Academic literature on the topic 'Network pathway'
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Journal articles on the topic "Network pathway"
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Milano, Marianna, Giuseppe Agapito, and Mario Cannataro. "Challenges and Limitations of Biological Network Analysis." BioTech 11, no. 3 (July 7, 2022): 24. http://dx.doi.org/10.3390/biotech11030024.
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High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
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Zheng, Fang, Le Wei, Liang Zhao, and FuChuan Ni. "Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks." BioMed Research International 2018 (July 30, 2018): 1–12. http://dx.doi.org/10.1155/2018/5670210.
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Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.
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Wong, Yung-Hao, Chia-Chou Wu, Chih-Lung Lin, Ting-Shou Chen, Tzu-Hao Chang, and Bor-Sen Chen. "Applying NGS Data to Find Evolutionary Network Biomarkers from the Early and Late Stages of Hepatocellular Carcinoma." BioMed Research International 2015 (2015): 1–27. http://dx.doi.org/10.1155/2015/391475.
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Hepatocellular carcinoma (HCC) is a major liver tumor (~80%), besides hepatoblastomas, angiosarcomas, and cholangiocarcinomas. In this study, we used a systems biology approach to construct protein-protein interaction networks (PPINs) for early-stage and late-stage liver cancer. By comparing the networks of these two stages, we found that the two networks showed some common mechanisms and some significantly different mechanisms. To obtain differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two stages of liver cancer by systems biology method using NGS data from cancer cells and adjacent noncancer cells. Using carcinogenesis relevance values (CRVs), we identified 43 and 80 significant proteins and their PPINs (network markers) for early-stage and late-stage liver cancer. To investigate the evolution of network biomarkers in the carcinogenesis process, a primary pathway analysis showed that common pathways of the early and late stages were those related to ordinary cancer mechanisms. A pathway specific to the early stage was the mismatch repair pathway, while pathways specific to the late stage were the spliceosome pathway, lysine degradation pathway, and progesterone-mediated oocyte maturation pathway. This study provides a new direction for cancer-targeted therapies at different stages.
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Villeneuve, Daniel L., Michelle M. Angrish, Marie C. Fortin, Ioanna Katsiadaki, Marc Leonard, Luigi Margiotta-Casaluci, Sharon Munn, et al. "Adverse outcome pathway networks II: Network analytics." Environmental Toxicology and Chemistry 37, no. 6 (May 7, 2018): 1734–48. http://dx.doi.org/10.1002/etc.4124.
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Loney, Fred, and Guanming Wu. "Automation of ReactomeFIViz via CyREST API." F1000Research 7 (May 2, 2018): 531. http://dx.doi.org/10.12688/f1000research.14776.1.
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Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.
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Loney, Fred, and Guanming Wu. "Automation of ReactomeFIViz via CyREST API." F1000Research 7 (May 23, 2018): 531. http://dx.doi.org/10.12688/f1000research.14776.2.
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Pathway- and network-based approaches project seemingly unrelated genes onto the context of pathways and networks, enhancing the analysis power that cannot be achieved via gene-based approaches. Pathway and network approaches are routinely applied in large-scale data analysis for cancer and other complicated diseases. ReactomeFIViz is a Cytoscape app, providing features for researchers to perform pathway- and network-based data analysis and visualization by leveraging manually curated Reactome pathways and highly reliable Reactome functional interaction network. To facilitate adoption of this app in bioinformatics software pipeline and workflow development, we develop a CyREST API for ReactomeFIViz by exposing some major features in the app. We describe a use case to demonstrate the use of this API in a Python-based notebook, and believe the new API will provide the community a convenient and powerful tool to perform pathway- and network-based data analysis and visualization using our app in an automatic way.
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Ellershaw, John, and Deborah Murphy. "The National Pathway Network of Palliative Care Pathways." Journal of integrated Care Pathways 7, no. 1 (April 2003): 11–13. http://dx.doi.org/10.1177/147322970300700104.
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Cocco, Nicoletta, Mercè Llabrés, Mariana Reyes-Prieto, and Marta Simeoni. "MetNet: A two-level approach to reconstructing and comparing metabolic networks." PLOS ONE 16, no. 2 (February 12, 2021): e0246962. http://dx.doi.org/10.1371/journal.pone.0246962.
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Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.
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Li, Chaoxing, Li Liu, and Valentin Dinu. "Pathways of topological rank analysis (PoTRA): a novel method to detect pathways involved in hepatocellular carcinoma." PeerJ 6 (April 9, 2018): e4571. http://dx.doi.org/10.7717/peerj.4571.
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Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes of HCC are HCC subtype-associated specifically. In conclusion, PoTRA is a new approach to explore and discover pathways involved in cancer. PoTRA can be used as a complement to other existing methods to broaden our understanding of the biological mechanisms behind cancer at the system-level.
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Li, Xiao, Xu Feng, Chunkang Chang, Qi He, and Wu Lingyun. "Identification of microRNA-Regulated Pathways through a Integration of Mcrorna-mRNA Microarray and Bioinformatics Analysis in CD34+ Cells of Myelodysplastic Syndromes." Blood 124, no. 21 (December 6, 2014): 3238. http://dx.doi.org/10.1182/blood.v124.21.3238.3238.
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Abstract Background MicroRNAs (miRNAs) are considered to play a key role in the pathogenesis of myelodysplastic syndromes (MDS). However, the effect of miRNA and targeted mRNA on signal transduction is not fully understood in MDS. Objective The objective of this study is to identify the miRNAs-regulated pathways. Methods Affymetrix GeneChip microRNA and PrimeView Array were used to analyze miRNAs and gene expression profile of CD34+ cells in 12 MDS patients and 6 healthy controls. Comprehensive bioinformatics analysis of the coordinate expression of miRNAs and mRNAs including Difference, Go, Pathway, Pathway-network, miRNA-Gene-Network and miRNA-Go-Network analysis was performed to identify the miRNAs-regulated networks. Results 1. 34 differentially expressed miRNAs (5 up- and 29 down-regulated miRNAs) and 1783 mRNAs (405 up- and 1378 down-regulated mRNAs) in CD34+ cells from MDS and Healthy controls were identified by miRNA and mRNA microarray, respectively (Fig.1). 2. 25 dysregulated miRNAs and 234 targeted mRNAs were identified by a combination of Pearson's correlation analysis and prediction by TargetScan; 394 target relationship of miRNAs was established (Fig.2). 3. Go analysis revealed that these miRNA-mRNAs pairs were involved in signal transduction, apoptotic process, DNA-dependent transcription regulation, protein phosphophoration, etc. Pathway analysis showed that MAPK, JAK/STAT and PI3K/Akt signaling pathways might be regulated by these miRNA-mRNAs pairs (Fig.3). 4. The pathway-network analysis revealed that MAPK signaling pathway, Jak-Stat signaling pathway and apoptosis signaling pathway (displayed by red cycle) located in the downstream of signal networks (Fig. 3E). Dysregulation of These pathways may be more meaningful for explaining the pathogenesis of MDS. 5. Through a combination of Pathway, miRNA-Gene-Network and miRNA-Go- Network analysis, 29 miRNA-mRNA-regulated pathways were identified such as miR-148a/TEK/PI3K-Akt signaling pathway, miR-195/BDNF/MAPK signaling pathway, miR-195/DLL1/Notch signaling pathway, miR-145/CCND2/ JAK-STAT signaling pathway, etc. (Table 1). Conclusion Alteration expression of several miRNAs and targeted mRNAs might have an important impact on cancer-related cellular pathways including MAPK, PI3K/Akt, JAK/STAT, etc. The role of these miRNAs-mediated pathways in pathogenesis of MDS merit further investigation. Fig. 1 Affymetrix mcroRNA and mRNA microarray in MDS Fig. 1. Affymetrix mcroRNA and mRNA microarray in MDS Fig. 2 Significant miRNA-mRNA pairs identified through a integration of mcroRNA-mRNA microarray Fig. 2. Significant miRNA-mRNA pairs identified through a integration of mcroRNA-mRNA microarray Table 1. Parts of dysregulated miRNAs, genes and targeted pathway in MDS MicroRNA Style Gene_synbol Pathway miR-148a Down TEK PI3K-Akt signaling pathway ITGA9 PI3K-Akt signaling pathway KIT PI3K-Akt signaling pathway HMGA2 Transcriptional misregulation in cancer miR-145 Down HHEX Transcriptional misregulation in cancer MEIS1 Transcriptional misregulation in cancer miR-200c Down EFNA1 PI3K-Akt signaling pathway KLF3 Transcriptional misregulation in cancer miR-195 Up BDNF MAPK signaling pathway CDC25B MAPK signaling pathway DLL1 Notch signaling pathway MRAS MAPK signaling pathway miR-17 Up CAMK2D Calcium signaling pathway miR-19a Up MAML1 Notch signaling pathway SLC8A1 Calcium signaling pathway THBS1 Proteoglycans in cancer TNF MAPK signaling pathway TNFRSF1B Adipocytokine signaling pathway ACSL1 Adipocytokine signaling pathway EDNRB Calcium signaling pathway miR-19b Up CALM1 Calcium signaling pathway TNF Proteoglycans in cancer Fig. 3 Go and pathway analysis Fig. 3. Go and pathway analysis Disclosures No relevant conflicts of interest to declare.
Dissertations / Theses on the topic "Network pathway"
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Isik, Zerrin. "Network Structure Based Pathway Enrichment System To Analyze Pathway Activities." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612951/index.pdf.
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Current approaches integrating large scale data and information from a
variety of sources to reveal molecular basis of cellular events do not
adequately benefit from pathway information. Here, we portray a network structure based pathway enrichment system that fuses and exploits model and data: signalling pathways are taken as the biological models while microarray and ChIP-seq data are the sample input data sources among many other alternatives. Our model- and data-driven hybrid system allows to quantitatively assessing the biological activity of a cyclic pathway and simultaneous enrichment of the significant paths leading to the ultimate cellular response. Signal Transduction Score Flow (SiTSFlow) algorithm is the fundamental constituent of proposed network structure based pathway enrichment system. SiTSFlow algorithm converts each pathway into a cascaded graph and then gene scores are mapped onto the protein nodes. Gene scores are transferred to en route of the pathway to form a final activity score describing behaviour of a specific process in the pathway while enriching of the gene node scores. Because of cyclic pathways, the algorithm runs in an iterative manner and it terminates when the node scores converge. The converged final activity score provides a quantitative measure to assess the biological significance of a process under the given experimental conditions. The conversion of cyclic pathways into cascaded graphs is performed by using a linear time multiple source Breadth First Search Algorithm. Furthermore, proposed network structure based pathway enrichment system works in linear time in terms of nodes and edges of given pathways.
In order to explore various biological responses of several processes in a
global signalling network, the selected small pathways have been unified based on their common gene and process nodes. The merge algorithm for pathways also runs in linear time in terms of nodes and edges of given pathways.
In the experiments, SiTSFlow algorithm proved the convergence behaviour of activity scores for several cyclic pathways and for a global signalling network. The biological results obtained by assessing of experimental data by described network structure based pathway enrichment system were in correlation with the expected cellular behaviour under the given experimental conditions.
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Wang, Chen. "From network to pathway: integrative network analysis of genomic data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77121.
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The advent of various types of high-throughput genomic data has enabled researchers to investigate complex biological systems in a systemic way and started to shed light on the underlying molecular mechanisms in cancers. To analyze huge amounts of genomic data, effective statistical and machine learning tools are clearly needed; more importantly, integrative approaches are especially needed to combine different types of genomic data for a network or pathway view of biological systems. Motivated by such needs, we make efforts in this dissertation to develop integrative framework for pathway analysis. Specifically, we dissect the molecular pathway into two parts: protein-DNA interaction network and protein-protein interaction network. Several novel approaches are proposed to integrate gene expression data with various forms of biological knowledge, such as protein-DNA interaction and protein-protein interaction for reliable molecular network identification.
The first part of this dissertation seeks to infer condition-specific transcriptional regulatory network by integrating gene expression data and protein-DNA binding information. Protein-DNA binding information provides initial relationships between transcription factors (TFs) and their target genes, and this information is essential to derive biologically meaningful integrative algorithms. Based on the availability of this information, we discuss the inference task based on two different situations:
(a) if protein-DNA binding information of multiple TFs is available:
based on the protein-DNA data of multiple TFs, which are derived from sequence analysis between DNA motifs and gene promoter regions, we can construct initial connection matrix and solve the network inference using a constraint least-squares approach named motif-guided network component analysis (mNCA). However, connection matrix usually contains a considerable amount of false positives and false negatives that make inference results questionable. To circumvent this problem, we propose a knowledge based stability analysis (kSA) approach to test the conditional relevance of individual TFs, by checking the discrepancy of multiple estimations of transcription factor activity with respect to different perturbations on the connections. The rationale behind stability analysis is that the consistency of observed gene expression and true network connection shall remain stable after small perturbations are applied to initial connection matrix. With condition-specific TFs prioritized by kSA, we further propose to use multivariate regression to highlight condition-specific target genes. Through simulation studies comparing with several competing methods, we show that the proposed schemes are more sensitive to detect relevant TFs and target genes for network inference purpose. Experimentally, we have applied stability analysis to yeast cell cycle experiment and further to a series of anti-estrogen breast cancer studies. In both experiments not only biologically relevant regulators are highlighted, the condition-specific transcriptional regulatory networks are also constructed, which could provide further insights into the corresponding cellular mechanisms.
(b) if only single TF's protein-DNA information is available:
this happens when protein-DNA binding relationship of individual TF is measured through experiments. Since original mNCA requires a complete connection matrix to perform estimation, an incomplete knowledge of single TF is not applicable for such approach. Moreover, binding information derived from experiments could still be inconsistent with gene expression levels. To overcome these limitations, we propose a linear extraction scheme called regulatory component analysis (RCA), which can infer underlying regulation relationships, even with partial biological knowledge. Numerical simulations show significant improvement of RCA over other traditional methods to identify target genes, not only in low signal-to-noise-ratio situations and but also when the given biological knowledge is incomplete and inconsistent to data. Furthermore, biological experiments on Escherichia coli regulatory network inferences are performed to fairly compare traditional methods, where the effectiveness and superior performance of RCA are confirmed.
The second part of the dissertation moves from protein-DNA interaction network up to protein-protein interaction network, to identify dys-regulated protein sub-networks by integrating gene expression data and protein-protein interaction information. Specifically, we propose a statistically principled method, namely Metropolis random walk on graph (MRWOG), to highlight condition-specific PPI sub-networks in a probabilistic way. The method is based on the Markov chain Monte Carlo (MCMC) theory to generate a series of samples that will eventually converge to some desired equilibrium distribution, and each sample indicates the selection of one particular sub-network during the process of Metropolis random walk. The central idea of MRWOG is built upon that the essentiality of one gene to be included in a sub-network depends on not only its expression but also its topological importance. Contrasted to most existing methods constructing sub-networks in a deterministic way and therefore lacking relevance score for each protein, MRWOG is capable of assessing the importance of each individual protein node in a global way, not only reflecting its individual association with clinical outcome but also indicating its topological role (hub, bridge) to connect other important proteins. Moreover, each protein node is associated with a sampling frequency score, which enables the statistical justification of each individual node and flexible scaling of sub-network results. Based on MRWOG approach, we further propose two strategies: one is bootstrapping used for assessing statistical confidence of detected sub-networks; the other is graphic division to separate a large sub-network to several smaller sub-networks for facilitating interpretations. MRWOG is easy to use with only two parameters need to be adjusted, one is beta value for performing random walk and another is Quantile level for calculating truncated posteriori mean. Through extensive simulations, we show that the proposed scheme is not sensitive to these two parameters in a relatively wide range. We also compare MRWOG with deterministic approaches for identifying sub-network and prioritizing topologically important proteins, in both cases MRWG outperforms existing methods in terms of both precision and recall. By utilizing MRWOG generated node/edge sampling frequency, which is actually posteriori mean of corresponding protein node/interaction edge, we illustrate that condition-specific nodes/interactions can be better prioritized than the schemes based on scores of individual node/interaction. Experimentally, we have applied MRWOG to study yeast knockout experiment for galactose utilization pathways to reveal important components of corresponding biological functions; we also applied MRWSOG to study breast cancer patient prognostics problems, where the sub-network analysis could lead to an understanding of the molecular mechanisms of antiestrogen resistance in breast cancer.
Finally, we conclude this dissertation with a summary of the original contributions, and the future work for deepening the theoretical justification of the proposed methods and broadening their potential biological applications such as cancer studies.Ph. D.
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Gao, Yipeng. "Transformation Pathway Network Analysis for Martensitic Transformations." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385978073.
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Ogris, Christoph. "Global functional association network inference and crosstalk analysis for pathway annotation." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146703.
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Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association. FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms. BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.
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Eguchi, Akihiro. "Neural network modelling of the primate ventral visual pathway." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:99277b9c-00ee-45e3-8adb-47190d716912.
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The aim of this doctoral research is to advance understanding of how the primate brain learns to process the detailed spatial form of natural visual scenes. Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects and faces. However, it remains a difficult challenge to understand exactly how these neurons develop their response properties through visually guided learning. This thesis approaches this problem through the use of computational modelling. In particular, I first show how the brain may learn to represent the spatial structure of objects and faces through a series of processing stages along the ventral visual pathway. Then I propose how understanding the two complementary unsupervised learning mechanisms of translation invariance may have useful applications in clinical psychology. Next, the potential functional role of top-down (feedback) propagation of visual information in the brain in driving the development of border ownership cells, which are thought to play a role in binding visual features such as boundary edges to their respective objects, is investigated. In particular, the limitations of traditional rate-coded neural networks in modelling these cells are identified. Finally, a general solution to such binding problems with the use of a more biologically realistic spiking neural network is presented. This work is set to make an important contribution towards understanding how the visual system learns to encode the detailed spatial structure of objects and faces within scenes, including representing the binding relations between the visual features that comprise those objects and faces.
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Chou, I.-Chun. "Parameter estimation and network identification in metabolic pathway systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26513.
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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.Committee Chair: Voit, Eberhard O.; Committee Member: Borodovsky, Mark; Committee Member: Butera, Robert; Committee Member: Kemp, Melissa; Committee Member: Park, Haesun. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Oswal, Vipul Kantilal. "Pathway Pioneer: Heterogenous Server Architecture for Scientific Visualization and Pathway Search in Metabolic Network Using Informed Search." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/2775.
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There is a huge demand for analysis and visualization of the biological models. PathwayPioneer is a web-based tool to analyze and visually represent complex biological models. PathwayPioneer generates the initial layout of the model and allows users to customize it. It is developed using .net technologies (C#) and hosted on the Internet Information Service (IIS) server. At back-end it interacts with python-based COBRApy library for biological calculations like Flux Balance Analysis (FBA). We have developed a parallel processing architecture to accommodate processing of large models and enable message-based communication between the .net webserver and python engine. We compared the performance of our online system by loading a website with multiple concurrent dummy users and performed different time intensive operations in parallel.
Given two metabolites of interest, millions of pathways can be found between them even in a small metabolic network. Depth First Search or Breadth First search algorithm retrieves all the possible pathways, thereby requiring huge computational time and resources. In Pathway Search using Informed Method, we have implemented, compared, and analyzed three different informed search techniques (Selected Subsystem, Selected Compartment, and Dynamic Search) and traditional exhaustive search technique. We found that the Dynamic approach performs exceedingly well with respect to time and total number of pathways searches. During our implementation we developed a SBML parser which outperforms the commercial libSBML parser in C#.
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Stoney, Ruth. "Using pathway networks to model context dependent cellular function." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/using-pathway-networks-to-model-context-dependent-cellular-function(562db48d-5e8b-40bb-8457-47c9a3455f9c).html.
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Molecular networks are commonly used to explore cellular organisation and disease mechanisms. Function is studied using molecular interaction networks, such as protein-protein networks. Although much biological insight has been gained using these models of molecular function, they are hindered by their reliance on available experimental data and an inability to capture the complexity of biological processes. Functional modules can be identified based on molecular network topology, making it essential that the edges accurately depict molecular interactions. However, these networks struggle to depict the temporal nature of interactions, giving the impression that all interactions are constant. This misrepresentation can result in functionally heterogeneous clusters. The notoriously inaccurate nature of experimental protein interaction data, along with variable conformity among network clusters and functional modules further impedes functional module extraction. Representation of genes by single nodes artificially merges the functions of pleiotropic genes, distorting the arrangement of function within molecular networks. This thesis therefore explores a more suitable model for representing function. Pathways are composed of sets of proteins that are known to interact within a particular cellular context, corresponding to a discernible biological function. Their representation of context dependent cellular activity makes them ideal for use as nodes within a new pathway level model. Using combinatorial algorithms a reduced redundancy pathway set was produced to represent global cellular systems. Enrichment analysis provides reliable functional annotations for each pathway node, attributing independent functions to pleiotropic genes. Edges are based on functional semantic similarity, generating a network representation of functional organisation. Both yeast and human biological systems are presented as functionally connected pathway networks. Pathway annotation and experimentation with semantic similarity measures provides insight into the cross-talk between biological processes. Pathway functional modules elucidate the intracellular implementation of processes. Disease modules highlight the effects of functional perturbations and disease mechanisms. The pathway model provides a complementary, high-level functional model that begins to bridge the gap between molecular data and phenotype. The utilisation of pathway data provides a large, well-validated data source, avoiding the inaccuracies inherent with molecular data. Pathway models better represent components of biological complexity such as pleiotropy and linear implementation of functions.
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Kaur, Dipendra. "Mapping and Filling Metabolic Pathway Holes." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/biology_theses/14.
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The network-mapping tool integrated with protein database search can be used for filling pathway holes. A metabolic pathway under consideration (pattern) is mapped into a known metabolic pathway (text), to find pathway holes. Enzymes that do not show up in the pattern may be a hole in the pattern pathway or an indication of alternative pattern pathway. We present a data-mining framework for filling holes in the pattern metabolic pathway based on protein function, prosite scan and protein sequence homology. Using this framework we suggest several fillings found with the same EC notation, with group neighbors (enzymes with same EC number in first three positions, different in the fourth position), and instances where the function of an enzyme has been taken up by the left or right neighboring enzyme in the pathway. The percentile scores are better when closely related organisms are mapped as compared to mapping distantly related organisms.
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Liu, Boqi. "The gene regulatory network in the anterior neural plate border of ascidian embryos." Kyoto University, 2020. http://hdl.handle.net/2433/253119.
Full textBooks on the topic "Network pathway"
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Tatarinova, Tatiana V., and Yuri Nikolsky, eds. Biological Networks and Pathway Analysis. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7027-8.
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Nikolsky, Yuri, and Julie Bryant, eds. Protein Networks and Pathway Analysis. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-175-2.
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Protein networks and pathway analysis. Dordrecht: Humana Press, 2009.
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Eileen, Zuber, Nelson Steve, and Pathways from Poverty Workshop for the Northeast Region (1995 : Boston, Mass.), eds. Pathways from poverty educational network. University Park, PA: Northeast Regional Center for Rural Development, Pennsylvania State University, 1996.
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Tanabe, Shihori, ed. Cancer Stem Cell Markers and Related Network Pathways. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12974-2.
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Diego, Minciacchi, ed. Thalamic networks for relay and modulation. Oxford [England]: Pergamon Press, 1993.
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Maranas, Costas D. Optimization methods in metabolic networks. Hoboken, New Jersey: John Wiley & Sons Inc., 2016.
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Pearce, Nigel G. Applicability of network analysis to pathways of care for cancer patients. Manchester: UMIST, 1997.
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D, Smolke Christina, ed. The metabolic pathway engineering handbook: Fundamentals. Boca Raton: CRC Press/Taylor & Francis, 2010.
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Learning and categorization in modular neural networks. Hillsdale, NJ: L. Erlbaum Associates, 1992.
Find full textBook chapters on the topic "Network pathway"
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Mal, Chittabrata, Ayushman Kumar Banerjee, and Joyabrata Mal. "Genome Scale Pathway-Pathway Co-functional Synergistic Network (PcFSN) in Oryza Sativa." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 47–57. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_6.
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AbstractCohesive network modelling and systems biology have emerged as extremely potent tools which helps understanding the combinatorial effects of biomolecules. Synergistic modulation among biomolecules (e.g., enzymes, transcription factors, microRNAs, drugs, etc.) are significant in finding out complex regulatory mechanisms in biological networks and pathways. In some cases, although combinatorial interactions among some biomolecules in specific biological networks is available, our knowledge in that particular domain is very limited with context to a genomic scale. Here we explore the pathway-pathway network to identify and understand the network architecture of metabolic pathway mediated regulations at genomic and co-functional levels, in rice. Using network transformation methods, a genome scale pathway-pathway co-functional synergistic network (PcFSN) was constructed. Finally, the PcFSN modules are extracted. This in turn helps to identify the miRNAs and genes associated with the pathways, especially linked to the central metabolic network in rice.
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Li, Yong. "Pathway Crosstalk Network." In Systems Biology for Signaling Networks, 491–504. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5797-9_20.
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Ekins, Sean, and Craig N. Giroux. "Mammalian Proteome and Toxicant Network Analysis." In Pathway Analysis for Drug Discovery, 165–93. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470399279.ch8.
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Slenter, Denise N., Martina Kutmon, and Egon L. Willighagen. "WikiPathways: Integrating Pathway Knowledge with Clinical Data." In Physician's Guide to the Diagnosis, Treatment, and Follow-Up of Inherited Metabolic Diseases, 1457–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67727-5_73.
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SummaryThroughout the chapters in this book, pathways are used to visualize how genetically inheritable metabolic disorders are related. These pathways provide common conceptual models which explain groups of chemical reactions within their biological context. Visual representations of the reactions in biological pathway diagrams provide intuitive ways to study the complex metabolic processes. In order to link (clinical) data to these pathways, they have to be understood by computers. Understanding how to move from a regular pathway drawing to its machine-readable counterpart is pertinent for creating proper models. This chapter outlines the various aspects of the digital counterparts of the pathway diagrams in this book, connecting them to databases and using them in data integration and analysis. This is followed by three examples of bioinformatics applications including a pathway enrichment analysis, a biological network extension, and a final example that integrates pathways with clinical biomarker data.
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Kim, Ju Han. "Network Biology, Sequence, Pathway and Ontology Informatics." In Genome Data Analysis, 175–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_10.
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Chang, Aaron N. "Prioritizing Genes for Pathway Impact Using Network Analysis." In Methods in Molecular Biology, 141–56. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-175-2_8.
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Brennan, Richard J., Tatiana Nikolskya, and Svetlana Bureeva. "Network and Pathway Analysis of Compound–Protein Interactions." In Methods in Molecular Biology, 225–47. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-274-2_10.
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van der Kolk, Marion, Arjan Kouwen, Joris Fuijkschot, and Ingeborg P. M. Griffioen. "From Care Pathway to a Personalized Metro Network." In Personalized Specialty Care, 71–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63746-0_10.
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Kim, Haseong, Rengul Atalay, and Erol Gelenbe. "G-Network Modelling Based Abnormal Pathway Detection in Gene Regulatory Networks." In Computer and Information Sciences II, 257–63. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2155-8_32.
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Bhalla, Parinishtha, Anukriti Verma, Bhawna Rathi, Shivani Sharda, and Pallavi Somvanshi. "Exploring Molecular Signatures in Spondyloarthritis: A Step Towards Early Diagnosis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 142–55. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_15.
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AbstractSpondyloarthritis is an acute inflammatory disorder of the musculoskeletal system often accompanied by pain, stiffness, bone and tissue damage. It majorly consists of ankylosing spondylitis, psoriatic arthritis and reactive arthritis. It follows a differential diagnosis pattern for demarcation between the spondyloarthritis subtypes and other arthritic subtypes such as rheumatoid arthritis, juvenile arthritis and osteoarthritis due to the heterogeneity causing gradual chronicity and complications. Presence of definite molecular markers can not only improve diagnosis efficiency but also aid in their prognosis and therapy. This study is an attempt to compose a refined list of such unique and common molecular signatures of the considered subtypes, by employing a reductionist approach amalgamating gene retrieval, protein-protein interaction network, functional, pathway, micro-RNA-gene and transcription factor-gene regulatory network analysis. Gene retrieval and protein-protein interaction network analysis resulted in unique and common interacting genes of arthritis subtypes. Functional annotation and pathway analysis found vital functions and pathways unique and common in arthritis subtypes. Furthermore, miRNA-gene and transcription factor-gene interaction networks retrieved unique and common miRNA’s and transcription factors in arthritis subtypes. Furthermore, the study identified important signatures of arthritis subtypes that can serve as markers assisting in prognosis, early diagnosis and personalized treatment of arthritis patients requiring validation via prospective experimental studies.
Conference papers on the topic "Network pathway"
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Liu, Lu, and Jianhua Ruan. "Network-based pathway enrichment analysis." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732493.
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Hong, SeulGi, and Min-Kook Choi. "Blockwise Temporal-Spatial Pathway Network." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506113.
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Rubel, Tobias, Pramesh Singh, and Anna Ritz. "Reconciling Signaling Pathway Databases with Network Topologies." In Pacific Symposium on Biocomputing 2022. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/9789811250477_0020.
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Kawai, Shigeru, Hisakazu Kurita, and Keiichi Kubota. "Design of Electro-Photonic Computer-Networks with Non-Blocking and Self-Routing Functions." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.otha2.
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In massively parallel computers, electrical networks have the serious problems regarding pin bottle-necks in switches, and the number of pathways between processor elements (PEs). In electrical networks, the number of pins for a chip decides the channel size for electrical crossbar switches, and network size. The number of pins for 16 ch crossbar switches exceeds more than IK (1,024), when the 32 ch external-buses are used. The 16 ch size may be limited in a chip for an electrical switch. By using the 16 ch switches, a maximum 128 ch Clos network[l], with only a strictly non-blocking function, may be accomplished. Photonic technologies may serve larger size crossbar switches, and they achieve more than 1K ch networks. Free-space optics may also overcome pathway problems, because light beams can cross each other with no mutual interference. Various data multiplexing technologies may be used in optical networks.
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CHEN, LI, JIANHUA XUAN, JINGHUA GU, YUE WANG, ZHEN ZHANG, TIAN-LI WANG, and IE-MING SHIH. "INTEGRATIVE NETWORK ANALYSIS TO IDENTIFY ABERRANT PATHWAY NETWORKS IN OVARIAN CANCER." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814366496_0004.
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Wilson, Hugh R. "Interaction of first- and second-order processes in 2D motion perception." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mnn1.
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Recent psychophysical experiments have shown that the perceived direction of motion of plaids, composed of two cosine gratings moving in different directions, can deviate by up to 50° from the true direction of rigid translation. We have developed a dynamic neural network model that predicts these and other motion data. The model incorporates two motion pathways that are subsequently combined by using a vector sum operation. The first motion pathway extracts the directions of motion of the component gratings, i.e., the Fourier motion signals, while the second pathway employs filtering and full-wave rectification to extract a non-Fourier motion signal. The vector sum of these motion pathways quantitatively predicts the psychophysical data, and it explains the existence of parallel input pathways from both V1 and V2 to area MT. The model correctly predicts that non-Fourier plaids will move in the vector sum direction, and interactions across spatial scales in the model accurately predict transitions from rigid to transparent motion.
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Mallavarapu, Rama Srikanth, TaeJin Ahn, Subhankar Mukherjee, Ajit S. Bopardikar, Garima Agarwal, and Taesung Park. "Estimating cancer gene pathway proximity using network interaction." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999383.
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Jiang, Bo, Jiahong Yu, Lei Zhou, Kailin Wu, and Yang Yang. "Two-Pathway Transformer Network for Video Action Recognition." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506453.
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Jain, V., G. P. Parr, P. J. Morrow, and M. Polaine. "Intelligent energy efficient network management across the access networks to CPE pathway." In 2011 IFIP/IEEE International Symposium on Integrated Network Management (IM 2011). IEEE, 2011. http://dx.doi.org/10.1109/inm.2011.5990648.
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Li, G., S. S. Nair, S. J. Lees, and F. W. Booth. "Regulation of G2/M Transition in Mammalian Cells by Oxidative Stress." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82349.
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The regulation of the G2/M transition for the mammalian cell cycle has been modeled using 19 states to investigate the G2 checkpoint dynamics in response to oxidative stress. A detailed network model of G2/M regulation is presented and then a “core” subsystem is extracted from the full network. An existing model of Mitosis control is extended by adding two important pathways regulating G2/M transition in response to DNA damage induced by oxidative stress. Model predictions indicate that the p53 dependent pathway is not required for initial G2 arrest as the Chk1/Cdc25C pathway can arrest the cell in G2 right after DNA damage. However, p53 and p21 expression is important for a more sustained G2 arrest by inhibiting the Thr161 phosphorylation by CAK. By eliminating the phosphorylation effect of Chk1 on p53, two completely independent pathways are obtained and it is shown that it does not affect the G2 arrest much. So the p53/p21 pathway makes an important, independent contribution to G2 arrest in response to oxidative stress, and any defect in this pathway may lead to genomic instability and predisposition to cancer. Such strict control mechanisms probably provide protection for survival in the face of various environmental changes. The controversial issue related to the mechanism of inactivation of Cdc2 by p21 is addressed and simulation predictions indicate that G2 arrest would not be affected much by considering the direct binding of p21 to Cdc2/Cyclin B given that the inhibition of CAK by p21 is already present if the binding efficiency is within a certain range. Lastly, we show that the G2 arrest time in response to oxidative stress is sensitive to the p53 synthesis rate.
Reports on the topic "Network pathway"
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Moe, Allison. Education, Training and Career Pathway Opportunities for Buildings Energy Efficiency Programs Within the Corps Network. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1894480.
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Friedman, Haya, Julia Vrebalov, and James Giovannoni. Elucidating the ripening signaling pathway in banana for improved fruit quality, shelf-life and food security. United States Department of Agriculture, October 2014. http://dx.doi.org/10.32747/2014.7594401.bard.
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Background : Banana being a monocot and having distinct peel and pulp tissues is unique among the fleshy fruits and hence can provide a more comprehensive understanding of fruit ripening. Our previous research which translated ripening discoveries from tomato, led to the identification of six banana fruit-associated MADS-box genes, and we confirmed the positive role of MaMADS1/2 in banana ripening. The overall goal was to further elucidate the banana ripening signaling pathway as mediated by MADS-boxtranscriptional regulators. Specific objectives were: 1) characterize transcriptional profiles and quality of MaMADS1/2 repressed fruit; 2) reveal the role of additional MaMADSgenes in ripening; 3) develop a model of fruit MaMADS-box mode of action; and 4) isolate new components of the banana ripening signaling pathway. Major conclusion: The functions of the banana MaMADS1-5 have been examined by complimenting the rinor the TAGL1-suppressed lines of tomato. Only MaMADS5 exhibited partial complementation of TAGL1-suppressed and rinlines, suggesting that while similar genes play corresponding roles in ripening, evolutionary divergence makes heterologous complementation studies challenging. Nevertheless, the partial complementation of tomato TAGL1-surpessed and rinlines with MaMADS5 suggests this gene is likely an important ripening regulator in banana, worthy of further study. RNA-seqtranscriptome analysis during ripening was performed on WT and MaMADS2-suppressed lines revealing additional candidate genes contributing to ripening control mechanisms. In summary, we discovered 39 MaMADS-box genes in addition to homologues of CNR, NOR and HB-1 expressed in banana fruits, and which were shown in tomato to play necessary roles in ripening. For most of these genes the expression in peel and pulp was similar. However, a number of key genes were differentially expressed between these tissues indicating that the regulatory components which are active in peel and pulp include both common and tissue-specific regulatory systems, a distinction as compared to the more uniform tomato fruit pericarp. Because plant hormones are well documented to affect fruit ripening, the expressions of genes within the auxin, gibberellin, abscisic acid, jasmonic acid, salicylic and ethylene signal transduction and synthesis pathways were targeted in our transcriptome analysis. Genes’ expression associated with these pathways generally declined during normal ripening in both peel and pulp, excluding cytokinin and ethylene, and this decline was delayed in MaMADS2-suppressed banana lines. Hence, we suggest that normal MaMADS2 activity promotes the observed downward expression within these non-ethylene pathways (especially in the pulp), thus enabling ripening progression. In contrast, the expressions of ACSand ACOof the ethylene biosynthesis pathway increase in peel and pulp during ripening and are delayed/inhibited in the transgenic bananas, explaining the reduced ethylene production of MaMADS2-suppressed lines. Inferred by the different genes’ expression in peel and pulp of the gibberellins, salicylic acid and cytokinins pathways, it is suggested that hormonal regulation in these tissues is diverse. These results provide important insights into possible avenues of ripening control in the diverse fruit tissues of banana which was not previously revealed in other ripening systems. As such, our transcriptome analysis of WT and ripening delayed banana mutants provides a starting point for further characterization of ripening. In this study we also developed novel evidence that the cytoskeleton may have a positive role in ripening as components of this pathway were down-regulated by MaMADS2 suppression. The mode of cytoskeleton involvement in fruit ripening remains unclear but presents a novel new frontier in ripening investigations. In summary, this project yielded functional understanding of the role and mode of action of MaMADS2 during ripening, pointing to both induction of ethylene and suppression of non-ethylene hormonal singling pathways. Furthermore, our data suggest important roles for cytoskeleton components and MaMADS5 in the overall banana ripening control network. Implications: The project revealed new molecular components/genes involved in banana ripening and refines our understanding of ripening responses in the peel and pulp tissues of this important species. This information is novel as compared to that derived from the more uniform carpel tissues of other highly studied ripening systems including tomato and grape. The work provides specific target genes for potential modification through genetic engineering or for exploration of useful genetic diversity in traditional breeding. The results from the project might point toward improved methods or new treatments to improve banana fruit storage and quality.
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Lee, L. Parallel Extreme Pathway Computation for Metabolic Networks. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/827001.
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Nadeau, Joseph H. Pathways, Networks and Systems Medicine Conferences. Office of Scientific and Technical Information (OSTI), November 2013. http://dx.doi.org/10.2172/1107799.
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Hibbett, David. Gene regulatory networks controlling carbohydrate-selective deconstruction pathways in fungi. Office of Scientific and Technical Information (OSTI), November 2022. http://dx.doi.org/10.2172/1896855.
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Lers, Amnon, and Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7591734.bard.
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Postharvest leaf senescence contributes to quality losses in flowers and leafy vegetables. The general goal of this research project was to investigate the regulatory mechanisms involved in dark-induced leaf senescence. The regulatory system involved in senescence induction and control is highly complex and possibly involves a network of senescence promoting pathways responsible for activation of the senescence-associated genes. Pathways involving different internal signals and environmental factors may have distinctive importance in different leaf senescence systems. Darkness is known to have a role in enhancement of postharvest leaf senescence and for getting an insight into its regulatory mechanism/s we have applied molecular genetics and functional genomics approaches. The original objectives were: 1. Identification of dark-induced SAGs in Arabidopsis using enhancer/promoter trap lines and microarray approaches; 2. Molecular and functional characterization of the identified genes by analyzing their expression and examining the phenotypes in related knockout mutant plants; 3. Initial studies of promoter sequences for selected early dark-induced SAGs. Since genomic studies of senescence, with emphasis on dark-induced senescence, were early-on published which included information on potential regulatory genes we decided to use this new information. This is instead of using the uncharacterized enhancer/promoter trap lines as originally planned. We have also focused on specific relevant genes identified in the two laboratories. Based on the available genomic analyses of leaf senescence 10 candidate genes hypothesized to have a regulatory role in dark-induced senescence were subjected to both expression as well as functional analyses. For most of these genes senescence-specific regulation was confirmed, however, functional analyses using knock-out mutants indicated no consequence to senescence progression. The transcription factor WARK75 was found to be specifically expressed during natural and dark-induced leaf senescence. Functional analysis demonstrated that in detached leaves senescence under darkness was significantly delayed while no phenotypic consequences could be observed on growth and development, including no effect on natural leaf senescence,. Thus, WARKY75 is suggested to have a role in dark-induced senescence, but not in natural senescence. Another regulatory gene identified to have a role in senescence is MKK9 encoding for a Mitogen-Activated Protein Kinase Kinase 9 which is upregulated during senescence in harvested leaves as well as in naturally senescing leaves. MKK9 can specifically phosphorylate another kinase, MPK6. Both knockouts of MKK9 and MPK6 displayed a significantly senescence delay in harvested leaves and possibly function as a phosphorelay that regulates senescence. To our knowledge, this is the first report that clearly demonstrates the involvement of a MAP kinase pathway in senescence. This research not only revealed a new signal transduction pathway, but more important provided significant insights into the regulatory mechanisms underlying senescence in harvested leaves. In an additional line of research we have employed the promoter of the senescence-induced BFN1 gene as a handle for identifying components of the regulatory mechanism. This gene was shown to be activated during darkinduced senescence of detached leaves, as well as natural senescence. This was shown by following protein accumulation and promoter activity which demonstrated that this promoter is activated during dark-induced senescence. Analysis of the promoter established that, at least some of the regulatory sequences reside in an 80 bps long fragment of the promoter. Overall, progress was made in identification of components with a role in dark-induced senescence in this project. Further studies should be done in order to better understand the function of these components and develop approaches for modulating the progress of senescence in crop plants for the benefit of agriculture.
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Taylor, Joe, Evert-jan Quak, James Georgalakis, and Louise Clark. Pathways to Impact in the Pandemic. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/cc.2022.003.
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Implementing and ascertaining impact and outcomes of research is a prolonged process that may take several years due to complexities in bureaucratic, social, and economic systems. At the macro level, collective reflection on the different methods and approaches that research projects use to promote uptake and impact is rare but has potential to encourage learning and exchanges between different funders and projects around impact pathways as useful road maps for research. The Covid-19 pandemic has changed the nature of research – while it has increased the demand for evidence to inform decision-making, it has further disrupted both the policy-influencing and engagement activities that would usually accompany such research. This report is based on an analysis of 90 research projects supported by the Covid Collective, COVID CIRCLE, and Covid Response for Equity (CORE) initiatives. It provides an overview and insight into how different funders and initiatives were working to facilitate change in the context of the Covid-19 pandemic. In line with the Economic and Social Research Council (ESRC) definitions of ‘impact’, and subsequent work by the ESRC-FCDO’s (Foreign, Commonwealth & Development Office) Impact Initiative, four categories were used to map the emerging outcomes and different types of change. These outcome areas comprise capacity, networks, conceptual, and instrumental outcomes. Outcome examples were then classified into more detailed descriptive groups highlighted in Table 1.
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Tucker-Blackmon, Angelicque. Engagement in Engineering Pathways “E-PATH” An Initiative to Retain Non-Traditional Students in Engineering Year Three Summative External Evaluation Report. Innovative Learning Center, LLC, July 2020. http://dx.doi.org/10.52012/tyob9090.
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The summative external evaluation report described the program's impact on faculty and students participating in recitation sessions and active teaching professional development sessions over two years. Student persistence and retention in engineering courses continue to be a challenge in undergraduate education, especially for students underrepresented in engineering disciplines. The program's goal was to use peer-facilitated instruction in core engineering courses known to have high attrition rates to retain underrepresented students, especially women, in engineering to diversify and broaden engineering participation. Knowledge generated around using peer-facilitated instruction at two-year colleges can improve underrepresented students' success and participation in engineering across a broad range of institutions. Students in the program participated in peer-facilitated recitation sessions linked to fundamental engineering courses, such as engineering analysis, statics, and dynamics. These courses have the highest failure rate among women and underrepresented minority students. As a mixed-methods evaluation study, student engagement was measured as students' comfort with asking questions, collaboration with peers, and applying mathematics concepts. SPSS was used to analyze pre-and post-surveys for statistical significance. Qualitative data were collected through classroom observations and focus group sessions with recitation leaders. Semi-structured interviews were conducted with faculty members and students to understand their experiences in the program. Findings revealed that women students had marginalization and intimidation perceptions primarily from courses with significantly more men than women. However, they shared numerous strategies that could support them towards success through the engineering pathway. Women and underrepresented students perceived that they did not have a network of peers and faculty as role models to identify within engineering disciplines. The recitation sessions had a positive social impact on Hispanic women. As opportunities to collaborate increased, Hispanic womens' social engagement was expected to increase. This social engagement level has already been predicted to increase women students' persistence and retention in engineering and result in them not leaving the engineering pathway. An analysis of quantitative survey data from students in the three engineering courses revealed a significant effect of race and ethnicity for comfort in asking questions in class, collaborating with peers outside the classroom, and applying mathematical concepts. Further examination of this effect for comfort with asking questions in class revealed that comfort asking questions was driven by one or two extreme post-test scores of Asian students. A follow-up ANOVA for this item revealed that Asian women reported feeling excluded in the classroom. However, it was difficult to determine whether these differences are stable given the small sample size for students identifying as Asian. Furthermore, gender differences were significant for comfort in communicating with professors and peers. Overall, women reported less comfort communicating with their professors than men. Results from student metrics will inform faculty professional development efforts to increase faculty support and maximize student engagement, persistence, and retention in engineering courses at community colleges. Summative results from this project could inform the national STEM community about recitation support to further improve undergraduate engineering learning and educational research.
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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7699862.bard.
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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.
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Takyiakwaa, Dorothy, Prince S. K. Tetteh, and Kofi Takyi Asante. Explaining the Weakness of Associational Life in Oil Palm Growing Communities in Southwestern Ghana. Institute of Development Studies (IDS), October 2021. http://dx.doi.org/10.19088/apra.2021.028.
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As the second most important industrial crop in Ghana, oil palm holds the potential of improving farmers’ livelihoods and alleviating rural poverty. For smallholder farmers, collective action through farmer-based organisations (FBOs) could provide a pathway to inclusive participation in agricultural commercialisation. There is ample evidence in the literature that collective action can help smallholders gain access to credit, improved inputs, or even networks of social support. Thus, collective action is widely recognised as a viable pathway out of poverty for the agrarian poor. However, our findings show that FBOs were either weak or non-existent. Indeed, we find that economic relations between farmers tend to be more individualised than one would expect to find in rural communities. This paper presents these findings, and explores why this is the case.