic receptor signaling including the role of transactivation of epidermal growth factor receptors and activation of the ERK and PI3K/AKT signaling pathways, the structural biology and metabolism of bile acids and evidence for functional interaction of bile acids with muscarinic receptors on human colon cancer cells. In murine colon cancer models, deficiency of subtype 3 muscarinic receptors attenuates intestinal neoplasia; a proof-of-concept supporting muscarinic receptor signaling as a therapeutic target for colon cancer.

  8. Muscarinic Receptor Signaling in Colon Cancer

    Directory of Open Access Journals (Sweden)

    Jean-Pierre Raufman

    2011-03-01

    Full Text Available According to the adenoma-carcinoma sequence, colon cancer results from accumulating somatic gene mutations; environmental growth factors accelerate and augment this process. For example, diets rich in meat and fat increase fecal bile acids and colon cancer risk. In rodent cancer models, increased fecal bile acids promote colon dysplasia. Conversely, in rodents and in persons with inflammatory bowel disease, low-dose ursodeoxycholic acid treatment alters fecal bile acid composition and attenuates colon neoplasia. In the course of elucidating the mechanism underlying these actions, we discovered that bile acids interact functionally with intestinal muscarinic receptors. The present communication reviews muscarinic receptor expression in normal and neoplastic colon epithelium, the role of autocrine signaling following synthesis and release of acetylcholine from colon cancer cells, post-muscarinic receptor signaling including the role of transactivation of epidermal growth factor receptors and activation of the ERK and PI3K/AKT signaling pathways, the structural biology and metabolism of bile acids and evidence for functional interaction of bile acids with muscarinic receptors on human colon cancer cells. In murine colon cancer models, deficiency of subtype 3 muscarinic receptors attenuates intestinal neoplasia; a proof-of-concept supporting muscarinic receptor signaling as a therapeutic target for colon cancer.

  9. Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

    Directory of Open Access Journals (Sweden)

    Korf Ulrike

    2011-07-01

    Full Text Available Abstract Background Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. Results We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. Conclusions The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

  10. MSAT signalling and network management architectures

    Science.gov (United States)

    Garland, Peter; Keelty, J. Malcolm

    1989-01-01

    Spar Aerospace has been active in the design and definition of Mobile Satellite Systems since the mid 1970's. In work sponsored by the Canadian Department of Communications, various payload configurations have evolved. In addressing the payload configuration, the requirements of the mobile user, the service provider and the satellite operator have always been the most important consideration. The current Spar 11 beam satellite design is reviewed, and its capabilities to provide flexibility and potential for network growth within the WARC87 allocations are explored. To enable the full capabilities of the payload to be realized, a large amount of ground based Switching and Network Management infrastructure will be required, when space segment becomes available. Early indications were that a single custom designed Demand Assignment Multiple Access (DAMA) switch should be implemented to provide efficient use of the space segment. As MSAT has evolved into a multiple service concept, supporting many service providers, this architecture should be reviewed. Some possible signalling and Network Management solutions are explored.

  11. [Intracellular signaling mechanisms in thyroid cancer].

    Science.gov (United States)

    Mondragón-Terán, Paul; López-Hernández, Luz Berenice; Gutiérrez-Salinas, José; Suárez-Cuenca, Juan Antonio; Luna-Ceballos, Rosa Isela; Erazo Valle-Solís, Aura

    2016-01-01

    Thyroid cancer is the most common malignancy of the endocrine system, the papillary variant accounts for 80-90% of all diagnosed cases. In the development of papillary thyroid cancer, BRAF and RAS genes are mainly affected, resulting in a modification of the system of intracellular signaling proteins known as «protein kinase mitogen-activated» (MAPK) which consist of «modules» of internal signaling proteins (Receptor/Ras/Raf/MEK/ERK) from the cell membrane to the nucleus. In thyroid cancer, these signanling proteins regulate diverse cellular processes such as differentiation, growth, development and apoptosis. MAPK play an important role in the pathogenesis of thyroid cancer as they are used as molecular biomarkers for diagnostic, prognostic and as possible therapeutic molecular targets. Mutations in BRAF gene have been correlated with poor response to treatment with traditional chemotherapy and as an indicator of poor prognosis. To review the molecular mechanisms involved in intracellular signaling of BRAF and RAS genes in thyroid cancer. Molecular therapy research is in progress for this type of cancer as new molecules have been developed in order to inhibit any of the components of the signaling pathway (RET/PTC)/Ras/Raf/MEK/ERK; with special emphasis on the (RET/PTC)/Ras/Raf section, which is a major effector of ERK pathway. Copyright © 2016 Academia Mexicana de Cirugía A.C. Publicado por Masson Doyma México S.A. All rights reserved.

  12. Cancer Genetics and Signaling | Center for Cancer Research

    Science.gov (United States)

    The Cancer, Genetics, and Signaling (CGS) Group at the National Cancer Institute at Frederick  offers a competitive postdoctoral training and mentoring program focusing on molecular and genetic aspects of cancer. The CGS Fellows Program is designed to attract and train exceptional postdoctoral fellows interested in pursuing independent research career tracks. CGS Fellows participate in a structured mentoring program designed for scientific and career development and transition to independent positions.

  13. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  14. Leptin signaling and cancer chemoresistance: Perspectives.

    Science.gov (United States)

    Candelaria, Pierre V; Rampoldi, Antonio; Harbuzariu, Adriana; Gonzalez-Perez, Ruben R

    2017-04-10

    Obesity is a major health problem and currently is endemic around the world. Obesity is a risk factor for several different types of cancer, significantly promoting cancer incidence, progression, poor prognosis and resistance to anti-cancer therapies. The study of this resistance is critical as development of chemoresistance is a serious drawback for the successful and effective drug-based treatments of cancer. There is increasing evidence that augmented adiposity can impact on chemotherapeutic treatment of cancer and the development of resistance to these treatments, particularly through one of its signature mediators, the adipokine leptin. Leptin is a pro-inflammatory, pro-angiogenic and pro-tumorigenic adipokine that has been implicated in many cancers promoting processes such as angiogenesis, metastasis, tumorigenesis and survival/resistance to apoptosis. Several possible mechanisms that could potentially be developed by cancer cells to elicit drug resistance have been suggested in the literature. Here, we summarize and discuss the current state of the literature on the role of obesity and leptin on chemoresistance, particularly as it relates to breast and pancreatic cancers. We focus on the role of leptin and its significance in possibly driving these proposed chemoresistance mechanisms, and examine its effects on cancer cell survival signals and expansion of the cancer stem cell sub-populations.

  15. ROS signalling in the biology of cancer.

    Science.gov (United States)

    Moloney, Jennifer N; Cotter, Thomas G

    2017-06-03

    Increased reactive oxygen species (ROS) production has been detected in various cancers and has been shown to have several roles, for example, they can activate pro-tumourigenic signalling, enhance cell survival and proliferation, and drive DNA damage and genetic instability. Counterintuitively ROS can also promote anti-tumourigenic signalling, initiating oxidative stress-induced tumour cell death. Tumour cells express elevated levels of antioxidant proteins to detoxify elevated ROS levels, establish a redox balance, while maintaining pro-tumourigenic signalling and resistance to apoptosis. Tumour cells have an altered redox balance to that of their normal counterparts and this identifies ROS manipulation as a potential target for cancer therapies. This review discusses the generation and sources of ROS within tumour cells, the regulation of ROS by antioxidant defence systems, as well as the effect of elevated ROS production on their signalling targets in cancer. It also provides an insight into how pro- and anti-tumourigenic ROS signalling pathways could be manipulated in the treatment of cancer. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Global Optimization for Transport Network Expansion and Signal Setting

    Directory of Open Access Journals (Sweden)

    Haoxiang Liu

    2015-01-01

    Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.

  17. Proinflammatory signaling functions of thrombin in cancer.

    Science.gov (United States)

    Ebrahimi, Safieh; Rahmani, Farzad; Behnam-Rassouli, Reihane; Hoseinkhani, Fatemeh; Parizadeh, Mohammad Reza; Keramati, Mohammad Reza; Khazaie, Majid; Avan, Amir; Hassanian, Seyed Mahdi

    2017-09-01

    Thrombin-induced activation of protease-activated receptors (PARs) represents a link between inflammation and cancer. Proinflammatory signaling functions of thrombin are associated with several inflammatory diseases including neurodegenerative, cardiovascular, and of special interest in this review cancer. Thrombin-induced inflammatory responses up-regulates expression of cytokines, adhesion molecules, angiogenic factors, and matrix-degrading proteases that facilitate tumor cells proliferation, angiogenesis, invasion, and metastasis. This review summarizes the current knowledge about the mechanisms of thrombin-mediated proinflammatory responses in cancer pathology for a better understanding and hence a better management of this disease. © 2016 Wiley Periodicals, Inc.

  18. Ror2 Signaling and Its Relevance in Breast Cancer Progression

    Directory of Open Access Journals (Sweden)

    Michaela Bayerlová

    2017-06-01

    Full Text Available Breast cancer is a heterogeneous disease and has been classified into five molecular subtypes based on gene expression profiles. Signaling processes linked to different breast cancer molecular subtypes and different clinical outcomes are still poorly understood. Aberrant regulation of Wnt signaling has been implicated in breast cancer progression. In particular Ror1/2 receptors and several other members of the non-canonical Wnt signaling pathway were associated with aggressive breast cancer behavior. However, Wnt signals are mediated via multiple complex pathways, and it is clinically important to determine which particular Wnt cascades, including their domains and targets, are deregulated in poor prognosis breast cancer. To investigate activation and outcome of the Ror2-dependent non-canonical Wnt signaling pathway, we overexpressed the Ror2 receptor in MCF-7 and MDA-MB231 breast cancer cells, stimulated the cells with its ligand Wnt5a, and we knocked-down Ror1 in MDA-MB231 cells. We measured the invasive capacity of perturbed cells to assess phenotypic changes, and mRNA was profiled to quantify gene expression changes. Differentially expressed genes were integrated into a literature-based non-canonical Wnt signaling network. The results were further used in the analysis of an independent dataset of breast cancer patients with metastasis-free survival annotation. Overexpression of the Ror2 receptor, stimulation with Wnt5a, as well as the combination of both perturbations enhanced invasiveness of MCF-7 cells. The expression–responsive targets of Ror2 overexpression in MCF-7 induced a Ror2/Wnt module of the non-canonical Wnt signaling pathway. These targets alter regulation of other pathways involved in cell remodeling processing and cell metabolism. Furthermore, the genes of the Ror2/Wnt module were assessed as a gene signature in patient gene expression data and showed an association with clinical outcome. In summary, results of this study

  19. RMOD: a tool for regulatory motif detection in signaling network.

    Directory of Open Access Journals (Sweden)

    Jinki Kim

    Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  20. RMOD: a tool for regulatory motif detection in signaling network.

    Science.gov (United States)

    Kim, Jinki; Yi, Gwan-Su

    2013-01-01

    Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  1. Cancer systems biology: signal processing for cancer research

    Science.gov (United States)

    Yli-Harja, Olli; Ylipää, Antti; Nykter, Matti; Zhang, Wei

    2011-01-01

    In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts. PMID:21439242

  2. Defining a modular signalling network from the fly interactome.

    Science.gov (United States)

    Baudot, Anaïs; Angelelli, Jean-Baptiste; Guénoche, Alain; Jacq, Bernard; Brun, Christine

    2008-05-19

    Signalling pathways relay information by transmitting signals from cell surface receptors to intracellular effectors that eventually activate the transcription of target genes. Since signalling pathways involve several types of molecular interactions including protein-protein interactions, we postulated that investigating their organization in the context of the global protein-protein interaction network could provide a new integrated view of signalling mechanisms. Using a graph-theory based method to analyse the fly protein-protein interaction network, we found that each signalling pathway is organized in two to three different signalling modules. These modules contain canonical proteins of the signalling pathways, known regulators as well as other proteins thereby predicted to participate to the signalling mechanisms. Connections between the signalling modules are prominent as compared to the other network's modules and interactions within and between signalling modules are among the more central routes of the interaction network. Altogether, these modules form an interactome sub-network devoted to signalling with particular topological properties: modularity, density and centrality. This finding reflects the integration of the signalling system into cell functioning and its important role connecting and coordinating different biological processes at the level of the interactome.

  3. Signaling networks: information flow, computation, and decision making.

    Science.gov (United States)

    Azeloglu, Evren U; Iyengar, Ravi

    2015-04-01

    Signaling pathways come together to form networks that connect receptors to many different cellular machines. Such networks not only receive and transmit signals but also process information. The complexity of these networks requires the use of computational models to understand how information is processed and how input-output relationships are determined. Two major computational approaches used to study signaling networks are graph theory and dynamical modeling. Both approaches are useful; network analysis (application of graph theory) helps us understand how the signaling network is organized and what its information-processing capabilities are, whereas dynamical modeling helps us determine how the system changes in time and space upon receiving stimuli. Computational models have helped us identify a number of emergent properties that signaling networks possess. Such properties include ultrasensitivity, bistability, robustness, and noise-filtering capabilities. These properties endow cell-signaling networks with the ability to ignore small or transient signals and/or amplify signals to drive cellular machines that spawn numerous physiological functions associated with different cell states. Copyright © 2015 Cold Spring Harbor Laboratory Press; all rights reserved.

  4. Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

    Directory of Open Access Journals (Sweden)

    Masanao Sato

    Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

  5. Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action.

    Science.gov (United States)

    Sun, Jingchun; Zhao, Min; Jia, Peilin; Wang, Lily; Wu, Yonghui; Iverson, Carissa; Zhou, Yubo; Bowton, Erica; Roden, Dan M; Denny, Joshua C; Aldrich, Melinda C; Xu, Hua; Zhao, Zhongming

    2015-06-01

    A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways

  6. Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

    Science.gov (United States)

    Sun, Jingchun; Zhao, Min; Jia, Peilin; Wang, Lily; Wu, Yonghui; Iverson, Carissa; Zhou, Yubo; Bowton, Erica; Roden, Dan M.; Denny, Joshua C.; Aldrich, Melinda C.; Xu, Hua; Zhao, Zhongming

    2015-01-01

    A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways

  7. Calcium wave signaling in cancer cells

    Science.gov (United States)

    PARKASH, JAI; ASOTRA, KAMLESH

    2010-01-01

    Ca2+ functions as an important signaling messenger right from beginning of the life to final moment of the end of the life. Ca2+ is needed at several steps of the cell cycle such as early G1, at the G1/S, and G2/M transitions. The Ca2+ signals in the form of time-dependent changes in intracellular Ca2+ concentrations, [Ca2+]i, are presented as brief spikes organized into regenerative Ca2+ waves. Ca2+-mediated signaling pathways have also been shown to play important roles in carcinogenesis such as transformation of normal cells to cancerous cells, tumor formation and growth, invasion, angiogenesis and metastasis. Since the global Ca2+ oscillations arise from Ca2+ waves initiated locally, it results in stochastic oscillations because although each cell has many IP3Rs and Ca2+ ions, the law of large numbers does not apply to the initiating event which is restricted to very few IP3Rs due to steep Ca2+ concentration gradients. The specific Ca2+ signaling information is likely to be encoded in a calcium code as the amplitude, duration, frequency, waveform or timing of Ca2+ oscillations and decoded again at a later stage. Since Ca2+ channels or pumps involved in regulating Ca2+ signaling pathways show altered expression in cancer, one can target these Ca2+ channels and pumps as therapeutic options to decrease proliferation of cancer cells and to promote their apoptosis. These studies can provide novel insights into alterations in Ca2+ wave patterns in carcinogenesis and lead to development of newer technologies based on Ca2+ waves for the diagnosis and therapy of cancer. PMID:20875431

  8. Using Artificial Neural Networks for ECG Signals Denoising

    Directory of Open Access Journals (Sweden)

    Zoltán Germán-Salló

    2010-12-01

    Full Text Available The authors have investigated some potential applications of artificial neural networks in electrocardiografic (ECG signal prediction. For this, the authors used an adaptive multilayer perceptron structure to predict the signal. The proposed procedure uses an artificial neural network based learning structure to estimate the (n+1th sample from n previous samples To train and adjust the network weights, the backpropagation (BP algorithm was used. In this paper, prediction of ECG signals (as time series using multi-layer feedforward neural networks will be described. The results are evaluated through approximation error which is defined as the difference between the predicted and the original signal.The prediction procedure is carried out (simulated in MATLAB environment, using signals from MIT-BIH arrhythmia database. Preliminary results are encouraging enough to extend the proposed method for other types of data signals.

  9. Modeling evolution of crosstalk in noisy signal transduction networks

    Science.gov (United States)

    Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

    2018-02-01

    Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

  10. Dopamine D1 signaling organizes network dynamics underlying working memory.

    Science.gov (United States)

    Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

    2016-06-01

    Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

  11. Information flow in a network of dispersed signalers-receivers

    Science.gov (United States)

    Halupka, Konrad

    2017-11-01

    I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

  12. Intelligent sensor networks the integration of sensor networks, signal processing and machine learning

    CERN Document Server

    Hu, Fei

    2012-01-01

    Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, incl

  13. Differential network entropy reveals cancer system hallmarks

    Science.gov (United States)

    West, James; Bianconi, Ginestra; Severini, Simone; Teschendorff, Andrew E.

    2012-01-01

    The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets. PMID:23150773

  14. Signaling by Extracellular Vesicles Advances Cancer Hallmarks.

    Science.gov (United States)

    Kanada, Masamitsu; Bachmann, Michael H; Contag, Christopher H

    2016-02-01

    Mammalian cells secrete various extracellular vesicles (EVs; exosomes, microvesicles, and apoptotic bodies) that differ in biogenesis, composition, and function. Each vesicle type can originate from normal or cancerous cells, transfer molecular cargo to both neighboring and distant cells, and modulate cellular behaviors involved in eubiology and pathology, such as tumor development. Here, we review evidence for the role of EVs in the establishment and maintenance of cancer hallmarks, including sustaining proliferative signaling, evading growth suppression, resisting cell death, reprogramming energy metabolism, acquiring genomic instability, and remodeling the tumor microenvironment. We also discuss how EVs are implicated in the induction of angiogenesis, control of cellular invasion, initiation of premetastatic niches, maintenance of inflammation, and evasion of immune surveillance. The deeper understanding of the biology of EVs and their contribution to the development and progression of tumors is leading to new opportunities in the diagnosis and treatment of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Focal adhesion signaling and therapy resistance in cancer.

    Science.gov (United States)

    Eke, Iris; Cordes, Nils

    2015-04-01

    Interlocking gene mutations, epigenetic alterations and microenvironmental features perpetuate tumor development, growth, infiltration and spread. Consequently, intrinsic and acquired therapy resistance arises and presents one of the major goals to solve in oncologic research today. Among the myriad of microenvironmental factors impacting on cancer cell resistance, cell adhesion to the extracellular matrix (ECM) has recently been identified as key determinant. Despite the differentiation between cell adhesion-mediated drug resistance (CAMDR) and cell adhesion-mediated radioresistance (CAMRR), the underlying mechanisms share great overlap in integrin and focal adhesion hub signaling and differ further downstream in the complexity of signaling networks between tumor entities. Intriguingly, cell adhesion to ECM is per se also essential for cancer cells similar to their normal counterparts. However, based on the overexpression of focal adhesion hub signaling receptors and proteins and a distinct addiction to particular integrin receptors, targeting of focal adhesion proteins has been shown to potently sensitize cancer cells to different treatment regimes including radiotherapy, chemotherapy and novel molecular therapeutics. In this review, we will give insight into the role of integrins in carcinogenesis, tumor progression and metastasis. Additionally, literature and data about the function of focal adhesion molecules including integrins, integrin-associated proteins and growth factor receptors in tumor cell resistance to radio- and chemotherapy will be elucidated and discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling*

    Science.gov (United States)

    Ryall, Karen A.; Holland, David O.; Delaney, Kyle A.; Kraeutler, Matthew J.; Parker, Audrey J.; Saucerman, Jeffrey J.

    2012-01-01

    Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression. PMID:23091058

  17. Protein-intrinsic and signaling network-based sources of resistance to EGFR- and ErbB family-targeted therapies in head and neck cancer

    OpenAIRE

    Mehra, Ranee; Serebriiskii, Ilya G.; Dunbrack, Roland L.; Robinson, Matthew K.; Burtness, Barbara; Golemis, Erica A.

    2011-01-01

    Agents targeting EGFR and related ErbB family proteins are valuable therapies for the treatment of many cancers. For some tumor types, including squamous cell carcinomas of the head and neck (SCCHN), antibodies targeting EGFR were the first protein-directed agents to show clinical benefit, and remain a standard component of clinical strategies for management of the disease. Nevertheless, many patients display either intrinsic or acquired resistance to these drugs; hence, major research goals ...

  18. Using Neural Networks in Diagnosing Breast Cancer

    National Research Council Canada - National Science Library

    Fogel, David

    1997-01-01

    .... In the current study, evolutionary programming is used to train neural networks and linear discriminant models to detect breast cancer in suspicious and microcalcifications using radiographic features and patient age...

  19. Transforming Growth Factor β Superfamily Signaling in Development of Colorectal Cancer.

    Science.gov (United States)

    Jung, Barbara; Staudacher, Jonas J; Beauchamp, Daniel

    2017-01-01

    Transforming growth factor (TGF)-β cytokines signal via a complex network of pathways to regulate proliferation, differentiation, adhesion, migration, and other functions in many cell types. A high percentage of colorectal tumors contain mutations that disrupt TGF-β family member signaling. We review how TGF-β family member signaling is altered during development of colorectal cancer, models of study, interaction of pathways, and potential therapeutic strategies. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

  20. MicroRNA-gene signaling pathways in pancreatic cancer

    Directory of Open Access Journals (Sweden)

    Alexandra Drakaki

    2013-10-01

    Full Text Available Pancreatic cancer is the fourth most frequent cause of cancer-related deaths and is characterized by early metastasis and pronounced resistance to chemotherapy and radiation therapy. Despite extensive esearch efforts, there is not any substantial progress regarding the identification of novel drugs against pancreatic cancer. Although the introduction of the chemotherapeutic agent gemcitabine improved clinical response, the prognosis of these patients remained extremely poor with a 5-year survival rate of 3-5%. Thus, the identification of the novel molecular pathways involved in pancreatic oncogenesis and the development of new and potent therapeutic options are highly desirable. Here, we describe how microRNAs control signaling pathways that are frequently deregulated during pancreatic oncogenesis. In addition, we provide evidence that microRNAs could be potentially used as novel pancreatic cancer therapeutics through reversal of chemotherapy and radiotherapy resistance or regulation of essential molecular pathways. Further studies should integrate the deregulated genes and microRNAs into molecular networks in order to identify the central regulators of pancreatic oncogenesis. Targeting these central regulators could lead to the development of novel targeted therapeutic approaches for pancreatic cancer patients.

  1. Optimizing Signal Behavior of Femtocells for Improved Network

    Directory of Open Access Journals (Sweden)

    Meera Joseph

    2016-10-01

    Full Text Available The high demand for network coverage in an indoor setting brought about the acceptance of femtocell technology as a solution using the backhaul connectivity in the existing network. The quality of signal, voice calling, Internet, security and data are improved through the use femtocell at the indoor environment. Here the service provider attempts to reduce their operation cost by presenting self-organizing mechanisms for optimization of the network. The remarkable part is that, femtocells improves coverage, enhances the data rate at the indoor environment. Therefore, the challenges of the femtocell also known as interference deteriorates the capacity and quality performance of the whole cellular network. In this paper we simulate the bit error rate against signal behaviour at the indoor environment and we also simulate the transmitting power over signal for both macrocells and femtocells. We focus on the transmitting power that might cause interference within the cellular network.

  2. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  3. Hybrid digital signal processing and neural networks applications in PWRs

    Energy Technology Data Exchange (ETDEWEB)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  4. How to target apoptosis signaling pathways for the treatment of pediatric cancers

    Directory of Open Access Journals (Sweden)

    Simone eFulda

    2013-02-01

    Full Text Available Apoptosis represents one of the most important forms of cell death in higher organisms and is typically dysregulated in human cancers, including pediatric tumors. This implies that ineffective engagement of cell death programs can contribute to tumor formation as well as tumor progression. In addition, the majority of cytotoxic therapeutic principles rely on the activation of cell death signaling pathways in cancer cells. Blockade of signaling networks that lead to cell death can therefore confer treatment resistance. A variety of genetic and epigenetic events as well as dysfunctional regulation of signaling networks have been identified as underlying causes of cell death resistance in childhood malignancies. Apoptosis pathways can be therapeutically exploited by enhancing proapoptotic signals or by neutralizing antiapoptotic programs. The challenge in the coming years will be to successfully transfer this knowledge into the development of innovative treatment approaches for children with cancer.

  5. MAPK cascade signalling networks in plant defence.

    Science.gov (United States)

    Pitzschke, Andrea; Schikora, Adam; Hirt, Heribert

    2009-08-01

    The sensing of stress signals and their transduction into appropriate responses is crucial for the adaptation and survival of plants. Kinase cascades of the mitogen-activated protein kinase (MAPK) class play a remarkably important role in plant signalling of a variety of abiotic and biotic stresses. MAPK cascade-mediated signalling is an essential step in the establishment of resistance to pathogens. Here, we describe the most recent insights into MAPK-mediated pathogen defence response regulation with a particular focus on the cascades involving MPK3, MPK4 and MPK6. We also discuss the strategies developed by plant pathogens to circumvent, inactivate or even 'hijack' MAPK-mediated defence responses.

  6. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    on formation of the primary cilium and CDE at the pocket region. The ciliary protein Inversin functions as a molecular switch between canonical and non-canonical Wnt signaling. In paper II, we show that Inversin and the primary cilium control Wnt signaling and are required for polarization and cell migration....... A number of central Wnt components localize to the fibroblast primary cilium, including the Wnt5a-receptor, Fzd3, and Dvl proteins. Inversin-deficient MEFs have an elevated expression of canonical Wnt-associated genes and proteins, in addition to dysregulation of components in non-canonical Wnt signaling......, which leads to uncontrolled cell movements. Together, the results obtained from my PhD studies reflect the high level of complexity within signaling systems regulated by the primary cilium that control cellular processes during embryonic development and in tissue homeostasis. As such, this dissertation...

  7. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    composition of receptors and signal components in the cilium to regulate cellular processes such as transcriptional control or cytoskeletal reorganization. This dissertation focuses on selected signaling systems regulated by the primary cilium, including the PDGFRα, TGFβ and Wnt pathways, and how......, which leads to uncontrolled cell movements. Together, the results obtained from my PhD studies reflect the high level of complexity within signaling systems regulated by the primary cilium that control cellular processes during embryonic development and in tissue homeostasis. As such, this dissertation......-dependent regulation of signal transduction. Upon ligand-binding and activation in the cilium, TGFβ receptors accumulate and are internalized at the ciliary base together with Smad2/3 transcription factors that are phosphorylated here and translocated to the nucleus for target gene expression. These processes depend...

  8. Decoding signalling networks by mass spectrometry-based proteomics

    DEFF Research Database (Denmark)

    Choudhary, Chuna Ram; Mann, Matthias

    2010-01-01

    Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system...

  9. Social multimedia signals a signal processing approach to social network phenomena

    CERN Document Server

    Roy, Suman Deb

    2014-01-01

    This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social me

  10. Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.

    Science.gov (United States)

    Neapolitan, Richard; Xue, Diyang; Jiang, Xia

    2014-01-01

    This paper concerns a study indicating that the expression levels of genes in signaling pathways can be modeled using a causal Bayesian network (BN) that is altered in tumorous tissue. These results open up promising areas of future research that can help identify driver genes and therapeutic targets. So, it is most appropriate for the cancer informatics community. Our central hypothesis is that the expression levels of genes that code for proteins on a signal transduction network (STP) are causally related and that this causal structure is altered when the STP is involved in cancer. To test this hypothesis, we analyzed 5 STPs associated with breast cancer, 7 STPs associated with other cancers, and 10 randomly chosen pathways, using a breast cancer gene expression level dataset containing 529 cases and 61 controls. We identified all the genes related to each of the 22 pathways and developed separate gene expression datasets for each pathway. We obtained significant results indicating that the causal structure of the expression levels of genes coding for proteins on STPs, which are believed to be implicated in both breast cancer and in all cancers, is more altered in the cases relative to the controls than the causal structure of the randomly chosen pathways.

  11. Emerging connections in the ethylene signaling network.

    Science.gov (United States)

    Yoo, Sang-Dong; Cho, Younghee; Sheen, Jen

    2009-05-01

    The gaseous plant hormone ethylene acts as a pivotal mediator to respond to and coordinate internal and external cues in modulating plant growth dynamics and developmental programs. Genetic analysis of Arabidopsis thaliana has been used to identify key components and to build a linear ethylene-signaling pathway from the receptors through to the nuclear transcription factors. Studies applying integrative approaches have revealed new regulators, molecular connections and mechanisms in ethylene signaling and unexpected links to other plant hormones. Here, we review and discuss recent discoveries about the functional mode of ethylene receptor complexes, dual mitogen-activated protein kinase cascade signaling, stability control of the master nuclear transcription activator ETHYLENE INSENSITIVE 3 (EIN3), and the contextual relationships between ethylene and other plant hormones, such as auxin and gibberellins, in organ-specific growth regulation.

  12. The relationship between modularity and robustness in signalling networks.

    Science.gov (United States)

    Tran, Tien-Dzung; Kwon, Yung-Keun

    2013-11-06

    Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.

  13. Signaling network dynamics investigated by quantitative phosphoproteomics

    NARCIS (Netherlands)

    Giansanti, Piero

    2014-01-01

    This thesis describes the application of proteomics technologies to get insight into several aspects of phosphorylation signaling dynamics. The core tool in all performed experiments is mass spectrometry (MS)-based phosphoproteomics. In Chapter 1, a general introduction is given into proteomics and

  14. Towards blueprints for network architecture, biophysical dynamics and signal transduction.

    Science.gov (United States)

    Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric

    2006-12-15

    We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

  15. Techniques for labeling of optical signals in bust switched networks

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Koonen, A. M. J.; Zhang, Jianfeng

    2003-01-01

    We present a review of significant issues related to labeled optical burst switched (LOBS) networks and technologies enabling future optical internet networks. Labeled optical burst switching provides a quick and efficient forwarding mechanism of IP packets/bursts over wavelength division...... multiplexed (WDM) networks due to its single forwarding algorithm, thus yielding low latency, and it enables scaling to terabit rates. Moreover, LOBS is compatible with the general multiprotocol label switching (GMPLS) framework for a unified control plane. We present a review on techniques for labeling...... of optical signals for LOBS networks, including experimental results, we discuss as well issues for further research....

  16. Inferring cell-scale signalling networks via compressive sensing.

    Directory of Open Access Journals (Sweden)

    Lei Nie

    Full Text Available Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: 1 variations of concentrations are sparse due to separations of timescales; 2 several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements.

  17. Emerging connections in the ethylene signaling network

    OpenAIRE

    Yoo, Sang-Dong; Cho, Younghee; Sheen, Jen

    2009-01-01

    The gaseous plant hormone ethylene acts as a pivotal mediator to respond to and coordinate internal and external cues in modulating plant growth dynamics and developmental programs. Genetic analysis of Arabidopsis thaliana has been used to identify key components and to build a linear ethylene-signaling pathway from the receptors through to the nuclear transcription factors. Studies applying integrative approaches have revealed new regulators, molecular connections and mechanisms in ethylene ...

  18. Wnt-signalling pathways and microRNAs network in carcinogenesis: experimental and bioinformatics approaches.

    Science.gov (United States)

    Onyido, Emenike K; Sweeney, Eloise; Nateri, Abdolrahman Shams

    2016-09-02

    Over the past few years, microRNAs (miRNAs) have not only emerged as integral regulators of gene expression at the post-transcriptional level but also respond to signalling molecules to affect cell function(s). miRNAs crosstalk with a variety of the key cellular signalling networks such as Wnt, transforming growth factor-β and Notch, control stem cell activity in maintaining tissue homeostasis, while if dysregulated contributes to the initiation and progression of cancer. Herein, we overview the molecular mechanism(s) underlying the crosstalk between Wnt-signalling components (canonical and non-canonical) and miRNAs, as well as changes in the miRNA/Wnt-signalling components observed in the different forms of cancer. Furthermore, the fundamental understanding of miRNA-mediated regulation of Wnt-signalling pathway and vice versa has been significantly improved by high-throughput genomics and bioinformatics technologies. Whilst, these approaches have identified a number of specific miRNA(s) that function as oncogenes or tumour suppressors, additional analyses will be necessary to fully unravel the links among conserved cellular signalling pathways and miRNAs and their potential associated components in cancer, thereby creating therapeutic avenues against tumours. Hence, we also discuss the current challenges associated with Wnt-signalling/miRNAs complex and the analysis using the biomedical experimental and bioinformatics approaches.

  19. The redox biology network in cancer pathophysiology and therapeutics

    Directory of Open Access Journals (Sweden)

    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  20. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  1. p53 as the main traffic controller of the cell signaling network.

    Science.gov (United States)

    Sebastian, Sinto; Azzariti, Amalia; Silvestris, Nicola; Porcelli, Letizia; Russo, Antonio; Paradiso, Angelo

    2010-06-01

    Among different pathological conditions that affect human beings, cancer has received a great deal of attention primarily because it leads to significant morbidity and mortality. This is essentially due to increasing world-wide incidence of this disease and the inability to discover the cause and molecular mechanisms by which normal human cells acquire the characteristics that define cancer cells. Since the discovery of p53 over a quarter of a century ago, it is now recognized that virtually all cell fate pathways of live cells and the decision to die are under the control of p53. Such extensive involvement indicates that p53 protein is acting as a major traffic controller in the cell signaling network. In cancer cells, many cell signaling pathways of normal human cells are rerouted towards immortalization and this is accomplished by the corruption of the main controllers of cell signaling pathways such as p53. This review highlights how p53 signaling activity is altered in cancer cells so that cells acquire the hallmarks of cancer including deregulated infinite self replicative potential.

  2. Efficient Mobility Management Signalling in Network Mobility Supported PMIPV6

    Directory of Open Access Journals (Sweden)

    Ananthi Jebaseeli Samuelraj

    2015-01-01

    Full Text Available Proxy Mobile IPV6 (PMIPV6 is a network based mobility management protocol which supports node’s mobility without the contribution from the respective mobile node. PMIPV6 is initially designed to support individual node mobility and it should be enhanced to support mobile network movement. NEMO-BSP is an existing protocol to support network mobility (NEMO in PMIPV6 network. Due to the underlying differences in basic protocols, NEMO-BSP cannot be directly applied to PMIPV6 network. Mobility management signaling and data structures used for individual node’s mobility should be modified to support group nodes’ mobility management efficiently. Though a lot of research work is in progress to implement mobile network movement in PMIPV6, it is not yet standardized and each suffers with different shortcomings. This research work proposes modifications in NEMO-BSP and PMIPV6 to achieve NEMO support in PMIPV6. It mainly concentrates on optimizing the number and size of mobility signaling exchanged while mobile network or mobile network node changes its access point.

  3. 1st International Conference on Signal, Networks, Computing, and Systems

    CERN Document Server

    Mohapatra, Durga; Nagar, Atulya; Sahoo, Manmath

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the first International Conference on Signal, Networks, Computing, and Systems (ICSNCS 2016) held at Jawaharlal Nehru University, New Delhi, India during February 25–27, 2016. The book is organized in to two volumes and primarily focuses on theory and applications in the broad areas of communication technology, computer science and information security. The book aims to bring together the latest scientific research works of academic scientists, professors, research scholars and students in the areas of signal, networks, computing and systems detailing the practical challenges encountered and the solutions adopted.

  4. Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

    Science.gov (United States)

    Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

    2017-01-01

    Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

  5. Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

  6. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    , with genomic modifications giving rise to differential protein dynamics, ultimately resulting in disease. The exact molecular signaling networks underlying specific disease phenotypes remain elusive, as the definition thereof requires extensive analysis of not only the genomic and proteomic landscapes within...... of my PhD in an attempt to positively contribute to this fundamental challenge. The thesis is divided into four parts. In Chapter I, we introduce the complexity of cancer, and describe some underlying causes and ways to study the disease from different molecular perspectives. There is a nearly infinite...... understanding of molecular processes which are fundamental to tumorigenesis. In Article 1, we propose a novel framework for how cancer mutations can be studied by taking into account their effect at the protein network level. In Article 2, we demonstrate how global, quantitative data on phosphorylation dynamics...

  7. Use of artificial neural networks in biosensor signal classification

    Directory of Open Access Journals (Sweden)

    Vlastimil Dohnal

    2008-01-01

    Full Text Available Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

  8. Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data

    Science.gov (United States)

    Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie

    2016-10-01

    Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.

  9. Adenylate Kinase and AMP Signaling Networks: Metabolic Monitoring, Signal Communication and Body Energy Sensing

    Directory of Open Access Journals (Sweden)

    Andre Terzic

    2009-04-01

    Full Text Available Adenylate kinase and downstream AMP signaling is an integrated metabolic monitoring system which reads the cellular energy state in order to tune and report signals to metabolic sensors. A network of adenylate kinase isoforms (AK1-AK7 are distributed throughout intracellular compartments, interstitial space and body fluids to regulate energetic and metabolic signaling circuits, securing efficient cell energy economy, signal communication and stress response. The dynamics of adenylate kinase-catalyzed phosphotransfer regulates multiple intracellular and extracellular energy-dependent and nucleotide signaling processes, including excitation-contraction coupling, hormone secretion, cell and ciliary motility, nuclear transport, energetics of cell cycle, DNA synthesis and repair, and developmental programming. Metabolomic analyses indicate that cellular, interstitial and blood AMP levels are potential metabolic signals associated with vital functions including body energy sensing, sleep, hibernation and food intake. Either low or excess AMP signaling has been linked to human disease such as diabetes, obesity and hypertrophic cardiomyopathy. Recent studies indicate that derangements in adenylate kinase-mediated energetic signaling due to mutations in AK1, AK2 or AK7 isoforms are associated with hemolytic anemia, reticular dysgenesis and ciliary dyskinesia. Moreover, hormonal, food and antidiabetic drug actions are frequently coupled to alterations of cellular AMP levels and associated signaling. Thus, by monitoring energy state and generating and distributing AMP metabolic signals adenylate kinase represents a unique hub within the cellular homeostatic network.

  10. Mapping the follicle-stimulating hormone-induced signalling networks

    Directory of Open Access Journals (Sweden)

    Pauline eGloaguen

    2011-10-01

    Full Text Available Follicle-stimulating hormone (FSH is a central regulator of male and female reproductive function. Over the last decade, there has been a growing perception of the complexity associated with FSH-induced cellular signalling. It is now clear that the canonical Gs/cAMP/PKA pathway is not the sole mechanism that must be considered in FSH biological actions. In parallel, consistent with the emerging concept of biased agonism, several examples of ligand-mediated selective signalling pathway activation by gonadotropin receptors have been reported. In this context, it is important to gain an integrative view of the signalling pathways induced by FSH and how they interconnect to form a network. In this review, we propose a first attempt at building topological maps of various pathways known to be involved in the FSH-induced signalling network. We discuss the multiple facets of FSH-induced signalling and how they converge to the hormone integrated biological response. Despite of their incompleteness, these maps of the FSH-induced signalling network represent a first step towards gaining a system-level comprehension of this hormone’s actions, which may ultimately facilitate the discovery of novel regulatory processes and therapeutic strategies for infertilities and non-steroidal contraception.

  11. The fidelity of dynamic signaling by noisy biomolecular networks.

    Directory of Open Access Journals (Sweden)

    Clive G Bowsher

    Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.

  12. Science Signaling Podcast for 14 March 2017: The Cancer Moonshot.

    Science.gov (United States)

    Yaffe, Michael B; VanHook, Annalisa M

    2017-03-14

    This Podcast features a conversation with Science Signaling's Chief Scientific Editor Michael Yaffe about opportunities for signaling researchers to contribute to the Cancer Moonshot, a federally funded initiative to accelerate cancer research. Administered by the National Cancer Institute (NCI), the goal of the program is to improve the prevention, diagnosis, and treatment of cancer. Signaling pathways are not only critical for the initiation and progression of cancer; they are also critical targets for treatment. In addition to developing new therapies, there are many other opportunities for signaling researchers to advance the goals of the Cancer Moonshot, such as improving methods of diagnosis and prevention.Listen to Podcast. Copyright © 2017, American Association for the Advancement of Science.

  13. Emergent decision-making in biological signal transduction networks

    Science.gov (United States)

    Helikar, Tomáš; Konvalina, John; Heidel, Jack; Rogers, Jim A.

    2008-01-01

    The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making. PMID:18250321

  14. Network analysis of breast cancer progression and reversal using a tree-evolving network algorithm.

    Directory of Open Access Journals (Sweden)

    Ankur P Parikh

    2014-07-01

    Full Text Available The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a "pan-cell-state" strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.

  15. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

    Directory of Open Access Journals (Sweden)

    Kirouac Daniel C

    2012-05-01

    Full Text Available Abstract Background Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID, PANTHER, Reactome, I2D, and STRING. We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data. Results We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless, we find a multiplicity of network topologies in which receptors couple to downstream

  16. The Prostate Cancer Biorepository Network (PCBN)

    Science.gov (United States)

    2015-10-01

    Biorepository Network (PCBN) is a public bioresource that provides high quality, well annotated specimens that can be used by prostate cancer researchers...will no longer attempt collection unless an effective technology for the identification and isolation of intact CTC becomes available. Serum and

  17. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  18. Targeting endothelin-1 receptor/β-arrestin1 network for the treatment of ovarian cancer.

    Science.gov (United States)

    Rosanò, Laura; Cianfrocca, Roberta; Sestito, Rosanna; Tocci, Piera; Di Castro, Valeriana; Bagnato, Anna

    2017-10-01

    Endothelin-1 receptor (ET-1R)/β-arrestin1 (β-arr1) signaling is dysregulated in ovarian cancer. This signaling circuit enables cancer cells to engage several signaling and transcriptional networks that are pervasively intertwined, and represent a potential therapeutic target for developing novel agents for ovarian cancer treatment. Areas covered: In this article, we discuss the role of the signaling network between ET-1R and key pathways mediated by the scaffold protein β-arr1, as part of signaling complex, or as a transcription co-activator, promoting precise control of transcription of different genes, including ET-1. Therefore ET-1R/β-arr1 is an actionable node involved in the activation of a persistent feedback loop that contributes to bypass signaling. Targeting ET-1R empowering this circuit can represent a necessary measure to reach clinical efficacy. Preclinical studies demonstrate that blocking ET-1R by FDA approved dual ETAR/ETBR antagonist prevents β-arr1 network formation, offering a novel therapeutic strategy in ovarian cancer patients. Expert opinion: The information provided in this review about the ET-1R/β-arr1 hub represents an invaluable tool for both identifying the interconnected pathways involved in ovarian cancer and targeting them more effectively. The new perspective arising from ET-1R therapeutics will likely prompt a valuable frame for the design of new promising combinatorial therapy, blocking compensatory networks.

  19. Multiplexed quantum dot labeling of activated c-Met signaling in castration-resistant human prostate cancer.

    Directory of Open Access Journals (Sweden)

    Peizhen Hu

    Full Text Available The potential application of multiplexed quantum dot labeling (MQDL for cancer detection and prognosis and monitoring therapeutic responses has attracted the interests of bioengineers, pathologists and cancer biologists. Many published studies claim that MQDL is effective for cancer biomarker detection and useful in cancer diagnosis and prognosis, these studies have not been standardized against quantitative biochemical and molecular determinations. In the present study, we used a molecularly characterized human prostate cancer cell model exhibiting activated c-Met signaling with epithelial to mesenchymal transition (EMT and lethal metastatic progression to bone and soft tissues as the gold standard, and compared the c-Met cell signaling network in this model, in clinical human prostate cancer tissue specimens and in a castration-resistant human prostate cancer xenograft model. We observed c-Met signaling network activation, manifested by increased phosphorylated c-Met in all three. The downstream survival signaling network was mediated by NF-κB and Mcl-1 and EMT was driven by receptor activator of NF-κB ligand (RANKL, at the single cell level in clinical prostate cancer specimens and the xenograft model. Results were confirmed by real-time RT-PCR and western blots in a human prostate cancer cell model. MQDL is a powerful tool for assessing biomarker expression and it offers molecular insights into cancer progression at both the cell and tissue level with high degree of sensitivity.

  20. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  1. On the distribution of signal phase in body area networks

    NARCIS (Netherlands)

    Wilson, S.K.; Cotton, Simon L.; Dias, Ugo S.; Scanlon, W.G.; Yacoub, Michel D.

    2010-01-01

    In this letter, we investigate the distribution of the phase component of the complex received signal observed in practical experiments using body area networks. Two phase distributions, the recently proposed κ-μ and η-μ probability densities, which together encompass the most widely used fading

  2. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  3. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2013-07-01

    PCBN is a public bioresource that provides tissue and other biospecimens to all prostate cancer investigators through an application process (http...number (RIN) was assessed by 2100 bioanalyzer (Agilent Technologies ). Additional information regarding PCBN SOPs for DNA and RNA extraction can be found

  4. Reverse engineering GTPase programming languages with reconstituted signaling networks.

    Science.gov (United States)

    Coyle, Scott M

    2016-07-02

    The Ras superfamily GTPases represent one of the most prolific signaling currencies used in Eukaryotes. With these remarkable molecules, evolution has built GTPase networks that control diverse cellular processes such as growth, morphology, motility and trafficking. (1-4) Our knowledge of the individual players that underlie the function of these networks is deep; decades of biochemical and structural data has provided a mechanistic understanding of the molecules that turn GTPases ON and OFF, as well as how those GTPase states signal by controlling the assembly of downstream effectors. However, we know less about how these different activities work together as a system to specify complex dynamic signaling outcomes. Decoding this molecular "programming language" would help us understand how different species and cell types have used the same GTPase machinery in different ways to accomplish different tasks, and would also provide new insights as to how mutations to these networks can cause disease. We recently developed a bead-based microscopy assay to watch reconstituted H-Ras signaling systems at work under arbitrary configurations of regulators and effectors. (5) Here we highlight key observations and insights from this study and propose extensions to our method to further study this and other GTPase signaling systems.

  5. Hedgehog Signaling in Prostate Development, Regeneration and Cancer

    Directory of Open Access Journals (Sweden)

    Wade Bushman

    2016-10-01

    Full Text Available The prostate is a developmental model system study of prostate growth regulation. Historically the research focus was on androgen regulation of development and growth and instructive interactions between the mesenchyme and epithelium. The study of Hh signaling in prostate development revealed important roles in ductal morphogenesis and in epithelial growth regulation that appear to be recapitulated in prostate cancer. This overview of Hh signaling in the prostate will address the well-described role of paracrine signaling prostate development as well as new evidence suggesting a role for autocrine signaling, the role of Hh signaling in prostate regeneration and reiterative activities in prostate cancer.

  6. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

    Science.gov (United States)

    Knapp, Bettina; Kaderali, Lars

    2013-01-01

    Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

  7. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

    Directory of Open Access Journals (Sweden)

    Bettina Knapp

    Full Text Available Perturbation experiments for example using RNA interference (RNAi offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

  8. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  9. The statistical mechanics of complex signaling networks: nerve growth factor signaling

    Science.gov (United States)

    Brown, K. S.; Hill, C. C.; Calero, G. A.; Myers, C. R.; Lee, K. H.; Sethna, J. P.; Cerione, R. A.

    2004-10-01

    The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'

  10. Shooting movies of signaling network dynamics with multiparametric cytometry.

    Science.gov (United States)

    Claassen, Manfred

    2014-01-01

    Single-cell technologies like mass cytometry enable researchers to comprehensively monitor signaling network responses in the context of heterogeneous cell populations. Cell-to-cell variability, the possibly nonlinear topology of signaling processes, and the destructive nature of mass cytometry necessitate nontrivial computational approaches to reconstruct and sensibly describe signaling dynamics. Modeling of signaling states depends on a set of coherent examples, that is, a set of cell events representing the same cell state. This requirement is frequently compromized by process asynchrony phenomena or nonlinear process topologies. We discuss various computational deconvolution approaches to define molecular process coordinates and enable compilation of coherent data sets for cell state inference. In addition to the conceptual presentation of these approaches, we discuss the application of these methods to modeling of TRAIL-induced apoptosis. Due to their generic applicability these computational approaches will contribute to the elucidation of dynamic intracellular signaling networks in various settings. The resulting signaling maps constitute a promising source for novel interventions and are expected to be particularly valuable in clinical settings.

  11. Maryland's Special Populations Cancer Network: cancer health disparities reduction model.

    Science.gov (United States)

    Baquet, Claudia R; Mack, Kelly M; Bramble, Joy; DeShields, Mary; Datcher, Delores; Savoy, Mervin; Hummel, Kery; Mishra, Shiraz I; Brooks, Sandra E; Boykin-Brown, Stephanie

    2005-05-01

    Cancer in Maryland is a serious health concern for minority and underserved populations in rural and urban areas. This report describes the National Cancer Institute (NCI) supported Maryland Special Populations Cancer Network (MSPN), a community-academic partnership. The MSPN's priority populations include African Americans, Native Americans, and other medically underserved residents of rural and urban areas. The MSPN has established a community infrastructure through formal collaborations with several community partners located in Baltimore City, the rural Eastern Shore, and Southern and Western Maryland, and among the Piscataway Conoy Tribe and the other 27 Native American Tribes in Maryland. Key partners also include the University of Maryland Eastern Shore and the University of Maryland Statewide Health Network. The MSPN has implemented innovative and successful programs in cancer health disparities research, outreach, and training; clinical trials education, health disparities policy, and resource leveraging. The MSPN addresses the goal of the NCI and the Department of Health and Human Services (DHHS) to reduce and eventually eliminate cancer health disparities. Community-academic partnerships are the foundation of this successful network.

  12. Deranged Wnt signaling is frequent in hereditary nonpolyposis colorectal cancer

    DEFF Research Database (Denmark)

    Isinger-Ekstrand, Anna; Therkildsen, Christina; Bernstein, Inge

    2011-01-01

    The Wnt signaling pathway is frequently deranged in colorectal cancer and is a key target for future preventive and therapeutic approaches. Colorectal cancers associated with the hereditary nonpolyposis colorectal cancer (HNPCC) syndrome are characterized by wide-spread microsatellite instability......, but show few gross genomic alterations. We characterized expression of the Wnt signaling pathway markers β-catenin, E-cadherin, TCF-4, and PTEN using immunohistochemical staining in colorectal cancers from individuals with HNPCC. Reduced membranous staining for β-catenin was found in 64% and for E......% of the tumors. In summary, altered expression of target molecules in the Wnt signaling pathway was demonstrated in the vast majority of the HNPCC-associated tumors, which support deranged Wnt-signaling as a central tumorigenic mechanism also in MMR defective colorectal cancer....

  13. Deranged Wnt signaling is frequent in hereditary nonpolyposis colorectal cancer

    DEFF Research Database (Denmark)

    Isinger-Ekstrand, Anna; Therkildsen, Christina; Bernstein, Inge

    2011-01-01

    The Wnt signaling pathway is frequently deranged in colorectal cancer and is a key target for future preventive and therapeutic approaches. Colorectal cancers associated with the hereditary nonpolyposis colorectal cancer (HNPCC) syndrome are characterized by wide-spread microsatellite instability......, but show few gross genomic alterations. We characterized expression of the Wnt signaling pathway markers ß-catenin, E-cadherin, TCF-4, and PTEN using immunohistochemical staining in colorectal cancers from individuals with HNPCC. Reduced membranous staining for ß-catenin was found in 64% and for E......% of the tumors. In summary, altered expression of target molecules in the Wnt signaling pathway was demonstrated in the vast majority of the HNPCC-associated tumors, which support deranged Wnt-signaling as a central tumorigenic mechanism also in MMR defective colorectal cancer....

  14. Early-warning signals of topological collapse in interbank networks

    CERN Document Server

    Squartini, Tiziano; Garlaschelli, Diego

    2013-01-01

    The financial crisis marked a paradigm shift, from traditional studies of individual risk to recent research on the "systemic risk" generated by whole networks of institutions. However, the reverse effects of realized defaults on network topology are poorly understood. Here we analyze the Dutch interbank network over the period 1998-2008, ending with the global crisis. We find that many topological properties, after controlling for overall density effects, display an abrupt change in 2008, thus providing a clear but unpredictable signature of the crisis. By contrast, if the intrinsic heterogeneity of banks is controlled for, the same properties undergo a slow and continuous transition, gradually connecting the crisis period to a much earlier stationary phase. This early-warning signal begins in 2005, and is preceded by an even earlier period of "risk autocatalysis" characterized by anomalous debt loops. These remarkable precursors are undetectable if the network is reconstructed from partial bank-specific inf...

  15. Computational modeling of signal transduction networks: a pedagogical exposition.

    Science.gov (United States)

    Prasad, Ashok

    2012-01-01

    We give a pedagogical introduction to computational modeling of signal transduction networks, starting from explaining the representations of chemical reactions by differential equations via the law of mass action. We discuss elementary biochemical reactions such as Michaelis-Menten enzyme kinetics and cooperative binding, and show how these allow the representation of large networks as systems of differential equations. We discuss the importance of looking for simpler or reduced models, such as network motifs or dynamical motifs within the larger network, and describe methods to obtain qualitative behavior by bifurcation analysis, using freely available continuation software. We then discuss stochastic kinetics and show how to implement easy-to-use methods of rule-based modeling for stochastic simulations. We finally suggest some methods for comprehensive parameter sensitivity analysis, and discuss the insights that it could yield. Examples, including code to try out, are provided based on a paper that modeled Ras kinetics in thymocytes.

  16. Signal Processing in Periodically Forced Gradient Frequency Neural Networks.

    Science.gov (United States)

    Kim, Ji Chul; Large, Edward W

    2015-01-01

    Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

  17. Consumer involvement in cancer research: example from a Cancer Network.

    Science.gov (United States)

    Arain, Mubashir; Pyne, Sarah; Thornton, Nigel; Palmer, Susan; Sharma, Ricky A

    2015-10-01

    The involvement of consumers and the general public in improving cancer services is an important component of health services. However, consumer involvement in cancer research is relatively unexplored. The objective of this study was to explore different ways of involving consumers in cancer research in one regional network. Thames Valley Cancer Network Consumer Research Partnership (CRP) group was formed in 2009. The group consists of consumers and professionals to help in promoting consumer involvement in Cancer Research in the Thames Valley. This study evaluated the project of consumer involvement in cancer research in the Thames Valley from March 2010 to March 2011. We used different indices to judge the level of consumer involvement: number of projects involving consumers through the group, types of projects, level of involvement (ranged from consultation on research documents to collaborating in preparing grant applications) and the methods of involving consumers in cancer research. Fifteen projects were submitted to the CRP group during the 12-month period studied. Of these, eight projects were clinical trials, three were qualitative research projects, two were patients' surveys and two were non-randomized interventional studies. Seven projects requested consumer involvement on patient information sheets for clinical trials. Of these seven applications, three also requested consumers' help in designing research questionnaires and another three requested that consumers should be involved in their project management group. In addition, four projects involved consumers in the proposal development phase and another four projects asked for advice on how to increase trial recruitment, conduct patient interviews or help with grant applications. The creation of the CRP and this audit of its activity have documented consumer involvement in cancer research in the Thames Valley. We have clearly shown that consumers can be involved in designing and managing cancer

  18. Nonlinear Silicon Photonic Signal Processing Devices for Future Optical Networks

    Directory of Open Access Journals (Sweden)

    Cosimo Lacava

    2017-01-01

    Full Text Available In this paper, we present a review on silicon-based nonlinear devices for all optical nonlinear processing of complex telecommunication signals. We discuss some recent developments achieved by our research group, through extensive collaborations with academic partners across Europe, on optical signal processing using silicon-germanium and amorphous silicon based waveguides as well as novel materials such as silicon rich silicon nitride and tantalum pentoxide. We review the performance of four wave mixing wavelength conversion applied on complex signals such as Differential Phase Shift Keying (DPSK, Quadrature Phase Shift Keying (QPSK, 16-Quadrature Amplitude Modulation (QAM and 64-QAM that dramatically enhance the telecom signal spectral efficiency, paving the way to next generation terabit all-optical networks.

  19. Advances of Liver X Receptor Signaling Pathways in Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Yun LIU

    2016-12-01

    Full Text Available Liver X receptors (LXRs, a kind of ligand-activated transcription factors, belong to the nuclear receptor superfamily (NRS, and function as central transcriptional regulators for lipid homeostasis, for which agonists have been developed as potential drugs for cardiovascular diseases and metabolic syndromes. In sex hormone-dependent cancers, dysregulation of lipid metabolism has been manifested. Prostate cancer is the most frequent cancer in males in Europe and a leading cause of cancer deaths, with similar proportion in other developed countries. A lot of studies have confirmed that both LXR and its agonists may play some roles in the progression of prostate cancer, and LXR signaling pathways may have a close association with the development and progression of prostate cancer. Hence, to investigate the signaling pathways mediated by LXR and its agonists and their effects in prostate cancer is favorable to optimization of new treatment methods.

  20. Design principles of nuclear receptor signaling: How complex networking improves signal transduction

    NARCIS (Netherlands)

    A.N. Kolodkin (Alexey); F.J. Bruggeman (Frank); N. Plant (Nick); M.J. Moné (Martijn); B.M. Bakker (Barbara); M.J. Campbell (Moray); J.P.T.M. van Leeuwen (Hans); C. Carlberg (Carsten); J.L. Snoep (Jacky); H.V. Westerhoff (Hans)

    2010-01-01

    textabstractThe topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of design aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic

  1. Design principles of nuclear receptor signaling : how complex networking improves signal transduction

    NARCIS (Netherlands)

    Kolodkin, Alexey N.; Bruggeman, Frank J.; Plant, Nick; Mone, Martijn J.; Bakker, Barbara M.; Campbell, Moray J.; van Leeuwen, Johannes P. T. M.; Carlberg, Carsten; Snoep, Jacky L.; Westerhoff, Hans V.

    2010-01-01

    The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of 'design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of

  2. Music Signal Processing Using Vector Product Neural Networks

    Science.gov (United States)

    Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

    2017-05-01

    We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

  3. Distributed Signal Processing for Wireless EEG Sensor Networks.

    Science.gov (United States)

    Bertrand, Alexander

    2015-11-01

    Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this tutorial paper presents a conceptual and exploratory study of wireless electroencephalography (EEG) sensor networks (WESNs), with an emphasis on distributed signal processing aspects. A WESN is conceived as a modular neuromonitoring platform for high-density EEG recordings, in which each node is equipped with an electrode array, a signal processing unit, and facilities for wireless communication. We first address the advantages of such a modular approach, and we explain how distributed signal processing algorithms make WESNs more power-efficient, in particular by avoiding data centralization. We provide an overview of distributed signal processing algorithms that are potentially applicable in WESNs, and for illustration purposes, we also provide a more detailed case study of a distributed eye blink artifact removal algorithm. Finally, we study the power efficiency of these distributed algorithms in comparison to their centralized counterparts in which all the raw sensor signals are centralized in a near-end or far-end fusion center.

  4. Modeling signaling networks with different formalisms: a preview.

    Science.gov (United States)

    MacNamara, Aidan; Henriques, David; Saez-Rodriguez, Julio

    2013-01-01

    In the last 30 years, many of the mechanisms behind signal transduction, the process by which the cell takes extracellular signals as an input and converts them to a specific cellular phenotype, have been experimentally determined. With these discoveries, however, has come the realization that the architecture of signal transduction, the signaling network, is incredibly complex. Although the main pathways between receptor and output are well-known, there is a complex net of regulatory features that include crosstalk between different pathways, spatial and temporal effects, and positive and negative feedbacks. Hence, modeling approaches have been used to try and unravel some of these complexities. We use the mitogen-activated protein kinase cascade to illustrate chemical kinetic and logic approaches to modeling signaling networks. By using a common well-known model, we illustrate here the assumptions and level of detail behind each modeling approach, which serves as an introduction to the more detailed discussions of each in the accompanying chapters in this book.

  5. Wireless sensor networks for monitoring physiological signals of multiple patients.

    Science.gov (United States)

    Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C

    2011-08-01

    This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time.

  6. Application of computational approaches to study signalling networks of nuclear and Tyrosine kinase receptors

    Directory of Open Access Journals (Sweden)

    Rebaï Ahmed

    2010-10-01

    Full Text Available Abstract Background Nuclear receptors (NRs and Receptor tyrosine kinases (RTKs are essential proteins in many cellular processes and sequence variations in their genes have been reported to be involved in many diseases including cancer. Although crosstalk between RTK and NR signalling and their contribution to the development of endocrine regulated cancers have been areas of intense investigation, the direct coupling of their signalling pathways remains elusive. In our understanding of the role and function of nuclear receptors on the cell membrane the interactions between nuclear receptors and tyrosine kinase receptors deserve further attention. Results We constructed a human signalling network containing nuclear receptors and tyrosine kinase receptors that identified a network topology involving eleven highly connected hubs. We further developed an integrated knowledge database, denominated NR-RTK database dedicated to human RTKs and NRs and their vertebrate orthologs and their interactions. These interactions were inferred using computational tools and those supported by literature evidence are indicated. NR-RTK database contains links to other relevant resources and includes data on receptor ligands. It aims to provide a comprehensive interaction map that identifies complex dynamics and potential crosstalk involved. Availability: NR-RTK database is accessible at http://www.bioinfo-cbs.org/NR-RTK/ Conclusions We infer that the NR-RTK interaction network is scale-free topology. We also uncovered the key receptors mediating the signal transduction between these two types of receptors. Furthermore, NR-RTK database is expected to be useful for researchers working on various aspects of the molecular basis of signal transduction by RTKs and NRs. Reviewers This article was reviewed by Professor Paul Harrison (nominated by Dr. Mark Gerstein, Dr. Arcady Mushegian and Dr. Anthony Almudevar.

  7. Nonlinear transfer of signal and noise correlations in cortical networks.

    Science.gov (United States)

    Lyamzin, Dmitry R; Barnes, Samuel J; Donato, Roberta; Garcia-Lazaro, Jose A; Keck, Tara; Lesica, Nicholas A

    2015-05-27

    Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks. Copyright © 2015 Lyamzin et al.

  8. AR-Signaling in Human Malignancies: Prostate Cancer and Beyond

    Directory of Open Access Journals (Sweden)

    Michael T. Schweizer

    2017-01-01

    Full Text Available In the 1940s Charles Huggins reported remarkable palliative benefits following surgical castration in men with advanced prostate cancer, and since then the androgen receptor (AR has remained the main therapeutic target in this disease. Over the past couple of decades, our understanding of AR-signaling biology has dramatically improved, and it has become apparent that the AR can modulate a number of other well-described oncogenic signaling pathways. Not surprisingly, mounting preclinical and epidemiologic data now supports a role for AR-signaling in promoting the growth and progression of several cancers other than prostate, and early phase clinical trials have documented preliminary signs of efficacy when AR-signaling inhibitors are used in several of these malignancies. In this article, we provide an overview of the evidence supporting the use of AR-directed therapies in prostate as well as other cancers, with an emphasis on the rationale for targeting AR-signaling across tumor types.

  9. Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

    Energy Technology Data Exchange (ETDEWEB)

    Stueckle, Todd A., E-mail: tstueckle@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Lu, Yongju, E-mail: yongju6@hotmail.com [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States); Davis, Mary E., E-mail: mdavis@wvu.edu [Department of Physiology, West Virginia University, Morgantown, WV 26506 (United States); Wang, Liying, E-mail: lmw6@cdc.gov [Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Jiang, Bing-Hua, E-mail: bhjiang@jefferson.edu [Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA 19107 (United States); Holaskova, Ida, E-mail: iholaskova@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Schafer, Rosana, E-mail: rschafer@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Barnett, John B., E-mail: jbarnett@hsc.wvu.edu [Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506 (United States); Rojanasakul, Yon, E-mail: yrojan@hsc.wvu.edu [Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506 (United States)

    2012-06-01

    Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As{sub 2}O

  10. Random Deep Belief Networks for Recognizing Emotions from Speech Signals

    Directory of Open Access Journals (Sweden)

    Guihua Wen

    2017-01-01

    Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

  11. CBL-CIPK network for calcium signaling in higher plants

    Science.gov (United States)

    Luan, Sheng

    Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

  12. Speech Subvocal Signal Processing using Packet Wavelet and Neuronal Network

    Directory of Open Access Journals (Sweden)

    Luis E. Mendoza

    2013-11-01

    Full Text Available This paper presents the results obtained from the recording, processing and classification of words in the Spanish language by means of the analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop. In this work, the signals were sensed with surface electrodes placed on the surface of the throat and acquired with a sampling frequency of 50 kHz. The signal conditioning consisted in: the location of area of interest using energy analysis, and filtering using Discrete Wavelet Transform. Finally, the feature extraction was made in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. The classification was carried out with a backpropagation neural network whose training was performed with 70% of the database obtained. The correct classification rate was 75%±2.

  13. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

    Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.

  14. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Science.gov (United States)

    Welcome, Menizibeya O.; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2015-01-01

    Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose) regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning. PMID:25653876

  15. BIOMARKER IDENTIFICATION IN BREAST CANCER: BETA-ADRENERGIC RECEPTOR SIGNALING AND PATHWAYS TO THERAPEUTIC RESPONSE

    Directory of Open Access Journals (Sweden)

    Liana E. Kafetzopoulou

    2013-03-01

    Full Text Available Recent preclinical studies have associated beta-adrenergic receptor (β-AR signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2 cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.

  16. Biomarker identification in breast cancer: Beta-adrenergic receptor signaling and pathways to therapeutic response.

    Science.gov (United States)

    Kafetzopoulou, Liana E; Boocock, David J; Dhondalay, Gopal Krishna R; Powe, Desmond G; Ball, Graham R

    2013-01-01

    Recent preclinical studies have associated beta-adrenergic receptor (β-AR) signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs) to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2) cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.

  17. Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

    Directory of Open Access Journals (Sweden)

    Holger Fröhlich

    Full Text Available Aberrant activation of sonic Hegdehog (SHH signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs. To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays. We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

  18. Opening new doors: Hedgehog signaling and the pancreatic cancer stroma

    NARCIS (Netherlands)

    Damhofer, H.

    2015-01-01

    In pancreatic cancer, a very difficult to treat tumor type with a dismal prognosis, Hedgehog (Hh) ligands are produced by tumor cells and signal to the surrounding tumor microenvironment. This thesis gives new insights into the different aspects of stromal biology and Hh signaling by describing for

  19. Focal adhesion signaling in breast cancer treatment

    NARCIS (Netherlands)

    Ma, Yafeng

    2009-01-01

    Understanding the molecular mechanisms of survival and migratory pathways in cancer cells is essential to better comprehending cancer progression, metastasis formation and drug resistance, thereby benefiting the development of novel anticancer treatments. The overall goal of the work is to better

  20. Aberrant activation of notch signaling in human breast cancer.

    Science.gov (United States)

    Stylianou, Spyros; Clarke, Rob B; Brennan, Keith

    2006-02-01

    A role for Notch signaling in human breast cancer has been suggested by both the development of adenocarcinomas in the murine mammary gland following pathway activation and the loss of Numb expression, a negative regulator of the Notch pathway, in a large proportion of breast carcinomas. However, it is not clear currently whether Notch signaling is frequently activated in breast tumors, and how it causes cellular transformation. Here, we show accumulation of the intracellular domain of Notch1 and hence increased Notch signaling in a wide variety of human breast carcinomas. In addition, we show that increased RBP-Jkappa-dependent Notch signaling is sufficient to transform normal breast epithelial cells and that the mechanism of transformation is most likely through the suppression of apoptosis. More significantly, we show that attenuation of Notch signaling reverts the transformed phenotype of human breast cancer cell lines, suggesting that inhibition of Notch signaling may be a therapeutic strategy for this disease.

  1. Can calcium signaling be harnessed for cancer immunotherapy?

    Science.gov (United States)

    Rooke, Ronald

    2014-10-01

    Experimental evidence shows the importance of the immune system in controlling tumor appearance and growth. Immunotherapy is defined as the treatment of a disease by inducing, enhancing or suppressing an immune response. In the context of cancer treatment, it involves breaking tolerance to a cancer-specific self-antigen and/or enhancing the existing anti-tumor immune response, be it specific or not. Part of the complexity in developing such treatment is that cancers are selected to escape adaptive or innate immune responses. These escape mechanisms are numerous and they may cumulate in one cancer. Moreover, different cancers of a same type may present different combinations of escape mechanisms. The limited success of immunotherapeutics in the clinic as stand-alone products may in part be explained by the fact that most of them only activate one facet of the immune response. It is important to identify novel methods to broaden the efficacy of immunotherapeutics. Calcium signaling is central to numerous cellular processes, leading to immune responses, cancer growth and apoptosis induced by cancer treatments. Calcium signaling in cancer therapy and control will be integrated to current cancer immunotherapy approaches. This article is part of a Special Issue entitled: Calcium Signaling in Health and Disease. Guest Editors: Geert Bultynck, Jacques Haiech, Claus W. Heizmann, Joachim Krebs, and Marc Moreau. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. NT2 derived neuronal and astrocytic network signalling.

    Directory of Open Access Journals (Sweden)

    Eric J Hill

    Full Text Available A major focus of stem cell research is the generation of neurons that may then be implanted to treat neurodegenerative diseases. However, a picture is emerging where astrocytes are partners to neurons in sustaining and modulating brain function. We therefore investigated the functional properties of NT2 derived astrocytes and neurons using electrophysiological and calcium imaging approaches. NT2 neurons (NT2Ns expressed sodium dependent action potentials, as well as responses to depolarisation and the neurotransmitter glutamate. NT2Ns exhibited spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling. Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, NT2 astrocytes (NT2As exhibited morphology and functional properties consistent with this glial cell type. NT2As responded to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. NT2As also generated propagating calcium waves that were gap junction and purinergic signalling dependent. Our results show that NT2 derived astrocytes exhibit appropriate functionality and that NT2N networks interact with NT2A networks in co-culture. These findings underline the utility of such cultures to investigate human brain cell type signalling under controlled conditions. Furthermore, since stem cell derived neuron function and survival is of great importance therapeutically, our findings suggest that the presence of complementary astrocytes may be valuable in supporting stem cell derived neuronal networks. Indeed, this also supports the intriguing possibility of selective therapeutic replacement of astrocytes in diseases where these cells are either lost or lose functionality.

  3. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  4. Notch signaling as a therapeutic target for breast cancer treatment?

    Science.gov (United States)

    Han, Jianxun; Hendzel, Michael J; Allalunis-Turner, Joan

    2011-05-31

    Aberrant Notch signaling can induce mammary gland carcinoma in transgenic mice, and high expressions of Notch receptors and ligands have been linked to poor clinical outcomes in human patients with breast cancer. This suggests that inhibition of Notch signaling may be beneficial for breast cancer treatment. In this review, we critically evaluate the evidence that supports or challenges the hypothesis that inhibition of Notch signaling would be advantageous in breast cancer management. We find that there are many remaining uncertainties that must be addressed experimentally if we are to exploit inhibition of Notch signaling as a treatment approach in breast cancer. Nonetheless, Notch inhibition, in combination with other therapies, is a promising avenue for future management of breast cancer. Furthermore, since aberrant Notch4 activity can induce mammary gland carcinoma in the absence of RBPjκ, a better understanding of the components of RBPjκ-independent oncogenic Notch signaling pathways and their contribution to Notch-induced tumorigenesis would facilitate the deployment of Notch inhibition strategies for effective treatment of breast cancer.

  5. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  6. Lossless Compression Schemes for ECG Signals Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    C. Eswaran

    2007-01-01

    Full Text Available This paper presents lossless compression schemes for ECG signals based on neural network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction in the first stage and encoding of the residues is done by using lossless entropy encoders in the second stage. Different types of lossless encoders, such as Huffman, arithmetic, and runlength encoders, are used. The performances of the proposed neural network predictor-based compression schemes are evaluated using standard distortion and compression efficiency measures. Selected records from MIT-BIH arrhythmia database are used for performance evaluation. The proposed compression schemes are compared with linear predictor-based compression schemes and it is shown that about 11% improvement in compression efficiency can be achieved for neural network predictor-based schemes with the same quality and similar setup. They are also compared with other known ECG compression methods and the experimental results show that superior performances in terms of the distortion parameters of the reconstructed signals can be achieved with the proposed schemes.

  7. Signalling design and architecture for a proposed mobile satellite network

    Science.gov (United States)

    Yan, T.-Y.; Cheng, U.; Wang, C.

    1990-01-01

    In a frequency-division/demand-assigned multiple-access (FD/DAMA) architecture, each mobile subscriber must make a connection request to the Network Management Center before transmission for either open-end or closed-end services. Open-end services are for voice calls and long file transfer and are processed on a blocked-call-cleared basis. Closed-end services are for transmitting burst data and are processed on a first-come first-served basis. This paper presents the signalling design and architecture for non-voice services of an FD/DAMA mobile satellite network. The connection requests are made through the recently proposed multiple channel collision resolution scheme which provides a significantly higher throughput than the traditional slotted ALOHA scheme. For non-voice services, it is well known that retransmissions are necessary to ensure the delivery of a message in its entirety from the source to destination. Retransmission protocols for open-end and closed-end data transfer are investigated. The signal structure for the proposed network is derived from X-25 standards with appropriate modifications. The packet types and their usages are described in this paper.

  8. Microglia Control Neuronal Network Excitability via BDNF Signalling

    Directory of Open Access Journals (Sweden)

    Francesco Ferrini

    2013-01-01

    Full Text Available Microglia-neuron interactions play a crucial role in several neurological disorders characterized by altered neural network excitability, such as epilepsy and neuropathic pain. While a series of potential messengers have been postulated as substrates of the communication between microglia and neurons, including cytokines, purines, prostaglandins, and nitric oxide, the specific links between messengers, microglia, neuronal networks, and diseases have remained elusive. Brain-derived neurotrophic factor (BDNF released by microglia emerges as an exception in this riddle. Here, we review the current knowledge on the role played by microglial BDNF in controlling neuronal excitability by causing disinhibition. The efforts made by different laboratories during the last decade have collectively provided a robust mechanistic paradigm which elucidates the mechanisms involved in the synthesis and release of BDNF from microglia, the downstream TrkB-mediated signals in neurons, and the biophysical mechanism by which disinhibition occurs, via the downregulation of the K+-Cl− cotransporter KCC2, dysrupting Cl−homeostasis, and hence the strength of GABAA- and glycine receptor-mediated inhibition. The resulting altered network activity appears to explain several features of the associated pathologies. Targeting the molecular players involved in this canonical signaling pathway may lead to novel therapeutic approach for ameliorating a wide array of neural dysfunctions.

  9. Hedgehog signaling regulates telomerase reverse transcriptase in human cancer cells.

    Directory of Open Access Journals (Sweden)

    Tapati Mazumdar

    Full Text Available The Hedgehog (HH signaling pathway is critical for normal embryonic development, tissue patterning and cell differentiation. Aberrant HH signaling is involved in multiple human cancers. HH signaling involves a multi-protein cascade activating the GLI proteins that transcriptionally regulate HH target genes. We have previously reported that HH signaling is essential for human colon cancer cell survival and inhibition of this signal induces DNA damage and extensive cell death. Here we report that the HH/GLI axis regulates human telomerase reverse transcriptase (hTERT, which determines the replication potential of cancer cells. Suppression of GLI1/GLI2 functions by a C-terminus truncated GLI3 repressor mutant (GLI3R, or by GANT61, a pharmacological inhibitor of GLI1/GLI2, reduced hTERT protein expression in human colon cancer, prostate cancer and Glioblastoma multiforme (GBM cell lines. Expression of an N-terminus deleted constitutively active mutant of GLI2 (GLI2ΔN increased hTERT mRNA and protein expression and hTERT promoter driven luciferase activity in human colon cancer cells while GANT61 inhibited hTERT mRNA expression and hTERT promoter driven luciferase activity. Chromatin immunoprecipitation with GLI1 or GLI2 antibodies precipitated fragments of the hTERT promoter in human colon cancer cells, which was reduced upon exposure to GANT61. In contrast, expression of GLI1 or GLI2ΔN in non-malignant 293T cells failed to alter the levels of hTERT mRNA and protein, or hTERT promoter driven luciferase activity. Further, expression of GLI2ΔN increased the telomerase enzyme activity, which was reduced by GANT61 administration in human colon cancer, prostate cancer, and GBM cells. These results identify hTERT as a direct target of the HH signaling pathway, and reveal a previously unknown role of the HH/GLI axis in regulating the replication potential of cancer cells. These findings are of significance in understanding the important regulatory

  10. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  11. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  12. Deep Space Network Capabilities for Receiving Weak Probe Signals

    Science.gov (United States)

    Asmar, Sami; Johnston, Doug; Preston, Robert

    2005-01-01

    Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

  13. A modular analysis of the auxin signalling network.

    Directory of Open Access Journals (Sweden)

    Etienne Farcot

    Full Text Available Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF and Aux/IAA (IAA transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants.

  14. A modular analysis of the auxin signalling network.

    Science.gov (United States)

    Farcot, Etienne; Lavedrine, Cyril; Vernoux, Teva

    2015-01-01

    Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF) and Aux/IAA (IAA) transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants.

  15. Security Enhancement of Wireless Sensor Networks Using Signal Intervals.

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y; Yoo, Jaesoo

    2017-04-02

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  16. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Directory of Open Access Journals (Sweden)

    Jaegeun Moon

    2017-04-01

    Full Text Available Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP, the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users.

  17. Security Enhancement of Wireless Sensor Networks Using Signal Intervals

    Science.gov (United States)

    Moon, Jaegeun; Jung, Im Y.; Yoo, Jaesoo

    2017-01-01

    Various wireless technologies, such as RF, Bluetooth, and Zigbee, have been applied to sensor communications. However, the applications of Bluetooth-based wireless sensor networks (WSN) have a security issue. In one pairing process during Bluetooth communication, which is known as simple secure pairing (SSP), the devices are required to specify I/O capability or user interference to prevent man-in-the-middle (MITM) attacks. This study proposes an enhanced SSP in which a nonce to be transferred is converted to a corresponding signal interval. The quantization level, which is used to interpret physical signal intervals, is renewed at every connection by the transferred nonce and applied to the next nonce exchange so that the same signal intervals can represent different numbers. Even if attackers eavesdrop on the signals, they cannot understand what is being transferred because they cannot determine the quantization level. Furthermore, the proposed model does not require exchanging passkeys as data, and the devices are secure in the case of using a fixed PIN. Subsequently, the new quantization level is calculated automatically whenever the same devices attempt to connect with each other. Therefore, the pairing process can be protected from MITM attacks and be convenient for users. PMID:28368341

  18. Modelling intracellular signalling networks using behaviour-based systems and the blackboard architecture

    OpenAIRE

    Perez, Pedro Pablo Gonzalez; Gershenson, Carlos; Garcia, Maura Cardenas; Otero, Jaime Lagunez

    2002-01-01

    This paper proposes to model the intracellular signalling networks using a fusion of behaviour-based systems and the blackboard architecture. In virtue of this fusion, the model developed by us, which has been named Cellulat, allows to take account two essential aspects of the intracellular signalling networks: (1) the cognitive capabilities of certain types of networks' components and (2) the high level of spatial organization of these networks. A simple example of modelling of Ca2+ signalli...

  19. CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method.

    Science.gov (United States)

    Wang, Kai; Hu, Fuyan; Xu, Kejia; Cheng, Hua; Jiang, Meng; Feng, Ruili; Li, Jing; Wen, Tieqiao

    2011-05-17

    Signal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways. We propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results. CASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/.

  20. Understanding cancer complexome using networks, spectral graph theory and multilayer framework

    Science.gov (United States)

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-01

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  1. Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

    Science.gov (United States)

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-03

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  2. Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems.

    Science.gov (United States)

    Parodi, Silvio; Riccardi, Giuseppe; Castagnino, Nicoletta; Tortolina, Lorenzo; Maffei, Massimo; Zoppoli, Gabriele; Nencioni, Alessio; Ballestrero, Alberto; Patrone, Franco

    2016-01-01

    Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.

  3. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  4. Gedunin inhibits pancreatic cancer by altering sonic hedgehog signaling pathway.

    Science.gov (United States)

    Subramani, Ramadevi; Gonzalez, Elizabeth; Nandy, Sushmita Bose; Arumugam, Arunkumar; Camacho, Fernando; Medel, Joshua; Alabi, Damilola; Lakshmanaswamy, Rajkumar

    2017-02-14

    The lack of efficient treatment options for pancreatic cancer highlights the critical need for the development of novel and effective chemotherapeutic agents. The medicinal properties found in plants have been used to treat many different illnesses including cancers. This study focuses on the anticancer effects of gedunin, a natural compound isolated from Azadirachta indica. Anti-proliferative effect of gedunin on pancreatic cancer cells was assessed using MTS assay. We used matrigel invasion assay, scratch assay, and soft agar colony formation assay to measure the anti-metastatic potential of gedunin. Immunoblotting was performed to analyze the effect of gedunin on the expression of key proteins involved in pancreatic cancer growth and metastasis. Gedunin induced apoptosis was measured using flow cytometric analysis. To further validate, xenograft studies with HPAC cells were performed. Gedunin treatment is highly effective in inducing death of pancreatic cancer cells via intrinsic and extrinsic mediated apoptosis. Our data further indicates that gedunin inhibited metastasis of pancreatic cancer cells by decreasing their EMT, invasive, migratory and colony formation capabilities. Gedunin treatment also inhibited sonic hedgehog signaling pathways. Further, experiments with recombinant sonic hedgehog protein and Gli inhibitor (Gant-61) demonstrated that gedunin induces its anti-metastatic effect through inhibition of sonic hedgehog signaling. The anti-cancer effect of gedunin was further validated using xenograft mouse model. Overall, our data suggests that gedunin could serve as a potent anticancer agent against pancreatic cancers.

  5. Molecular cues on obesity signals, tumor markers and endometrial cancer.

    Science.gov (United States)

    Daley-Brown, Danielle; Oprea-Ilies, Gabriela M; Lee, Regina; Pattillo, Roland; Gonzalez-Perez, Ruben R

    2015-01-01

    Tumor markers are important tools for early diagnosis, prognosis, therapy response and endometrial cancer monitoring. A large number of molecular and pathologic markers have been described in types I and II endometrial cancers, which has served to define the main oncogenic, epidemiological, genetic, clinical and histopathological features. Ongoing attempts to stratify biological markers of endometrial cancer are presented. However, data on changes in tumor marker profiles in obesity-related endometrial cancer are scarce. Obesity is a pandemic in Western countries that has an important impact on endometrial cancers, albeit through not very well-defined mechanisms. Although endometrial cancer is more common in Caucasian women, higher mortality is found in African Americans who also show higher incidence of obesity. Here, we describe how obesity signals (estrogen, leptin, leptin induced-molecules, Notch; cytokines and growth factors) could affect endometrial cancer. Leptin signaling and its crosstalk may be associated to the more aggressive and poor prognosis type II endometrial cancer, which affects more postmenopausal and African-American women. In this regard, studies on expression of novel molecular markers (Notch, interleukin-1 and leptin crosstalk outcome) may provide essential clues for detection, prevention, treatment and prognosis.

  6. Molecular cues on obesity signals, tumor markers and endometrial cancer

    Science.gov (United States)

    Daley-Brown, Danielle; Oprea-Ilies, Gabriela M.; Lee, Regina; Pattillo, Roland

    2018-01-01

    Tumor markers are important tools for early diagnosis, prognosis, therapy response and endometrial cancer monitoring. A large number of molecular and pathologic markers have been described in types I and II endometrial cancers, which has served to define the main oncogenic, epidemiological, genetic, clinical and histopathological features. Ongoing attempts to stratify biological markers of endometrial cancer are presented. However, data on changes in tumor marker profiles in obesity-related endometrial cancer are scarce. Obesity is a pandemic in Western countries that has an important impact on endometrial cancers, albeit through not very well-defined mechanisms. Although endometrial cancer is more common in Caucasian women, higher mortality is found in African Americans who also show higher incidence of obesity. Here, we describe how obesity signals (estrogen, leptin, leptin induced-molecules, Notch; cytokines and growth factors) could affect endometrial cancer. Leptin signaling and its crosstalk may be associated to the more aggressive and poor prognosis type II endometrial cancer, which affects more postmenopausal and African-American women. In this regard, studies on expression of novel molecular markers (Notch, interleukin-1 and leptin crosstalk outcome) may provide essential clues for detection, prevention, treatment and prognosis. PMID:25781554

  7. Barcoding of GPCR trafficking and signaling through the various trafficking roadmaps by compartmentalized signaling networks.

    Science.gov (United States)

    Bahouth, Suleiman W; Nooh, Mohammed M

    2017-08-01

    Proper signaling by G protein coupled receptors (GPCR) is dependent on the specific repertoire of transducing, enzymatic and regulatory kinases and phosphatases that shape its signaling output. Activation and signaling of the GPCR through its cognate G protein is impacted by G protein-coupled receptor kinase (GRK)-imprinted "barcodes" that recruit β-arrestins to regulate subsequent desensitization, biased signaling and endocytosis of the GPCR. The outcome of agonist-internalized GPCR in endosomes is also regulated by sequence motifs or "barcodes" within the GPCR that mediate its recycling to the plasma membrane or retention and eventual degradation as well as its subsequent signaling in endosomes. Given the vast number of diverse sequences in GPCR, several trafficking mechanisms for endosomal GPCR have been described. The majority of recycling GPCR, are sorted out of endosomes in a "sequence-dependent pathway" anchored around a type-1 PDZ-binding module found in their C-tails. For a subset of these GPCR, a second "barcode" imprinted onto specific GPCR serine/threonine residues by compartmentalized kinase networks was required for their efficient recycling through the "sequence-dependent pathway". Mutating the serine/threonine residues involved, produced dramatic effects on GPCR trafficking, indicating that they played a major role in setting the trafficking itinerary of these GPCR. While endosomal SNX27, retromer/WASH complexes and actin were required for efficient sorting and budding of all these GPCR, additional proteins were required for GPCR sorting via the second "barcode". Here we will review recent developments in GPCR trafficking in general and the human β 1 -adrenergic receptor in particular across the various trafficking roadmaps. In addition, we will discuss the role of GPCR trafficking in regulating endosomal GPCR signaling, which promote biochemical and physiological effects that are distinct from those generated by the GPCR signal transduction

  8. Proteomic Analysis of the Downstream Signaling Network of PARP1.

    Science.gov (United States)

    Zhen, Yuanli; Yu, Yonghao

    2018-01-19

    Poly-ADP-ribosylation (PARylation) is a protein posttranslational modification (PTM) that is critically involved in many biological processes that are linked to cell stress responses. It is catalyzed by a class of enzymes known as poly-ADP-ribose polymerases (PARPs). In particular, PARP1 is a nuclear protein that is activated upon sensing nicked DNA. Once activated, PARP1 is responsible for the synthesis of a large number of PARylated proteins and initiation of the DNA damage response mechanisms. This observation provided the rationale for developing PARP1 inhibitors for the treatment of human malignancies. Indeed, three PARP1 inhibitors (Olaparib, Rucaparib, and Niraparib) have recently been approved by the Food and Drug Administration for the treatment of ovarian cancer. Moreover, in 2017, both Olaparib and Niraparib have also been approved for the treatment of fallopian tube cancer and primary peritoneal cancer. Despite this very exciting progress in the clinic, the basic signaling mechanism that connects PARP1 to a diverse array of biological processes is still poorly understood. This is, in large part, due to the inherent technical difficulty associated with the analysis of protein PARylation, which is a low-abundance, labile, and heterogeneous PTM. The study of PARylation has been greatly facilitated by the recent advances in mass spectrometry-based proteomic technologies tailored to the analysis of this modification. In this Perspective, we discuss these breakthroughs, including their technical development, and applications that provide a global view of the many biological processes regulated by this important protein modification.

  9. Automated Measurement and Signaling Systems for the Transactional Network

    Energy Technology Data Exchange (ETDEWEB)

    Piette, Mary Ann; Brown, Richard; Price, Phillip; Page, Janie; Granderson, Jessica; Riess, David; Czarnecki, Stephen; Ghatikar, Girish; Lanzisera, Steven

    2013-12-31

    The Transactional Network Project is a multi-lab activity funded by the US Department of Energy?s Building Technologies Office. The project team included staff from Lawrence Berkeley National Laboratory, Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The team designed, prototyped and tested a transactional network (TN) platform to support energy, operational and financial transactions between any networked entities (equipment, organizations, buildings, grid, etc.). PNNL was responsible for the development of the TN platform, with agents for this platform developed by each of the three labs. LBNL contributed applications to measure the whole-building electric load response to various changes in building operations, particularly energy efficiency improvements and demand response events. We also provide a demand response signaling agent and an agent for cost savings analysis. LBNL and PNNL demonstrated actual transactions between packaged rooftop units and the electric grid using the platform and selected agents. This document describes the agents and applications developed by the LBNL team, and associated tests of the applications.

  10. Customising the therapeutic response of signalling networks to promote antitumor responses by drug combinations

    Directory of Open Access Journals (Sweden)

    Alexey eGoltsov

    2014-02-01

    Full Text Available Drug resistance, de novo and acquired, pervades cellular signalling networks from one signalling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anticancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potency. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of drug combinations and design methods to determine advanced targets for drug combination therapy. Based on a joint systems analysis of cellular signalling network (SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyse the targets of drug combinations. The method explores mechanisms of sensitizing the SN through combination of two drugs targeting vertical signalling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to the customization of the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the downstream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects along with the

  11. Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction.

    Science.gov (United States)

    Reyes-Gibby, Cielito C; Yuan, Christine; Wang, Jian; Yeung, Sai-Ching J; Shete, Sanjay

    2015-06-05

    Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction. Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling. Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

  12. Singapore Cancer Network (SCAN) Guidelines for Systemic Therapy of Colorectal Cancer.

    Science.gov (United States)

    2015-10-01

    The SCAN colorectal cancer systemic therapy workgroup aimed to develop Singapore Cancer Network (SCAN) clinical practice guidelines for systemic therapy for colorectal cancer in Singapore. The workgroup utilised a modified ADAPTE process to calibrate high quality international evidence-based clinical practice guidelines to our local setting. Five international guidelines were evaluated-those developed by the National Comprehensive Cancer Network for colon (2014) and rectal (2014) cancer, the European Society of Medical Oncology for advanced (2012) and early (2013) cancer and the National Institute of Clinical Excellence (2011). Recommendations on systemic therapy in colorectal cancer were produced. These adapted guidelines form the SCAN Guidelines 2015 for systemic therapy of colorectal cancer.

  13. Cysteine-rich 61-connective tissue growth factor-nephroblastoma-overexpressed 5 (CCN5)/Wnt-1-induced signaling protein-2 (WISP-2) regulates microRNA-10b via hypoxia-inducible factor-1α-TWIST signaling networks in human breast cancer cells.

    Science.gov (United States)

    Haque, Inamul; Banerjee, Snigdha; Mehta, Smita; De, Archana; Majumder, Monami; Mayo, Matthew S; Kambhampati, Suman; Campbell, Donald R; Banerjee, Sushanta K

    2011-12-16

    MicroRNAs (miRNAs) are naturally occurring single-stranded RNA molecules that post-transcriptionally regulate the expression of target mRNA transcripts. Many of these target mRNA transcripts are involved in regulating processes commonly altered during tumorigenesis and metastatic growth. These include cell proliferation, differentiation, apoptosis, migration, and invasion. Among the several miRNAs, miRNA-10b (miR-10b) expression is increased in metastatic breast cancer cells and positively regulates cell migration and invasion through the suppression of the homeobox D10 (HOXD10) tumor suppressor signaling pathway. In breast metastatic cells, miR-10b expression is enhanced by a transcription factor TWIST1. We find that miR-10b expression in breast cancer cells can be suppressed by CCN5, and this CCN5 effect is mediated through the inhibition of TWIST1 expression. Moreover, CCN5-induced inhibition of TWIST1 expression is mediated through the translational inhibition/modification of hypoxia-inducible factor-1α via impeding JNK signaling pathway. Collectively, these studies suggest a novel regulatory pathway exists through which CCN5 exerts its anti-invasive function. On the basis of these findings, it is plausible that reactivation of CCN5 in miR-10b-positive invasive/metastatic breast cancers alone or in combination with current therapeutic regimens could provide a unique, alternative strategy to existing breast cancer therapy.

  14. Online Social Networks - Opportunities for Empowering Cancer Patients.

    Science.gov (United States)

    Mohammadzadeh, Zeinab; Davoodi, Somayeh; Ghazisaeidi, Marjan

    2016-01-01

    Online social network technologies have become important to health and apply in most health care areas. Particularly in cancer care, because it is a disease which involves many social aspects, online social networks can be very useful. Use of online social networks provides a suitable platform for cancer patients and families to present and share information about their medical conditions, address their educational needs, support decision making, and help to coping with their disease and improve their own outcomes. Like any other new technologies, online social networks, along with many benefits, have some negative effects such as violation of privacy and publication of incorrect information. However, if these effects are managed properly, they can empower patients to manage cancer through changing behavioral patterns and enhancing the quality of cancer patients lives This paper explains some application of online social networks in the cancer patient care process. It also covers advantages and disadvantages of related technologies.

  15. Extracellular matrix component signaling in cancer

    DEFF Research Database (Denmark)

    Multhaupt, Hinke A. B.; Leitinger, Birgit; Gullberg, Donald

    2016-01-01

    Cell responses to the extracellular matrix depend on specific signaling events. These are important from early development, through differentiation and tissue homeostasis, immune surveillance, and disease pathogenesis. Signaling not only regulates cell adhesion cytoskeletal organization...... and motility but also provides survival and proliferation cues. The major classes of cell surface receptors for matrix macromols. are the integrins, discoidin domain receptors, and transmembrane proteoglycans such as syndecans and CD44. Cells respond not only to specific ligands, such as collagen, fibronectin......, or basement membrane glycoproteins, but also in terms of matrix rigidity. This can regulate the release and subsequent biol. activity of matrix-bound growth factors, for example, transforming growth factor-β. In the environment of tumors, there may be changes in cell populations and their receptor profiles...

  16. Multimodality imaging of TGFβ signaling in breast cancer metastases

    Science.gov (United States)

    Serganova, Inna; Moroz, Ekaterina; Vider, Jelena; Gogiberidze, George; Moroz, Maxim; Pillarsetty, Nagavarakishore; Doubrovin, Michael; Minn, Andy; Thaler, Howard T.; Massague, Joan; Gelovani, Juri; Blasberg, Ronald

    2009-01-01

    The skeleton is a preferred site for breast cancer metastasis. We have developed a multimodality imaging approach to monitor the transforming growth factor β (TGFβ) signaling pathway in bone metastases, sequentially over time in the same animal. As model systems, two MDA-MB-231 breast cancer cells lines with different metastatic tropisms, SCP2 and SCP3, were transduced with constitutive and TGFβ-inducible reporter genes and were tested in vitro and in living animals. The sites and expansion of metastases were visualized by bioluminescence imaging using a constitutive firefly luciferase reporter, while TGFβ signaling in metastases was monitored by microPET imaging of HSV1-TK/GFP expression with [18F]FEAU and by a more sensitive and cost-effective bioluminescence reporter, based on nonsecreted Gaussia luciferase. Concurrent and sequential imaging of metastases in the same animals provided insight into the location and progression of metastases, and the timing and course of TGFβ signaling. The anticipated and newly observed differences in the imaging of tumors from two related cell lines have demonstrated that TGFβ signal transduction pathway activity can be noninvasively imaged with high sensitivity and reproducibility, thereby providing the opportunity for an assessment of novel treatments that target TGFβ signaling.—Serganova, I., Moroz, E., Vider, J., Gogiberidze, G., Moroz, M., Pillarsetty, N., Doubrovin, M., Minn, A., Thaler, H. T., Massague, J., Gelovani, J., Blasberg, R. Multimodality imaging of TGFβ signaling in breast cancer metastases. PMID:19325038

  17. Computational study of noise in a large signal transduction network

    Directory of Open Access Journals (Sweden)

    Ruohonen Keijo

    2011-06-01

    Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

  18. Reconstructing cancer drug response networks using multitask learning.

    Science.gov (United States)

    Ruffalo, Matthew; Stojanov, Petar; Pillutla, Venkata Krishna; Varma, Rohan; Bar-Joseph, Ziv

    2017-10-10

    Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

  19. Deciphering genomic alterations in colorectal cancer through transcriptional subtype-based network analysis.

    Directory of Open Access Journals (Sweden)

    Jing Zhu

    Full Text Available Both transcriptional subtype and signaling network analyses have proved useful in cancer genomics research. However, these two approaches are usually applied in isolation in existing studies. We reason that deciphering genomic alterations based on cancer transcriptional subtypes may help reveal subtype-specific driver networks and provide insights for the development of personalized therapeutic strategies. In this study, we defined transcriptional subtypes for colorectal cancer (CRC and identified driver networks/pathways for each subtype. Applying consensus clustering to a patient cohort with 1173 samples identified three transcriptional subtypes, which were validated in an independent cohort with 485 samples. The three subtypes were characterized by different transcriptional programs related to normal adult colon, early colon embryonic development, and epithelial mesenchymal transition, respectively. They also showed statistically different clinical outcomes. For each subtype, we mapped somatic mutation and copy number variation data onto an integrated signaling network and identified subtype-specific driver networks using a random walk-based strategy. We found that genomic alterations in the Wnt signaling pathway were common among all three subtypes; however, unique combinations of pathway alterations including Wnt, VEGF and Notch drove distinct molecular and clinical phenotypes in different CRC subtypes. Our results provide a coherent and integrated picture of human CRC that links genomic alterations to molecular and clinical consequences, and which provides insights for the development of personalized therapeutic strategies for different CRC subtypes.

  20. Network modeling links breast cancer susceptibility and centrosome dysfunction.

    Science.gov (United States)

    Pujana, Miguel Angel; Han, Jing-Dong J; Starita, Lea M; Stevens, Kristen N; Tewari, Muneesh; Ahn, Jin Sook; Rennert, Gad; Moreno, Víctor; Kirchhoff, Tomas; Gold, Bert; Assmann, Volker; Elshamy, Wael M; Rual, Jean-François; Levine, Douglas; Rozek, Laura S; Gelman, Rebecca S; Gunsalus, Kristin C; Greenberg, Roger A; Sobhian, Bijan; Bertin, Nicolas; Venkatesan, Kavitha; Ayivi-Guedehoussou, Nono; Solé, Xavier; Hernández, Pilar; Lázaro, Conxi; Nathanson, Katherine L; Weber, Barbara L; Cusick, Michael E; Hill, David E; Offit, Kenneth; Livingston, David M; Gruber, Stephen B; Parvin, Jeffrey D; Vidal, Marc

    2007-11-01

    Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

  1. Cancer classification: Mutual information, target network and strategies of therapy.

    Science.gov (United States)

    Hsu, Wen-Chin; Liu, Chan-Cheng; Chang, Fu; Chen, Su-Shing

    2012-10-02

    Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy. We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms. These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative. We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves

  2. Selective control of the apoptosis signaling network in heterogeneous cell populations.

    Directory of Open Access Journals (Sweden)

    Diego Calzolari

    Full Text Available BACKGROUND: Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome. However, in both cases these responses must occur with minimal response in other cells exposed to treatment; that is, the response must be selective. METHODOLOGY AND PRINCIPAL FINDINGS: Apoptosis signaling in heterogeneous cells is described by an ensemble of gene networks with identical topology but different link strengths. Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics. Parallel pathways promote normal statistics while series pathways promote skew distributions, which in the most extreme cases become log-normal. We also show that feedback and non-linearity can produce bimodal signaling statistics, as can discreteness and non-linearity. Two methods for optimizing selective control are presented. The first is an exhaustive search method and the second is a linear programming based approach. Though control of a single gene in the signaling network yields little selectivity, control of a few genes typically yields higher levels of selectivity. The statistics of gene combinations susceptible to selective control in heterogeneous apoptosis networks is studied and is used to identify general control strategies. CONCLUSIONS AND SIGNIFICANCE: We have explored two methods for the study of selectivity in cell populations. The first is an exhaustive search method limited to three node perturbations. The second is an effective linear model, based on

  3. Signaling Pathways in Cancer: a Matter of Dosage

    NARCIS (Netherlands)

    C.F.C. Gaspar (Claudia)

    2009-01-01

    textabstractThe main issue addressed in this thesis is how different levels of signaling activity of the Wnt and TGF-β/BMP pathways can affect transcriptional responses in particular relevant for self-renewal and differentiation both in homeostasis and in cancer. Chapter 1 presents an overview

  4. Multisite phosphorylation networks as signal processors for Cdk1.

    Science.gov (United States)

    Kõivomägi, Mardo; Ord, Mihkel; Iofik, Anna; Valk, Ervin; Venta, Rainis; Faustova, Ilona; Kivi, Rait; Balog, Eva Rose M; Rubin, Seth M; Loog, Mart

    2013-12-01

    The order and timing of cell-cycle events is controlled by changing substrate specificity and different activity thresholds of cyclin-dependent kinases (CDKs). However, it is not understood how a single protein kinase can trigger hundreds of switches in a sufficiently time-resolved fashion. We show that cyclin-Cdk1-Cks1-dependent phosphorylation of multisite targets in Saccharomyces cerevisiae is controlled by key substrate parameters including distances between phosphorylation sites, distribution of serines and threonines as phosphoacceptors and positioning of cyclin-docking motifs. The component mediating the key interactions in this process is Cks1, the phosphoadaptor subunit of the cyclin-Cdk1-Cks1 complex. We propose that variation of these parameters within networks of phosphorylation sites in different targets provides a wide range of possibilities for differential amplification of Cdk1 signals, thus providing a mechanism to generate a wide range of thresholds in the cell cycle.

  5. Detecting malicious chaotic signals in wireless sensor network

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Kumari, Sangeeta

    2018-02-01

    In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

  6. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  7. Modulation of signal transduction pathways by natural compounds in cancer.

    Science.gov (United States)

    Ranjan, Alok; Fofaria, Neel M; Kim, Sung-Hoon; Srivastava, Sanjay K

    2015-10-01

    Cancer is generally regarded as the result of abnormal growth of cells. According to World Health Organization, cancer is the leading cause of mortality worldwide. Mother nature provides a large source of bioactive compounds with excellent therapeutic efficacy. Numerous phytochemicals from nature have been investigated for anticancer properties. In this review article, we discuss several natural compounds, which have shown anti-cancer activity. Natural compounds induce cell cycle arrest, activate intrinsic and extrinsic apoptosis pathways, generate Reactive Oxygen Species (ROS), and down-regulate activated signaling pathways, resulting in inhibition of cell proliferation, progression and metastasis of cancer. Several preclinical studies have suggested that natural compounds can also increase the sensitivity of resistant cancers to available chemotherapy agents. Furthermore, combining FDA approved anti-cancer drugs with natural compounds results in improved efficacy. On the basis of these exciting outcomes of natural compounds against several cancer types, several agents have already advanced to clinical trials. In conclusion, preclinical results and clinical outcomes against cancer suggest promising anticancer efficacy of agents from natural sources. Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

  8. Consensus-based sparse signal reconstruction algorithm for wireless sensor networks

    National Research Council Canada - National Science Library

    Peng, Bao; Zhao, Zhi; Han, Guangjie; Shen, Jian

    2016-01-01

    This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter...

  9. Integrative signaling networks of membrane guanylate cyclases: Biochemistry and physiology

    Directory of Open Access Journals (Sweden)

    Rameshwar K Sharma

    2016-09-01

    Full Text Available This monograph presents a historical perspective of cornerstone developments on the biochemistry and physiology of mammalian membrane guanylate cyclases (MGCs, highlighting contributions made by the authors and their collaborators. Upon resolution of early, contentious studies, cyclic GMP emerged, alongside cyclic AMP, as an important intracellular second messenger for hormonal signaling. However, the two signaling pathways differ in significant ways. In the cyclic AMP pathway, hormone binding to a G protein coupled receptor leads to stimulation or inhibition of an adenylate cyclase, whereas the cyclic GMP pathway dispenses with intermediaries; hormone binds to an MGC to affect its activity. Although the cyclic GMP pathway is direct, it is by no means simple. The modular design of the molecule incorporates regulation by ATP binding and phosphorylation. MGCs can form complexes with Ca2+-sensing subunits that either increase or decrease cyclic GMP synthesis, depending on subunit identity. In some systems, co-expression of two Ca2+ sensors, GCAP1 and S100B with ROS-GC1 confers bimodal signaling marked by increases in cyclic GMP synthesis when intracellular Ca2+ concentration rises or falls. Some MGCs monitor or are modulated by carbon dioxide via its conversion to bicarbonate. One MGC even functions as a thermosensor as well as a chemosensor; activity reaches a maximum with a mild drop in temperature. The complexity afforded by these multiple limbs of operation enables MGC networks to perform transductions traditionally reserved for G protein coupled receptors and TRP (Transient Receptor Potential channels and to serve a diverse array of functions, including control over cardiac vasculature, smooth muscle relaxation, blood pressure regulation, cellular growth, sensory transductions, neural plasticity and memory.

  10. LRP-1 promotes cancer cell invasion by supporting ERK and inhibiting JNK signaling pathways.

    Directory of Open Access Journals (Sweden)

    Benoit Langlois

    Full Text Available BACKGROUND: The low-density lipoprotein receptor-related protein-1 (LRP-1 is an endocytic receptor mediating the clearance of various extracellular molecules involved in the dissemination of cancer cells. LRP-1 thus appeared as an attractive receptor for targeting the invasive behavior of malignant cells. However, recent results suggest that LRP-1 may facilitate the development and growth of cancer metastases in vivo, but the precise contribution of the receptor during cancer progression remains to be elucidated. The lack of mechanistic insights into the intracellular signaling networks downstream of LRP-1 has prevented the understanding of its contribution towards cancer. METHODOLOGY/PRINCIPAL FINDINGS: Through a short-hairpin RNA-mediated silencing approach, we identified LRP-1 as a main regulator of ERK and JNK signaling in a tumor cell context. Co-immunoprecipitation experiments revealed that LRP-1 constitutes an intracellular docking site for MAPK containing complexes. By using pharmacological agents, constitutively active and dominant-negative kinases, we demonstrated that LRP-1 maintains malignant cells in an adhesive state that is favorable for invasion by activating ERK and inhibiting JNK. We further demonstrated that the LRP-1-dependent regulation of MAPK signaling organizes the cytoskeletal architecture and mediates adhesive complex turnover in cancer cells. Moreover, we found that LRP-1 is tethered to the actin network and to focal adhesion sites and controls ERK and JNK targeting to talin-rich structures. CONCLUSIONS: We identified ERK and JNK as the main molecular relays by which LRP-1 regulates focal adhesion disassembly of malignant cells to support invasion.

  11. Hedgehog Signaling Inhibitors as Anti-Cancer Agents in Osteosarcoma

    Energy Technology Data Exchange (ETDEWEB)

    Ram Kumar, Ram Mohan, E-mail: rkumar@research.balgrist.ch; Fuchs, Bruno [Laboratory for Orthopaedic Research, Balgrist University Hospital, Sarcoma Center-UZH University of Zurich, Zurich 8008 (Switzerland)

    2015-05-13

    Osteosarcoma is a rare type of cancer associated with a poor clinical outcome. Even though the pathologic characteristics of OS are well established, much remains to be understood, particularly at the molecular signaling level. The molecular mechanisms of osteosarcoma progression and metastases have not yet been fully elucidated and several evolutionary signaling pathways have been found to be linked with osteosarcoma pathogenesis, especially the hedgehog signaling (Hh) pathway. The present review will outline the importance and targeting the hedgehog signaling (Hh) pathway in osteosarcoma tumor biology. Available data also suggest that aberrant Hh signaling has pro-migratory effects and leads to the development of osteoblastic osteosarcoma. Activation of Hh signaling has been observed in osteosarcoma cell lines and also in primary human osteosarcoma specimens. Emerging data suggests that interference with Hh signal transduction by inhibitors may reduce osteosarcoma cell proliferation and tumor growth thereby preventing osteosarcomagenesis. From this perspective, we outline the current state of Hh pathway inhibitors in osteosarcoma. In summary, targeting Hh signaling by inhibitors promise to increase the efficacy of osteosarcoma treatment and improve patient outcome.

  12. Vitamin D Signaling Modulators in Cancer Therapy.

    Science.gov (United States)

    Luo, Wei; Johnson, Candace S; Trump, Donald L

    2016-01-01

    The antiproliferative and pro-apoptotic effects of 1α,25-dihydroxycholecalciferol (1,25(OH)2D3, 1,25D3, calcitriol) have been demonstrated in various tumor model systems in vitro and in vivo. However, limited antitumor effects of 1,25D3 have been observed in clinical trials. This may be attributed to a variety of factors including overexpression of the primary 1,25D3 degrading enzyme, CYP24A1, in tumors, which would lead to rapid local inactivation of 1,25D3. An alternative strategy for improving the antitumor activity of 1,25D3 involves the combination with a selective CYP24A1 inhibitor. The validity of this approach is supported by numerous preclinical investigations, which demonstrate that CYP24A1 inhibitors suppress 1,25D3 catabolism in tumor cells and increase the effects of 1,25D3 on gene expression and cell growth. Studies are now required to determine whether selective CYP24A1 inhibitors+1,25D3 can be used safely and effectively in patients. CYP24A1 inhibitors plus 1,25D3 can cause dose-limiting toxicity of vitamin D (hypercalcemia) in some patients. Dexamethasone significantly reduces 1,25D3-mediated hypercalcemia and enhances the antitumor activity of 1,25D3, increases VDR-ligand binding, and increases VDR protein expression. Efforts to dissect the mechanisms responsible for CYP24A1 overexpression and combinational effect of 1,25D3/dexamethasone in tumors are underway. Understanding the cross talk between vitamin D receptor (VDR) and glucocorticoid receptor (GR) signaling axes is of crucial importance to the design of new therapies that include 1,25D3 and dexamethasone. Insights gained from these studies are expected to yield novel strategies to improve the efficacy of 1,25D3 treatment. © 2016 Elsevier Inc. All rights reserved.

  13. The role of nutraceuticals in pancreatic cancer prevention and therapy: Targeting cellular signaling, miRNAs and epigenome

    Science.gov (United States)

    Li, Yiwei; Go, Vay Liang W.; Sarkar, Fazlul H.

    2014-01-01

    Pancreatic cancer is one of the most aggressive malignancies in US adults. The experimental studies have found that antioxidant nutrients could reduce oxidative DNA damage, suggesting that these antioxidants may protect against pancreatic carcinogenesis. Several epidemiologic studies showed that dietary intake of antioxidants was inversely associated with the risk of pancreatic cancer, demonstrating the inhibitory effects of antioxidants on pancreatic carcinogenesis. Moreover, nutraceuticals, the anti-cancer agents from diet or natural plants, have been found to inhibit the development and progression of pancreatic cancer through the regulation of cellular signaling pathways. Importantly, nutraceuticals also up-regulate the expression of tumor suppressive miRNAs and down-regulate the expression of oncogenic miRNAs, leading to the inhibition of pancreatic cancer cell growth and pancreatic Cancer Stem Cell (CSC) self-renewal through modulation of cellular signaling network. Furthermore, nutraceuticals also regulate epigenetically deregulated DNAs and miRNAs, leading to the normalization of altered cellular signaling in pancreatic cancer cells. Therefore, nutraceuticals could have much broader use in the prevention and/or treatment of pancreatic cancer in combination with conventional chemotherapeutics. However, more in vitro mechanistic experiments, in vivo animal studies, and clinical trials are needed to realize the true value of nutraceuticals in the prevention and/or treatment of pancreatic cancer. PMID:25493373

  14. Rnd3 regulates lung cancer cell proliferation through notch signaling.

    Directory of Open Access Journals (Sweden)

    Yongjun Tang

    Full Text Available Rnd3/RhoE is a small Rho GTPase involved in the regulation of different cell behaviors. Dysregulation of Rnd3 has been linked to tumorigenesis and metastasis. Lung cancers are the leading cause of cancer-related death in the West and around the world. The expression of Rnd3 and its ectopic role in non-small cell lung cancer (NSCLC remain to be explored. Here, we reported that Rnd3 was down-regulated in three NSCLC cell lines: H358, H520 and A549. The down-regulation of Rnd3 led to hyper-activation of Rho Kinase and Notch signaling. The reintroduction of Rnd3 or selective inhibition of Notch signaling, but not Rho Kinase signaling, blocked the proliferation of H358 and H520 cells. Mechanistically, Notch intracellular domain (NICD protein abundance in H358 cells was regulated by Rnd3-mediated NICD proteasome degradation. Rnd3 regulated H358 and H520 cell proliferation through a Notch1/NICD/Hes1 signaling axis independent of Rho Kinase.

  15. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

    Science.gov (United States)

    Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

    2016-08-26

    Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and

  16. Oxygen sensing and hypoxia signalling pathways in animals: the implications of physiology for cancer.

    Science.gov (United States)

    Ratcliffe, Peter J

    2013-04-15

    Studies of regulation of the haematopoietic growth factor erythropoietin led to the unexpected discovery of a widespread system of direct oxygen sensing that regulates gene expression in animals. The oxygen-sensitive signal is generated by a series of non-haem Fe(II)- and 2-oxoglutarate-dependent dioxygenases that catalyse the post-translational hydroxylation of specific residues in the transcription factor hypoxia-inducible factor (HIF). These hydroxylations promote both oxygen-dependent degradation and oxygen-dependent inactivation of HIF, but are suppressed in hypoxia, leading to the accumulation of HIF and assembly of an active transcriptional complex in hypoxic cells. Hypoxia-inducible factor activates an extensive transcriptional cascade that interfaces with other cell signalling pathways, microRNA networks and RNA-protein translational control systems. The relationship of these cellular signalling pathways to the integrated physiology of oxygen homeostasis and the implication of dysregulating these massive physiological pathways in diseases such as cancer are discussed.

  17. Protein Phosphatase 2A in the Regulation of Wnt Signaling, Stem Cells, and Cancer

    Directory of Open Access Journals (Sweden)

    Joshua J. Thompson

    2018-02-01

    Full Text Available Protein phosphorylation is a ubiquitous cellular process that allows for the nuanced and reversible regulation of protein activity. Protein phosphatase 2A (PP2A is a heterotrimeric serine-threonine phosphatase—composed of a structural, regulatory, and catalytic subunit—that controls a variety of cellular events via protein dephosphorylation. While much is known about PP2A and its basic biochemistry, the diversity of its components—especially the multitude of regulatory subunits—has impeded the determination of PP2A function. As a consequence of this complexity, PP2A has been shown to both positively and negatively regulate signaling networks such as the Wnt pathway. Wnt signaling modulates major developmental processes, and is a dominant mediator of stem cell self-renewal, cell fate, and cancer stem cells. Because PP2A affects Wnt signaling both positively and negatively and at multiple levels, further understanding of this complex dynamic may ultimately provide insight into stem cell biology and how to better treat cancers that result from alterations in Wnt signaling. This review will summarize literature that implicates PP2A as a tumor suppressor, explore PP2A mutations identified in human malignancy, and focus on PP2A in the regulation of Wnt signaling and stem cells so as to better understand how aberrancy in this pathway can contribute to tumorigenesis.

  18. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  19. The Primary Cilium in Cell Signaling and Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Michaud III, Edward J [ORNL; Yoder, Bradley [University of Alabama, Birmingham

    2006-01-01

    The primary cilium is a microtubule-based antenna-like structure that emanates from the surface of virtually all cells in the mammalian body. It is anchored to the cell by the basal body, which develops from the mother centriole of the centrosome in a manner that is coordinately regulated with the cell cycle. The primary cilium is a sensory organelle that receives both mechanical and chemical signals from other cells and the environment, and transmits these signals to the nucleus to elicit a cellular response. Recent studies revealed that multiple components of the Sonic hedgehog and plateletderived growth factor receptor-A signal transduction pathways localize to the primary cilium, and that loss of the cilium blocks ligand-induced signaling by both pathways. In light of the major role that these pathways play in numerous types of cancer, we anticipate that the emerging discoveries being made about the function of the primary cilium in signaling pathways that are critical for embryonic development and tissue homeostasis in adults will also provide novel insights into the molecular mechanisms of carcinogenesis. (Cancer Res 2006; 66 13): 6463-7)

  20. Activin type IB receptor signaling in prostate cancer cells promotes lymph node metastasis in a xenograft model

    Energy Technology Data Exchange (ETDEWEB)

    Nomura, Masatoshi, E-mail: nomura@med.kyushu-u.ac.jp [Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan); Tanaka, Kimitaka; Wang, Lixiang; Goto, Yutaka; Mukasa, Chizu; Ashida, Kenji; Takayanagi, Ryoichi [Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan)

    2013-01-04

    Highlights: Black-Right-Pointing-Pointer ActRIB signaling induces Snail and S100A4 expressions in prostate cancer cells. Black-Right-Pointing-Pointer The prostate cancer cell lines expressing an active form of ActRIB were established. Black-Right-Pointing-Pointer ActRIB signaling promotes EMT and lymph node metastasis in xenograft model. -- Abstract: Activin, a member of the transforming growth factor-{beta} family, has been known to be a growth and differentiating factor. Despite its pluripotent effects, the roles of activin signaling in prostate cancer pathogenesis are still unclear. In this study, we established several cell lines that express a constitutive active form of activin type IB receptor (ActRIBCA) in human prostate cancer cells, ALVA41 (ALVA-ActRIBCA). There was no apparent change in the proliferation of ALVA-ActRIBCA cells in vitro; however, their migratory ability was significantly enhanced. In a xenograft model, histological analysis revealed that the expression of Snail, a cell-adhesion-suppressing transcription factor, was dramatically increased in ALVA-ActRIBCA tumors, indicating epithelial mesenchymal transition (EMT). Finally, mice bearing ALVA-ActRIBCA cells developed multiple lymph node metastases. In this study, we demonstrated that ActRIBCA signaling can promote cell migration in prostate cancer cells via a network of signaling molecules that work together to trigger the process of EMT, and thereby aid in the aggressiveness and progression of prostate cancers.

  1. Discovering cancer genes by integrating network and functional properties

    Directory of Open Access Journals (Sweden)

    Davis David P

    2009-09-01

    Full Text Available Abstract Background Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO annotations, to facilitate the identification of cancer genes. Methods Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.

  2. Targeting cancer stem cells and signaling pathways by phytochemicals: Novel approach for breast cancer therapy.

    Science.gov (United States)

    Dandawate, Prasad R; Subramaniam, Dharmalingam; Jensen, Roy A; Anant, Shrikant

    2016-10-01

    Breast cancer is the most common form of cancer diagnosed in women worldwide and the second leading cause of cancer-related deaths in the USA. Despite the development of newer diagnostic methods, selective as well as targeted chemotherapies and their combinations, surgery, hormonal therapy, radiotherapy, breast cancer recurrence, metastasis and drug resistance are still the major problems for breast cancer. Emerging evidence suggest the existence of cancer stem cells (CSCs), a population of cells with the capacity to self-renew, differentiate and be capable of initiating and sustaining tumor growth. In addition, CSCs are believed to be responsible for cancer recurrence, anticancer drug resistance, and metastasis. Hence, compounds targeting breast CSCs may be better therapeutic agents for treating breast cancer and control recurrence and metastasis. Naturally occurring compounds, mainly phytochemicals have gained immense attention in recent times because of their wide safety profile, ability to target heterogeneous populations of cancer cells as well as CSCs, and their key signaling pathways. Therefore, in the present review article, we summarize our current understanding of breast CSCs and their signaling pathways, and the phytochemicals that affect these cells including curcumin, resveratrol, tea polyphenols (epigallocatechin-3-gallate, epigallocatechin), sulforaphane, genistein, indole-3-carbinol, 3, 3'-di-indolylmethane, vitamin E, retinoic acid, quercetin, parthenolide, triptolide, 6-shogaol, pterostilbene, isoliquiritigenin, celastrol, and koenimbin. These phytochemicals may serve as novel therapeutic agents for breast cancer treatment and future leads for drug development. Copyright © 2016. Published by Elsevier Ltd.

  3. Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

    Directory of Open Access Journals (Sweden)

    Vahid Emamian

    2003-03-01

    Full Text Available Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA, and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

  4. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    Science.gov (United States)

    Samarasinghe, S; Ling, H

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  5. A Medical Center Network for Optimized Lung Cancer Biospecimen Banking

    Science.gov (United States)

    2015-10-01

    1 Award Number: W81XWH-10-1-0818 TITLE: “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” PRINCIPAL INVESTIGATOR: Christopher...To) 20Sep2014 - 19Sep2015 4. TITLE AND SUBTITLE “A Medical Center Network for Optimized Lung Cancer Biospecimen Banking ” 5a. CONTRACT NUMBER 5b...Although new subject enrollments and specimen collection have ceased, the LCBRN is committed to the outcome of this project, which is a bank of

  6. Influence of the complex-shape light signal on the neural network

    Science.gov (United States)

    Melnikov, Leonid A.; Novosselova, Anna V.; Blinova, Nadejda V.

    1999-03-01

    The effect of external signals of different shapes (constant, serrated and others) on the ring neural network modeling the visual perception is investigated numerically. New specific features in the dynamics of the neural network, such as the excitation, the swapping and the depression, were observed. The cooperative amplication of the external signal and the memory effect have been observed.

  7. ERBB receptors in cancer: signaling from the inside.

    Science.gov (United States)

    Arteaga, Carlos L

    2011-03-16

    ERBB receptor tyrosine kinases are activated by ligand-induced dimerization followed by activation and transphosphorylation of their intracellular kinase domains. A recent study by Bill and colleagues demonstrates that receptor transphosphorylation can be regulated from inside the cell by members of the cytohesin protein family. These data highlight a novel mechanism of amplification of ERBB receptor signaling output that may contribute to embryogenesis and cancer progression.

  8. Crosstalk between Wnt Signaling and RNA Processing in Colorectal Cancer

    Directory of Open Access Journals (Sweden)

    Michael Bordonaro

    2013-01-01

    Full Text Available RNA processing involves a variety of processes affecting gene expression, including the removal of introns through RNA splicing, as well as 3' end processing (cleavage and polyadenylation. Alternative RNA processing is fundamentally important for gene regulation, and aberrant processing is associated with the initiation and progression of cancer. Deregulated Wnt signaling, which is the initiating event in the development of most cases of human colorectal cancer (CRC, has been linked to modified RNA processing, which may contribute to Wnt-mediated colonic carcinogenesis. Crosstalk between Wnt signaling and alternative RNA splicing with relevance to CRC includes effects on the expression of Rac1b, an alternatively spliced gene associated with tumorigenesis, which exhibits alternative RNA splicing that is influenced by Wnt activity. In addition, Tcf4, a crucial component of Wnt signaling, also exhibits alternative splicing, which is likely involved in colonic tumorigenesis. Modulation of 3' end formation, including of the Wnt target gene COX-2, also can influence the neoplastic process, with implications for CRC. While many human genes are dependent on introns and splicing for normal levels of gene expression, naturally intronless genes exist with a unique metabolism that allows for intron-independent gene expression. Effects of Wnt activity on the RNA metabolism of the intronless Wnt-target gene c-jun is a likely contributor to cancer development. Further, butyrate, a breakdown product of dietary fiber and a histone deacetylase inhibitor, upregulates Wnt activity in CRC cells, and also modulates RNA processing; therefore, the interplay between Wnt activity, the modulation of this activity by butyrate, and differential RNA metabolism in colonic cells can significantly influence tumorigenesis. Determining the role played by altered RNA processing in Wnt-mediated neoplasia may lead to novel interventions aimed at restoring normal RNA metabolism for

  9. EGFR and HER2 signaling in breast cancer brain metastasis

    Science.gov (United States)

    Sirkisoon, Sherona R.; Carpenter, Richard L.; Rimkus, Tadas; Miller, Lance; Metheny-Barlow, Linda; Lo, Hui-Wen

    2016-01-01

    Breast cancer occurs in approximately 1 in 8 women and 1 in 37 women with breast cancer succumbed to the disease. Over the past decades, new diagnostic tools and treatments have substantially improved the prognosis of women with local diseases. However, women with metastatic disease still have a dismal prognosis without effective treatments. Among different molecular subtypes of breast cancer, the HER2-enriched and basal-like subtypes typically have higher rates of metastasis to the brain. Basal-like metastatic breast tumors frequently express EGFR. Consequently, HER2- and EGFR-targeted therapies are being used in the clinic and/or evaluated in clinical trials for treating breast cancer patients with brain metastases. In this review, we will first provide an overview of the HER2 and EGFR signaling pathways. The roles that EGFR and HER2 play in breast cancer metastasis to the brain will then be discussed. Finally, we will summarize the preclinical and clinical effects of EGFR- and HER2-targeted therapies on breast cancer metastasis. PMID:26709660

  10. Targeting the human cancer pathway protein interaction network by structural genomics.

    Science.gov (United States)

    Huang, Yuanpeng Janet; Hang, Dehua; Lu, Long Jason; Tong, Liang; Gerstein, Mark B; Montelione, Gaetano T

    2008-10-01

    Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

  11. Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics*

    Science.gov (United States)

    Huang, Yuanpeng Janet; Hang, Dehua; Lu, Long Jason; Tong, Liang; Gerstein, Mark B.; Montelione, Gaetano T.

    2008-01-01

    Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as “hubs” or “bottlenecks” in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects. PMID:18487680

  12. International network of cancer genome projects

    NARCIS (Netherlands)

    Hudson, Thomas J.; Anderson, Warwick; Aretz, Axel; Barker, Anna D.; Bell, Cindy; Bernabé, Rosa R.; Bhan, M. K.; Calvo, Fabien; Eerola, Iiro; Gerhard, Daniela S.; Guttmacher, Alan; Guyer, Mark; Hemsley, Fiona M.; Jennings, Jennifer L.; Kerr, David; Klatt, Peter; Kolar, Patrik; Kusuda, Jun; Lane, David P.; Laplace, Frank; Lu, Youyong; Nettekoven, Gerd; Ozenberger, Brad; Peterson, Jane; Rao, T. S.; Remacle, Jacques; Schafer, Alan J.; Shibata, Tatsuhiro; Stratton, Michael R.; Vockley, Joseph G.; Watanabe, Koichi; Yang, Huanming; Yuen, Matthew M. F.; Knoppers, Bartha M.; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn G.; Dyke, Stephanie O. M.; Joly, Yann; Kato, Kazuto; Kennedy, Karen L.; Nicolás, Pilar; Parker, Michael J.; Rial-Sebbag, Emmanuelle; Romeo-Casabona, Carlos M.; Shaw, Kenna M.; Wallace, Susan; Wiesner, Georgia L.; Zeps, Nikolajs; Lichter, Peter; Biankin, Andrew V.; Chabannon, Christian; Chin, Lynda; Clément, Bruno; de Alava, Enrique; Degos, Françoise; Ferguson, Martin L.; Geary, Peter; Hayes, D. Neil; Johns, Amber L.; Kasprzyk, Arek; Nakagawa, Hidewaki; Penny, Robert; Piris, Miguel A.; Sarin, Rajiv; Scarpa, Aldo; van de Vijver, Marc; Futreal, P. Andrew; Aburatani, Hiroyuki; Bayés, Mónica; Bowtell, David D. L.; Campbell, Peter J.; Estivill, Xavier; Grimmond, Sean M.; Gut, Ivo; Hirst, Martin; López-Otín, Carlos; Majumder, Partha; Marra, Marco; McPherson, John D.; Ning, Zemin; Puente, Xose S.; Ruan, Yijun; Stunnenberg, Hendrik G.; Swerdlow, Harold; Velculescu, Victor E.; Wilson, Richard K.; Xue, Hong H.; Yang, Liu; Spellman, Paul T.; Bader, Gary D.; Boutros, Paul C.; Flicek, Paul; Getz, Gad; Guigó, Roderic; Guo, Guangwu; Haussler, David; Heath, Simon; Hubbard, Tim J.; Jiang, Tao; Jones, Steven M.; Li, Qibin; López-Bigas, Nuria; Luo, Ruibang; Muthuswamy, Lakshmi; Ouellette, B. F. Francis; Pearson, John V.; Quesada, Victor; Raphael, Benjamin J.; Sander, Chris; Speed, Terence P.; Stein, Lincoln D.; Stuart, Joshua M.; Teague, Jon W.; Totoki, Yasushi; Tsunoda, Tatsuhiko; Valencia, Alfonso; Wheeler, David A.; Wu, Honglong; Zhao, Shancen; Zhou, Guangyu; Lathrop, Mark; Thomas, Gilles; Yoshida, Teruhiko; Axton, Myles; Gunter, Chris; Miller, Linda J.; Zhang, Junjun; Haider, Syed A.; Wang, Jianxin; Yung, Christina K.; Cross, Anthony; Liang, Yong; Gnaneshan, Saravanamuttu; Guberman, Jonathan; Hsu, Jack; Chalmers, Don R. C.; Hasel, Karl W.; Kaan, Terry S. H.; Lowrance, William W.; Masui, Tohru; Rodriguez, Laura Lyman; Vergely, Catherine; Cloonan, Nicole; Defazio, Anna; Eshleman, James R.; Etemadmoghadam, Dariush; Gardiner, Brooke A.; Kench, James G.; Sutherland, Robert L.; Tempero, Margaret A.; Waddell, Nicola J.; Wilson, Peter J.; Gallinger, Steve; Tsao, Ming-Sound; Shaw, Patricia A.; Petersen, Gloria M.; Mukhopadhyay, Debabrata; DePinho, Ronald A.; Thayer, Sarah; Shazand, Kamran; Beck, Timothy; Sam, Michelle; Timms, Lee; Ballin, Vanessa; Ji, Jiafu; Zhang, Xiuqing; Chen, Feng; Hu, Xueda; Yang, Qi; Tian, Geng; Zhang, Lianhai; Xing, Xiaofang; Li, Xianghong; Zhu, Zhenggang; Yu, Yingyan; Yu, Jun; Tost, Jörg; Brennan, Paul; Holcatova, Ivana; Zaridze, David; Brazma, Alvis; Egevad, Lars; Prokhortchouk, Egor; Banks, Rosamonde Elizabeth; Uhlén, Mathias; Viksna, Juris; Ponten, Fredrik; Skryabin, Konstantin; Birney, Ewan; Borg, Ake; Børresen-Dale, Anne-Lise; Caldas, Carlos; Foekens, John A.; Martin, Sancha; Reis-Filho, Jorge S.; Richardson, Andrea L.; Sotiriou, Christos; van't Veer, Laura; Birnbaum, Daniel; Blanche, Hélène; Boucher, Pascal; Boyault, Sandrine; Masson-Jacquemier, Jocelyne D.; Pauporté, Iris; Pivot, Xavier; Vincent-Salomon, Anne; Tabone, Eric; Theillet, Charles; Treilleux, Isabelle; Bioulac-Sage, Paulette; Decaens, Thomas; Franco, Dominique; Gut, Marta; Samuel, Didier; Zucman-Rossi, Jessica; Eils, Roland; Brors, Benedikt; Korbel, Jan O.; Korshunov, Andrey; Landgraf, Pablo; Lehrach, Hans; Pfister, Stefan; Radlwimmer, Bernhard; Reifenberger, Guido; Taylor, Michael D.; von Kalle, Christof; Majumder, Partha P.; Pederzoli, Paolo; Lawlor, Rita T.; Delledonne, Massimo; Bardelli, Alberto; Gress, Thomas; Klimstra, David; Zamboni, Giuseppe; Nakamura, Yusuke; Miyano, Satoru; Fujimoto, Akihiro; Campo, Elias; de Sanjosé, Silvia; Montserrat, Emili; González-Díaz, Marcos; Jares, Pedro; Himmelbaue, Heinz; Bea, Silvia; Aparicio, Samuel; Easton, Douglas F.; Collins, Francis S.; Compton, Carolyn C.; Lander, Eric S.; Burke, Wylie; Green, Anthony R.; Hamilton, Stanley R.; Kallioniemi, Olli P.; Ley, Timothy J.; Liu, Edison T.; Wainwright, Brandon J.

    2010-01-01

    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the

  13. The European Cancer and Work Network: CANWON

    NARCIS (Netherlands)

    de Boer, Angela G. E. M.

    2014-01-01

    The number of cancer survivors is rapidly growing due to improved treatment and ageing population. Almost half of cancer patients will experience a cancer diagnosis during working age when career and work-related issues play an important role. Many cancer survivors are at risk for unemployment which

  14. Non-Canonical Hh Signaling in Cancer-Current Understanding and Future Directions.

    Science.gov (United States)

    Gu, Dongsheng; Xie, Jingwu

    2015-08-27

    As a major regulatory pathway for embryonic development and tissue patterning, hedgehog signaling is not active in most adult tissues, but is reactivated in a number of human cancer types. A major milestone in hedgehog signaling in cancer is the Food and Drug Administration (FDA) approval of a smoothened inhibitor Vismodegib for treatment of basal cell carcinomas. Vismodegib can block ligand-mediated hedgehog signaling, but numerous additional clinical trials have failed to show significant improvements in cancer patients. Amounting evidence indicate that ligand-independent hedgehog signaling plays an essential role in cancer. Ligand-independent hedgehog signaling, also named non-canonical hedgehog signaling, generally is not sensitive to smoothened inhibitors. What we know about non-canonical hedgehog signaling in cancer, and how should we prevent its activation? In this review, we will summarize recent development of non-canonical hedgehog signaling in cancer, and will discuss potential ways to prevent this type of hedgehog signaling.

  15. Proceedings of the IEEE 2003 Neural Networks for Signal Processing Workshop

    DEFF Research Database (Denmark)

    Larsen, Jan

    methodology and real-world application domains and is widely entering into everyday solutions adopted by research and industry, going far beyond “traditional” neural networks and academic examples. As reflected in this collection, contemporary neural networks for signal processing combine many ideas from......This proceeding contains refereed papers presented at the thirteenth IEEE Workshop on Neural Networks for Signal Processing (NNSP’2003), held at the Atria-Mercure Conference Center, Toulouse, France, September 17-19, 2003. The Neural Networks for Signal Processing Technical Committee of the IEEE...... Signal Processing Society organized the workshop with sponsorship of the Signal Processing Society and the co-operation of the IEEE Neural Networks Society. The IEEE Press published the previous twelve volumes of the NNSP Workshop proceedings in a hardbound volume. This year, the bound volume...

  16. Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

    DEFF Research Database (Denmark)

    Dheer, D.K.; Doolla, S.; Bandyopadhyay, S.

    2017-01-01

    For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues......, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete...... dynamic model of an islanded microgrid was developed. From stability analysis, the study reports that both location of DGs and choice of droop coefficient have a significant effect on small signal stability, transient response of the system and network losses. The trade-off associated with the network...

  17. Calcium sensing receptor signalling in physiology and cancer.

    Science.gov (United States)

    Brennan, Sarah C; Thiem, Ursula; Roth, Susanne; Aggarwal, Abhishek; Fetahu, Irfete Sh; Tennakoon, Samawansha; Gomes, Ana Rita; Brandi, Maria Luisa; Bruggeman, Frank; Mentaverri, Romuald; Riccardi, Daniela; Kallay, Enikö

    2013-07-01

    The calcium sensing receptor (CaSR) is a class C G-protein-coupled receptor that is crucial for the feedback regulation of extracellular free ionised calcium homeostasis. While extracellular calcium (Ca(2+)o) is considered the primary physiological ligand, the CaSR is activated physiologically by a plethora of molecules including polyamines and l-amino acids. Activation of the CaSR by different ligands has the ability to stabilise unique conformations of the receptor, which may lead to preferential coupling of different G proteins; a phenomenon termed 'ligand-biased signalling'. While mutations of the CaSR are currently not linked with any malignancies, altered CaSR expression and function are associated with cancer progression. Interestingly, the CaSR appears to act both as a tumour suppressor and an oncogene, depending on the pathophysiology involved. Reduced expression of the CaSR occurs in both parathyroid and colon cancers, leading to loss of the growth suppressing effect of high Ca(2+)o. On the other hand, activation of the CaSR might facilitate metastasis to bone in breast and prostate cancer. A deeper understanding of the mechanisms driving CaSR signalling in different tissues, aided by a systems biology approach, will be instrumental in developing novel drugs that target the CaSR or its ligands in cancer. This article is part of a Special Issue entitled: 12th European Symposium on Calcium. Copyright © 2012 Elsevier B.V. All rights reserved.

  18. Managing cancer care through service delivery networks: The role of professional collaboration in two European cancer networks.

    Science.gov (United States)

    Prades, Joan; Morando, Verdiana; Tozzi, Valeria D; Verhoeven, Didier; Germà, Jose R; Borras, Josep M

    2017-01-01

    Background The study examines two meso-strategic cancer networks, exploring to what extent collaboration can strengthen or hamper network effectiveness. Unlike macro-strategic networks, meso-strategic networks have no hierarchical governance structures nor are they institutionalised within healthcare services' delivery systems. This study aims to analyse the models of professional cooperation and the tools developed for managing clinical practice within two meso-strategic, European cancer networks. Methods Multiple case study design based on the comparative analysis of two cancer networks: Iridium, in Antwerp, Belgium and the Institut Català d'Oncologia in Catalonia, Spain. The case studies applied mixed methods, with qualitative research based on semi-structured interviews ( n = 35) together with case-site observation and material collection. Results The analysis identified four levels of collaborative intensity within medical specialties as well as in multidisciplinary settings, which became both platforms for crosscutting clinical work between hubs' experts and local care teams and the levers for network-based tools development. The organisation of clinical practice relied on professional-based cooperative processes and tiers, lacking vertical integration mechanisms. Conclusions The intensity of professional linkages largely shaped the potential of meso-strategic cancer networks to influence clinical practice organisation. Conversely, the introduction of managerial techniques or network governance structures, without introducing vertical hierarchies, was found to be critical solutions.

  19. Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis.

    Directory of Open Access Journals (Sweden)

    Alessandro Bertuzzi

    Full Text Available Insulin resistance is the common denominator of several diseases including type 2 diabetes and cancer, and investigating the mechanisms responsible for insulin signaling impairment is of primary importance. A mathematical model of the insulin signaling network (ISN is proposed and used to investigate the dose-response curves of components of this network. Experimental data of C2C12 myoblasts with phosphatase and tensin homologue (PTEN suppressed and data of L6 myotubes with induced insulin resistance have been analyzed by the model. We focused particularly on single and double Akt phosphorylation and pointed out insulin signaling changes related to insulin resistance. Moreover, a new characterization of the upstream signaling of the mammalian target of rapamycin complex 2 (mTORC2 is presented. As it is widely recognized that ISN proteins have a crucial role also in cell proliferation and death, the ISN model was linked to a cell population model and applied to data of a cell line of acute myeloid leukemia treated with a mammalian target of rapamycin inhibitor with antitumor activity. The analysis revealed simple relationships among the concentrations of ISN proteins and the parameters of the cell population model that characterize cell cycle progression and cell death.

  20. Induction of Synthetic Lethality by Natural Compounds Targeting Cancer Signaling.

    Science.gov (United States)

    Farrand, Lee; Byun, Sanguine

    2017-11-16

    Despite the breakthroughs that have been achieved, significant unmet needs relating to the inadequate efficacy and toxicity of currently-available cancer therapies remain. Kinase inhibitors are a class of agents that target signaling factors responsible for the survival of malignant cells, and may address at least some of these issues. The concept of synthetic lethality provides a potential solution to counteract pathway redundancies, and refers to situations in which a mutation in one of two particular genes alone permits cell survival, while simultaneous mutation in both results in cell death. When exploited in the context of cancer therapy, pathways that are uniquely upregulated in cancer cells become selective targets, with reduced off-target toxicity toward their healthy counterparts. Natural compounds represent a large and readily-accessible library of bioactive structures that can be screened for synthetically lethal interactions by testing for the inhibition of kinases relevant to cancer cell survival. In this review, we discuss the concept of synthetic lethality and focus on scenarios in which natural compounds that target kinases may be applied to tip the balance in favor of cancer cell death during therapeutic challenge. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Multiple roles and therapeutic implications of Akt signaling in cancer

    Directory of Open Access Journals (Sweden)

    Emiliano Calvo

    2009-06-01

    Full Text Available Emiliano Calvo1, Victoria Bolós2, Enrique Grande21Centro Integral Oncológico Clara Campal (CiOCC, Madrid. Spain; 2Pfizer Oncology, Alcobendas-Madrid, SpainAbstract: The prominence of the PI3K-Akt signaling pathway in several tumors indicates a relationship with tumor grade and proliferation. Critical cellular processes are driven through this pathway. More detailed knowledge of the pathogenesis of tumors would enable us to design targeted drugs to block both membrane tyrosine kinase receptors and the intracellular kinases involved in the transmission of the signal. The newly approved molecular inhibitors sunitinib (an inhibitor of vascular endothelial growth factor receptor, platelet-derived growth factor receptor, and other tyrosine kinase receptors, sorafenib (a serine–threonine kinase inhibitor that acts against B-Raf and temsirolimus (an mTOR inhibitor shown clinical activity in advanced kidney cancer. Chronic myeloid leukemia has changed its natural history thanks to imatinib and dasatinib, both of which inhibit the intracellular bcr/abl protein derived from the alteration in the Philadelphia chromosome. Intracellular pathways are still important in cancer development and their blockade directly affects outcome. Cross-talk has been observed but is not well understood. Vertical and horizontal pathway blockade are promising anticancer strategies. Indeed, preclinical and early clinical data suggest that combining superficial and intracellular blocking agents can synergize and leverage single-agent activity. The implication of the Akt signaling pathway in cancer is well established and has led to the development of new anticancer agents that block its activation.Keywords: Akt, cancer, therapeutic target, Akt inhibitors

  2. The mTOR Signalling Pathway in Human Cancer

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    Paula Soares

    2012-02-01

    Full Text Available The conserved serine/threonine kinase mTOR (the mammalian target of rapamycin, a downstream effector of the PI3K/AKT pathway, forms two distinct multiprotein complexes: mTORC1 and mTORC2. mTORC1 is sensitive to rapamycin, activates S6K1 and 4EBP1, which are involved in mRNA translation. It is activated by diverse stimuli, such as growth factors, nutrients, energy and stress signals, and essential signalling pathways, such as PI3K, MAPK and AMPK, in order to control cell growth, proliferation and survival. mTORC2 is considered resistant to rapamycin and is generally insensitive to nutrients and energy signals. It activates PKC-α and AKT and regulates the actin cytoskeleton. Deregulation of multiple elements of the mTOR pathway (PI3K amplification/mutation, PTEN loss of function, AKT overexpression, and S6K1, 4EBP1 and eIF4E overexpression has been reported in many types of cancers, particularly in melanoma, where alterations in major components of the mTOR pathway were reported to have significant effects on tumour progression. Therefore, mTOR is an appealing therapeutic target and mTOR inhibitors, including the rapamycin analogues deforolimus, everolimus and temsirolimus, are submitted to clinical trials for treating multiple cancers, alone or in combination with inhibitors of other pathways. Importantly, temsirolimus and everolimus were recently approved by the FDA for the treatment of renal cell carcinoma, PNET and giant cell astrocytoma. Small molecules that inhibit mTOR kinase activity and dual PI3K-mTOR inhibitors are also being developed. In this review, we aim to survey relevant research, the molecular mechanisms of signalling, including upstream activation and downstream effectors, and the role of mTOR in cancer, mainly in melanoma.

  3. Blockade of Fas signaling in breast cancer cells suppresses tumor growth and metastasis via disruption of Fas signaling-initiated cancer-related inflammation.

    Science.gov (United States)

    Liu, Qiuyan; Tan, Qinchun; Zheng, Yuanyuan; Chen, Kun; Qian, Cheng; Li, Nan; Wang, Qingqing; Cao, Xuetao

    2014-04-18

    Mechanisms for cancer-related inflammation remain to be fully elucidated. Non-apoptotic functions of Fas signaling have been proposed to play an important role in promoting tumor progression. It has yet to be determined if targeting Fas signaling can control tumor progression through suppression of cancer-related inflammation. In the current study we found that breast cancer cells with constitutive Fas expression were resistant to apoptosis induction by agonistic anti-Fas antibody (Jo2) ligation or Fas ligand cross-linking. Higher expression of Fas in human breast cancer tissue has been significantly correlated with poorer prognosis in breast cancer patients. To determine whether blockade of Fas signaling in breast cancer could suppress tumor progression, we prepared an orthotopic xenograft mouse model with mammary cancer cells 4T1 and found that blockade of Fas signaling in 4T1 cancer cells markedly reduced tumor growth, inhibited tumor metastasis in vivo, and prolonged survival of tumor-bearing mice. Mechanistically, blockade of Fas signaling in cancer cells significantly decreased systemic or local recruitment of myeloid derived suppressor cells (MDSCs) in vivo. Furthermore, blockade of Fas signaling markedly reduced IL-6, prostaglandin E2 production from breast cancer cells by impairing p-p38, and activity of the NFκB pathway. In addition, administration of a COX-2 inhibitor and anti-IL-6 antibody significantly reduced MDSC accumulation in vivo. Therefore, blockade of Fas signaling can suppress breast cancer progression by inhibiting proinflammatory cytokine production and MDSC accumulation, indicating that Fas signaling-initiated cancer-related inflammation in breast cancer cells may be a potential target for treatment of breast cancer.

  4. Network Medicine Strikes a Blow against Breast Cancer

    DEFF Research Database (Denmark)

    Erler, Janine Terra; Linding, Rune

    2012-01-01

    Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative bre...

  5. Extending pathways and processes using molecular interaction networks to analyse cancer genome data

    Directory of Open Access Journals (Sweden)

    Krasnogor Natalio

    2010-12-01

    Full Text Available Abstract Background Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways. Results We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes. Conclusions The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

  6. International network of cancer genome projects.

    OpenAIRE

    Aretz, Axel; Bernabé, Rosa R.; Calvo, Fabien; Eerola, Iiro; Hemsley, Fiona M.; Jennings, Jennifer L; Kerr, David; Klatt, Peter; Kolar, Patrik; Lane, David P; Laplace, Frank; Nettekoven, Gerd; Remacle, Jacques; Watanabe, Koichi; Matthew M. F. Yuen

    2010-01-01

    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically relevant subtypes for prognosis and therapeut...

  7. Stochastic effects as a force to increase the complexity of signaling networks

    KAUST Repository

    Kuwahara, Hiroyuki

    2013-07-29

    Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects - called deviant effects - in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

  8. Clinical Implications of Hedgehog Pathway Signaling in Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Daniel L. Suzman

    2015-09-01

    Full Text Available Activity in the Hedgehog pathway, which regulates GLI-mediated transcription, is important in organogenesis and stem cell regulation in self-renewing organs, but is pathologically elevated in many human malignancies. Mutations leading to constitutive activation of the pathway have been implicated in medulloblastoma and basal cell carcinoma, and inhibition of the pathway has demonstrated clinical responses leading to the approval of the Smoothened inhibitor, vismodegib, for the treatment of advanced basal cell carcinoma. Aberrant Hedgehog pathway signaling has also been noted in prostate cancer with evidence suggesting that it may render prostate epithelial cells tumorigenic, drive the epithelial-to-mesenchymal transition, and contribute towards the development of castration-resistance through autocrine and paracrine signaling within the tumor microenvironment and cross-talk with the androgen pathway. In addition, there are emerging clinical data suggesting that inhibition of the Hedgehog pathway may be effective in the treatment of recurrent and metastatic prostate cancer. Here we will review these data and highlight areas of active clinical research as they relate to Hedgehog pathway inhibition in prostate cancer.

  9. Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation.

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Ma

    Full Text Available A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.

  10. Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data.

    Directory of Open Access Journals (Sweden)

    Jason W Locasale

    2009-08-01

    Full Text Available Advances in mass spectrometry among other technologies have allowed for quantitative, reproducible, proteome-wide measurements of levels of phosphorylation as signals propagate through complex networks in response to external stimuli under different conditions. However, computational approaches to infer elements of the signaling network strictly from the quantitative aspects of proteomics data are not well established. We considered a method using the principle of maximum entropy to infer a network of interacting phosphotyrosine sites from pairwise correlations in a mass spectrometry data set and derive a phosphorylation-dependent interaction network solely from quantitative proteomics data. We first investigated the applicability of this approach by using a simulation of a model biochemical signaling network whose dynamics are governed by a large set of coupled differential equations. We found that in a simulated signaling system, the method detects interactions with significant accuracy. We then analyzed a growth factor mediated signaling network in a human mammary epithelial cell line that we inferred from mass spectrometry data and observe a biologically interpretable, small-world structure of signaling nodes, as well as a catalog of predictions regarding the interactions among previously uncharacterized phosphotyrosine sites. For example, the calculation places a recently identified tumor suppressor pathway through ARHGEF7 and Scribble, in the context of growth factor signaling. Our findings suggest that maximum entropy derived network models are an important tool for interpreting quantitative proteomics data.

  11. Targeting multiple pro-apoptotic signaling pathways with curcumin in prostate cancer cells.

    Directory of Open Access Journals (Sweden)

    Mariela Rivera

    Full Text Available Curcumin, an extract from the turmeric rhizome (Curcuma longa, is known to exhibit anti-inflammatory, antioxidant, chemopreventive and antitumoral activities against aggressive and recurrent cancers. Accumulative data indicate that curcumin may induce cancer cell death. However, the detailed mechanism underlying its pro-apoptotic and anti-cancer effects remains to be elucidated. In the present study, we examined the signaling pathways triggered by curcumin, specifically, the exact molecular mechanisms of curcumin-induced apoptosis in highly metastatic human prostate cancer cells. The effect of curcumin was evaluated using for the first time in prostate cancer, a gel-free shotgun quantitative proteomic analysis coupled with Tandem Mass Tag isobaric labeling-based-signaling networks. Results were confirmed at the gene expression level by qRT-PCR and at the protein expression level by western blot and flow cytometry. Our findings revealed that curcumin induced an Endoplasmic Reticulum stress-mediated apoptosis in PC3. The mechanisms by which curcumin promoted cell death in these cells were associated with cell cycle arrest, increased reactive oxygen species, autophagy and the Unfolded Protein Response. Furthermore, the upregulation of ER stress was measured using key indicators of ER stress: Glucose-Regulated Protein 78, Inositol-Requiring Enzyme 1 alpha, Protein Disulfide isomerase and Calreticulin. Chronic ER stress induction was concomitant with the upregulation of pro-apoptotic markers (caspases 3,9,12 and Poly (ADP-ribose polymerase. The downregulated proteins include anti-apoptotic and anti-tumor markers, supporting their curcumin-induced pro-apoptotic role in prostate cancer cells. Taken together, these data suggest that curcumin may serve as a promising anticancer agent by inducing a chronic ER stress mediated cell death and activation of cell cycle arrest, UPR, autophagy and oxidative stress responses.

  12. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  13. Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

    Science.gov (United States)

    Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

    2017-03-01

    Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

  14. NDEx - the Network Data Exchange, A Network Commons for Biologists | Informatics Technology for Cancer Research (ITCR)

    Science.gov (United States)

    Network models of biology, whether curated or derived from large-scale data analysis, are critical tools in the understanding of cancer mechanisms and in the design and personalization of therapies. The NDEx Project (Network Data Exchange) will create, deploy, and maintain an open-source, web-based software platform and public website to enable scientists, organizations, and software applications to share, store, manipulate, and publish biological networks.

  15. Digital Signal Processing and Control for the Study of Gene Networks.

    Science.gov (United States)

    Shin, Yong-Jun

    2016-04-22

    Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

  16. TGIF Governs a Feed-Forward Network that Empowers Wnt Signaling to Drive Mammary Tumorigenesis

    OpenAIRE

    Zhang, Ming-Zhu; Ferrigno, Olivier; Wang, Zhe; Ohnishi, Mutsuko; Prunier, Céline; Levy, Laurence; Razzaque, Mohammed; Horne, Williams C.; Romero, Damian; Tzivion, Guri; Colland, Frédéric; Baron, Roland; Atfi, Azeddine

    2015-01-01

    Many types of human cancers having hyperactivated Wnt signaling display no causative alterations in known effectors of this pathway. Here, we report a function of TGIF in Wnt signaling. TGIF associates with and diverts Axin1 and Axin2 from the β-Catenin destruction complex therefore allowing β-Catenin accrual. Intriguingly, activation of Wnt signaling induces the expression of TGIF, which unveils a feed-forward loop that ensures effective integration of Wnt signaling. In triple negative breas...

  17. Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network

    OpenAIRE

    Kale, S. N.; Dudul, S. V.

    2009-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical application and modern human computer interaction. EMG signals acquire noise while traveling through tissue, inherent noise in electronics equipment, ambient noise, and so forth. ANN approach is studied for reduction of noise in EMG signal. In this paper, it is shown that Focused Time-Lagged Recurrent Neural Network (FTLRNN) can elegantly solve to reduce the noise from EMG signal. After rigorous computer simulations, authors d...

  18. Quantitative Models of Imperfect Deception in Network Security using Signaling Games with Evidence

    OpenAIRE

    Pawlick, Jeffrey; Zhu, Quanyan

    2017-01-01

    Deception plays a critical role in many interactions in communication and network security. Game-theoretic models called "cheap talk signaling games" capture the dynamic and information asymmetric nature of deceptive interactions. But signaling games inherently model undetectable deception. In this paper, we investigate a model of signaling games in which the receiver can detect deception with some probability. This model nests traditional signaling games and complete information Stackelberg ...

  19. TGF-β signaling in liver and gastrointestinal cancers.

    Science.gov (United States)

    Katz, L H; Likhter, M; Jogunoori, W; Belkin, M; Ohshiro, K; Mishra, L

    2016-09-01

    Transforming Growth Factor-β (TGF-β) plays crucial and complex roles in liver and gastrointestinal cancers. These include a multitude of distinct functions, such as maintaining stem cell homeostasis, promoting fibrosis, immune modulating, as a tumor suppressor and paradoxically, as a tumor progressor. However, key mechanisms for the switches responsible for these distinct actions are poorly understood, and remain a challenge. The Cancer Genome Atlas (TCGA) analyses and genetically engineered mouse models now provide an integrated approach to dissect these multifaceted and context-dependent driving roles of the TGF-β pathway. In this review, we will discuss the molecular mechanisms of TGF-β signaling, focusing on colorectal, gastric, pancreatic, and liver cancers. Novel drugs targeting the TGF-β pathway have been developed over the last decade, and some have been proven effective in clinical trials. A better understanding of the TGF-β pathway may improve our ability to target it, thus providing more tools to the armamentarium against these deadly cancers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. SIRT1 regulates endothelial Notch signaling in lung cancer.

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    Mian Xie

    Full Text Available BACKGROUND: Sirtuin 1 (SIRT1 acts as a key regulator of vascular endothelial homeostasis, angiogenesis, and endothelial dysfunction. However, the underlying mechanism for SIRT1-mediated lung carcinoma angiogenesis remains unknown. Herein, we report that the nicotinamide adenine dinucleotide 1 (NAD1-dependent deacetylase SIRT1 can function as an intrinsic negative modulator of Delta-like ligand 4 (DLL4/Notch signaling in Lewis lung carcinoma (LLC xenograft-derived vascular endothelial cells (lung cancer-derived ECs. PRINCIPAL FINDINGS: SIRT1 negatively regulates Notch1 intracellular domain (N1IC and Notch1 target genes HEY1 and HEY2 in response to Delta-like ligand 4 (DLL4 stimulation. Furthermore, SIRT1 deacetylated and repressed N1IC expression. Quantitative chromatin immunoprecipitation (qChIP analysis and gene reporter assay demonstrated that SIRT1 bound to one highly conserved region, which was located at approximately -500 bp upstream of the transcriptional start site of Notch1,and repressed Notch1 transcription. Inhibition of endothelial cell growth and sprouting angiogenesis by DLL4/Notch signaling was enhanced in SIRT1-silenced lung cancer-derived EC and rescued by Notch inhibitor DAPT. In vivo, an increase in proangiogenic activity was observed in Matrigel plugs from endothelial-specific SIRT1 knock-in mice. SIRT1 also enhanced tumor neovascularization and tumor growth of LLC xenografts. CONCLUSIONS: Our results show that SIRT1 facilitates endothelial cell branching and proliferation to increase vessel density and promote lung tumor growth through down-regulation of DLL4/Notch signaling and deacetylation of N1IC. Thus, targeting SIRT1 activity or/and gene expression may represent a novel mechanism in the treatment of lung cancer.

  1. Integrative network biology: graph prototyping for co-expression cancer networks.

    Directory of Open Access Journals (Sweden)

    Karl G Kugler

    Full Text Available Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure.

  2. Integrative modelling of the influence of MAPK network on cancer cell fate decision.

    Directory of Open Access Journals (Sweden)

    Luca Grieco

    2013-10-01

    Full Text Available The Mitogen-Activated Protein Kinase (MAPK network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3 activating mutations.

  3. Enzalutamide: targeting the androgen signalling pathway in metastatic castration-resistant prostate cancer

    NARCIS (Netherlands)

    Schalken, J.A.; Fitzpatrick, J.M.

    2016-01-01

    Significant progress has been made in the understanding of the underlying cancer biology of castration-resistant prostate cancer (CRPC) with the androgen receptor (AR) signalling pathway remaining implicated throughout the prostate cancer disease continuum. Reactivation of the AR signalling pathway

  4. Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.

    Science.gov (United States)

    Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B

    2014-11-01

    Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks. Copyright © 2014. Published by Elsevier Ltd.

  5. DMPD: The interferon signaling network and transcription factor C/EBP-beta. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 18163952 The interferon signaling network and transcription factor C/EBP-beta. Li H... The interferon signaling network and transcription factor C/EBP-beta. PubmedID 18163952 Title The interfero...n signaling network and transcription factor C/EBP-beta. Authors Li H, Gade P, Xi

  6. Oncogenic role and therapeutic target of leptin signaling in breast cancer and cancer stem cells

    Science.gov (United States)

    Guo, Shanchun; Liu, Mingli; Wang, Guangdi; Torroella-Kouri, Marta; Gonzalez-Perez, Ruben R.

    2012-01-01

    Significant correlations between obesity and incidence of various cancers have been reported. Obesity, considered a mild inflammatory process, is characterized by a high level of secretion of several cytokines from adipose tissue. These molecules have disparate effects, which could be relevant to cancer development. Among the inflammatory molecules, leptin, mainly produced by adipose tissue and overexpressed with its receptor (Ob-R) in cancer cells is the most studied adipokine. Mutations of leptin or Ob-R genes associated with obesity or cancer are rarely found. However, leptin is an anti-apoptotic molecule in many cell types, and its central roles in obesity-related cancers are based on its pro-angiogenic, pro-inflammatory and mitogenic actions. Notably, these leptin actions are commonly reinforced through entangled crosstalk with multiple oncogenes, cytokines and growth factors. Leptin-induced signals comprise several pathways commonly triggered by many cytokines (i.e, canonical: JAK2/STAT; MAPK/ERK1/2 and PI-3K/AKT1 and, non-canonical signaling pathways: PKC, JNK and p38 MAP kinase). Each of these leptin-induced signals is essential to its biological effects on food intake, energy balance, adiposity, immune and endocrine systems, as well as oncogenesis. This review is mainly focused on the current knowledge of the oncogenic role of leptin in breast cancer. Additionally, leptin pro-angiogenic molecular mechanisms and its potential role in breast cancer stem cells will be reviewed. Strict biunivocal binding-affinity and activation of leptin/Ob-R complex makes it a unique molecular target for prevention and treatment of breast cancer, particularly in obesity contexts. PMID:22289780

  7. Digital Signal Processing for a Sliceable Transceiver for Optical Access Networks

    DEFF Research Database (Denmark)

    Saldaña Cercos, Silvia; Wagner, Christoph; Vegas Olmos, Juan José

    2015-01-01

    also for implementing full signal path symmetry in real-time oscilloscopes to provide performance and signal fidelity (i.e. lower noise and jitter). In this paper the key digital signal processing (DSP) subsystems required to achieve signal slicing are surveyed. It also presents, for the first time......Methods to upgrade the network infrastructure to cope with current traffic demands has attracted increasing research efforts. A promising alternative is signal slicing. Signal slicing aims at re-using low bandwidth equipment to satisfy high bandwidth traffic demands. This technique has been used...... penalty is reported for 10 Gbps. Power savings of the order of hundreds of Watts can be obtained when using signal slicing as an alternative to 10 Gbps implemented access networks....

  8. Pathway and network analysis of cancer genomes

    DEFF Research Database (Denmark)

    Creixell, Pau; Reimand, Jueri; Haider, Syed

    2015-01-01

    Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been ...

  9. Predicting selective drug targets in cancer through metabolic networks

    Science.gov (United States)

    Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

    2011-01-01

    The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

  10. Model-based design of self-Adapting networked signal processing systems

    NARCIS (Netherlands)

    Oliveira Filho, J.A. de; Papp, Z.; Djapic, R.; Oostveen, J.C.

    2013-01-01

    The paper describes a model based approach for architecture design of runtime reconfigurable, large-scale, networked signal processing applications. A graph based modeling formalism is introduced to describe all relevant aspects of the design (functional, concurrency, hardware, communication,

  11. Jasmonate Signalling Network in Arabidopsis thaliana: Crucial Regulatory Nodes and New Physiological Scenarios

    National Research Council Canada - National Science Library

    Virginia Balbi; Alessandra Devoto

    2008-01-01

    .... In this review, we focus on the latest published work on jasmonate (JA) signalling components and new regulatory nodes in the transcriptional network that regulates a number of diverse plant responses to developmental and environmental cues...

  12. Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

    Science.gov (United States)

    Zhang, Zipeng; Fan, Tao; Wang, Shuqing

    It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

  13. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  14. Simulation of mixed switched-capacitor/digital networks with signal-driven switches

    Science.gov (United States)

    Suyama, Ken; Tsividis, Yannis P.; Fang, San-Chin

    1990-12-01

    The simulation of mixed switched-capacitor/digital (SC/D) networks containing capacitors, independent and linear-dependent voltage sources, switches controlled either by periodic or nonperiodic Boolean signals, latched comparators, and logic gates is considered. A unified linear switched-capacitor network (SCN) and mixed SC/D network simulator, SWITCAP2, and its applications to several widely used and novel nonlinear SCNs are discussed. The switches may be controlled by periodic waveforms and by nonperiodic waveforms from the outputs of comparators and logic gates. The signal-dependent modification of network topology through the comparators, logic gates, and signal-driven switches makes the modeling of various nonlinear switched-capacitor circuits possible. Simulation results for a pulse-code modulation (PCM) voice encoder, a sigma-delta modulator, a neural network, and a phase-locked loop (PLL) are presented to demonstrate the flexibility of the approach.

  15. Complex networks of self-incompatibility signaling in the Brassicaceae.

    Science.gov (United States)

    Tantikanjana, Titima; Nasrallah, Mikhail E; Nasrallah, June B

    2010-10-01

    The self-pollination barrier of self-incompatibility in the Brassicaceae is based on the activity of a polymorphic stigma receptor and its pollen ligand, whose allele-specific interaction triggers a signaling cascade within the stigma epidermal cell that culminates in the inhibition of pollen tube development. Recent analyses have identified signaling intermediates and revealed unexpected cross-talk between self-incompatibility signaling and pistil development. The self-incompatibility response is now thought to be based on a phosphorylation and ubiquitin-mediated degradation pathway that inhibits the secretion of factors required for successful pollination. Because manipulation of the identified signaling intermediates results in only partial disruption of the self-incompatibility reaction, this pathway likely functions in conjunction with other as-yet unidentified signaling pathways to effect complete inhibition of self-pollen. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Social networks and survival after breast cancer diagnosis.

    Science.gov (United States)

    Beasley, Jeannette M; Newcomb, Polly A; Trentham-Dietz, Amy; Hampton, John M; Ceballos, Rachel M; Titus-Ernstoff, Linda; Egan, Kathleen M; Holmes, Michelle D

    2010-12-01

    Evidence has been inconsistent regarding the impact of social networks on survival after breast cancer diagnosis. We prospectively examined the relation between components of social integration and survival in a large cohort of breast cancer survivors. Women (N=4,589) diagnosed with invasive breast cancer were recruited from a population-based, multi-center, case-control study. A median of 5.6 years (Interquartile Range 2.7-8.7) after breast cancer diagnosis, women completed a questionnaire on recent post-diagnosis social networks and other lifestyle factors. Social networks were measured using components of the Berkman-Syme Social Networks Index to create a measure of social connectedness. Based on a search of the National Death Index, 552 deaths (146 related to breast cancer) were identified. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. Higher scores on a composite measure of social connectedness as determined by the frequency of contacts with family and friends, attendance of religious services, and participation in community activities was associated with a 15-28% reduced risk of death from any cause (p-trend=0.02). Inverse trends were observed between all-cause mortality and frequency of attendance at religious services (p-trend=0.0001) and hours per week engaged in community activities (p-trend=0.0005). No material associations were identified between social networks and breast cancer-specific mortality. Engagement in activities outside the home was associated with lower overall mortality after breast cancer diagnosis.

  17. Experimental Demonstration of Mixed Formats and Bit Rates Signal Allocation for Spectrum-flexible Optical Networking

    DEFF Research Database (Denmark)

    Borkowski, Robert; Karinou, Fotini; Angelou, Marianna

    2012-01-01

    We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks.......We report on an extensive experimental study for adaptive allocation of 16-QAM and QPSK signals inside spectrum flexible heterogeneous superchannel. Physical-layer performance parameters are extracted for use in resource allocation mechanisms of future flexible networks....

  18. CancerLinker: Explorations of Cancer Study Network

    OpenAIRE

    Nguyen, Vinh; Kabir, Md Yasin; Dang, Tommy

    2017-01-01

    Interactive visualization tools are highly desirable to biologist and cancer researchers to explore the complex structures, detect patterns and find out the relationships among bio-molecules responsible for a cancer type. A pathway contains various bio-molecules in different layers of the cell which is responsible for specific cancer type. Researchers are highly interested in understanding the relationships among the proteins of different pathways and furthermore want to know how those protei...

  19. Large-Scale Analysis of Network Bistability for Human Cancers

    OpenAIRE

    Tetsuya Shiraishi; Shinako Matsuyama; Hiroaki Kitano

    2010-01-01

    Author Summary Since most disease states exhibit a certain level of resilience against therapeutic interventions, each disease state can be considered to be homeostatic to some extent. There must be one or more mechanisms that cause the gene-regulatory network to maintain a certain state, and one such mechanism is a bistable switch. In this work, bistable switch networks were constructed and their ON(upregulated)/OFF(downregulated) states were compared between human cancers and healthy contro...

  20. Profiling metabolic networks to study cancer metabolism.

    Science.gov (United States)

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention. Copyright © 2012 Elsevier Ltd. All rights reserved.

  1. Epsin is required for Dishevelled stability and Wnt signaling activation in colon cancer development

    Science.gov (United States)

    Chang, Baojun; Tessneer, Kandice L.; McManus, John; Liu, Xiaolei; Hahn, Scott; Pasula, Satish; Wu, Hao; Song, Hoogeun; Chen, Yiyuan; Cai, Xiaofeng; Dong, Yunzhou; Brophy, Megan L.; Rahman, Ruby; Ma, Jian-Xing; Xia, Lijun; Chen, Hong

    2015-01-01

    Uncontrolled canonical Wnt signaling supports colon epithelial tumor expansion and malignant transformation. Understanding the regulatory mechanisms involved is crucial for elucidating the pathogenesis of and will provide new therapeutic targets for colon cancer. Epsins are ubiquitin-binding adaptor proteins upregulated in several human cancers; however, epsins’ involvement in colon cancer is unknown. Here we show that loss of intestinal epithelial epsins protects against colon cancer by significantly reducing the stability of the crucial Wnt signaling effector, dishevelled (Dvl2), and impairing Wnt signaling. Consistently, epsins and Dvl2 are correspondingly upregulated in colon cancer. Mechanistically, epsin binds Dvl2 via its epsin N-terminal homology domain and ubiquitin-interacting motifs and prohibits Dvl2 polyubiquitination and degradation. Our findings reveal an unconventional role for epsins in stabilizing Dvl2 and potentiating Wnt signaling in colon cancer cells to ensure robust colon cancer progression. Epsins’ pro-carcinogenic role suggests they are potential therapeutic targets to combat colon cancer. PMID:25871009

  2. Androgen-Independent Prostate Cancer: Potential Role of Androgen and ErbB Receptor Signal Transduction Crosstalk

    Directory of Open Access Journals (Sweden)

    Soha Salama El Sheikh

    2003-03-01

    Full Text Available In prostate cancer (PC, increasing evidence suggests that androgen receptor (AR signalling is functional under conditions of maximal androgen blockade. PC cells survive and proliferate in the altered hormonal environment possibly by interactions between growth factor-activated pathways and AR signalling. The present review article summarizes the current evidence of this crosstalk and focuses on the interactions among the ErbB receptor network, its downstream pathways, the AR. The potential role of this crosstalk in the development of androgen independence and in relation to antiandrogen therapy is discussed. Such interactions provide insight into possible complementary or additional strategies in the management of PC.

  3. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  4. Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks.

    Science.gov (United States)

    Park, Heewon; Shimamura, Teppei; Imoto, Seiya; Miyano, Satoru

    2017-10-20

    There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L1-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L1-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L1-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample

  5. Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

    1999-01-01

    This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...

  6. Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks

    DEFF Research Database (Denmark)

    Postnov, D.E.; Koreshkov, R.N.; Brazhe, N.A.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission...... in astrocytic-neuronal networks. We reproduce local and global dynamical patterns observed experimentally....

  7. The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

    Directory of Open Access Journals (Sweden)

    Frank eEmmert-Streib

    2014-02-01

    Full Text Available In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome $21$ is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

  8. Array signal processing in the NASA Deep Space Network

    Science.gov (United States)

    Pham, Timothy T.; Jongeling, Andre P.

    2004-01-01

    In this paper, we will describe the benefits of arraying and past as well as expected future use of this application. The signal processing aspects of array system are described. Field measurements via actual tracking spacecraft are also presented.

  9. Stability of multispecies bacterial communities: signaling networks may stabilize microbiomes.

    Directory of Open Access Journals (Sweden)

    Ádám Kerényi

    Full Text Available Multispecies bacterial communities can be remarkably stable and resilient even though they consist of cells and species that compete for environmental resources. In silico models suggest that common signals released into the environment may help selected bacterial species cluster at common locations and that sharing of public goods (i.e. molecules produced and released for mutual benefit can stabilize this coexistence. In contrast, unilateral eavesdropping on signals produced by a potentially invading species may protect a community by keeping invaders away from limited resources. Shared bacterial signals, such as those found in quorum sensing systems, may thus play a key role in fine tuning competition and cooperation within multi-bacterial communities. We suggest that in addition to metabolic complementarity, signaling dynamics may be important in further understanding complex bacterial communities such as the human, animal as well as plant microbiomes.

  10. Communication networks of men facing a diagnosis of prostate cancer.

    Science.gov (United States)

    Brown, Dot; Oetzel, John; Henderson, Alison

    2016-11-01

    This study seeks to identify the factors that shape the communication networks of men who face a potential diagnosis of prostate cancer, and how these factors relate to their disclosure about their changing health status. Men facing a potential diagnosis of prostate cancer are in a challenging situation; the support benefits of disclosing their changing health status to others in their communication networks is set against a backdrop of the potential stigma and uncertainty of the diagnosis. All men on a prostate biopsy waiting list were eligible for inclusion in an exploratory and interpretive study. Semi-structured interviews with 40 men explored their network structures and disclosure of health information. Thematic analysis highlighted the factors which contributed to their network structures and their disclosure about their health status. Four network factors shaped men's perspectives about disclosing their health status: (1) tie strength, comprising both strong and weak ties; (2) knowledgeable others, with a focus on medical professionals in the family; (3) homophily, which included other individuals with a similar medical condition; and (4) geographical proximity, with a preference for face-to-face communication. Communication networks influence men's disclosure of their health status and in particular weak ties with medical knowledge have an important role. Men who use the potential for support in their networks may experience improved psychosocial outcomes. Using these four network factors-tie strength, knowledgeable others, homophily or geographical proximity-to forecast men's willingness to disclose helps identify men who lack potential support and so are at risk of poor psychosocial health. Those with few strong ties or knowledgeable others in their networks may be in the at-risk cohort. The support provided in communication networks complements formal medical care from nurses and other health professionals, and encouraging patients to use their

  11. Learning Signaling Network Structures with Sparsely Distributed Data

    OpenAIRE

    Sachs, Karen; Itani, Solomon; Carlisle, Jennifer; Nolan, Garry P.; Pe'er, Dana; Lauffenburger, Douglas A.

    2009-01-01

    Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, f...

  12. Targeting Stromal-Cancer Cell Crosstalk Networks in Ovarian Cancer Treatment

    Directory of Open Access Journals (Sweden)

    Tsz-Lun Yeung

    2016-01-01

    Full Text Available Ovarian cancer is a histologically, clinically, and molecularly diverse disease with a five-year survival rate of less than 30%. It has been estimated that approximately 21,980 new cases of epithelial ovarian cancer will be diagnosed and 14,270 deaths will occur in the United States in 2015, making it the most lethal gynecologic malignancy. Ovarian tumor tissue is composed of cancer cells and a collection of different stromal cells. There is increasing evidence that demonstrates that stromal involvement is important in ovarian cancer pathogenesis. Therefore, stroma-specific signaling pathways, stroma-derived factors, and genetic changes in the tumor stroma present unique opportunities for improving the diagnosis and treatment of ovarian cancer. Cancer-associated fibroblasts (CAFs are one of the major components of the tumor stroma that have demonstrated supportive roles in tumor progression. In this review, we highlight various types of signaling crosstalk between ovarian cancer cells and stromal cells, particularly with CAFs. In addition to evaluating the importance of signaling crosstalk in ovarian cancer progression, we discuss approaches that can be used to target tumor-promoting signaling crosstalk and how these approaches can be translated into potential ovarian cancer treatment.

  13. Targeting Stromal-Cancer Cell Crosstalk Networks in Ovarian Cancer Treatment.

    Science.gov (United States)

    Yeung, Tsz-Lun; Leung, Cecilia S; Li, Fuhai; Wong, Stephen S T; Mok, Samuel C

    2016-01-06

    Ovarian cancer is a histologically, clinically, and molecularly diverse disease with a five-year survival rate of less than 30%. It has been estimated that approximately 21,980 new cases of epithelial ovarian cancer will be diagnosed and 14,270 deaths will occur in the United States in 2015, making it the most lethal gynecologic malignancy. Ovarian tumor tissue is composed of cancer cells and a collection of different stromal cells. There is increasing evidence that demonstrates that stromal involvement is important in ovarian cancer pathogenesis. Therefore, stroma-specific signaling pathways, stroma-derived factors, and genetic changes in the tumor stroma present unique opportunities for improving the diagnosis and treatment of ovarian cancer. Cancer-associated fibroblasts (CAFs) are one of the major components of the tumor stroma that have demonstrated supportive roles in tumor progression. In this review, we highlight various types of signaling crosstalk between ovarian cancer cells and stromal cells, particularly with CAFs. In addition to evaluating the importance of signaling crosstalk in ovarian cancer progression, we discuss approaches that can be used to target tumor-promoting signaling crosstalk and how these approaches can be translated into potential ovarian cancer treatment.

  14. Intertwining of Activin A and TGFβ Signaling: Dual Roles in Cancer Progression and Cancer Cell Invasion

    Energy Technology Data Exchange (ETDEWEB)

    Loomans, Holli A. [Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN 37232 (United States); Andl, Claudia D., E-mail: claudia.andl@vanderbilt.edu [Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN 37232 (United States); Department of Surgery, Vanderbilt University Medical Center, Nashville, TN 37232 (United States); Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232 (United States); Vanderbilt Digestive Disease Center, Vanderbilt University Medical Center, Nashville, TN 37232 (United States); Vanderbilt Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN 37232 (United States)

    2014-12-30

    In recent years, a significant amount of research has examined the controversial role of activin A in cancer. Activin A, a member of the transforming growth factor β (TGFβ) superfamily, is best characterized for its function during embryogenesis in mesoderm cell fate differentiation and reproduction. During embryogenesis, TGFβ superfamily ligands, TGFβ, bone morphogenic proteins (BMPs) and activins, act as potent morphogens. Similar to TGFβs and BMPs, activin A is a protein that is highly systemically expressed during early embryogenesis; however, post-natal expression is overall reduced and remains under strict spatiotemporal regulation. Of importance, normal post-natal expression of activin A has been implicated in the migration and invasive properties of various immune cell types, as well as endometrial cells. Aberrant activin A signaling during development results in significant morphological defects and premature mortality. Interestingly, activin A has been found to have both oncogenic and tumor suppressor roles in cancer. Investigations into the role of activin A in prostate and breast cancer has demonstrated tumor suppressive effects, while in lung and head and neck squamous cell carcinoma, it has been consistently shown that activin A expression is correlated with increased proliferation, invasion and poor patient prognosis. Activin A signaling is highly context-dependent, which is demonstrated in studies of epithelial cell tumors and the microenvironment. This review discusses normal activin A signaling in comparison to TGFβ and highlights how its dysregulation contributes to cancer progression and cell invasion.

  15. TGIF Governs a Feed-Forward Network That Empowers Wnt Signaling to Drive Mammary Tumorigenesis

    Science.gov (United States)

    Zhang, Ming-Zhu; Ferrigno, Olivier; Wang, Zhe; Ohnishi, Mutsuko; Prunier, Céline; Levy, Laurence; Razzaque, Mohammed; Horne, Williams C.; Romero, Damian; Tzivion, Guri; Colland, Frédéric; Baron, Roland; Atfi, Azeddine

    2015-01-01

    SUMMARY Many types of human cancers having hyperactivated Wnt signaling display no causative alterations in known effectors of this pathway. Here, we report a function of TGIF in Wnt signaling. TGIF associates with and diverts Axin1 and Axin2 from the β-Catenin destruction complex therefore allowing β-Catenin accrual. Intriguingly, activation of Wnt signaling induces the expression of TGIF, which unveils a feed-forward loop that ensures effective integration of Wnt signaling. In triple negative breast cancers (TNBC), elevated levels of TGIF correlate with high Wnt signaling and poor survival of patients. Moreover, genetic experiments revealed that Tgif1 ablation impeded mammary tumor development in MMTV-Wnt1 mice, further underscoring a requirement of TGIF for oncogenic Wnt signaling. PMID:25873176

  16. A TDoA Localization Scheme for Underwater Sensor Networks with Use of Multilinear Chirp Signals

    Directory of Open Access Journals (Sweden)

    En Cheng

    2016-01-01

    Full Text Available Due to the multipath, Doppler, and other effects, the node location signals have high probability of access collision in the underwater acoustic sensor networks (UW-ASNs, and therefore, it causes the signal lost and the access block; therefore, it constrains the networks performance. In this paper, we take the multilinear chirp (MLC signals as the location signal to improve the anticollision ability. In order to increase the detection efficiency of MLC, we propose a fast efficient detection method called mixing change rate-fractional Fourier transform (MCR-FrFT. This method transforms the combined rates of MLC into symmetry triangle rates and then separates the multiuser signals based on the transformed rates by using FrFT. Theoretical derivation and simulation results show that the proposed method can detect the locations signals, estimate the time difference of arrival (TDoA, reduce the multiple access interference, and improve the location performance.

  17. Network evolution: rewiring and signatures of conservation in signaling.

    Directory of Open Access Journals (Sweden)

    Mark G F Sun

    Full Text Available The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3 domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.

  18. Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-01-01

    Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

  19. Increased STAT1 signaling in endocrine-resistant breast cancer.

    Directory of Open Access Journals (Sweden)

    Rui Huang

    that STAT signaling is important in endocrine resistance, and that STAT inhibitors may represent potential therapies in breast cancer, even in the resistant setting.

  20. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

    Science.gov (United States)

    Samaga, Regina; Klamt, Steffen

    2013-06-26

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input-output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical

  1. Genome network medicine: innovation to overcome huge challenges in cancer therapy.

    Science.gov (United States)

    Roukos, Dimitrios H

    2014-01-01

    The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.

  2. Surface sensing and signaling networks in plant pathogenic fungi.

    Science.gov (United States)

    Kou, Yanjun; Naqvi, Naweed I

    2016-09-01

    Pathogenic fungi have evolved highly varied and remarkable strategies to invade and infect their plant hosts. Typically, such fungal pathogens utilize highly specialized infection structures, morphologies or cell types produced from conidia or ascospores on the cognate host surfaces to gain entry therein. Such diverse infection strategies require intricate coordination in cell signaling and differentiation in phytopathogenic fungi. Here, we present an overview of our current understanding of cell signaling and infection-associated development that primes host penetration in the top ten plant pathogenic fungi, which utilize specific receptors to sense and respond to different surface cues, such as topographic features, hydrophobicity, hardness, plant lipids, phytohormones, and/or secreted enzymes. Subsequently, diverse signaling components such as G proteins, cyclic AMP/Protein Kinase A and MAP kinases are activated to enable the differentiation of infection structures. Recent studies have also provided fascinating insights into the spatio-temporal dynamics and specialized sequestration and trafficking of signaling moieties required for proper development of infection structures in phytopathogenic fungi. Molecular insight in such infection-related morphogenesis and cell signaling holds promise for identifying novel strategies for intervention of fungal diseases in plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Directory of Open Access Journals (Sweden)

    Samra Khalid

    2016-10-01

    Full Text Available Background Breast cancer (BC is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s. It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α associated Biological Regulatory Network (BRN for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR for wet lab experiments as well as provided valuable insights in the treatment of cancers

  4. CDO, an Hh-coreceptor, mediates lung cancer cell proliferation and tumorigenicity through Hedgehog signaling.

    Science.gov (United States)

    Leem, Young-Eun; Ha, Hye-Lim; Bae, Ju-Hyeon; Baek, Kwan-Hyuck; Kang, Jong-Sun

    2014-01-01

    Hedgehog (Hh) signaling plays essential roles in various developmental processes, and its aberrant regulation results in genetic disorders or malignancies in various tissues. Hyperactivation of Hh signaling is associated with lung cancer development, and there have been extensive efforts to investigate how to control Hh signaling pathway and regulate cancer cell proliferation. In this study we investigated a role of CDO, an Hh co-receptor, in non-small cell lung cancer (NSCLC). Inhibition of Hh signaling by SANT-1 or siCDO in lung cancer cells reduced proliferation and tumorigenicity, along with the decrease in the expression of the Hh components. Histological analysis with NSCLC mouse tissue demonstrated that CDO was expressed in advanced grade of the cancer, and precisely co-localized with GLI1. These data suggest that CDO is required for proliferation and survival of lung cancer cells via Hh signaling.

  5. Dose-to-duration encoding and signaling beyond saturation in intracellular signaling networks.

    Directory of Open Access Journals (Sweden)

    Marcelo Behar

    2008-10-01

    Full Text Available The cellular response elicited by an environmental cue typically varies with the strength of the stimulus. For example, in the yeast Saccharomyces cerevisiae, the concentration of mating pheromone determines whether cells undergo vegetative growth, chemotropic growth, or mating. This implies that the signaling pathways responsible for detecting the stimulus and initiating a response must transmit quantitative information about the intensity of the signal. Our previous experimental results suggest that yeast encode pheromone concentration as the duration of the transmitted signal. Here we use mathematical modeling to analyze possible biochemical mechanisms for performing this "dose-to-duration" conversion. We demonstrate that modulation of signal duration increases the range of stimulus concentrations for which dose-dependent responses are possible; this increased dynamic range produces the counterintuitive result of "signaling beyond saturation" in which dose-dependent responses are still possible after apparent saturation of the receptors. We propose a mechanism for dose-to-duration encoding in the yeast pheromone pathway that is consistent with current experimental observations. Most previous investigations of information processing by signaling pathways have focused on amplitude encoding without considering temporal aspects of signal transduction. Here we demonstrate that dose-to-duration encoding provides cells with an alternative mechanism for processing and transmitting quantitative information about their surrounding environment. The ability of signaling pathways to convert stimulus strength into signal duration results directly from the nonlinear nature of these systems and emphasizes the importance of considering the dynamic properties of signaling pathways when characterizing their behavior. Understanding how signaling pathways encode and transmit quantitative information about the external environment will not only deepen our

  6. Distributed Bandpass Filtering and Signal Demodulation in Cortical Network Models

    Science.gov (United States)

    McDonnell, Mark D.

    Experimental recordings of cortical activity often exhibit narrowband oscillations, at various center frequencies ranging in the order of 1-200 Hz. Many neuronal mechanisms are known to give rise to oscillations, but here we focus on a population effect known as sparsely synchronised oscillations. In this effect, individual neurons in a cortical network fire irregularly at slow average spike rates (1-10 Hz), but the population spike rate oscillates at gamma frequencies (greater than 40 Hz) in response to spike bombardment from the thalamus. These cortical networks form recurrent (feedback) synapses. Here we describe a model of sparsely synchronized population oscillations using the language of feedback control engineering, where we treat spiking as noisy feedback. We show, using a biologically realistic model of synaptic current that includes a delayed response to inputs, that the collective behavior of the neurons in the network is like a distributed bandpass filter acting on the network inputs. Consequently, the population response has the character of narrowband random noise, and therefore has an envelope and instantaneous frequency with lowpass characteristics. Given that there exist biologically plausible neuronal mechanisms for demodulating the envelope and instantaneous frequency, we suggest there is potential for similar effects to be exploited in nanoscale electronics implementations of engineered communications receivers.

  7. Attractor for a Reaction-Diffusion System Modeling Cancer Network

    Directory of Open Access Journals (Sweden)

    Xueyong Chen

    2014-01-01

    Full Text Available A reaction-diffusion cancer network regulated by microRNA is considered in this paper. We study the asymptotic behavior of solution and show the existence of global uniformly bounded solution to the system in a bounded domain Ω⊂Rn. Some estimates and asymptotic compactness of the solutions are proved. As a result, we establish the existence of the global attractor in L2(Ω×L2(Ω and prove that the solution converges to stable steady states. These results can help to understand the dynamical character of cancer network and propose a new insight to study the mechanism of cancer. In the end, the numerical simulation shows that the analytical results agree with numerical simulation.

  8. EGFR signaling in colorectal cancer: a clinical perspective

    Directory of Open Access Journals (Sweden)

    Saletti P

    2015-01-01

    Full Text Available Piercarlo Saletti,1 Francesca Molinari,2 Sara De Dosso,1 Milo Frattini2 1Oncology Institute of Southern Switzerland, Bellinzona, 2Laboratory of Molecular Pathology, Institute of Pathology, Locarno, Switzerland Abstract: Colorectal cancer (CRC remains a formidable health burden worldwide, with up to 50% of patients developing metastases during the course of their disease. This group of CRC patients, characterized by the worst prognosis, has been extensively investigated to improve their life expectancy. Main efforts, focused on the epidermal growth-factor receptor (EGFR, which plays a pivotal role in CRC pathogenesis, have led to the development and introduction in clinical practice of specific targeted therapies (ie, monoclonal antibodies. Subsequently, the scientific community has tried to identify molecular predictors of the efficacy of such therapies. However, it has become clear that EGFR alterations occurring in CRC are difficult to investigate, and therefore their predictive role is unclear. In contrast, the clinical role of two downstream members (KRAS and NRAS has been clearly demonstrated. Currently, EGFR-targeted therapies can be administered only to patients with wild-type KRAS and NRAS genes. Our review addresses the medical management of metastatic CRC. Specifically, we describe in detail the molecular biology of metastatic CRC, focusing on the EGFR signaling pathway, and we discuss the role of current and emerging related biomarkers and therapies in this field. We also summarize the clinical evidence regarding anti-EGFR monoclonal antibodies and examine potential future perspectives. Keywords: colorectal cancer, EGFR, gene mutations, cetuximab, panitumumab

  9. Novel Small Molecule Inhibitors of Cancer Stem Cell Signaling Pathways.

    Science.gov (United States)

    Abetov, Danysh; Mustapova, Zhanar; Saliev, Timur; Bulanin, Denis; Batyrbekov, Kanat; Gilman, Charles P

    2015-12-01

    The main aim of oncologists worldwide is to understand and then intervene in the primary tumor initiation and propagation mechanisms. This is essential to allow targeted elimination of cancer cells without altering normal mitotic cells. Currently, there are two main rival theories describing the process of tumorigenesis. According to the Stochastic Model, potentially any cell, once defunct, is capable of initiating carcinogenesis. Alternatively the Cancer Stem Cell (CSC) Model posits that only a small fraction of undifferentiated tumor cells are capable of triggering carcinogenesis. Like healthy stem cells, CSCs are also characterized by a capacity for self-renewal and the ability to generate differentiated progeny, possibly mediating treatment resistance, thus leading to tumor recurrence and metastasis. Moreover, molecular signaling profiles are similar between CSCs and normal stem cells, including Wnt, Notch and Hedgehog pathways. Therefore, development of novel chemotherapeutic agents and proteins (e.g., enzymes and antibodies) specifically targeting CSCs are attractive pharmaceutical candidates. This article describes small molecule inhibitors of stem cell pathways Wnt, Notch and Hedgehog, and their recent chemotherapy clinical trials.

  10. Nitric oxide signaling in human ovarian cancer: A potential therapeutic target.

    Science.gov (United States)

    El-Sehemy, Ahmed; Postovit, Lynne-Marie; Fu, YangXin

    2016-04-01

    Ovarian cancer is the leading cause of death due to gynecologic malignancies worldwide. Current therapy regimens are ineffective to treat advanced ovarian cancers, presenting a need to develop novel therapeutic strategies. Nitric oxide (NO) is a multifunctional gaseous molecule that is generated by cancer, stromal and endothelial cells and plays a multifaceted role in cancer biology through multiple mechanisms. Accumulating evidence suggests that NO signaling is involved in multiple aspects of ovarian cancer, including growth, apoptosis, cancer-stromal cell interaction, angiogenesis and response to chemotherapy. This review will discuss the experimental and clinical evidence of the involvement of NO signaling in ovarian cancer and the therapeutic potential of targeting NO signaling in ovarian cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Guard Cell Signal Transduction Network: Advances in Understanding Abscisic Acid, CO2, and Ca2+ Signaling

    KAUST Repository

    Kim, Tae-Houn

    2010-05-04

    Stomatal pores are formed by pairs of specialized epidermal guard cells and serve as major gateways for both CO2 influx into plants from the atmosphere and transpirational water loss of plants. Because they regulate stomatal pore apertures via integration of both endogenous hormonal stimuli and environmental signals, guard cells have been highly developed as a model system to dissect the dynamics and mechanisms of plant-cell signaling. The stress hormone ABA and elevated levels of CO2 activate complex signaling pathways in guard cells that are mediated by kinases/phosphatases, secondary messengers, and ion channel regulation. Recent research in guard cells has led to a new hypothesis for how plants achieve specificity in intracellular calcium signaling: CO2 and ABA enhance (prime) the calcium sensitivity of downstream calcium-signaling mechanisms. Recent progress in identification of early stomatal signaling components are reviewed here, including ABA receptors and CO2-binding response proteins, as well as systems approaches that advance our understanding of guard cell-signaling mechanisms.

  12. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may...

  13. The National Cancer Institute's Physical Sciences - Oncology Network

    Science.gov (United States)

    Espey, Michael Graham

    In 2009, the NCI launched the Physical Sciences - Oncology Centers (PS-OC) initiative with 12 Centers (U54) funded through 2014. The current phase of the Program includes U54 funded Centers with the added feature of soliciting new Physical Science - Oncology Projects (PS-OP) U01 grant applications through 2017; see NCI PAR-15-021. The PS-OPs, individually and along with other PS-OPs and the Physical Sciences-Oncology Centers (PS-OCs), comprise the Physical Sciences-Oncology Network (PS-ON). The foundation of the Physical Sciences-Oncology initiative is a high-risk, high-reward program that promotes a `physical sciences perspective' of cancer and fosters the convergence of physical science and cancer research by forming transdisciplinary teams of physical scientists (e.g., physicists, mathematicians, chemists, engineers, computer scientists) and cancer researchers (e.g., cancer biologists, oncologists, pathologists) who work closely together to advance our understanding of cancer. The collaborative PS-ON structure catalyzes transformative science through increased exchange of people, ideas, and approaches. PS-ON resources are leveraged to fund Trans-Network pilot projects to enable synergy and cross-testing of experimental and/or theoretical concepts. This session will include a brief PS-ON overview followed by a strategic discussion with the APS community to exchange perspectives on the progression of trans-disciplinary physical sciences in cancer research.

  14. Network-based reading system for lung cancer screening CT

    Science.gov (United States)

    Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio

    2006-03-01

    This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.

  15. Chemical signal amplification in two-dimensional paper networks

    OpenAIRE

    Fu, Elain; Kauffman, Peter; Lutz, Barry; Yager, Paul

    2010-01-01

    Two-dimensional paper networks (2DPNs) hold great potential for extending the utility of paper-based chemical and biochemical diagnostics at a cost and ease-of-use that is comparable to conventional lateral flow strips. 2DPNs enable the automated sequential delivery of multiple reagents to a detection region with a single user activation step, and therefore have the potential to extend the processing capabilities of inexpensive paper-based assays with comparable ease of use to conventional la...

  16. An inductive signalling network regulates mammalian tooth morphogenesis with implications for tooth regeneration.

    Science.gov (United States)

    Li, Z; Yu, M; Tian, W

    2013-10-01

    Sequential and reciprocal epithelial-mesenchymal interactions, essential throughout such aspects of tooth morphogenesis as patterning, size and number of teeth, involves a well-ordered series of inductive and permissive signals that exert global control over cell proliferation, differentiation and organogenesis. In particular, growth factors, transcription factors and their corresponding receptors, as well as other soluble morphogens, make up a regulatory network at the molecular level that synergistically or antagonistically controls intra-/inter-cellular signal transduction during odontogenesis. This review summarizes recent advances in the study of crucial signalling pathways, for example of BMPs, Wnt, Notch, Shh and FGF, with emphasis on the potential integrated signalling network responsible for tooth formation. Our work probes into the complexity of these inductive signalling pathways to promote the understanding of tooth regeneration. Additionally, our study provides further insights into therapeutic strategies for various dental abnormalities in patterning and number, such as tooth agenesis and supernumerary teeth. © 2013 John Wiley & Sons Ltd.

  17. Singularity analysis of the AKT signaling pathway reveals connections between cancer and metabolic diseases

    Science.gov (United States)

    Wang, Guanyu

    2010-12-01

    Connections between cancer and metabolic diseases may consist in the complex network of interactions among a common set of biomolecules. By applying singularity and bifurcation analysis, the phenotypes constrained by the AKT signaling pathway are identified and mapped onto the parameter space, which include cancer and certain metabolic diseases. By considering physiologic properties (sensitivity, robustness and adaptivity) the AKT pathway must possess in order to efficiently sense growth factors and nutrients, the region of normal responses is located. To optimize these properties, the intracellular concentration of the AKT protein must be sufficiently high to saturate its enzymes; the strength of the positive feedback must be stronger than that of the negative feedback. The analysis illuminates the parameter space and reveals system-level mechanisms in regulating biological functions (cell growth, survival, proliferation and metabolism) and how their deregulation may lead to the development of diseases. The analytical expressions summarize the synergistic interactions among many molecules, which provides valuable insights into therapeutic interventions. In particular, a strategy for overcoming the limitations of mTOR inhibition is proposed for cancer therapy.

  18. Design The Cervical Cancer Detector Use The Artificial Neural Network

    Science.gov (United States)

    Intan Af'idah, Dwi; Didik Widianto, Eko; Setyawan, Budi

    2013-06-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  19. Modulation of Notch signaling as a therapeutic approach for liver cancer.

    Science.gov (United States)

    Wu, Guang; Wilson, George; George, Jacob; Qiao, Liang

    2015-01-01

    Notch signaling is an evolutionarily conserved pathway crucial for the development and homeostasis of many organs including liver. Aberrant Notch signaling has been mechanistically linked to cancer development including liver cancer. The two principal types of liver cancer are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (IHCC). HCCs comprise over 80% of all liver cancers and are the 6(th) most common malignancy worldwide. Hepatocellular carcinoma is also a cancer with an extremely poor prognosis, being the 3(rd) commonest cause of cancer-related mortality. Accumulating evidence indicates that the role of Notch signaling in liver cancer is more complex than once thought and is dependent on the expression of specific Notch signaling components and the complex crosstalk with other signaling pathways. Currently there are a variety of gene therapy based approaches used to target Notch signaling in preclinical studies to successfully treat liver cancer including neutralizing antibodies, siRNA, shRNA and miRNA. The use of targeted anti-Notch therapy in the clinic to treat liver cancer will require considerable refinement of our current knowledge on the regulation of Notch signaling components and their effects in both normal and malignant liver cells in order to target specific Notch subunits which are critical to liver cancer tumorigenesis but not to the homeostasis of normal cells. Using this approach will reduce the major side effects, which are frequently seen in patients treated with GSIs to inhibit the entire Notch signaling pathway. Here our understanding of Notch signaling is reviewed, highlighting its function in liver cancer initiation, progress and opportunities for liver cancer therapies.

  20. Patterns of human gene expression variance show strong associations with signaling network hierarchy.

    Science.gov (United States)

    Komurov, Kakajan; Ram, Prahlad T

    2010-11-12

    Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV) of genes and their relationship to cellular functions and physiological responses is poorly understood. To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

  1. Patterns of human gene expression variance show strong associations with signaling network hierarchy

    Directory of Open Access Journals (Sweden)

    Ram Prahlad T

    2010-11-01

    Full Text Available Abstract Background Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV of genes and their relationship to cellular functions and physiological responses is poorly understood. Results To understand global principles of relationship between transcriptional regulation of human genes and their functions, we have leveraged large-scale datasets of human gene expression measurements across a wide spectrum of cell conditions. We report that human genes are highly diverse in terms of their EV; while some genes have highly variable expression pattern, some seem to be relatively ubiquitously expressed across a wide range of conditions. The wide spectrum of gene EV strongly correlates with the positioning of proteins within the signaling network hierarchy, such that, secreted extracellular receptor ligands and membrane receptors have the highest EV, and intracellular signaling proteins have the lowest EV in the genome. Our analysis shows that this pattern of EV reflects functional centrality: proteins with highly specific signaling functions are modulated more frequently than those with highly central functions in the network, which is also consistent with previous studies on tissue-specific gene expression. Interestingly, these patterns of EV along the signaling network hierarchy have significant correlations with promoter architectures of respective genes. Conclusion Our analyses suggest a generic systems level mechanism of regulation of the cellular signaling network at the transcriptional level.

  2. Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

    Science.gov (United States)

    Das, A; Reddy, N P; Narayanan, J

    2001-02-01

    Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

  3. Global Analysis of miRNA-mRNA Interaction Network in Breast Cancer with Brain Metastasis.

    Science.gov (United States)

    Li, Zhixin; Peng, Zhiqiang; Gu, Siyu; Zheng, Junfang; Feng, Duiping; Qin, Qiong; He, Junqi

    2017-08-01

    MicroRNAs (miRNAs) have been linked to a number of cancer types including breast cancer. The rate of brain metastases is 10-30% in patients with advanced breast cancer which is associated with poor prognosis. The potential application of miRNAs in the diagnostics and therapeutics of breast cancer with brain metastasis is an area of intense interest. In an initial effort to systematically address the differential expression of miRNAs and mRNAs in primary breast cancer which may provide clues for early detection of brain metastasis, we analyzed the consequent changes in global patterns of gene expression in Gene Expression Omnibus (GEO) data set obtained by microarray from patients with in situ carcinoma and patients with brain metastasis. The miRNA-pathway regulatory network and miRNA-mRNA regulatory network were investigated in breast cancer specimens from patients with brain metastasis to screen for significantly dysregulated miRNAs followed by prediction of their target genes and pathways by Gene Ontology (GO) analysis. Functional coordination of the changes of gene expression can be modulated by individual miRNAs. Two miRNAs, hsa-miR-17-5p and hsa-miR-16-5p, were identified as having the highest associations with targeted mRNAs [such as B-cell lymphoma 2 (BCL2), small body size/mothers against decapentaplegic 3 (SMAD3) and suppressor of cytokine signaling 1 (SOCS1)] and pathways associated with epithelial-mesenchymal transitions and other processes linked with cancer metastasis (including cell cycle, adherence junctions and extracellular matrix-receptor interaction). mRNAs for two genes [HECT, UBA and WWE domain containing 1 (HUWE1) and BCL2] were found to have the highest associations with miRNAs, which were down-regulated in brain metastasis specimens of breast cancer. The change of 11 selected miRNAs was verified in The Cancer Genome Atlas (TCGA) breast cancer dataset. Up-regulation of hsa-miR-17-5p was detected in triple-negative breast cancer tissues in

  4. Protein and signaling networks in vertebrate photoreceptor cells

    Directory of Open Access Journals (Sweden)

    Karl-Wilhelm eKoch

    2015-11-01

    Full Text Available Vertebrate photoreceptor cells are exquisite light detectors operating under very dim and bright illumination. The photoexcitation and adaptation machinery in photoreceptor cells consists of protein complexes that can form highly ordered supramolecular structures and control the homeostasis and mutual dependence of the secondary messengers cGMP and Ca2+. The visual pigment in rod photoreceptors, the G protein-coupled receptor rhodopsin is organized in tracks of dimers thereby providing a signaling platform for the dynamic scaffolding of the G protein transducin. Illuminated rhodopsin is turned off by phosphorylation catalyzed by rhodopsin kinase GRK1 under control of Ca2+-recoverin. The GRK1 protein complex partly assembles in lipid raft structures, where shutting off rhodopsin seems to be more effective. Re-synthesis of cGMP is another crucial step in the recovery of the photoresponse after illumination. It is catalyzed by membrane bound sensory guanylate cyclases and is regulated by specific neuronal Ca2+-sensor proteins called GCAPs. At least one guanylate cyclase (ROS-GC1 was shown to be part of a multiprotein complex having strong interactions with the cytoskeleton and being controlled in a multimodal Ca2+-dependent fashion. The final target of the cGMP signaling cascade is a cyclic nucleotide-gated channel that is a hetero-oligomeric protein located in the plasma membrane and interacting with accessory proteins in highly organized microdomains. We summarize results and interpretations of findings related to the inhomogeneous organization of signaling units in photoreceptor outer segments.

  5. Fibroblast growth factor receptor 3 interacts with and activates TGFβ-activated kinase 1 tyrosine phosphorylation and NFκB signaling in multiple myeloma and bladder cancer.

    Directory of Open Access Journals (Sweden)

    Lisa Salazar

    Full Text Available Cancer is a major public health problem worldwide. In the United States alone, 1 in 4 deaths is due to cancer and for 2013 a total of 1,660,290 new cancer cases and 580,350 cancer-related deaths are projected. Comprehensive profiling of multiple cancer genomes has revealed a highly complex genetic landscape in which a large number of altered genes, varying from tumor to tumor, impact core biological pathways and processes. This has implications for therapeutic targeting of signaling networks in the development of treatments for specific cancers. The NFκB transcription factor is constitutively active in a number of hematologic and solid tumors, and many signaling pathways implicated in cancer are likely connected to NFκB activation. A critical mediator of NFκB activity is TGFβ-activated kinase 1 (TAK1. Here, we identify TAK1 as a novel interacting protein and target of fibroblast growth factor receptor 3 (FGFR3 tyrosine kinase activity. We further demonstrate that activating mutations in FGFR3 associated with both multiple myeloma and bladder cancer can modulate expression of genes that regulate NFκB signaling, and promote both NFκB transcriptional activity and cell adhesion in a manner dependent on TAK1 expression in both cancer cell types. Our findings suggest TAK1 as a potential therapeutic target for FGFR3-associated cancers, and other malignancies in which TAK1 contributes to constitutive NFκB activation.

  6. Fibroblast Growth Factor Receptor 3 Interacts with and Activates TGFβ-Activated Kinase 1 Tyrosine Phosphorylation and NFκB Signaling in Multiple Myeloma and Bladder Cancer

    Science.gov (United States)

    Krejci, Pavel; Meyer, April N.; Casale, Malcolm; Hallowell, Matthew; Wilcox, William R.; Donoghue, Daniel J.; Thompson, Leslie Michels

    2014-01-01

    Cancer is a major public health problem worldwide. In the United States alone, 1 in 4 deaths is due to cancer and for 2013 a total of 1,660,290 new cancer cases and 580,350 cancer-related deaths are projected. Comprehensive profiling of multiple cancer genomes has revealed a highly complex genetic landscape in which a large number of altered genes, varying from tumor to tumor, impact core biological pathways and processes. This has implications for therapeutic targeting of signaling networks in the development of treatments for specific cancers. The NFκB transcription factor is constitutively active in a number of hematologic and solid tumors, and many signaling pathways implicated in cancer are likely connected to NFκB activation. A critical mediator of NFκB activity is TGFβ-activated kinase 1 (TAK1). Here, we identify TAK1 as a novel interacting protein and target of fibroblast growth factor receptor 3 (FGFR3) tyrosine kinase activity. We further demonstrate that activating mutations in FGFR3 associated with both multiple myeloma and bladder cancer can modulate expression of genes that regulate NFκB signaling, and promote both NFκB transcriptional activity and cell adhesion in a manner dependent on TAK1 expression in both cancer cell types. Our findings suggest TAK1 as a potential therapeutic target for FGFR3-associated cancers, and other malignancies in which TAK1 contributes to constitutive NFκB activation. PMID:24466111

  7. Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis.

    Science.gov (United States)

    Li, Rudong; Dong, Xiao; Ma, Chengcheng; Liu, Lei

    2014-08-23

    Prostate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis--the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development. Using the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer. We conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies.

  8. Atomic force microscopy and graph analysis to study the P-cadherin/SFK mechanotransduction signalling in breast cancer cells.

    Science.gov (United States)

    Ribeiro, A S; Carvalho, F A; Figueiredo, J; Carvalho, R; Mestre, T; Monteiro, J; Guedes, A F; Fonseca, M; Sanches, J; Seruca, R; Santos, N C; Paredes, J

    2016-11-24

    Physical forces mediated by cell-cell adhesion molecules, as cadherins, play a crucial role in preserving normal tissue architecture. Accordingly, altered cadherins' expression has been documented as a common event during cancer progression. However, in most studies, no data exist linking pro-tumorigenic signaling and variations in the mechanical balance mediated by adhesive forces. In breast cancer, P-cadherin overexpression increases in vivo tumorigenic ability, as well as in vitro cell invasion, by activating Src family kinase (SFK) signalling. However, it is not known how P-cadherin and SFK activation impact cell-cell biomechanical properties. In the present work, using atomic force microscopy (AFM) images, cell stiffness and cell-cell adhesion measurements, and undirected graph analysis based on microscopic images, we have demonstrated that P-cadherin overexpression promotes significant alterations in cell's morphology, by decreasing cellular height and increasing its area. It also affects biomechanical properties, by decreasing cell-cell adhesion and cell stiffness. Furthermore, cellular network analysis showed alterations in intercellular organization, which is associated with cell-cell adhesion dysfunction, destabilization of an E-cadherin/p120ctn membrane complex and increased cell invasion. Remarkably, inhibition of SFK signaling, using dasatinib, reverted the pathogenic P-cadherin induced effects by increasing cell's height, cell-cell adhesion and cell stiffness, and generating more compact epithelial aggregates, as quantified by intercellular network analysis. In conclusion, P-cadherin/SFK signalling induces topological, morphological and biomechanical cell-cell alterations, which are associated with more invasive breast cancer cells. These effects could be further reverted by dasatinib treatment, demonstrating the applicability of AFM and cell network diagrams for measuring the epithelial biomechanical properties and structural organization.

  9. Plant gravitropic signal transduction: A network analysis leads to gene discovery

    Science.gov (United States)

    Wyatt, Sarah

    Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.” Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes. Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections. Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected

  10. New Concepts in Phospholipase D Signaling in Inflammation and Cancer

    Directory of Open Access Journals (Sweden)

    Julian Gomez-Cambronero

    2010-01-01

    Full Text Available Phospholipase D (PLD catalyzes the hydrolysis of phosphatidylcholine to generate the lipid second messenger phosphatidic acid (PA and choline. PLD regulation in cells falls into two major signaling categories. One is via growth factors/mitogens, such as EGF, PDGF, insulin, and serum, and implicates tyrosine kinases; the other is via the small GTPase proteins Arf and Rho. We summarize here our lab's and other groups' contributions to those pathways and introduce several novel concepts. For the mitogen-induced signaling, new data indicate that an increase in cell transformation in PLD2-overexpressing cells is due to an increase of de novo DNA synthesis induced by PLD2, with the specific tyrosine residues involved in those functions being Y179 and Y511. Recent research has also implicated Grb2 in tyrosine phosphorylation of PLD2 that also involves Sos and the ERK pathway. The targets of phosphorylation within the PLD2 molecule that are key to its regulation have recently been precisely mapped. They are Y296, Y415, and Y511 and the responsible kinases are, respectively, EGFR, JAK3, and Src. Y296 is an inhibitory site and its phosphorylation explains the low PLD2 activity that exists in low-invasive MCF-7 breast cancer cells. Advances along the small GTPase front have implicated cell migration, as PLD1 and PLD2 cause an increase in chemotaxis of leukocytes and inflammation. PA is necessary for full chemotaxis. PA enriches the localization of the atypical guanine exchange factor (GEF, DOCK2, at the leading edge of polarized neutrophils. Further, extracellular PA serves as a neutrophil chemoattractant; PA enters the cell and activates the mTOR/S6K pathway (specifically, S6K. A clear connection between PLD with the mTOR/S6K pathway has been established, in that PA binds to mTOR and also binds to S6K independently of mTOR. Lastly, there is evidence in the upstream direction of cell signaling that mTOR and S6K keep PLD2 gene expression function down

  11. Tolerance to drought and salt stress in plants: Unraveling the signaling networks

    Directory of Open Access Journals (Sweden)

    Dortje eGolldack

    2014-04-01

    Full Text Available Tolerance of plants to abiotic stressors such as drought and salinity is triggered by complex multicomponent signaling pathways to restore cellular homeostasis and promote survival. Major plant transcription factor families such as bZIP, NAC, AP2/ERF and MYB orchestrate regulatory networks underlying abiotic stress tolerance. Sucrose nonfermenting 1-related protein kinase 2 (SnRK2 and MAPK pathways contribute to initiation of stress adaptive downstream responses and promote plant growth and development. As a convergent point of multiple abiotic cues, cellular effects of environmental stresses are not only imbalances of ionic and osmotic homeostasis but also impaired photosynthesis, cellular energy depletion, and redox imbalances. Recent evidence of regulatory systems that link sensing and signaling of environmental conditions and the intracellular redox status have shed light on interfaces of stress and energy signaling. ROS (reactive oxygen species cause severe cellular damage by peroxidation and de-esterification of membrane lipids, however, current models also define a pivotal signaling function of ROS in triggering tolerance against stress. Recent research advances suggest and support a regulatory role of ROS in the cross talks of stress triggered hormonal signaling such as the abscisic acid (ABA pathway and endogenously induced redox and metabolite signals. Here, we discuss and review the versatile molecular convergence in the abiotic stress responsive signaling networks in the context of ROS and lipid derived signals and the specific role of stomatal signaling.

  12. Cilia and coordination of signaling networks during heart development

    DEFF Research Database (Denmark)

    Koefoed, Karen; Veland, Iben Rønn; Pedersen, Lotte Bang

    2014-01-01

    Primary cilia are unique sensory organelles that coordinate a wide variety of different signaling pathways to control cellular processes during development and in tissue homeostasis. Defects in function or assembly of these antenna-like structures are therefore associated with a broad range...... of developmental disorders and diseases called ciliopathies. Recent studies have indicated a major role of different populations of cilia, including nodal and cardiac primary cilia, in coordinating heart development, and defects in these cilia are associated with congenital heart diseases. Here, we present...

  13. Altering the threshold of an excitable signal transduction network changes cell migratory modes.

    Science.gov (United States)

    Miao, Yuchuan; Bhattacharya, Sayak; Edwards, Marc; Cai, Huaqing; Inoue, Takanari; Iglesias, Pablo A; Devreotes, Peter N

    2017-04-01

    The diverse migratory modes displayed by different cell types are generally believed to be idiosyncratic. Here we show that the migratory behaviour of Dictyostelium was switched from amoeboid to keratocyte-like and oscillatory modes by synthetically decreasing phosphatidylinositol-4,5-bisphosphate levels or increasing Ras/Rap-related activities. The perturbations at these key nodes of an excitable signal transduction network initiated a causal chain of events: the threshold for network activation was lowered, the speed and range of propagating waves of signal transduction activity increased, actin-driven cellular protrusions expanded and, consequently, the cell migratory mode transitions ensued. Conversely, innately keratocyte-like and oscillatory cells were promptly converted to amoeboid by inhibition of Ras effectors with restoration of directed migration. We use computational analysis to explain how thresholds control cell migration and discuss the architecture of the signal transduction network that gives rise to excitability.

  14. Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Chennubhotla Chakra

    2006-10-01

    Full Text Available Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. In these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets. Conclusion As transcriptional regulation represents the 'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the TR network control robust integrated responses to complex stimuli.

  15. Role of Notch signalling pathway in cancer and its association with ...

    Indian Academy of Sciences (India)

    screening, detection, diagnosis, staging and risk stratification of various cancers. DNA methylation can be therapeutically reversed and demethylating drugs have proven to be promising in cancer treatment. This review focusses on the methylation status of genes in Notch signalling pathway from various cancers and how ...

  16. Dynamic protein interaction networks and new structural paradigms in signaling

    Science.gov (United States)

    Csizmok, Veronika; Follis, Ariele Viacava; Kriwacki, Richard W.; Forman-Kay, Julie D.

    2017-01-01

    Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins and regions (IDPs/IDRs), which represent ~30% of the proteome and enable unique regulatory mechanisms. In this review we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as “ultrasensitivity” and “regulated folding and unfolding”. We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information. PMID:26922996

  17. Addressing cancer disparities via community network mobilization and intersectoral partnerships: a social network analysis.

    Science.gov (United States)

    Ramanadhan, Shoba; Salhi, Carmel; Achille, Erline; Baril, Nashira; D'Entremont, Kerrie; Grullon, Milagro; Judge, Christine; Oppenheimer, Sarah; Reeves, Chrasandra; Savage, Clara; Viswanath, Kasisomayajula

    2012-01-01

    Community mobilization and collaboration among diverse partners are vital components of the effort to reduce and eliminate cancer disparities in the United States. We studied the development and impact of intersectoral connections among the members of the Massachusetts Community Network for Cancer Education, Research, and Training (MassCONECT). As one of the Community Network Program sites funded by the National Cancer Institute, this infrastructure-building initiative utilized principles of Community-based Participatory Research (CBPR) to unite community coalitions, researchers, policymakers, and other important stakeholders to address cancer disparities in three Massachusetts communities: Boston, Lawrence, and Worcester. We conducted a cross-sectional, sociometric network analysis four years after the network was formed. A total of 38 of 55 members participated in the study (69% response rate). Over four years of collaboration, the number of intersectoral connections reported by members (intersectoral out-degree) increased, as did the extent to which such connections were reported reciprocally (intersectoral reciprocity). We assessed relationships between these markers of intersectoral collaboration and three intermediate outcomes in the effort to reduce and eliminate cancer disparities: delivery of community activities, policy engagement, and grants/publications. We found a positive and statistically significant relationship between intersectoral out-degree and community activities and policy engagement (the relationship was borderline significant for grants/publications). We found a positive and statistically significant relationship between intersectoral reciprocity and community activities and grants/publications (the relationship was borderline significant for policy engagement). The study suggests that intersectoral connections may be important drivers of diverse intermediate outcomes in the effort to reduce and eliminate cancer disparities. The findings

  18. Fractal analysis of spontaneous fluctuations of the BOLD signal in the human brain networks.

    Science.gov (United States)

    Li, Yi-Chia; Huang, Yun-An

    2014-05-01

    To investigate what extent brain regions are continuously interacting during resting-state, independent component analyses (ICA) was applied to analyze resting-state functional MRI (RS-fMRI) data. According to the analyzed results, it was surprisingly found that low frequency fluctuations (LFFs), which belong to the 1/f signal (a signal with power spectrum whose power spectral density is inversely proportional to the frequency), have been classified into groups using ICA; furthermore, the spatial distributions of these groups within the brain were found to resemble the spatial distributions of different networks, which manifests that the signal characteristics of RS LFFs are distinct across networks. In our work, we applied the 1/f model in the fractal analyses to further investigate this distinction. Twenty healthy participants got involved in this study. They were scanned to acquire the RS-fMRI data. The acquired data were first processed with ICA to obtain the networks of the resting brain. Afterward, the blood-oxygenation level dependent (BOLD) signals of these networks were processed with the fractal analyses for obtaining the fractal parameter α. α was found to significantly vary across networks, which reveals that the fractal characteristic of LFFs differs across networks. According to prior literatures, this difference could be brought by the discrepancy of hemodynamic response amplitude (HRA) between networks. Hence, in our work, we also performed the computational simulation to discover the relationship between α and HRA. Based on the simulation results, HRA is highly linear-correlated with the fractal characteristics of LFFs which is revealed by α. Our results support that the origin of RS-fMRI signals contains arterial fluctuations. Hence, in addition to the commonly used method such as synchrony analysis and power spectral analysis, another approach, the fractal analysis, is suggested for acquiring the information of hemodynamic responses by means

  19. Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

    Directory of Open Access Journals (Sweden)

    Christopher R S Banerji

    2015-03-01

    Full Text Available The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers.

  20. Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer

    Science.gov (United States)

    Banerji, Christopher R. S.; Severini, Simone; Caldas, Carlos; Teschendorff, Andrew E.

    2015-01-01

    The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers. PMID:25793737

  1. Molecular signaling networks in regulation of immunity and disease

    DEFF Research Database (Denmark)

    Laursen, Janne Marie; Jensen, Stina Rikke; Sørensen, Morten

    The gut microbiota, host tissues, and the immune system form a complex network where extensive crosstalk and molecular interactions substantially impact the overall state of the system. Concomitantly, modulation of host immune function is recurrently a result of the interaction of complex......), plays a crucial role in shaping the nature of the adaptive/memorybased immune response after encountering inflammatory compounds. In the gut, the DC is continuously exposed to microbial and dietary components that are recognized by its innate pattern recognition receptors, and the phenotype developed...... and dynamic microbial communities with the immune cell compartment in the gut, and therefore the interaction between components from different gut bacteria can efficiently shape the phenotype of the immune response. A specialized antigenpresenting cell present at mucosal surfaces, the dendritic cell (DC...

  2. Stochasticity in the signalling network of a model microbe

    Science.gov (United States)

    Bischofs, Ilka; Foley, Jonathan; Battenberg, Eric; Fontaine-Bodin, Lisa; Price, Gavin; Wolf, Denise; Arkin, Adam

    2007-03-01

    The soil dwelling bacterium Bacillus subtilis is an excellent model organism for studying stochastic stress response induction in an isoclonal population. Subjected to the same stressor cells undergo different cell fates, including sporulation, competence, degradative enzyme synthesis and motility. For example, under conditions of nutrient deprivation and high cell density only a portion of the cell population forms an endospore. Here we use a combined experimental and theoretical approach to study stochastic sporulation induction in Bacillus subtilis. Using several fluorescent reporter strains we apply time lapse fluorescent microscopy in combination with quantitative image analysis to study cell fate progression on a single cell basis and elucidate key noise generators in the underlying cellular network.

  3. In vitro membrane reconstitution of the T cell receptor proximal signaling network

    OpenAIRE

    Hui, Enfu; Vale, Ronald D.

    2014-01-01

    T-cell receptor (TCR) phosphorylation is controlled by a complex network that includes Lck, a Src family kinase (SFK), the tyrosine phosphatase CD45, and the Lck-inhibitory kinase Csk. How these competing phosphorylation and dephosphorylation reactions are modulated to produce T-cell triggering is not fully understood. Here we reconstituted this signaling network using purified enzymes on liposomes, recapitulating the membrane environment in which they normally interact. We demonstrate that L...

  4. The structural network of Interleukin-10 and its implications in inflammation and cancer

    Science.gov (United States)

    2014-01-01

    Background Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies. Results Using PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing β-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix

  5. Gene expression, signal transduction pathways and functional networks associated with growth of sporadic vestibular schwannomas

    DEFF Research Database (Denmark)

    Sass, Hjalte C.R.; Borup, Rehannah; Alanin, Mikkel

    2017-01-01

    The objective of this study was to determine global gene expression in relation to Vestibular schwannomas (VS) growth rate and to identify signal transduction pathways and functional molecular networks associated with growth. Repeated magnetic resonance imaging (MRI) prior to surgery determined...... and analyzed by dChip software. Differential gene expression was defined as a 1.5-fold difference between fast and slow growing tumors (>... of signal transduction pathways and functional molecular networks associated with tumor growth. In total 109 genes were deregulated in relation to tumor growth rate. Genes associated with apoptosis, growth and cell proliferation were deregulated. Gene ontology included regulation of the cell cycle, cell...

  6. The behaviour of basic autocatalytic signalling modules in isolation and embedded in networks

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, J. [Department of Chemical Engineering, Centre for Process Systems Engineering, Institute for Systems and Synthetic Biology, Imperial College London, London SW7 2AZ (United Kingdom); Mois, Kristina; Suwanmajo, Thapanar [Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ (United Kingdom)

    2014-11-07

    In this paper, we examine the behaviour of basic autocatalytic feedback modules involving a species catalyzing its own production, either directly or indirectly. We first perform a systematic study of the autocatalytic feedback module in isolation, examining the effect of different factors, showing how this module is capable of exhibiting monostable threshold and bistable switch-like behaviour. We then study the behaviour of this module embedded in different kinds of basic networks including (essentially) irreversible cycles, open and closed reversible chains, and networks with additional feedback. We study the behaviour of the networks deterministically and also stochastically, using simulations, analytical work, and bifurcation analysis. We find that (i) there are significant differences between the behaviour of this module in isolation and in a network: thresholds may be altered or destroyed and bistability may be destroyed or even induced, even when the ambient network is simple. The global characteristics and topology of this network and the position of the module in the ambient network can play important and unexpected roles. (ii) There can be important differences between the deterministic and stochastic dynamics of the module embedded in networks, which may be accentuated by the ambient network. This provides new insights into the functioning of such enzymatic modules individually and as part of networks, with relevance to other enzymatic signalling modules as well.

  7. Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

    Directory of Open Access Journals (Sweden)

    Nan Jiang

    2017-01-01

    Full Text Available A signal design problem is studied for efficiently managing autonomous vehicles (AVs and regular vehicles (RVs simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN principle and RVs follow the user equilibrium (UE principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.

  8. Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks.

    Directory of Open Access Journals (Sweden)

    Song Feng

    2016-05-01

    Full Text Available Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.

  9. Traffic analysis and signal processing in optical packet switched networks

    DEFF Research Database (Denmark)

    Fjelde, Tina

    2002-01-01

    Gbit/s demultiplexing and 2x10 to 20 Gbit/s multiplexing. Lastly, the IWC’s capabilities as an optical logic gate for enabling more complex signal processing are demonstrated and four applications hereof are discussed. Logic OR and AND are verified in full at 10 Gbit/s using PRBS sequences coupled...... into an MI. Moreover, logic XOR is demonstrated in an MZI at 10 and 20 Gbit/s with good results. Using an MI, the excellent performance of a novel scheme for MPLS label swapping exploiting logic XOR is demonstrated at 10 Gbit/s with a negligible 0.4 dB penalty. Finally, three novel schemes are described...

  10. The role of protein interaction domains in the human cancer network

    Directory of Open Access Journals (Sweden)

    Shady S. Ibrahim

    2011-06-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Proteins interact largely through specific domains which constitute the main building blocks of an interaction network. Perturbed or dysfunctional protein interactions are linked to many diseases, including cancer. In this study we describe the major pathways and connections within the human cancer network by a novel approach in which we overlay the human cancer network with all protein interaction domain (PID superfamilies. Based on 38,777 experimentally derived interactions, we constructed a cancer network with 8 different levels and identified all major protein hubs within this cancer interactome. Only one percent of the cancer genes constitute over 50 percent of all interactions within the network. In addition, we mapped 56 PID superfamilies onto the cancer network, and discovered that over 10% of protein interaction domains are overrepresented within the cancer interactome when compared to the normal protein network. We present here a comprehensive list of all PIDs in the cancer network, identify the most important hubs within it and discover several individual genes which had previously not been linked to cancer. These proteins constitute excellent targets for the development of novel cancer therapeutics. Our results further hint to a partial molecular commonality between cancer and neurodegenerative diseases such as Alzheimer's and Huntington's.

  11. Luminal breast cancer metastasis is dependent on estrogen signaling

    NARCIS (Netherlands)

    Ganapathy, Vidya; Banach-Petrosky, Whitney; Xie, Wen; Kareddula, Aparna; Nienhuis, Hilde; Miles, Gregory; Reiss, Michael

    Luminal breast cancer is the most frequently encountered type of human breast cancer and accounts for half of all breast cancer deaths due to metastatic disease. We have developed new in vivo models of disseminated human luminal breast cancer that closely mimic the human disease. From initial

  12. Wireless Body Area Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting

    OpenAIRE

    Joonyoung Jung; Kiryong Ha; Jeonwoo Lee; Youngsung Kim; Daeyoung Kim

    2008-01-01

    We developed a ubiquitous healthcare system consisted of aphysiological signal devices, a mobile system, a device provider system, a healthcare service provider system, a physician system, and a healthcare personal system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We propose a scanning algorithm, dynamic discover...

  13. Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

    Directory of Open Access Journals (Sweden)

    Kohvakka Mikko

    2008-01-01

    Full Text Available Abstract ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

  14. Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

    Directory of Open Access Journals (Sweden)

    D. Hämäläinen

    2008-03-01

    Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

  15. Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy

    National Research Council Canada - National Science Library

    Fabrice Wendling; Fabrice Bartolomei; Lotfi Senhadji

    2009-01-01

    .... Scalp EEG or intracerebral EEG signals recorded in patients with drug-resistant partial epilepsy convey important information about epileptogenic networks that must be localized and understood prior...

  16. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  17. Identification of cancer fusion drivers using network fusion centrality

    OpenAIRE

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on ...

  18. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  19. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

    Science.gov (United States)

    Sriraam, N

    2011-01-01

    A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  20. Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

    Science.gov (United States)

    Hu, Peizhen; Chung, Leland W K; Berel, Dror; Frierson, Henry F; Yang, Hua; Liu, Chunyan; Wang, Ruoxiang; Li, Qinlong; Rogatko, Andre; Zhau, Haiyen E

    2013-01-01

    We reported (PLoS One 6 (12):e28670, 2011) that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC) tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE) tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1) expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

  1. Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

    Directory of Open Access Journals (Sweden)

    Peizhen Hu

    Full Text Available We reported (PLoS One 6 (12:e28670, 2011 that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1 expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

  2. The Rare Cancer Network: ongoing studies and future strategy

    Directory of Open Access Journals (Sweden)

    Mahmut Ozsahin

    2014-08-01

    Full Text Available The Rare Cancer Network (RCN was formed in the early 1990’s to create a global network that could pool knowledge and resources in the studies of rare malignancies whose infrequency prevented both their study with prospective clinical trials. To date, the RCN has initiated 74 studies resulting in 46 peer reviewed publications. The First International Symposium of the Rare Cancer Network took place in Nice in March of 2014. Status updates and proposals for new studies were heard for fifteen topics. Ongoing studies continue for cardiac sarcomas, thyroid cancers, glomus tumors, and adult medulloblastomas. New proposals were presented at the symposium for primary hepatic lymphoma, solitary fibrous tumors, Rosai-Dorfman disease, tumors of the ampulla of Vater, salivary gland tumors, anorectal melanoma, midline nuclear protein in testes carcinoma, pulmonary lymphoepithelioma-like carcinoma, adenoid cystic carcinoma of the trachea, osteosarcomas of the mandible, and extra-cranial hemangiopericytoma. This manuscript presents the abstracts of those proposals and updates on ongoing studies, as well a brief summary of the vision and future of the RCN.

  3. Combination therapy approaches to target insulin-like growth factor receptor signaling in breast cancer.

    Science.gov (United States)

    Ochnik, Aleksandra M; Baxter, Robert C

    2016-11-01

    Insulin-like growth factor receptor (IGF1R) signaling as a therapeutic target has been widely studied and clinically tested. Despite the vast amount of literature supporting the biological role of IGF1R in breast cancer, effective clinical translation in targeting its activity as a cancer therapy has not been successful. The intrinsic complexity of cancer cell signaling mediated by many tyrosine kinase growth factor receptors that work together to modulate each other and intracellular downstream mediators in the cell highlights that studying IGF1R expression and activity as a prognostic factor and therapeutic target in isolation is certainly associated with problems. This review discusses the current literature and clinical trials associated with IGF-1 signaling and attempts to look at new ways of designing novel IGF1R-directed breast cancer therapy approaches to target its activity 
and/or intracellular downstream signaling pathways in IGF1R-expressing breast cancers. © 2016 Society for Endocrinology.

  4. Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts

    Science.gov (United States)

    Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; Tremblay, Marie-Andrée; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

    2016-09-01

    Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. In vivo Raman spectroscopy can detect these invasive cancer cells in patients with grade 2 to 4 gliomas. The robustness of this Raman signal can be dampened by spectral artifacts generated by lights in the operating room. We found that artificial neural networks (ANNs) can overcome these spectral artifacts using nonparametric and adaptive models to detect complex nonlinear spectral characteristics. Coupling ANN with Raman spectroscopy simplifies the intraoperative use of Raman spectroscopy by limiting changes required to the standard neurosurgical workflow. The ability to detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery and improve patient survival.

  5. Gα12/13 signaling promotes cervical cancer invasion through the RhoA/ROCK-JNK signaling axis

    Energy Technology Data Exchange (ETDEWEB)

    Yuan, Bo; Cui, Jinquan, E-mail: jinquancuijqc@163.com; Wang, Wuliang; Deng, Kehong

    2016-05-13

    Several reports have indicated a role for the members of the G12 family of heterotrimeric G proteins (Gα12 and Gα13) in oncogenesis and tumor cell growth. The aims of the present study were to evaluate the role of G12 signaling in cervical cancer. We demonstrated that expression of the G12 proteins was highly upregulated in cervical cancer cells. Additionally, expression of the activated forms of Gα12/Gα13 but not expression of activated Gαq induced cell invasion through the activation of the RhoA family of G proteins, but had no effect on cell proliferation in the cervical cancer cells. Inhibition of G12 signaling by expression of the RGS domain of the p115-Rho-specific guanine nucleotide exchange factor (p115-RGS) blocked thrombin-stimulated cell invasion, but did not inhibit cell proliferation in cervical cells, whereas the inhibition of Gαq (RGS2) had no effect. Furthermore, G12 signaling was able to activate Rho proteins, and this stimulation was inhibited by p115-RGS, and Gα12-induced invasion was blocked by an inhibitor of RhoA/B/C (C3 toxin). Pharmacological inhibition of JNK remarkably decreased G12-induced JNK activation. Both a JNK inhibitor (SP600125) and a ROCK inhibitor (Y27632) reduced G12-induced JNK and c-Jun activation, and markedly inhibited G12-induced cellular invasion. Collectively, these findings demonstrate that stimulation of G12 proteins is capable of promoting invasion through RhoA/ROCK-JNK activation. -- Highlights: •Gα12/Gα13 is upregulated in cervical cancer cell lines. •Gα12/Gα13 is not involved in cervical cancer cell proliferation. •Gα12/Gα13 promotes cervical cancer cell invasion. •The role of Rho G proteins in G12-promoted cervical cancer cell invasion. •G12 promotes cell invasion through activation of the ROCK-JNK signaling axis.

  6. Induction of Cancer Cell Death by Isoflavone: The Role of Multiple Signaling Pathways

    Science.gov (United States)

    Li, Yiwei; Kong, Dejuan; Bao, Bin; Ahmad, Aamir; Sarkar, Fazlul H.

    2011-01-01

    Soy isoflavones have been documented as dietary nutrients broadly classified as “natural agents” which plays important roles in reducing the incidence of hormone-related cancers in Asian countries, and have shown inhibitory effects on cancer development and progression in vitro and in vivo, suggesting the cancer preventive or therapeutic activity of soy isoflavones against cancers. Emerging experimental evidence shows that isoflavones could induce cancer cell death by regulating multiple cellular signaling pathways including Akt, NF-κB, MAPK, Wnt, androgen receptor (AR), p53 and Notch signaling, all of which have been found to be deregulated in cancer cells. Therefore, homeostatic regulation of these important cellular signaling pathways by isoflavones could be useful for the activation of cell death signaling, which could result in the induction of apoptosis of both pre-cancerous and/or cancerous cells without affecting normal cells. In this article, we have attempted to summarize the current state-of-our-knowledge regarding the induction of cancer cell death pathways by isoflavones, which is believed to be mediated through the regulation of multiple cellular signaling pathways. The knowledge gained from this article will provide a comprehensive view on the molecular mechanism(s) by which soy isoflavones may exert their effects on the prevention of tumor progression and/or treatment of human malignancies, which would also aid in stimulating further in-depth mechanistic research and foster the initiation of novel clinical trials. PMID:22200028

  7. Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

    Science.gov (United States)

    Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

    Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

  8. Effect of signal noise on the learning capability of an artificial neural network

    Science.gov (United States)

    Vega, J. J.; Reynoso, R.; Calvet, H. Carrillo

    2009-07-01

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  9. Effect of signal noise on the learning capability of an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Vega, J.J. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico)], E-mail: jjvc@nuclear.inin.mx; Reynoso, R. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico); Calvet, H. Carrillo [Laboratorio de Dinamica no Lineal, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico, Mexico D.F. 04510 (Mexico)

    2009-07-21

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  10. Towards convergence of wireless and wireline signal transport in broadband access networks

    DEFF Research Database (Denmark)

    Yu, Xianbin; Prince, Kamau; Tafur Monroy, Idelfonso

    2010-01-01

    Hybrid optical wireless access networks are to play an important role in the realization of the vision of delivery of broadband services to the end-user any time, anywhere and at affordable costs. We present results of experiments conducted over a field deployed optical fibre links we successfully...... demonstrated converged wireless and wireline signal transport over a common fibre infrastructure. The type of signal used in this field deployed experiments cover WiMax, Impulse-radio ultra-wideband (UWB) and coherent transmission of baseband QPSK and radio-over-fibre signals....

  11. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  12. Analyzing and constraining signaling networks: parameter estimation for the user.

    Science.gov (United States)

    Geier, Florian; Fengos, Georgios; Felizzi, Federico; Iber, Dagmar

    2012-01-01

    The behavior of most dynamical models not only depends on the wiring but also on the kind and strength of interactions which are reflected in the parameter values of the model. The predictive value of mathematical models therefore critically hinges on the quality of the parameter estimates. Constraining a dynamical model by an appropriate parameterization follows a 3-step process. In an initial step, it is important to evaluate the sensitivity of the parameters of the model with respect to the model output of interest. This analysis points at the identifiability of model parameters and can guide the design of experiments. In the second step, the actual fitting needs to be carried out. This step requires special care as, on the one hand, noisy as well as partial observations can corrupt the identification of system parameters. On the other hand, the solution of the dynamical system usually depends in a highly nonlinear fashion on its parameters and, as a consequence, parameter estimation procedures get easily trapped in local optima. Therefore any useful parameter estimation procedure has to be robust and efficient with respect to both challenges. In the final step, it is important to access the validity of the optimized model. A number of reviews have been published on the subject. A good, nontechnical overview is provided by Jaqaman and Danuser (Nat Rev Mol Cell Biol 7(11):813-819, 2006) and a classical introduction, focussing on the algorithmic side, is given in Press (Numerical recipes: The art of scientific computing, Cambridge University Press, 3rd edn., 2007, Chapters 10 and 15). We will focus on the practical issues related to parameter estimation and use a model of the TGFβ-signaling pathway as an educative example. Corresponding parameter estimation software and models based on MATLAB code can be downloaded from the authors's web page ( http://www.bsse.ethz.ch/cobi ).

  13. TGF-β signaling alterations and susceptibility to colorectal cancer

    OpenAIRE

    Xu, Yanfei; Pasche, Boris

    2007-01-01

    In 2006, more than 55 000 patients died of colorectal cancer in the US, accounting for ∼10% of all cancer deaths. Despite significant progress in screening combined with the development of novel effective therapies, colorectal cancer ranks second to lung cancer as a cause of cancer death. Twin studies indicate that 35% of all colorectal cancers are inherited, but high-penetrance tumor susceptibility genes only account for ∼3–6% of all cases. The remainder of the unexplained familial risk is p...

  14. At the crossroads: EGFR and PTHrP signaling in cancer-mediated diseases of bone.

    Science.gov (United States)

    Foley, John; Nickerson, Nicole; Riese, David J; Hollenhorst, Peter C; Lorch, Gwendolen; Foley, Anne M

    2012-07-01

    The epidermal growth factor receptor is a well-established cancer therapeutic target due to its stimulation of proliferation, motility, and resistance to apoptosis. Recently, additional roles for the receptor have been identified in growth of metastases. Similar to development, metastatic spread requires signaling interactions between epithelial-derived tumor cells and mesenchymal derivatives of the microenvironment. This necessitates reactivation of developmental signaling molecules, including the hypercalcemia factor parathyroid hormone-related protein. This review covers the variations of epidermal growth factor receptor signaling in cancers that produce bone metastases, regulation of parathyroid hormone-related protein, and evidence that the two molecules drive cancer-mediated diseases of bone.

  15. Processing of signals from an ion-elective electrode array by a neural network

    NARCIS (Netherlands)

    Bos, M.; Bos, A.; van der Linden, W.E.

    1990-01-01

    Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous

  16. 47 CFR 73.4157 - Network signals which adversely affect affiliate broadcast service.

    Science.gov (United States)

    2010-10-01

    ... affiliate broadcast service. 73.4157 Section 73.4157 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) BROADCAST RADIO SERVICES RADIO BROADCAST SERVICES Rules Applicable to All Broadcast Stations § 73.4157 Network signals which adversely affect affiliate broadcast service. See Public Notice, FCC 79-387...

  17. Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

    Science.gov (United States)

    2009-03-01

    Jordan JD, Landau EM, Iyengar R (2000) Signaling networks: the origins of cellular multitasking. Cell 103: 193–200. 53. Eungdamrong NJ, Iyengar R...I, Shamir R (2009) Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25: 1158–1164

  18. Determination of outdoor signal propagation via visibility analysis in outdoor wireless networks

    Directory of Open Access Journals (Sweden)

    Mustafa Coşar

    2017-02-01

    Full Text Available Wireless networks on university campuses has gained importance in recent years. These networks in major areas such as university campuses, are faced with many problems during the planning, design and establishment. These problems are among the first that comes to mind, the physical properties of the campus and is selected according to the characteristics of network equipment. There is no doubt at all points of a wireless network set up in order to provide uninterrupted service and quality of the signal is expected to be good. However, it should be understood literally cannot meet these expectations. Therefore, to solve many problems to campus planning and design can be made to have acceptable signal distribution will have the appropriate use of and satisfaction with increasing effect. In this study, due to the start of construction on the North Campus of Hitit University, wireless signal spread using the current spread has been determined with the help of geographic information systems visibility analysis. An area of 56 hectares, with the total of 9 AP the acceptable signal distribution was obtained.

  19. A framework for mapping, visualisation and automatic model creation of signal-transduction networks.

    Science.gov (United States)

    Tiger, Carl-Fredrik; Krause, Falko; Cedersund, Gunnar; Palmér, Robert; Klipp, Edda; Hohmann, Stefan; Kitano, Hiroaki; Krantz, Marcus

    2012-04-24

    Intracellular signalling systems are highly complex. This complexity makes handling, analysis and visualisation of available knowledge a major challenge in current signalling research. Here, we present a novel framework for mapping signal-transduction networks that avoids the combinatorial explosion by breaking down the network in reaction and contingency information. It provides two new visualisation methods and automatic export to mathematical models. We use this framework to compile the presently most comprehensive map of the yeast MAP kinase network. Our method improves previous strategies by combining (I) more concise mapping adapted to empirical data, (II) individual referencing for each piece of information, (III) visualisation without simplifications or added uncertainty, (IV) automatic visualisation in multiple formats, (V) automatic export to mathematical models and (VI) compatibility with established formats. The framework is supported by an open source software tool that facilitates integration of the three levels of network analysis: definition, visualisation and mathematical modelling. The framework is species independent and we expect that it will have wider impact in signalling research on any system.

  20. Signal Quality Outage Analysis for Ultra-Reliable Communications in Cellular Networks

    DEFF Research Database (Denmark)

    Gerardino, Guillermo Andrés Pocovi; Alvarez, Beatriz Soret; Lauridsen, Mads

    2015-01-01

    Ultra-reliable communications over wireless will open the possibility for a wide range of novel use cases and applications. In cellular networks, achieving reliable communication is challenging due to many factors, particularly the fading of the desired signal and the interference. In this regard...

  1. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  2. Expansion of Sphingosine Kinase and Sphingosine-1-Phosphate Receptor Function in Normal and Cancer Cells: From Membrane Restructuring to Mediation of Estrogen Signaling and Stem Cell Programming.

    Science.gov (United States)

    Sukocheva, Olga A

    2018-01-31

    Sphingolipids, sphingolipid metabolizing enzymes, and their receptors network are being recognized as part of the signaling mechanisms, which govern breast cancer cell growth, migration, and survival during chemotherapy treatment. Approximately 70% of breast cancers are estrogen receptor (ER) positive and, thus, rely on estrogen signaling. Estrogen activates an intracellular network composed of many cytoplasmic and nuclear mediators. Some estrogen effects can be mediated by sphingolipids. Estrogen activates sphingosine kinase 1 (SphK1) and amplifies the intracellular concentration of sphingosine-1-phosphate (S1P) in breast cancer cells during stimulation of proliferation and survival. Specifically, Estrogen activates S1P receptors (S1PR) and induces growth factor receptor transactivation. SphK, S1P, and S1PR expression are causally associated with endocrine resistance and progression to advanced tumor stages in ER-positive breast cancers in vivo . Recently, the network of SphK/S1PR was shown to promote the development of ER-negative cancers and breast cancer stem cells, as well as stimulating angiogenesis. Novel findings confirm and broaden our knowledge about the cross-talk between sphingolipids and estrogen network in normal and malignant cells. Current S1PRs therapeutic inhibition was indicated as a promising chemotherapy approach in non-responsive and advanced malignancies. Considering that sphingolipid signaling has a prominent role in terminally differentiated cells, the impact should be considered when designing specific SphK/S1PR inhibitors. This study analyzes the dynamic of the transformation of sphingolipid axis during a transition from normal to pathological condition on the level of the whole organism. The sphingolipid-based mediation and facilitation of global effects of estrogen were critically accented as a bridging mechanism that should be explored in cancer prevention.

  3. Expansion of Sphingosine Kinase and Sphingosine-1-Phosphate Receptor Function in Normal and Cancer Cells: From Membrane Restructuring to Mediation of Estrogen Signaling and Stem Cell Programming

    Directory of Open Access Journals (Sweden)

    Olga A. Sukocheva

    2018-01-01

    Full Text Available Sphingolipids, sphingolipid metabolizing enzymes, and their receptors network are being recognized as part of the signaling mechanisms, which govern breast cancer cell growth, migration, and survival during chemotherapy treatment. Approximately 70% of breast cancers are estrogen receptor (ER positive and, thus, rely on estrogen signaling. Estrogen activates an intracellular network composed of many cytoplasmic and nuclear mediators. Some estrogen effects can be mediated by sphingolipids. Estrogen activates sphingosine kinase 1 (SphK1 and amplifies the intracellular concentration of sphingosine-1-phosphate (S1P in breast cancer cells during stimulation of proliferation and survival. Specifically, Estrogen activates S1P receptors (S1PR and induces growth factor receptor transactivation. SphK, S1P, and S1PR expression are causally associated with endocrine resistance and progression to advanced tumor stages in ER-positive breast cancers in vivo. Recently, the network of SphK/S1PR was shown to promote the development of ER-negative cancers and breast cancer stem cells, as well as stimulating angiogenesis. Novel findings confirm and broaden our knowledge about the cross-talk between sphingolipids and estrogen network in normal and malignant cells. Current S1PRs therapeutic inhibition was indicated as a promising chemotherapy approach in non-responsive and advanced malignancies. Considering that sphingolipid signaling has a prominent role in terminally differentiated cells, the impact should be considered when designing specific SphK/S1PR inhibitors. This study analyzes the dynamic of the transformation of sphingolipid axis during a transition from normal to pathological condition on the level of the whole organism. The sphingolipid-based mediation and facilitation of global effects of estrogen were critically accented as a bridging mechanism that should be explored in cancer prevention.

  4. Responses to olfactory signals reflect network structure of flower-visitor interactions.

    Science.gov (United States)

    Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

    2010-07-01

    1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.

  5. Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells

    Science.gov (United States)

    Johard, Helena; Mahdessian, Diana; Fedr, Radek; Marks, Carolyn; Medalová, Jiřina; Souček, Karel; Lundberg, Emma; Linnarsson, Sten; Bryja, Vítězslav; Sekyrova, Petra; Altun, Mikael; Andäng, Michael

    2017-01-01

    The cell cycle coordinates core functions such as replication and cell division. However, cell-cycle-regulated transcription in the control of non-core functions, such as cell identity maintenance through specific transcription factors (TFs) and signalling pathways remains unclear. Here, we provide a resource consisting of mapped transcriptomes in unsynchronized HeLa and U2OS cancer cells sorted for cell cycle phase by Fucci reporter expression. We developed a novel algorithm for data analysis that enables efficient visualization and data comparisons and identified cell cycle synchronization of Notch signalling and TFs associated with development. Furthermore, the cell cycle synchronizes with the circadian clock, providing a possible link between developmental transcriptional networks and the cell cycle. In conclusion we find that cell cycle synchronized transcriptional patterns are temporally compartmentalized and more complex than previously anticipated, involving genes, which control cell identity and development. PMID:29228002

  6. Crosstalk among hormones and signaling networks during stomatal development in Arabidopsis hypocotyls

    Directory of Open Access Journals (Sweden)

    Laura Serna

    2016-09-01

    Full Text Available During development, signaling networks specify stomatal cell fate and patterning in response to phytohormones. A number of studies in the past few years have revealed that brassinosteroids repress the signaling pathway that inactivates SPEECHLESS (SPCH, promoting stomatal cell fate determination in the hypocotyl. These plant hormones also control stomatal patterning specification by regulating genes in the TTG/BHLHs/MYBs/GL2 network. Gibberellins, like brassinosteroids, promote stomatal formation in the embryonic stem, which suggests that their signaling pathways may converge. These phytohormones also regulate LLM-domain B-GATA factors. The involvement of these factors as positive regulators of stomatal formation, which function upstream of SPCH, suggests that the brassinosteroid and gibberellin signaling pathways may converge to control stomatal cell fate specification. In addition, the leucine-rich repeat-containing receptor-like protein TOO MANY MOUTHS acts later than these hormones in the cell division sequence that triggers stomatal formation, and it also appears to control stomatal initiation in response to brassinosteroids. The emerging picture suggests that crosstalk among hormones and signaling networks guides stomatal cell fate determination and patterning in the hypocotyl.

  7. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  8. Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals.

    Science.gov (United States)

    Manivannan, R; Samidurai, R; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-03-01

    This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs. An improved exponential stability and strictly (Q,S,R)-γ-dissipative conditions of the addressed systems are represented by the linear matrix inequalities (LMIs). Finally, four interesting numerical examples are developed to verify the usefulness of the proposed method with a practical application to a biological network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. cGMP signaling as a target for the prevention and treatment of breast cancer.

    Science.gov (United States)

    Windham, Perrin F; Tinsley, Heather N

    2015-04-01

    One in eight women in the United States will be diagnosed with invasive breast cancer in her lifetime. Advances in therapeutic strategies, diagnosis, and improved awareness have resulted in a significant reduction in breast cancer related mortality. However, there is a continued need for more effective and less toxic drugs for both the prevention and the treatment of breast cancer in order to see a continued decline in the morbidity and mortality associated with this disease. Recent studies suggest that the cGMP signaling pathway may be aberrantly regulated in breast cancer. As such, this pathway may serve as a source of novel targets for future breast cancer drug discovery efforts. This review provides an overview of cGMP signaling in normal physiology and in breast cancer as well as current strategies being investigated for targeting this pathway in breast cancer. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. USP33, a new player in lung cancer, mediates Slit-Robo signaling.

    Science.gov (United States)

    Wen, Pushuai; Kong, Ruirui; Liu, Jianghong; Zhu, Li; Chen, Xiaoping; Li, Xiaofei; Nie, Yongzhan; Wu, Kaichun; Wu, Jane Y

    2014-09-01

    Ubiquitin specific protease 33 (USP33) is a multifunctional protein regulating diverse cellular processes. The expression and role of USP33 in lung cancer remain unexplored. In this study, we show that USP33 is down-regulated in multiple cohorts of lung cancer patients and that low expression of USP33 is associated with poor prognosis. USP33 mediates Slit-Robo signaling in lung cancer cell migration. Downregulation of USP33 reduces the protein stability of Robo1 in lung cancer cells, providing a previously unknown mechanism for USP33 function in mediating Slit activity in lung cancer cells. Taken together, USP33 is a new player in lung cancer that regulates Slit-Robo signaling. Our data suggest that USP33 may be a candidate tumor suppressor for lung cancer with potential as a prognostic marker.

  11. In vitro membrane reconstitution of the T-cell receptor proximal signaling network.

    Science.gov (United States)

    Hui, Enfu; Vale, Ronald D

    2014-02-01

    T-cell receptor (TCR) phosphorylation is controlled by a complex network that includes Lck, a Src family kinase (SFK), the tyrosine phosphatase CD45 and the Lck-inhibitory kinase Csk. How these competing phosphorylation and dephosphorylation reactions are modulated to produce T-cell triggering is not fully understood. Here we reconstituted this signaling network using purified enzymes on liposomes, recapitulating the membrane environment in which they normally interact. We demonstrate that Lck's enzymatic activity can be regulated over an ~10-fold range by controlling its phosphorylation state. By varying kinase and phosphatase concentrations, we constructed phase diagrams that reveal ultrasensitivity in the transition from the quiescent to the phosphorylated state and demonstrate that co-clustering TCR and Lck or detaching Csk from the membrane can trigger TCR phosphorylation. Our results provide insight into the mechanism of TCR signaling as well as other signaling pathways involving SFKs.

  12. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. On-Board Fiber-Optic Network Architectures for Radar and Avionics Signal Distribution

    Science.gov (United States)

    Alam, Mohammad F.; Atiquzzaman, Mohammed; Duncan, Bradley B.; Nguyen, Hung; Kunath, Richard

    2000-01-01

    Continued progress in both civil and military avionics applications is overstressing the capabilities of existing radio-frequency (RF) communication networks based on coaxial cables on board modem aircrafts. Future avionics systems will require high-bandwidth on- board communication links that are lightweight, immune to electromagnetic interference, and highly reliable. Fiber optic communication technology can meet all these challenges in a cost-effective manner. Recently, digital fiber-optic communication systems, where a fiber-optic network acts like a local area network (LAN) for digital data communications, have become a topic of extensive research and development. Although a fiber-optic system can be designed to transport radio-frequency (RF) signals, the digital fiber-optic systems under development today are not capable of transporting microwave and millimeter-wave RF signals used in radar and avionics systems on board an aircraft. Recent advances in fiber optic technology, especially wavelength division multiplexing (WDM), has opened a number of possibilities for designing on-board fiber optic networks, including all-optical networks for radar and avionics RF signal distribution. In this paper, we investigate a number of different novel approaches for fiber-optic transmission of on-board VHF and UHF RF signals using commercial off-the-shelf (COTS) components. The relative merits and demerits of each architecture are discussed, and the suitability of each architecture for particular applications is pointed out. All-optical approaches show better performance than other traditional approaches in terms of signal-to-noise ratio, power consumption, and weight requirements.

  14. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Science.gov (United States)

    Jain, Siddhartha; Gitter, Anthony; Bar-Joseph, Ziv

    2014-12-01

    Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  15. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Directory of Open Access Journals (Sweden)

    Siddhartha Jain

    2014-12-01

    Full Text Available Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  16. Prediction of breast cancer using artificial neural networks.

    Science.gov (United States)

    Saritas, Ismail

    2012-10-01

    In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.

  17. Lung cancer classification using neural networks for CT images.

    Science.gov (United States)

    Kuruvilla, Jinsa; Gunavathi, K

    2014-01-01

    Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer aided classification method in computed tomography (CT) images of lungs developed using artificial neural network. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The statistical parameters like mean, standard deviation, skewness, kurtosis, fifth central moment and sixth central moment are used for classification. The classification process is done by feed forward and feed forward back propagation neural networks. Compared to feed forward networks the feed forward back propagation network gives better classification. The parameter skewness gives the maximum classification accuracy. Among the already available thirteen training functions of back propagation neural network, the Traingdx function gives the maximum classification accuracy of 91.1%. Two new training functions are proposed in this paper. The results show that the proposed training function 1 gives an accuracy of 93.3%, specificity of 100% and sensitivity of 91.4% and a mean square error of 0.998. The proposed training function 2 gives a classification accuracy of 93.3% and minimum mean square error of 0.0942. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  18. Metastases and Colon Cancer Tumor Growth Display Divergent Responses to Modulation of Canonical WNT Signaling.

    Directory of Open Access Journals (Sweden)

    Chandan Seth

    Full Text Available Human colon cancers commonly harbor loss of function mutations in APC, a repressor of the canonical WNT pathway, thus leading to hyperactive WNT-TCF signaling. Re-establishment of Apc function in mice, engineered to conditionally repress Apc through RNAi, resolve the intestinal tumors formed due to hyperactivated Wnt-Tcf signaling. These and other results have prompted the search for specific WNT pathway antagonists as therapeutics for clinically problematic human colon cancers and associated metastases, which remain largely incurable. This widely accepted view seems at odds with a number of findings using patient-derived material: Canonical TCF targets are repressed, instead of being hyperactivated, in advanced colon cancers, and repression of TCF function does not generally result in tumor regression in xenografts. The results of a number of genetic mouse studies have also suggested that canonical WNT-TCF signaling drives metastases, but direct in vivo tests are lacking, and, surprisingly, TCF repression can enhance directly seeded metastatic growth. Here we have addressed the abilities of enhanced and blocked WNT-TCF signaling to alter tumor growth and distant metastases using xenografts of advanced human colon cancers in mice. We find that endogenous WNT-TCF signaling is mostly anti-metastatic since downregulation of TCF function with dnTCF generally enhances metastatic spread. Consistently, elevating the level of WNT signaling, by increasing the levels of WNT ligands, is not generally pro-metastatic. Our present and previous data reveal a heterogeneous response to modulating WNT-TCF signaling in human cancer cells. Nevertheless, the findings that a fraction of colon cancers tested require WNT-TCF signaling for tumor growth but all respond to repressed signaling by increasing metastases beg for a reevaluation of the goal of blocking WNT-TCF signaling to universally treat colon cancers. Our data suggest that WNT-TCF blockade may be effective

  19. Identification of cancer fusion drivers using network fusion centrality

    Science.gov (United States)

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on the theory of exon/domain shuffling. The model leads to a hypothesis that a fusion is more likely to be an oncogenic driver if its partner genes act like hubs in a network because the fusion mutation can deregulate normal functions of many other genes and their pathways. The hypothesis is supported by the observation that for most known cancer fusion genes, at least one of the fusion partners appears to be a hub in a network, and even for many fusions both partners appear to be hubs. Based on this model, we construct fusion centrality, a multi-gene-based network metric, and use it to score fusion drivers. We show that the fusion centrality outperforms other single gene-based methods. Specifically, the method successfully predicts most of 38 newly discovered fusions that had validated oncogenic importance. To our best knowledge, this is the first network-based approach for identifying fusion drivers. Availability: Matlab code implementing the fusion centrality method is available upon request from the corresponding authors. Contact: perwu777@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23505294

  20. Hierarchical attention networks for information extraction from cancer pathology reports.

    Science.gov (United States)

    Gao, Shang; Young, Michael T; Qiu, John X; Yoon, Hong-Jun; Christian, James B; Fearn, Paul A; Tourassi, Georgia D; Ramanthan, Arvind

    2017-11-16

    We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1-G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network. Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macro F-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460). HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

  1. Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling.

    Science.gov (United States)

    Otero-Muras, Irene; Yordanov, Pencho; Stelling, Joerg

    2017-04-01

    Bistability has important implications in signaling pathways, since it indicates a potential cell decision between alternative outcomes. We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks (both with and without mass conservation), and apply them to search for sources of bistability at different levels of the interferon signaling pathway. Different type I interferon subtypes and/or doses are known to elicit differential bioactivities (ranging from antiviral, antiproliferative to immunomodulatory activities). How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question. Here, we detect bistability at the level of early STAT signaling, showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity. This finding could contribute to explain the differential signaling (antiviral vs apoptotic) depending on interferon dose and subtype (α vs β) observed in type I interferons.

  2. Wnt signaling, stem cells, and cancer of the gastrointestinal tract

    NARCIS (Netherlands)

    Schepers, A.; Clevers, H.

    2012-01-01

    The Wnt signaling pathway was originally uncovered as one of the prototype developmental signaling cascades in invertebrates as well as in vertebrates. The first indication that Wnt signaling also plays a role in the adult animal came from the study of the intestine of Tcf-4 (Tcf7L2) knockout mice.

  3. Classification of breast cancer histology images using Convolutional Neural Networks.

    Directory of Open Access Journals (Sweden)

    Teresa Araújo

    Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

  4. ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.

    Science.gov (United States)

    Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul

    2014-03-01

    Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

  5. Experimental video signals distribution MMF network based on IEEE 802.11 standard

    Science.gov (United States)

    Kowalczyk, Marcin; Maksymiuk, Lukasz; Siuzdak, Jerzy

    2014-11-01

    The article was focused on presentation the achievements in a scope of experimental research on transmission of digital video streams in the frame of specially realized for this purpose ROF (Radio over Fiber) network. Its construction was based on the merge of wireless IEEE 802.11 network, popularly referred as Wi-Fi, with a passive optical network PON based on multimode fibers MMF. The proposed approach can constitute interesting proposal in area of solutions in the scope of the systems monitoring extensive, within which is required covering of a large area with ensuring of a relatively high degree of immunity on the interferences transmitted signals from video IP cameras to the monitoring center and a high configuration flexibility (easily change the deployment of cameras) of such network.

  6. Regulation of Hedgehog Signaling in Cancer by Natural and Dietary Compounds.

    Science.gov (United States)

    Bao, Cheng; Kramata, Pavel; Lee, Hong Jin; Suh, Nanjoo

    2018-01-01

    The aberrant Hedgehog (Hh) signaling induced by mutations or overexpression of the signaling mediators has been implicated in cancer, associated with processes including inflammation, tumor cell growth, invasion, and metastasis, as well as cancer stemness. Small molecules targeting the regulatory components of the Hh signaling pathway, especially Smoothened (Smo), have been developed for the treatment of cancer. However, acquired resistance to a Smo inhibitor vismodegib observed in clinical trials suggests that other Hh signaling components need to be explored as potential anticancer targets. Natural and dietary compounds provide a resource for the development of potent agents affecting intracellular signaling cascades, and numerous studies have been conducted to evaluate the efficacy of natural products in targeting the Hh signaling pathway. In this review, we summarize the role of Hh signaling in tumorigenesis, discuss results from recent studies investigating the effect of natural products and dietary components on Hh signaling in cancer, and provide insight on novel small molecules as potential Hh signaling inhibitors. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  8. Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2012-02-01

    Full Text Available Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI to investigate the maturation of control processes underlying problem solving skills in 7-9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI, ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC. We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive

  9. A Review: Phytochemicals Targeting JAK/STAT Signaling and IDO Expression in Cancer.

    Science.gov (United States)

    Arumuggam, Niroshaathevi; Bhowmick, Neil A; Rupasinghe, H P Vasantha

    2015-06-01

    Cancer remains a major health problem worldwide. Among many other factors, two regulatory defects that are present in most cancer cells are constitutive activation of Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway and the induction of indoleamine 2, 3-dioxygenase (IDO), an enzyme that catalyzes tryptophan degradation, through JAK/STAT signaling. Cytokine signaling activates STAT proteins in regulating cell proliferation, differentiation, and survival through modulation of target genes. Many phytochemicals can inhibit both JAK/STAT signaling and IDO expression in antigen-presenting cells by targeting different pathways. Some of the promising phytochemicals that are discussed in this review include resveratrol, cucurbitacin, curcumin, (-)-epigallocatechin gallate, and others. It is now evident that phytochemicals play key roles in inhibition of tumor proliferation and development and provide novel means for therapeutic targeting of cancer. Copyright © 2015 John Wiley & Sons, Ltd.

  10. Massively parallel network architectures for automatic recognition of visual speech signals. Final technical report

    Energy Technology Data Exchange (ETDEWEB)

    Sejnowski, T.J.; Goldstein, M.

    1990-01-01

    This research sought to produce a massively-parallel network architecture that could interpret speech signals from video recordings of human talkers. This report summarizes the project's results: (1) A corpus of video recordings from two human speakers was analyzed with image processing techniques and used as the data for this study; (2) We demonstrated that a feed forward network could be trained to categorize vowels from these talkers. The performance was comparable to that of the nearest neighbors techniques and to trained humans on the same data; (3) We developed a novel approach to sensory fusion by training a network to transform from facial images to short-time spectral amplitude envelopes. This information can be used to increase the signal-to-noise ratio and hence the performance of acoustic speech recognition systems in noisy environments; (4) We explored the use of recurrent networks to perform the same mapping for continuous speech. Results of this project demonstrate the feasibility of adding a visual speech recognition component to enhance existing speech recognition systems. Such a combined system could be used in noisy environments, such as cockpits, where improved communication is needed. This demonstration of presymbolic fusion of visual and acoustic speech signals is consistent with our current understanding of human speech perception.

  11. Bi-stability in type 2 diabetes mellitus multi-organ signalling network.

    Directory of Open Access Journals (Sweden)

    Shubhankar Kulkarni

    Full Text Available Type 2 diabetes mellitus (T2DM is believed to be irreversible although no component of the pathophysiology is irreversible. We show here with a network model that the apparent irreversibility is contributed by the structure of the network of inter-organ signalling. A network model comprising all known inter-organ signals in T2DM showed bi-stability with one insulin sensitive and one insulin resistant attractor. The bi-stability was made robust by multiple positive feedback loops suggesting an evolved allostatic system rather than a homeostatic system. In the absence of the complete network, impaired insulin signalling alone failed to give a stable insulin resistant or hyperglycemic state. The model made a number of correlational predictions many of which were validated by empirical data. The current treatment practice targeting obesity, insulin resistance, beta cell function and normalization of plasma glucose failed to reverse T2DM in the model. However certain behavioural and neuro-endocrine interventions ensured a reversal. These results suggest novel prevention and treatment approaches which need to be tested empirically.

  12. Genetic Gastric Cancer Susceptibility in the International Clinical Cancer Genomics Community Research Network.

    Science.gov (United States)

    Slavin, Thomas; Neuhausen, Susan L; Rybak, Christina; Solomon, Ilana; Nehoray, Bita; Blazer, Kathleen; Niell-Swiller, Mariana; Adamson, Aaron W; Yuan, Yate-Ching; Yang, Kai; Sand, Sharon; Castillo, Danielle; Herzog, Josef; Wu, Xiwei; Tao, Shu; Chavez, Tanya; Woo, Yanghee; Chao, Joseph; Mora, Pamela; Horcasitas, Darling; Weitzel, Jeffrey

    2017-10-01

    Few susceptibility genes for gastric cancer have been identified. We sought to identify germline susceptibility genes from participants with gastric cancer from an international hereditary cancer research network. Adults with gastric cancer of any histology, and with a germline DNA sample (n = 51), were retrospectively selected. For those without previously identified germline mutations (n = 43), sequencing was performed for 706 candidate genes. Twenty pathogenic or likely pathogenic variants were identified among 18 participants. Eight of the 18 participants had previous positive clinical testing, including six with CDH1 pathogenic or likely pathogenic variants, and two with pathogenic MSH2 and TP53 variants. Of the remaining 10, six were in BRCA1 DNA damage response pathway genes (ATM, ATR, BRCA2, BRIP1, FANCC, TP53), other variants were identified in CTNNA1, FLCN, SBDS, and GNAS. Participants identified with pathogenic or likely pathogenic variants were younger at gastric cancer diagnosis than those without, 39.1 versus 48.0 years, and over 50% had a close family member with gastric cancer (p-values < 0.0001). In conclusion, many participants were identified with mutations in clinically-actionable genes. Age of onset and family history of gastric cancer were mutation status predictors. Our findings support multigene panels in identifying gastric cancer predisposition. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Science, Science Signaling, and Science Translational Medicine – AAAS Special Collection on Cancer Research, March 2011

    Directory of Open Access Journals (Sweden)

    Forsythe, Katherine H.

    2011-10-01

    Full Text Available The National Cancer Act, signed in 1971, aimed to eliminate cancer deaths through a massive increase in research funding. The American Association for the Advancement of Science, the publisher of Science, Science Signaling, and Science Translational Medicine, observed the 40th anniversary of the Cancer Act in 2011, with special research articles and features, found in all three journals, on the state of cancer research 40 years later. This collection of articles explores both breakthroughs and the challenges in cancer research over the last four decades, and lets us know what we might expect in the future.

  14. Postdiagnosis social networks and breast cancer mortality in the After Breast Cancer Pooling Project.

    Science.gov (United States)

    Kroenke, Candyce H; Michael, Yvonne L; Poole, Elizabeth M; Kwan, Marilyn L; Nechuta, Sarah; Leas, Eric; Caan, Bette J; Pierce, John; Shu, Xiao-Ou; Zheng, Ying; Chen, Wendy Y

    2017-04-01

    Large social networks have been associated with better overall survival, though not consistently with breast cancer (BC)-specific outcomes. This study evaluated associations of postdiagnosis social networks and BC outcomes in a large cohort. Women from the After Breast Cancer Pooling Project (n = 9267) provided data on social networks within approximately 2 years of their diagnosis. A social network index was derived from information about the presence of a spouse/partner, religious ties, community ties, friendship ties, and numbers of living first-degree relatives. Cox models were used to evaluate associations, and a meta-analysis was used to determine whether effect estimates differed by cohort. Stratification by demographic, social, tumor, and treatment factors was performed. There were 1448 recurrences and 1521 deaths (990 due to BC). Associations were similar in 3 of 4 cohorts. After covariate adjustments, socially isolated women (small networks) had higher risks of recurrence (hazard ratio [HR], 1.43; 95% confidence interval [CI], 1.15-1.77), BC-specific mortality (HR, 1.64; 95% CI, 1.33-2.03), and total mortality (HR, 1.69; 95% CI, 1.43-1.99) than socially integrated women; associations were stronger in those with stage I/II cancer. In the fourth cohort, there were no significant associations with BC-specific outcomes. A lack of a spouse/partner (P = .02) and community ties (P = .04) predicted higher BC-specific mortality in older white women but not in other women. However, a lack of relatives (P = .02) and friendship ties (P = .01) predicted higher BC-specific mortality in nonwhite women only. In a large pooled cohort, larger social networks were associated with better BC-specific and overall survival. Clinicians should assess social network information as a marker of prognosis because critical supports may differ with sociodemographic factors. Cancer 2017;123:1228-1237. © 2016 American Cancer Society. © 2016 American Cancer Society.

  15. Plant morphogenesis, auxin, and the signal-trafficking network incompleteness theorem

    Directory of Open Access Journals (Sweden)

    Karl J. Niklas

    2012-03-01

    Full Text Available Plant morphogenesis (the development of form and function requires signal-trafficking and cross-talking among all levels of organization to coordinate the operation of metabolic and genomic networked systems. Many if not all of these biological features can be rendered as logic circuits supervising the operation of one or more signal-activated metabolic or genome networks. This approach simplifies complex morphogenetic phenomena and allows for their aggregation into diagrams of larger, more "global" networked systems. This conceptualization is illustrated for morphogenesis in model plants such as maize (Zea mays and Thale cress (Arabidopsis thaliana from an evolutionary perspective. The phytohormone indole-acetic acid (IAA is used as an example for a well-known signaling chemical and discussed in terms of the logic circuits and signal-activated sub-systems for hormone-mediated wall loosening and cell expansion as well as polar/lateral intercellular IAA transport. For each of these phenomena, a circuit/sub-system diagram highlights missing components, either in the logic circuit or in the sub-system it supervises, that must be identified experimentally if each of these basic phenomena is to be fully understood within a phylogen

  16. An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals

    Directory of Open Access Journals (Sweden)

    Marsel Mano

    2013-04-01

    Full Text Available Brain machine interface (BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.

  17. Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

    Science.gov (United States)

    Dasgupta, Hirak

    2016-12-01

    The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

  18. Gedunin inhibits pancreatic cancer by altering sonic hedgehog signaling pathway

    OpenAIRE

    Subramani, Ramadevi; Gonzalez, Elizabeth; Nandy, Sushmita Bose; Arumugam, Arunkumar; Camacho, Fernando; Medel, Joshua; Alabi, Damilola; Lakshmanaswamy, Rajkumar

    2016-01-01

    INTRODUCTION The lack of efficient treatment options for pancreatic cancer highlights the critical need for the development of novel and effective chemotherapeutic agents. The medicinal properties found in plants have been used to treat many different illnesses including cancers. This study focuses on the anticancer effects of gedunin, a natural compound isolated from Azadirachta indica. METHODS Anti?proliferative effect of gedunin on pancreatic cancer cells was assessed using MTS assay. We u...

  19. The Role of Erythropoietin Signaling in Human Cancer

    Science.gov (United States)

    2004-01-01

    would like to thank Dr. Rosemary Borke whose introduction to neuroanatomy offered a refreshing sense of awareness of the nervous system...neck cancer were terminated prematurely due to adverse outcomes in the Epo treated group, including increased local regional spread and increased...trials designed to evaluate the therapeutic benefit of Epo in breast cancer and head and neck cancer were terminated prematurely due to adverse

  20. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.

    Science.gov (United States)

    Chia, Joanne; Goh, Germaine; Racine, Victor; Ng, Susanne; Kumar, Pankaj; Bard, Frederic

    2012-01-01

    The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

  1. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  2. Signal-transduction networks and the regulation of muscle protein degradation.

    Science.gov (United States)

    Szewczyk, Nathaniel J; Jacobson, Lewis A

    2005-10-01

    Protein degradation in muscle functions in maintaining normal physiological homeostasis and adapting to new homeostatic states, and is required for muscle wasting or atrophy in various pathological states. The interplay between protein synthesis and degradation to maintain homeostasis is complex and responds to a variety of autocrine and intercellular signals from neuronal inputs, hormones, cytokines, growth factors and other regulatory molecules. The intracellular events that connect extracellular signals to the molecular control of protein degradation are incompletely understood, but likely involve interacting signal-transduction networks rather than isolated pathways. We review some examples of signal-transduction systems that regulate protein degradation, including effectors of proteolysis inducing factor (PIF), insulin and insulin-like growth factor (IGF) and their receptors, and fibroblast growth factor (FGF) and its receptors.

  3. RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

    Science.gov (United States)

    Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

    2008-10-01

    A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

  4. Prostate Cancer Stem Cells and Nanotechnology: A Focus on Wnt Signaling.

    Science.gov (United States)

    Qin, Wei; Zheng, Yongjiang; Qian, Bin-Zhi; Zhao, Meng

    2017-01-01

    Prostate cancer is the most common cancer among men worldwide. However, current treatments for prostate cancer patients in advanced stage often fail because of relapse. Prostate cancer stem cells (PCSCs) are resistant to most standard therapies, and are considered to be a major mechanism of cancer metastasis and recurrence. In this review, we summarized current understanding of PCSCs and their self-renewal signaling pathways with a specific focus on Wnt signaling. Although multiple Wnt inhibitors have been developed to target PCSCs, their application is still limited by inefficient delivery and toxicity in vivo. Recently, nanotechnology has opened a new avenue for cancer drug delivery, which significantly increases specificity and reduces toxicity. These nanotechnology-based drug delivery methods showed great potential in targeting PCSCs. Here, we summarized current advancement of nanotechnology-based therapeutic strategies for targeting PCSCs and highlighted the challenges and perspectives in designing future therapies to eliminate PCSCs.

  5. Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.

    Science.gov (United States)

    Bianconi, Fortunato; Baldelli, Elisa; Ludovini, Vienna; Luovini, Vienna; Petricoin, Emanuel F; Crinò, Lucio; Valigi, Paolo

    2015-10-19

    The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits. The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

  6. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  7. Methodologies for the modeling and simulation of biochemical networks, illustrated for signal transduction pathways: a primer.

    Science.gov (United States)

    ElKalaawy, Nesma; Wassal, Amr

    2015-03-01

    Biochemical networks depict the chemical interactions that take place among elements of living cells. They aim to elucidate how cellular behavior and functional properties of the cell emerge from the relationships between its components, i.e. molecules. Biochemical networks are largely characterized by dynamic behavior, and exhibit high degrees of complexity. Hence, the interest in such networks is growing and they have been the target of several recent modeling efforts. Signal transduction pathways (STPs) constitute a class of biochemical networks that receive, process, and respond to stimuli from the environment, as well as stimuli that are internal to the organism. An STP consists of a chain of intracellular signaling processes that ultimately result in generating different cellular responses. This primer presents the methodologies used for the modeling and simulation of biochemical networks, illustrated for STPs. These methodologies range from qualitative to quantitative, and include structural as well as dynamic analysis techniques. We describe the different methodologies, outline their underlying assumptions, and provide an assessment of their advantages and disadvantages. Moreover, publicly and/or commercially available implementations of these methodologies are listed as appropriate. In particular, this primer aims to provide a clear introduction and comprehensive coverage of biochemical modeling and simulation methodologies for the non-expert, with specific focus on relevant literature of STPs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  8. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  9. Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase

    Directory of Open Access Journals (Sweden)

    Nudelman Irina

    2010-10-01

    Full Text Available Abstract Background Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. Description We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to

  10. Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase.

    Science.gov (United States)

    Patil, Sonali; Pincas, Hanna; Seto, Jeremy; Nudelman, German; Nudelman, Irina; Sealfon, Stuart C

    2010-10-07

    Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to pathogen detection. This map represents a navigable

  11. Epsin is required for Dishevelled stability and Wnt signalling activation in colon cancer development.

    Science.gov (United States)

    Chang, Baojun; Tessneer, Kandice L; McManus, John; Liu, Xiaolei; Hahn, Scott; Pasula, Satish; Wu, Hao; Song, Hoogeun; Chen, Yiyuan; Cai, Xiaofeng; Dong, Yunzhou; Brophy, Megan L; Rahman, Ruby; Ma, Jian-Xing; Xia, Lijun; Chen, Hong

    2015-03-16

    Uncontrolled canonical Wnt signalling supports colon epithelial tumour expansion and malignant transformation. Understanding the regulatory mechanisms involved is crucial for elucidating the pathogenesis of and will provide new therapeutic targets for colon cancer. Epsins are ubiquitin-binding adaptor proteins upregulated in several human cancers; however, the involvement of epsins in colon cancer is unknown. Here we show that loss of intestinal epithelial epsins protects against colon cancer by significantly reducing the stability of the crucial Wnt signalling effector, dishevelled (Dvl2), and impairing Wnt signalling. Consistently, epsins and Dvl2 are correspondingly upregulated in colon cancer. Mechanistically, epsin binds Dvl2 via its epsin N-terminal homology domain and ubiquitin-interacting motifs and prohibits Dvl2 polyubiquitination and degradation. Our findings reveal an unconventional role for epsins in stabilizing Dvl2 and potentiating Wnt signalling in colon cancer cells to ensure robust colon cancer progression. The pro-carcinogenic role of Epsins suggests that they are potential therapeutic targets to combat colon cancer.

  12. Ligand-dependent Notch signaling is involved in tumor initiation and tumor maintenance in pancreatic cancer

    NARCIS (Netherlands)

    Mullendore, Michael E.; Koorstra, Jan-Bart; Li, Yue-Ming; Offerhaus, G. Johan; Fan, Xing; Henderson, Clark M.; Matsui, William; Eberhart, Charles G.; Maitra, Anirban; Feldmann, Georg

    2009-01-01

    PURPOSE: Aberrant activation of the Notch signaling pathway is commonly observed in human pancreatic cancer, although the mechanism(s) for this activation has not been elucidated. EXPERIMENTAL DESIGN: A panel of 20 human pancreatic cancer cell lines was profiled for the expression of Notch

  13. SRC Homology 2 Domain Binding Sites in Insulin, IGF-1 and FGF receptor mediated signaling networks reveal an extensive potential interactome

    Directory of Open Access Journals (Sweden)

    Liu Bernard A

    2012-09-01

    Full Text Available Abstract Specific peptide ligand recognition by modular interaction domains is essential for the fidelity of information flow through the signal transduction networks that control cell behavior in response to extrinsic and intrinsic stimuli. Src homology 2 (SH2 domains recognize distinct phosphotyrosine peptide motifs, but the specific sites that are phosphorylated and the complement of available SH2 domains varies considerably in individual cell types. Such differences are the basis for a wide range of available protein interaction microstates from which signaling can evolve in highly divergent ways. This underlying complexity suggests the need to broadly map the signaling potential of systems as a prerequisite for understanding signaling in specific cell types as well as various pathologies that involve signal transduction such as cancer, developmental defects and metabolic disorders. This report describes interactions between SH2 domains and potential binding partners that comprise initial signaling downstream of activated fibroblast growth factor (FGF, insulin (Ins, and insulin-like growth factor-1 (IGF-1 receptors. A panel of 50 SH2 domains screened against a set of 192 phosphotyrosine peptides defines an extensive potential interactome while demonstrating the selectivity of individual SH2 domains. The interactions described confirm virtually all previously reported associations while describing a large set of potential novel interactions that imply additional complexity in the signaling networks initiated from activated receptors. This study of pTyr ligand binding by SH2 domains provides valuable insight into the selectivity that underpins complex signaling networks that are assembled using modular protein interaction domains.

  14. MicroRNA Networks in Breast Cancer Cells.

    Science.gov (United States)

    Tahiri, Andliena; Aure, Miriam R; Kristensen, Vessela N

    2018-01-01

    A variety of molecular techniques can be used in order to unravel the molecular composition of cells. In particular, the microarray technology has been used to identify novel biomarkers that may be useful in the diagnosis, prognosis, or treatment of cancer. The microarray technology is ideal for biomarker discovery as it allows for the screening of a large number of molecules at once. In this review, we focus on microRNAs (miRNAs) which are key molecules in cells and regulate gene expression post-transcriptionally. miRNAs are small, single-stranded RNA molecules that bind to complementary mRNAs. Binding of miRNAs to mRNAs leads either to degradation, or translational inhibition of the target mRNA. Roughly one third of all the mRNAs are postulated to be regulated by miRNAs. miRNAs are known to be deregulated in different types of cancer, including breast cancer, and it has been demonstrated that deregulation of several miRNAs can be used as biological markers in cancer. miRNA expression can for example discriminate between normal, benign and malignant breast tissue, and between different breast cancer subtypes.In the post-genomic era, an important task of molecular biology is to understand gene regulation in the context of biological networks. Because miRNAs have such a pronounced role in cells, it is pivotal to understand the mechanisms that underlie their control, and to identify how miRNAs influence cancer development and progression.

  15. History of the Rare Cancer Network and past research

    Directory of Open Access Journals (Sweden)

    René-Olivier Mirimanoff

    2014-08-01

    Full Text Available Approximately, twenty years ago, the Rare Cancer Network (RCN was formed in Lausanne, Switzerland, to support the study of rare malignancies. The RCN has grown over the years and now includes 130 investigators from twenty-four nations on six continents. The network held its first international symposium in Nice, France, on March 21-22, 2014. The proceedings of that meeting are presented in two companion papers. This manuscript reviews the history of the growth of the RCN and contains the abstracts of fourteen oral presentations made at the meeting of prior RCN studies. From 1993 to 2014, 74 RCN studies have been initiated, of which 54 were completed, 10 are in progress or under analysis, and 9 were stopped due to poor accrual. Forty-four peer reviewed publications have been written on behalf of the RCN.

  16. Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

    Directory of Open Access Journals (Sweden)

    Lun Li

    2014-01-01

    Full Text Available Associating microRNAs (miRNAs with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.

  17. Quantitative analysis of HGF and EGF-dependent phosphotyrosine signaling networks

    DEFF Research Database (Denmark)

    Hammond, Dean E; Hyde, Russell; Kratchmarova, Irina

    2010-01-01

    between the respective signaling networks but also clear points of departure. A small number of HGF specific effectors were identified including myosin-X, galectin-1, ELMO2 and EphrinB1, while a larger set of EGF specific effectors (39 proteins) includes both novel (e.g., MAP4K3) and established......We have used stable isotope labeling by amino acids in cell culture (SILAC), in combination with high-resolution mass spectrometry, to identify common and discrete components of the respective receptor tyrosine kinase-dependent phosphotyrosine-associated networks induced by acute stimulation of A...

  18. The Oncogenic Palmitoyi-Protein Network in Prostate Cancer

    Science.gov (United States)

    2015-06-01

    e1004405. 13. Ong S -E, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach...Unlimited The views, opinions and/or findings contained in this report are those of the author( s ) and should not be construed as an official Department of...Protein Network in Prostate Cancer 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-10-1-0106 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) Michael R. Freeman

  19. CAMK2N1 inhibits prostate cancer progression through androgen receptor-dependent signaling

    OpenAIRE

    Wang, Tao; Guo, ShuiMing; Liu, Zhuo; Wu, Licheng; Li, Mingchao; Yang, Jun; Chen, Ruibao; Liu, Xiaming; Xu, Hua; Cai, Shaoxin; CHEN, Hui; LI, WEIYONG; Xu, Shaohua; Wang, Liang; Hu, Zhiquan

    2014-01-01

    Castration resistance is a major obstacle to hormonal therapy for prostate cancer patients. Although androgen independence of prostate cancer growth is a known contributing factor to endocrine resistance, the mechanism of androgen receptor deregulation in endocrine resistance is still poorly understood. Herein, the CAMK2N1 was shown to contribute to the human prostate cancer cell growth and survival through AR-dependent signaling. Reduced expression of CAMK2N1 was correlated to recurrence-fre...

  20. Distributed geolocation algorithm in mobile ad hoc networks using received signal strength differences

    Science.gov (United States)

    Guo, Shanzeng; Tang, Helen

    2012-05-01

    Future military wireless communication in a battlefield will be mobile ad hoc in nature. The ability to geolocate and track both friendly forces and enemies is very important in military command and control operations. However, current mobile ad hoc networks (MANET) have no capabilities to geolocate radio emitters that belong to enemy mobile ad hoc networks. This paper presents a distributed geolocation algorithm using received signal strength differences to geolocate enemy radio emitters by leveraging friendly force MANET infrastructure, and proposes a communication protocol for radio emitter geolocation applications. An enemy's radio emitter signal is detected, and its signal strength is measured by the nodes in a friendly mobile ad hoc network. The identity of the enemy radio emitter is extracted from the decoded message header of the medium access control layer. By correlating and associating the enemy's radio emitter identity with its received signal strength, the enemy radio emitter is identified. The enemy's radio emitter identity and its received signal strength are distributed and shared among friendly mobile ad hoc nodes. Using received signal strength differences, a master friendly node can calculate the enemy's radio emitter geolocation, and build a recognized MANET picture (RMP). This MANET picture is then distributed to all friendly nodes for effective command and control operations. An advantage of this method is that mobile ad hoc nodes do not need special RF antennas to geolocate the enemy radio emitter as conventional electronic warfare techniques do. MATLAB-based simulations are presented to evaluate the accuracy and reliability of the proposed distributed geolocation algorithm under different MANET placements.

  1. Simulation study on effects of signaling network structure on the developmental increase in complexity

    Energy Technology Data Exchange (ETDEWEB)

    Keranen, Soile V.E.

    2003-04-02

    The developmental increase in structural complexity in multicellular life forms depends on local, often non-periodic differences in gene expression. These depend on a network of gene-gene interactions coded within the organismal genome. To better understand how genomic information generates complex expression patterns, I have modeled the pattern forming behavior of small artificial genomes in virtual blastoderm embryos. I varied several basic properties of these genomic signaling networks, such as the number of genes, the distributions of positive (inductive) and negative (repressive) interactions, and the strengths of gene-gene interactions, and analyzed their effects on developmental pattern formation. The results show how even simple genomes can generate complex non-periodic patterns under suitable conditions. They also show how the frequency of complex patterns depended on the numbers and relative arrangements of positive and negative interactions. For example, negative co-regulation of signaling pathway components increased the likelihood of (complex) patterns relative to differential negative regulation of the pathway components. Interestingly, neither quantitative differences either in strengths of signaling interactions nor multiple response thresholds to signal concentration (as in morphogen gradients) were essential for formation of multiple, spatially unique cell types. Thus, with combinatorial code of gene regulation and hierarchical signaling interactions, it is theoretically possible to organize metazoan embryogenesis with just a small fraction of the metazoan genome. Because even small networks can generate complex patterns when they contain a suitable set of connections, evolution of metazoan complexity may have depended more on selection for favourable configurations of signaling interactions than on the increase in numbers of regulatory genes.

  2. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

    Science.gov (United States)

    Smith, Aaron; Evans, Michael; Downey, Joseph

    2017-01-01

    National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

  3. Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Elham Ghoochani

    2011-03-01

    Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

  4. Genetic algorithm for the optimization of features and neural networks in ECG signals classification.

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-31

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  5. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    Science.gov (United States)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  6. Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks.

    Science.gov (United States)

    Foremski, Paweł; Gorawski, Michał; Grochla, Krzysztof; Polys, Konrad

    2015-09-02

    The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.

  7. Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

    Science.gov (United States)

    Curilem, Gloria; Vergara, Jorge; Fuentealba, Gustavo; Acuña, Gonzalo; Chacón, Max

    2009-02-01

    Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The

  8. QoS signaling across heterogeneous wired/wireless networks: resource management in diffserv using the NSIS protocol suite

    NARCIS (Netherlands)

    Bader, Attila; Karagiannis, Georgios; Westberg, Lars; Kappler, Cornelia; Phelan, Tom; Tschofenig, Hannes; Heijenk, Geert; Shen, S.

    2005-01-01

    Reservation-based Quality of Service (QoS) in a mixed wireless and wireline environment requires an end-to-end signaling protocol that is capable of adapting to the idiosyncrasies of the different networks. The QoS NSIS Signaling Protocol (QoSNSLP) has been created by the Next Steps In Signaling

  9. Wireless Network of Collaborative Physiological Signal Devices in a U-Healthcare System

    Science.gov (United States)

    Jung, Joonyoung; Kim, Daeyoung

    We designed and implemented collaborative physiological signal devices in a u-healthcare(ubiquitous healthcare) system. In this system, wireless body area network (WBAN) such as ZigBee is used to communicate between physiological signal devices and the mobile system. WBAN device needs a specific function for ubiquitous healthcare application. We show several collaborative physiological devices and propose WBAN mechanism such as a fast scanning algorithm, a dynamic discovery and installation mechanism, a reliable data transmission, a device access control for security, and a healthcare profile for u-healthcare system.

  10. EU-ADR healthcare database network vs. spontaneous reporting system database: preliminary comparison of signal detection.

    Science.gov (United States)

    Trifirò, Gianluca; Patadia, Vaishali; Schuemie, Martijn J; Coloma, Preciosa M; Gini, Rosa; Herings, Ron; Hippisley-Cox, Julia; Mazzaglia, Giampiero; Giaquinto, Carlo; Scotti, Lorenza; Pedersen, Lars; Avillach, Paul; Sturkenboom, Miriam C J M; van der Lei, Johan; Eu-Adr Group

    2011-01-01

    The EU-ADR project aims to exploit different European electronic healthcare records (EHR) databases for drug safety signal detection. In this paper we report the preliminary results concerning the comparison of signal detection between EU-ADR network and two spontaneous reporting databases, the Food and Drug Administration and World Health Organization databases. EU-ADR data sources consist of eight databases in four countries (Denmark, Italy, Netherlands, and United Kingdom) that are virtually linked through distributed data network. A custom-built software (Jerboa©) elaborates harmonized input data that are produced locally and generates aggregated data which are then stored in a central repository. Those data are subsequently analyzed through different statistics (i.e. Longitudinal Gamma Poisson Shrinker). As potential signals, all the drugs that are associated to six events of interest (bullous eruptions - BE, acute renal failure - ARF, acute myocardial infarction - AMI, anaphylactic shock - AS, rhabdomyolysis - RHABD, and upper gastrointestinal bleeding - UGIB) have been detected via different data mining techniques in the two systems. Subsequently a comparison concerning the number of drugs that could be investigated and the potential signals detected for each event in the spontaneous reporting systems (SRSs) and EU-ADR network was made. SRSs could explore, as potential signals, a larger number of drugs for the six events, in comparison to EU-ADR (range: 630-3,393 vs. 87-856), particularly for those events commonly thought to be potentially drug-induced (i.e. BE: 3,393 vs. 228). The highest proportion of signals detected in SRSs was found for BE, ARF and AS, while for ARF, and UGIB in EU-ADR. In conclusion, it seems that EU-ADR longitudinal database network may complement traditional spontaneous reporting system for signal detection, especially for those adverse events that are frequent in general population and are not commonly thought to be drug

  11. Activation of NF-kappa B signaling promotes growth of prostate cancer cells in bone.

    Directory of Open Access Journals (Sweden)

    Renjie Jin

    Full Text Available Patients with advanced prostate cancer almost invariably develop osseous metastasis. Although many studies indicate that the activation of NF-κB signaling appears to be correlated with advanced cancer and promotes tumor metastasis by influencing tumor cell migration and angiogenesis, the influence of altered NF-κB signaling in prostate cancer cells within boney metastatic lesions is not clearly understood. While C4-2B and PC3 prostate cancer cells grow well in the bone, LNCaP cells are difficult to grow in murine bone following intraskeletal injection. Our studies show that when compared to LNCaP, NF-κB activity is significantly higher in C4-2B and PC3, and that the activation of NF-κB signaling in prostate cancer cells resulted in the increased expression of the osteoclast inducing genes PTHrP and RANKL. Further, conditioned medium derived from NF-κB activated LNCaP cells induce osteoclast differentiation. In addition, inactivation of NF-κB signaling in prostate cancer cells inhibited tumor formation in the bone, both in the osteolytic PC3 and osteoblastic/osteoclastic mixed C4-2B cells; while the activation of NF-κB signaling in LNCaP cells promoted tumor establishment and proliferation in the bone. The activation of NF-κB in LNCaP cells resulted in the formation of an osteoblastic/osteoclastic mixed tumor with increased osteoclasts surrounding the new formed bone, similar to metastases commonly seen in patients with prostate cancer. These results indicate that osteoclastic reaction is required even in the osteoblastic cancer cells and the activation of NF-κB signaling in prostate cancer cells increases osteoclastogenesis by up-regulating osteoclastogenic genes, thereby contributing to bone metastatic formation.

  12. Curcumin and emodin down-regulate TGF-β signaling pathway in human cervical cancer cells.

    Directory of Open Access Journals (Sweden)

    Pooja Chandrakant Thacker

    Full Text Available Cervical cancer is the major cause of cancer related deaths in women, especially in developing countries and Human Papilloma Virus infection in conjunction with multiple deregulated signaling pathways leads to cervical carcinogenesis. TGF-β signaling in later stages of cancer is known to induce epithelial to mesenchymal transition promoting tumor growth. Phytochemicals, curcumin and emodin, are effective as chemopreventive and chemotherapeutic compounds against several cancers including cervical cancer. The main objective of this work was to study the effect of curcumin and emodin on TGF-β signaling pathway and its functional relevance to growth, migration and invasion in two cervical cancer cell lines, SiHa and HeLa. Since TGF-β and Wnt/β-catenin signaling pathways are known to cross talk having common downstream targets, we analyzed the effect of TGF-β on β-catenin (an important player in Wnt/β-catenin signaling and also studied whether curcumin and emodin modulate them. We observed that curcumin and emodin effectively down regulate TGF-β signaling pathway by decreasing the expression of TGF-β Receptor II, P-Smad3 and Smad4, and also counterbalance the tumorigenic effects of TGF-β by inhibiting the TGF-β-induced migration and invasion. Expression of downstream effectors of TGF-β signaling pathway, cyclinD1, p21 and Pin1, was inhibited along with the down regulation of key mesenchymal markers (Snail and Slug upon curcumin and emodin treatment. Curcumin and emodin were also found to synergistically inhibit cell population and migration in SiHa and HeLa cells. Moreover, we found that TGF-β activates Wnt/β-catenin signaling pathway in HeLa cells, and curcumin and emodin down regulate the pathway by inhibiting β-catenin. Taken together our data provide a mechanistic basis for the use of curcumin and emodin in the treatment of cervical cancer.

  13. TCR signalling network organization at the immunological synapses of murine regulatory T cells.

    Science.gov (United States)

    van Ham, Marco; Teich, René; Philipsen, Lars; Niemz, Jana; Amsberg, Nicole; Wissing, Josef; Nimtz, Manfred; Gröbe, Lothar; Kliche, Stefanie; Thiel, Nadine; Klawonn, Frank; Hubo, Mario; Jonuleit, Helmut; Reichardt, Peter; Müller, Andreas J; Huehn, Jochen; Jänsch, Lothar

    2017-08-17

    Regulatory T (Treg) cells require T-cell receptor (TCR) signalling to exert their immunosuppressive activity, but the precise organization of the TCR signalling network compared to conventional T (Tconv) cells remains elusive. By using accurate mass spectrometry and multi-epitope ligand cartography (MELC) we characterized TCR signalling and recruitment of TCR signalling components to the immunological synapse (IS) in Treg cells and Tconv cells. With the exception of Themis which we detected in lower amounts in Treg cells, other major TCR signalling components were found equally abundant, however, their phosphorylation-status notably discriminates Treg cells from Tconv cells. Overall, this study identified 121 Treg cell-specific phosphorylations. Short-term triggering of T cell subsets via CD3 and CD28 widely harmonized these variations with the exception of eleven TCR signalling components that mainly regulate cytoskeleton dynamics and molecular transport. Accordingly, conjugation with B cells indeed caused variant cellular morphology and revealed a Treg cell-specific recruitment of TCR signalling components such as PKCθ, PLCγ1 and ZAP70 as well as B cell-derived CD86 into the IS. Together, results from this study support the existence of a Treg cell-specific IS and suggest Treg cell-specific cytoskeleton dynamics as a novel determinant for the unique functional properties of Treg cells. © 2017 The Authors. European Journal of Immunology published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Induction of epithelial-mesenchymal transition (EMT) in breast cancer cells is calcium signal dependent.

    Science.gov (United States)

    Davis, F M; Azimi, I; Faville, R A; Peters, A A; Jalink, K; Putney, J W; Goodhill, G J; Thompson, E W; Roberts-Thomson, S J; Monteith, G R

    2014-05-01

    Signals from the tumor microenvironment trigger cancer cells to adopt an invasive phenotype through epithelial-mesenchymal transition (EMT). Relatively little is known regarding key signal transduction pathways that serve as cytosolic bridges between cell surface receptors and nuclear transcription factors to induce EMT. A better understanding of these early EMT events may identify potential targets for the control of metastasis. One rapid intracellular signaling pathway that has not yet been explored during EMT induction is calcium. Here we show that stimuli used to induce EMT produce a transient increase in cytosolic calcium levels in human breast cancer cells. Attenuation of the calcium signal by intracellular calcium chelation significantly reduced epidermal growth factor (EGF)- and hypoxia-induced EMT. Intracellular calcium chelation also inhibited EGF-induced activation of signal transducer and activator of transcription 3 (STAT3), while preserving other signal transduction pathways such as Akt and extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation. To identify calcium-permeable channels that may regulate EMT induction in breast cancer cells, we performed a targeted siRNA-based screen. We found that transient receptor potential-melastatin-like 7 (TRPM7) channel expression regulated EGF-induced STAT3 phosphorylation and expression of the EMT marker vimentin. Although intracellular calcium chelation almost completely blocked the induction of many EMT markers, including vimentin, Twist and N-cadherin, the effect of TRPM7 silencing was specific for vimentin protein expression and STAT3 phosphorylation. These results indicate that TRPM7 is a partial regulator of EMT in breast cancer cells, and that other calcium-permeable ion channels are also involved in calcium-dependent EMT induction. In summary, this work establishes an important role for the intracellular calcium signal in the induction of EMT in human breast cancer cells. Manipulation of

  15. Aggressive surgery for borderline resectable pancreatic cancer: evaluation of National Comprehensive Cancer Network guidelines.

    Science.gov (United States)

    Yamada, Suguru; Fujii, Tsutomu; Sugimoto, Hiroyuki; Nomoto, Shuji; Takeda, Shin; Kodera, Yasuhiro; Nakao, Akimasa

    2013-08-01

    The objective of this study was to evaluate the relevance of defining borderline resectable (BR) pancreatic cancer as a distinct entity in the treatment scheme of pancreatic cancer as proposed by the National Comprehensive Cancer Network. Among 375 patients with pancreatic cancer, 137 patients were deemed to have resectable disease (R) by preoperative imaging studies, whereas 96 were found to have an unresectable disease during surgery. The remaining 142 patients fulfilled the definition of BR and were further classified into 3 subgroups based on the National Comprehensive Cancer Network guidelines: portal vein invasion (PV[+]), common hepatic artery invasion (CHA[+]), and superior mesenteric artery invasion (SMA[+]). PV(+) was subdivided into types B, C, and D according to the degree of portal vein invasion. Patients in the R group had significantly better survival than those in the PV(+) group (P = 0.0038), who in turn survived significantly longer than those classified as SMA(+) (P = 0.041). Type B patients survived significantly longer than did types C and D patients (P = 0.013 and P = 0.030, respectively). In PV(+) patients, compliance with postoperative chemotherapy at 3 and 6 months was 56.9% and 44.6%, respectively, substantially inferior to patients with resectable disease (72.6% and 54.7%, respectively). The optimal treatment strategy may differ among various subgroups within the BR category.

  16. Involvement of Different networks in mammary gland involution after the pregnancy/lactation cycle: Implications in breast cancer.

    Science.gov (United States)

    Zaragozá, Rosa; García-Trevijano, Elena R; Lluch, Ana; Ribas, Gloria; Viña, Juan R

    2015-04-01

    Early pregnancy is associated with a reduction in a woman's lifetime risk for breast cancer. However, different studies have demonstrated an increase in breast cancer risk in the years immediately following pregnancy. Early and long-term risk is even higher if the mother age is above 35 years at the time of first parity. The proinflammatory microenvironment within the mammary gland after pregnancy renders an "ideal niche" for oncogenic events. Signaling pathways involved in programmed cell death and tissue remodeling during involution are also activated in breast cancer. Herein, the major signaling pathways involved in mammary gland involution, signal transducer and activator of transcription (STAT3), nuclear factor-kappa B (NF-κB), tran