Relevant bibliographies by topics / Metabolic Networks and Pathways

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Metabolic Networks and Pathways'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Metabolic Networks and Pathways"

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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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García, Irene, Bessem Chouaia, Mercè Llabrés, and Marta Simeoni. "Exploring the expressiveness of abstract metabolic networks." PLOS ONE 18, no. 2 (February 9, 2023): e0281047. http://dx.doi.org/10.1371/journal.pone.0281047.

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Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms’ metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.
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Schuster, Stefan, Luís F. de Figueiredo, and Christoph Kaleta. "Predicting novel pathways in genome-scale metabolic networks." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1202–5. http://dx.doi.org/10.1042/bst0381202.

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Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.
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Jusufi, Ilir, Christian Klukas, Andreas Kerren, and Falk Schreiber. "Guiding the interactive exploration of metabolic pathway interconnections." Information Visualization 11, no. 2 (September 19, 2011): 136–50. http://dx.doi.org/10.1177/1473871611405677.

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Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas, especially Systems Biology. Biochemical networks as representations of processes are very important for a comprehensive understanding of living beings. Drawings of these networks are often visually overloaded and do not scale. A common solution to deal with this complexity is to divide the complete network, for example, the metabolism, into a large set of single pathways that are hierarchically structured. If those pathways are visualized, this strategy generates additional navigation and exploration problems as the user loses the context within the complete network. In this article, we present a general solution to this problem of visualizing interconnected pathways and discuss it in context of biochemical networks. Our new visualization approach supports the analyst in obtaining an overview to related pathways if they are working within a particular pathway of interest. By using glyphs, brushing, and topological information of the related pathways, our interactive visualization is able to intuitively guide the exploration and navigation process, and thus the analysis processes too. To deal with real data and current networks, our tool has been implemented as a plugin for the VANTED system.
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Petrovsky, Denis V., Kristina A. Malsagova, Vladimir R. Rudnev, Liudmila I. Kulikova, Vasiliy I. Pustovoyt, Evgenii I. Balakin, Ksenia A. Yurku, and Anna L. Kaysheva. "Bioinformatics Methods for Constructing Metabolic Networks." Processes 11, no. 12 (December 14, 2023): 3430. http://dx.doi.org/10.3390/pr11123430.

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Metabolic pathway prediction and reconstruction play crucial roles in solving fundamental and applied biomedical problems. In the case of fundamental research, annotation of metabolic pathways allows one to study human health in normal, stressed, and diseased conditions. In applied research, it allows one to identify novel drugs and drug targets and to design mimetics (biomolecules with tailored properties), as well as contributes to the development of such disciplines as toxicology and nutrigenomics. It is important to understand the role of a metabolite as a substrate (the product or intermediate participant of an enzymatic reaction) in cellular signaling and phenotype implementation according to the pivotal paradigm of biology: “one gene–one protein–one function (one trait)”. Due to the development of omics technologies, a vast body of data on the metabolome composition of living organisms has been accumulated over the past two decades. Systematization of the information on the roles played by metabolites in implementation of cellular signaling, as well as metabolic pathway reconstruction and refinement, have necessitated the development of bioinformatic tools for performing large-scale omics data mining. This paper reviews web-accessible databases relevant to metabolic pathways and considers the applications of the three types of bioinformatics methods for constructing metabolic networks (graphs for substrate–enzyme–product transformation; stoichiometric analysis of substrate–product transformation; and product retrosynthesis). It describes, step by step, a generalized algorithm for constructing biological pathway maps which explains to the researcher the workflow implemented in available bioinformatics tools and can be used to create new tools in projects requiring pathway reconstruction.
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Faust, Karoline, Didier Croes, and Jacques van Helden. "Prediction of metabolic pathways from genome-scale metabolic networks." Biosystems 105, no. 2 (August 2011): 109–21. http://dx.doi.org/10.1016/j.biosystems.2011.05.004.

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Cheng, Qiong, and Alexander Zelikovsky. "Combinatorial Optimization Algorithms for Metabolic Networks Alignments and Their Applications." International Journal of Knowledge Discovery in Bioinformatics 2, no. 1 (January 2011): 1–23. http://dx.doi.org/10.4018/jkdb.2011010101.

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The accumulation of high-throughput genomic and proteomic data allows for reconstruction of large and complex metabolic networks. To analyze accumulated data and reconstructed networks, it is critical to identify network patterns and evolutionary relations between metabolic networks; finding similar networks is computationally challenging. Based on gene duplication and function sharing in biological networks, a network alignment problem is formulated that asks the optimal vertex-to-vertex mapping allowing path contraction, different types of vertex deletion, and vertex insertions. This paper presents fixed parameter tractable combinatorial optimization algorithms, which take into account the similarity of both the enzymes’ functions arbitrary network topologies. Results are evaluated by the randomized P-Value computation. The authors perform pairwise alignments of all pathways for four organisms and find a set of statistically significant pathway similarities. The network alignment is used to identify pathway holes that are the result of inconsistencies and missing enzymes. The authors propose a framework of filling pathway holes by including database searches for missing enzymes and proteins with the matching prosites and further finding potential candidates with high sequence similarity.
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MITTENTHAL, JAY, BERTRAND CLARKE, and ALEXANDER SCHEELINE. "HOW CELLS AVOID ERRORS IN METABOLIC AND SIGNALING NETWORKS." International Journal of Modern Physics B 17, no. 10 (April 20, 2003): 2005–22. http://dx.doi.org/10.1142/s0217979203018028.

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We examine features of intracellular networks that make errors less probable and beneficial responses more probable. In a false negative (F-) error, a network does not respond to input. A network is reliable if it operates with a low probability of a F- error. Features that promote reliability include fewer reactions in sequence, more alternative pathways, no side reactions and negative feedback. In a false positive (F+) error, a network produces output without input. Here, a network is specific if it has a low probability of a F+ error. Conjunctions of signals within or between pathways can improve specificity through sigmoid steady-state response curves, kinetic proofreading and checkpoints. Both reliability and specificity are important in networks that regulate the fate of a cell and in networks with hubs or modules, and this includes scale-free networks. Some networks discriminate among several inputs by responding to each input through a different combination of pathways.
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Croes, Didier, Fabian Couche, Shoshana J. Wodak, and Jacques van Helden. "Inferring Meaningful Pathways in Weighted Metabolic Networks." Journal of Molecular Biology 356, no. 1 (February 2006): 222–36. http://dx.doi.org/10.1016/j.jmb.2005.09.079.

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Huang, Yiran, Yusi Xie, Cheng Zhong, and Fengfeng Zhou. "Finding branched pathways in metabolic network via atom group tracking." PLOS Computational Biology 17, no. 2 (February 2, 2021): e1008676. http://dx.doi.org/10.1371/journal.pcbi.1008676.

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Finding non-standard or new metabolic pathways has important applications in metabolic engineering, synthetic biology and the analysis and reconstruction of metabolic networks. Branched metabolic pathways dominate in metabolic networks and depict a more comprehensive picture of metabolism compared to linear pathways. Although progress has been developed to find branched metabolic pathways, few efforts have been made in identifying branched metabolic pathways via atom group tracking. In this paper, we present a pathfinding method called BPFinder for finding branched metabolic pathways by atom group tracking, which aims to guide the synthetic design of metabolic pathways. BPFinder enumerates linear metabolic pathways by tracking the movements of atom groups in metabolic network and merges the linear atom group conserving pathways into branched pathways. Two merging rules based on the structure of conserved atom groups are proposed to accurately merge the branched compounds of linear pathways to identify branched pathways. Furthermore, the integrated information of compound similarity, thermodynamic feasibility and conserved atom groups is also used to rank the pathfinding results for feasible branched pathways. Experimental results show that BPFinder is more capable of recovering known branched metabolic pathways as compared to other existing methods, and is able to return biologically relevant branched pathways and discover alternative branched pathways of biochemical interest. The online server of BPFinder is available at http://114.215.129.245:8080/atomic/. The program, source code and data can be downloaded from https://github.com/hyr0771/BPFinder.
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Dissertations / Theses on the topic "Metabolic Networks and Pathways"

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Leung, Shuen-yi, and 梁舜頤. "Predicting metabolic pathways from metabolic networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664317.

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Leung, Shuen-yi. "Predicting metabolic pathways from metabolic networks." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42664317.

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Morrison, Erin S., and Alexander V. Badyaev. "Structuring evolution: biochemical networks and metabolic diversification in birds." BioMed Central, 2016. http://hdl.handle.net/10150/620926.

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Background Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a “global” carotenoid network – comprising of all known enzymatic reactions among naturally occurring carotenoids – with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. Results We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network – compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. Conclusions The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.
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Faust, Karoline. "Development, assessment and application of bioinformatics tools for the extraction of pathways from metabolic networks." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210054.

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Genes can be associated in numerous ways, e.g. by co-expression in micro-arrays, co-regulation in operons and regulons or co-localization on the genome. Association of genes often indicates that they contribute to a common biological function, such as a pathway. The aim of this thesis is to predict metabolic pathways from associated enzyme-coding genes. The prediction approach developed in this work consists of two steps: First, the reactions are obtained that are carried out by the enzymes coded by the genes. Second, the gaps between these seed reactions are filled with intermediate compounds and reactions. In order to select these intermediates, metabolic data is needed. This work made use of metabolic data collected from the two major metabolic databases, KEGG and MetaCyc. The metabolic data is represented as a network (or graph) consisting of reaction nodes and compound nodes. Interme- diate compounds and reactions are then predicted by connecting the seed reactions obtained from the query genes in this metabolic network using a graph algorithm.

In large metabolic networks, there are numerous ways to connect the seed reactions. The main problem of the graph-based prediction approach is to differentiate biochemically valid connections from others. Metabolic networks contain hub compounds, which are involved in a large number of reactions, such as ATP, NADPH, H2O or CO2. When a graph algorithm traverses the metabolic network via these hub compounds, the resulting metabolic pathway is often biochemically invalid.

In the first step of the thesis, an already existing approach to predict pathways from two seeds was improved. In the previous approach, the metabolic network was weighted to penalize hub compounds and an extensive evaluation was performed, which showed that the weighted network yielded higher prediction accuracies than either a raw or filtered network (where hub compounds are removed). In the improved approach, hub compounds are avoided using reaction-specific side/main compound an- notations from KEGG RPAIR. As an evaluation showed, this approach in combination with weights increases prediction accuracy with respect to the weighted, filtered and raw network.

In the second step of the thesis, path finding between two seeds was extended to pathway prediction given multiple seeds. Several multiple-seed pathay prediction approaches were evaluated, namely three Steiner tree solving heuristics and a random-walk based algorithm called kWalks. The evaluation showed that a combination of kWalks with a Steiner tree heuristic applied to a weighted graph yielded the highest prediction accuracy.

Finally, the best perfoming algorithm was applied to a microarray data set, which measured gene expression in S. cerevisiae cells growing on 21 different compounds as sole nitrogen source. For 20 nitrogen sources, gene groups were obtained that were significantly over-expressed or suppressed with respect to urea as reference nitrogen source. For each of these 40 gene groups, a metabolic pathway was predicted that represents the part of metabolism up- or down-regulated in the presence of the investigated nitrogen source.

The graph-based prediction of pathways is not restricted to metabolic networks. It may be applied to any biological network and to any data set yielding groups of associated genes, enzymes or compounds. Thus, multiple-end pathway prediction can serve to interpret various high-throughput data sets.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Amon, Johannes. "Mining the genomes of actinomycetes : identification of metabolic pathways and regulatory networks." kostenfrei, 2010. http://d-nb.info/1002175534/34.

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Meggiato, Alberto <1987&gt. "Comparing metabolic networks at pathway level." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8501.

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Metabolic pathway comparison between different species is important to discover the differences in a metabolic function developed during the evolutionary process. This kind of analysis may allow the detection of important information useful also in drug engineering and medical science. In this thesis we propose a method for metabolic pathways comparison based on their representation as sets and multisets of chemical reactions. The information is taken from the KEGG database because it has a standardised representation of each pathway in the different organisms. The pathway comparison technique is then used in the context of metabolic networks comparison in order to solve the problems due to the size of the compared networks. The proposed methods have been implemented in Java as part of a tool for metabolic networks comparison.
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Dall'Olio, Giovanni Marco 1983. "Applications of network theory to human population genetics : from pathways to genotype networks." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/133454.

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In this thesis we developed two approaches to study positive selection and genetic adaptation in the human genome. Both approaches are based on applications of network theory. In the first approach, we studied how the signals of selection are distributed among the genes of a metabolic pathway. We use a network representation of the Asparagine N-Glycosylation pathway, and determine if given positions are more likely to be involved in selection events. We determined a different distribution of signals between the upstream part of this pathway, which has a linear structure and is involved in a conserved process, and the downstream part of the pathway, which has a complex network structure and is involved in adaptation to the environment. In the second approach, we applied a network representation of the set of genotypes observed in a population (Genotype Network) to next-generation sequencing data. The main result is a genome-wide picture of how the populations of the 1000 Genomes dataset have explored the genotype space. We found that the genotype networks of coding regions tend to be more connected and more expanded in the space than non coding regions, and that simulated sweeps have similar patterns compared to simulated neutral regions.
En esta tesis hemos desarrollado dos métodos para estudiar los patrones de selección positiva y adaptación genética en el genoma humano. Ambos métodos se basan en aplicaciones de teoría de redes. En la primera aplicación hemos investigado cómo las señales de selección están distribuidas a lo largo de una ruta metabólica. Hemos utilizado una representación de la ruta de N-Glicosilación, para estudiar si determinadas posiciones tienen más probabilidades de estar implicadas en eventos de selección positiva. Hemos comparado la distribución de las señales de selección entre la primera parte de la ruta metabólica, que tiene una estructura muy lineal y está involucrada en un proceso conservado, y la segunda parte de la ruta, que tiene una estructura de redes compleja y está involucrada en adaptación al ambiente. En la segunda aplicación hemos aplicado el concepto de redes de genotipos (Genotype Networks) a datos de secuencia de nueva generación. El resultado es un análisis completo de cómo las poblaciones de 1000 Genomas han explorado el espacio de genotipo. Las redes de genotipos de regiones codificantes suelen estar más conectadas y más expandidas que las regiones no-codificantes. Además, por medio de simulaciones hemos observado los patrones esperados para eventos de selección positiva.
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Cakmak, Ali. "Mining Metabolic Networks and Biomedical Literature." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1223490345.

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Ullah, Ehsan. "Pathway Analysis of Metabolic Networks using Graph Theoretical Approaches." Thesis, Tufts University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3640954.

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Cellular pathways defining biochemical transformational routes are often utilized as engineering targets to achieve industrial-scale production of commercially useful biomolecules including polyesters, building blocks for polymers, biofuels, and therapeutics derived from isoprenoids, polyketides, and non-ribosomal peptides. Identifying target pathways can be expedited using computational tools, leading to reduced development cost, time, and effort, and enabling new discoveries with potential positive impact on human health and the environment.

This thesis addresses three cellular pathway identification problems within metabolic networks. In the first problem, we identify all stoichiometrically balanced, thermodynamically feasible and genetically independent pathways, known as Elementary Flux Modes (EFMs), that can be used to express flux distributions and characterize cellular function. We develop an algorithm, gEFM, that incorporates structural information of the underlying network to enumerate all EFMs. The results show that gEFM is competitive with state-of-the art EFM computation techniques for several test cases, but less so for networks with larger number of EFMs. In the second and third problems, we identify individual target pathways with pre-specified characteristics. We develop an algorithm, PreProPath, for identifying a target pathway for up-regulation such that the path is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The results show that PreProPath can successfully identify high ethanol production pathways across multiple growth rates, and for succinate production in Escherichia coli (E. coli) as published in the literature. We also develop an algorithm, Dominant-Edge Pathway, that identifies thermodynamically-favored reactions along a pathway within the network from a given source metabolite to the desired destination. The algorithm is used to identify thermodynamically-limiting pathways in Zymomonas mobilis (Z. mobilis), E. coli and rat liver cell.

The novelty of this thesis is in utilizing graph-based methods to enumerate EFMs and to efficiently explore the pathway design space. Overall, the thesis advances the state-of-the-art techniques for metabolic pathway analysis.

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Xiang, Lu, and 项路. "Finding phenotype related pathways via biological networks comparison." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4715262X.

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 Why some species (or strains of a species) exhibit certain phenotypes (e.g. aerobic, anaerobic, pathogenic etc.) while the others do not is an important question to be answered. Apart from the conventional genomic study, studying the metabolism of the two groups of species may discover the corresponding pathways that are conserved in one group but not in the other. However, only a few tools provide functions to compare two groups of metabolic networks which are usually limited to the reaction level, not the pathway level. In this dissertation, a problem named DMP (Differentiating Metabolic Pathway) problem was formed. Given two groups of metabolic networks, it aims at finding conserved pathways exist in first group, but not the second group. The problem also captures the mutation in similar pathways and derives a measurement (p-value and e-score) for evaluating the significance of the pathways. An algorithm, DMPFinder, was developed to solve the DMP problem. Experimental results show that DMPFinder is able to identify pathways that are critical for the first group to exhibit a certain phenotype which is absent in the other group. Some of these pathways cannot be identified by other tools which only consider reaction level or do not take into account possible mutations among species.
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Computer Science
Master
Master of Philosophy
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Books on the topic "Metabolic Networks and Pathways"

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Maranas, Costas D. Optimization methods in metabolic networks. Hoboken, New Jersey: John Wiley & Sons Inc., 2016.

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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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D, Smolke Christina, ed. The metabolic pathway engineering handbook: Fundamentals. Boca Raton: CRC Press/Taylor & Francis, 2009.

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Skulachev, V. P. Principles of bioenergetics. Heidelberg: Springer, 2013.

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Rui-Sheng, Wang, and Zhang Xiang-Sun 1943-, eds. Biomolecular networks: Methods and applications in systems biology. Hoboken, N.J: Wiley, 2009.

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Inc, ebrary, ed. Biological petri nets. Washington, D.C: IOS Press, 2011.

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Jensen, Michael Krogh, and Jay D. Keasling, eds. Synthetic Metabolic Pathways. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7295-1.

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Roberts, Terry R., David H. Hutson, Philip W. Lee, Peter H. Nicholls, and Jack R. Plimmer, eds. Metabolic Pathways of Agrochemicals. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847551375.

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Roberts, Terry R., David H. Hutson, Philip W. Lee, Peter H. Nicholls, and Jack R. Plimmer, eds. Metabolic Pathways of Agrochemicals. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847551382.

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Wang, Xiaoyuan, Jian Chen, and Peter Quinn, eds. Reprogramming Microbial Metabolic Pathways. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5055-5.

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Book chapters on the topic "Metabolic Networks and Pathways"

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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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Arita, Masanori. "From Metabolic Reactions to Networks and Pathways." In Bacterial Molecular Networks, 93–106. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_6.

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Faust, Karoline, and Jacques van Helden. "Predicting Metabolic Pathways by Sub-network Extraction." In Bacterial Molecular Networks, 107–30. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-61779-361-5_7.

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Pitkänen, Esa, Ari Rantanen, Juho Rousu, and Esko Ukkonen. "Finding Feasible Pathways in Metabolic Networks." In Advances in Informatics, 123–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573036_12.

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Israelowitz, Meir, Birgit Weyand, Sabine Bohlmann, James Kramer, Christoph Gille, Syed W. H. Rizvi, Herbert P. von Schroeder, and Matthias Reuter. "Neural Networks for Modeling Metabolic Pathways." In Series in BioEngineering, 177–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-53214-1_12.

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Schreiber, Falk, Eva Grafahrend-Belau, Oliver Kohlbacher, and Huaiyu Mi. "Visualising Metabolic Pathways and Networks: Past, Present, Future." In Integrative Bioinformatics, 237–67. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6795-4_12.

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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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Rohrschneider, Markus, Alexander Ullrich, Andreas Kerren, Peter F. Stadler, and Gerik Scheuermann. "Visual Network Analysis of Dynamic Metabolic Pathways." In Advances in Visual Computing, 316–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_31.

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Saks, Valdur, Uwe Schlattner, Malgorzata Tokarska-Schlattner, Theo Wallimann, Rafaela Bagur, Sarah Zorman, Martin Pelosse, et al. "Systems Level Regulation of Cardiac Energy Fluxes Via Metabolic Cycles: Role of Creatine, Phosphotransfer Pathways, and AMPK Signaling." In Systems Biology of Metabolic and Signaling Networks, 261–320. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38505-6_11.

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Shah, Hayat Ali, Juan Liu, Zhihui Yang, and Jing Feng. "DeepMAT: Predicting Metabolic Pathways of Compounds Using a Message Passing and Attention-Based Neural Networks." In Lecture Notes in Computer Science, 428–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4749-2_37.

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Conference papers on the topic "Metabolic Networks and Pathways"

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Leung, S. Y., Henry C. M. Leung, Carlos L. Xiang, S. M. Yiu, and Francis Y. L. Chin. "Predicting metabolic pathways from metabolic networks with limited biological knowledge." In 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2010. http://dx.doi.org/10.1109/bibmw.2010.5703765.

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Gomez–Vela, Francisco, Norberto Diaz–Diaz, and Jesus Aguilar–Ruiz. "Gene Networks Validation based on Metabolic Pathways." In Bioengineering (BIBE). IEEE, 2011. http://dx.doi.org/10.1109/bibe.2011.10.

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Cheng, Qiong, Robert Harrison, and Alexander Zelikovsky. "Homomorphisms of Multisource Trees into Networks with Applications to Metabolic Pathways." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375587.

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Maniadi, Evaggelia M., and Ioannis G. Tollis. "Analysis and visualization of metabolic pathways and networks: A hypegraph approach." In 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2014. http://dx.doi.org/10.1109/bhi.2014.6864316.

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Pedersen, Jay, Ryan Patch, Lotfollah Najjar, and Dhundy R. Bastola. "PathwayLinks: Network analysis of metabolic pathways across bacterial organisms in a community." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999276.

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Fields, Karen T., Noel Fortun, Geoffrey A. Solano, and Angelyn Lao. "CRNet Translator: Building GMA, S-System Models and Chemical Reaction Networks of Disease and Metabolic Pathways." In 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2020. http://dx.doi.org/10.1109/iisa50023.2020.9284412.

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LI, YUNLEI, DICK DE RIDDER, MARCO J. L. DE GROOT, and MARCEL J. T. REINDERS. "METABOLIC PATHWAY ALIGNMENT (M-PAL) REVEALS DIVERSITY AND ALTERNATIVES IN CONSERVED NETWORKS." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0029.

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Sardar, Rahila, Kashif M. Shaikh, and Pavan P. Jutur. "Identification of transcription hubs that control lipid metabolism and carbon concentrating mechanism in model microalgae chlamydomonas reinhardtii using regulatory networks: Regulatory networks hubs in C. reinhardtii that control lipid and carbon concentrating metabolic pathways." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552116.

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Albrijawi, M. Taleb, Amrou Haj Ibrahim, and Reda Alhajj. "Predictions of drug metabolism pathways through CYP 3A4 enzyme by analysing drug-target interactions network graph." In ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3487351.3490959.

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Akella, Sridevi, and Chanchal K. Mitra. "Metabolic pathways as electronic circuits." In 2011 6th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2011. http://dx.doi.org/10.1109/hibit.2011.6450815.

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Reports on the topic "Metabolic Networks and Pathways"

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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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Droby, S., J. L. Norelli, M. E. Wisniewski, S. Freilich, A. Faigenboim, and C. Dardick. Microbial networks on harvested apples and the design of antagonistic consortia to control postharvest pathogens. Israel: United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134164.bard.

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We have demonstrated, at a global level, the existence of spatial variation in the fungal and bacterial composition of different fruit tissues. The composition, diversity and abundance varied in fruit harvested in different geographical locations and suggests a potential link between location and the type and rate of postharvest diseases that develop in each country. The global core microbiome of apple fruit was determined and found to be represented by several beneficial microbial taxa and accounted for a large fraction of the fruit microbial community. To further characterize apple fruit the microbiome after harvest, a detailed study was performed to evaluate effects of postharvest practices on the composition of the fruit peel. Microbiota. Results of this work conformed our findings that tissue-type is the main factor driving fungal and bacterial diversity and community composition on apple fruit. Both postharvest treatments and low temperature storage had a great impact on the fungal and bacterial diversity and community composition of these tissue types. Distinct spatial and temporal changes in the composition and diversity of the microbiota were observed in response to various postharvest management practices. Our results clearly indicated that apple fruit has a unique core microbiome that is universal. Analysis of the microbiome across Malus species indicates that the microbiome of domesticated apple has a higher diversity and abundance and is an admixture of the microbiome present in its wild progenitors, with clear evidence for introgression. These findings support the existence of co-evolution between Malus species and their microbiome during domestication. A network analysis of the metagenomics data was used to further elucidate functional differences between the microbiome of organic vs. conventional fruit. Our analysis predicted a link between Capnodiales and the degradation of aromatic compounds. Alternaria, a genus in the Capnodiales genus, is one of the main pathogens of stored apple fruit and was also abundant in our samples. The potential role of Alternaria in the degradation of aromatic compounds is in agreement with previous studies indicating a link between Alternaria and the metabolism of the aromatic compound, alphafarnesene38, a key volatile secreted by the fruit during maturation. A greater number of metabolic pathways related to plant defense substances (e.g. terpenoids and alkaloids) were identified in the microbiome of organic fruit samples, while more antibiotic-related metabolic pathways for compounds such as Erythromycin, Avermectin, Ansamycin, and Penicillin were present in the microbiome of apple fruit samples grown using conventional management practices.
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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.

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Fruit quality is determined by numerous genetic factors that affect taste, aroma, ‎color, texture, nutritional value and shelf life. To unravel the genetic components ‎involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.
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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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Fromm, Hillel, Paul Michael Hasegawa, and Aaron Fait. Calcium-regulated Transcription Factors Mediating Carbon Metabolism in Response to Drought. United States Department of Agriculture, June 2013. http://dx.doi.org/10.32747/2013.7699847.bard.

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Original objectives: The long-term goal of the proposed research is to elucidate the transcription factors, genes and metabolic networks involved in carbon metabolism and partitioning in response to water deficit. The proposed research focuses on the GTLcalcium/calmodulinbindingTFs and the gene and metabolic networks modulated by these TFs in Arabidopsis thaliana. The specific objectives are as follows. Objective-1 (USA): Physiological analyses of GTL1 loss- and gain-of-function plants under water sufficient and drought stress conditions Objective 2 (USA / Israel-TAU): Characterizion of GTL target genes and bioinformatic analysis of data to eulcidate gene-network topology. Objective-3 (Israel-TAU): Regulation of GTLmediated transcription by Ca²⁺/calmodulin: mechanism and biological significance. Objective-4 (Israel-BGU): Metabolic networks and carbon partitioning in response to drought. Additional direction: In the course of the project we added another direction, which was reported in the 2nd annual report, to elucidate genes controlling drought avoidance. The TAU team has isolated a few unhydrotropic (hyd) mutants and are in the process of mapping these mutations (of hyd13 and hyd15; see last year's report for a description of these mutants under salt stress) in the Arabidopsis genome by map-based cloning and deep sequencing. For this purpose, each hyd mutant was crossed with a wild type plant of the Landsberg ecotype, and at the F2 stage, 500-700 seedlings showing the unhydrotropic phenotype were collected separately and pooled DNA samples were subkected to the Illumina deep sequencing technology. Bioinformatics were used to identify the exact genomic positions of the mutations (based on a comparison of the genomic sequences of the two Arabidopsis thaliana ecotypes (Columbia and Landsberg). Background: To feed the 9 billion people or more, expected to live on Earth by the mid 21st century, the production of high-quality food must increase substantially. Based on a 2009 Declaration of the World Summit on Food Security, a target of 70% more global food production by the year 2050 was marked, an unprecedented food-production growth rate. Importantly, due to the larger areas of low-yielding land globally, low-yielding environments offer the greatest opportunity for substantial increases in global food production. Nowadays, 70% of the global available water is used by agriculture, and 40% of the world food is produced from irrigated soils. Therefore, much needs to be done towards improving the efficiency of water use by plants, accompanied by increased crop yield production under water-limiting conditions. Major conclusions, solutions and achievements: We established that AtGTL1 (Arabidopsis thaliana GT-2 LIKE1) is a focal determinant in water deficit (drought) signaling and tolerance, and water use efficiency (WUE). The GTL1 transcription factor is an upstream regulator of stomatal development as a transrepressor of AtSDD1, which encodes a subtilisin protease that activates a MAP kinase pathway that negatively regulates stomatal lineage and density. GTL1 binds to the core GT3 cis-element in the SDD1 promoter and transrepresses its expression under water-sufficient conditions. GTL1 loss-of-function mutants have reduced stomatal number and transpiration, and enhanced drought tolerance and WUE. In this case, higher WUE under water sufficient conditions occurs without reduction in absolute biomass accumulation or carbon assimilation, indicating that gtl1-mediated effects on stomatal conductance and transpiration do not substantially affect CO₂ uptake. These results are proof-of-concept that fine-tuned regulation of stomatal density can result in drought tolerance and higher WUE with maintenance of yield stability. Implications: Accomplishments during the IS-4243-09R project provide unique tools for continued discovery research to enhance plant drought tolerance and WUE.
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Knaff, David, and Hirasawa Mussakaz. Ferredoxin Dependent Plant Metabolic Pathways. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/1417307.

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Albert-Laszlo Barabasi. The Organization of Complex Metabolic Networks. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/881797.

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Jiao, Y., and A. Navid. Metabolic Engineering and Modeling of Metabolic Pathways to Improve Hydrogen Production by Photosynthetic Bacteria. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1179401.

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Karp, Peter D. Curation and Computational Design of Bioenergy-Related Metabolic Pathways. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1171111.

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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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