Journal articles: '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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Paley, Suzanne, Richard Billington, James Herson, Markus Krummenacker, and Peter D. Karp. "Pathway Tools Visualization of Organism-Scale Metabolic Networks." Metabolites 11, no. 2 (January 22, 2021): 64. http://dx.doi.org/10.3390/metabo11020064.

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Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.

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Hidalgo, José Francisco, Francisco Guil, and José Manuel García. "A new approach to obtaining EFMs using graph methods based on the shortest path between end nodes." Genomics and Computational Biology 2, no. 1 (September 12, 2016): 30. http://dx.doi.org/10.18547/gcb.2016.vol2.iss1.e30.

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Genome-scale metabolic networks let us understand the behaviour of the metabolism in the cells of living organisms. The availability of great amounts of such data gives the scientific community the opportunity to infer in silico new metabolic knowledge. Elementary Flux Modes (EFM) are minimal contained pathways or subsets of a metabolic network that are very useful to achieving the comprehension of a very specific metabolic function (as well as dysfunctions), and to get the knowledge to develop new drugs. Metabolic networks can have large connectivity and, therefore, EFMs resolution faces a combinational explosion challenge to be solved. In this paper we propose a new approach to obtain EFMs based on graph theory, the balanced graph concept and the shortest path between end nodes. Our proposal uses the shortest path between end nodes (input and output nodes) that finds all the pathways in the metabolic network and is able to prioritise the pathway search accounting the biological mean pursued. Our technique has two phases, the exploration phase and the characterisation one, and we show how it works in a well-known case study. We also demonstrate the relevance of the concept of balanced graph to achieve to the full list of EFMs.

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Veiga, Diogo Fernando, Pedro de Stege Cecconello, José Eduardo de Lucca, and Luismar Marques Porto. "Extension of the IsaViz software for the representation of metabolic and regulatory networks." Brazilian Archives of Biology and Technology 48, spe (June 2005): 197–205. http://dx.doi.org/10.1590/s1516-89132005000400025.

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In this work we developed an extension of IsaViz software, a RDF (Resource Description Framework) authoring tool, designed to be a graphical environment to build models of metabolic and regulatory networks. This environment, called Metabolic IsaViz, was linked to a genomic library of types and was modeled on the basis of ontologies. Biochemical pathways included data at sequence level (e.g., the amino acid sequence of enzymes), besides kinetic and thermodynamic parameters for the reactions. Models created with Metabolic IsaViz could be exported to pathways simulators through SBML (Systems Biology Markup Language), which allowed to analyze the pathway dynamics of target chemicals.

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Croes, D., F. Couche, S. J. Wodak, and J. van Helden. "Metabolic PathFinding: inferring relevant pathways in biochemical networks." Nucleic Acids Research 33, Web Server (July 1, 2005): W326—W330. http://dx.doi.org/10.1093/nar/gki437.

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Somvanshi, Pramod R., Anilkumar K. Patel, Sharad Bhartiya, and K. V. Venkatesh. "Influence of plasma macronutrient levels on hepatic metabolism: role of regulatory networks in homeostasis and disease states." RSC Advances 6, no. 17 (2016): 14344–71. http://dx.doi.org/10.1039/c5ra18128c.

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Multilevel regulations by metabolic, signaling and transcription pathways form a complex network that works to provide robust metabolic regulation in the liver. This analysis indicates that dietary perturbations in these networks can lead to insulin resistance.

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Tseng, Kuan-Chieh, Guan-Zhen Li, Yu-Cheng Hung, Chi-Nga Chow, Nai-Yun Wu, Yi-Ying Chien, Han-Qin Zheng, et al. "EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways." Plant and Cell Physiology 61, no. 10 (September 8, 2020): 1818–27. http://dx.doi.org/10.1093/pcp/pcaa115.

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Abstract Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user’s gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

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Gawthrop, Peter J., and Edmund J. Crampin. "Energy-based analysis of biomolecular pathways." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2202 (June 2017): 20160825. http://dx.doi.org/10.1098/rspa.2016.0825.

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Decomposition of biomolecular reaction networks into pathways is a powerful approach to the analysis of metabolic and signalling networks. Current approaches based on analysis of the stoichiometric matrix reveal information about steady-state mass flows (reaction rates) through the network. In this work, we show how pathway analysis of biomolecular networks can be extended using an energy-based approach to provide information about energy flows through the network. This energy-based approach is developed using the engineering-inspired bond graph methodology to represent biomolecular reaction networks. The approach is introduced using glycolysis as an exemplar; and is then applied to analyse the efficiency of free energy transduction in a biomolecular cycle model of a transporter protein [sodium-glucose transport protein 1 (SGLT1)]. The overall aim of our work is to present a framework for modelling and analysis of biomolecular reactions and processes which considers energy flows and losses as well as mass transport.

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VITABILE, SALVATORE, VINCENZO CONTI, BARBARA LANZA, DOMENICO CUSUMANO, and FLIPPO SORBELLO. "METABOLIC NETWORKS ROBUSTNESS: THEORY, SIMULATIONS AND RESULTS." Journal of Interconnection Networks 12, no. 03 (September 2011): 221–40. http://dx.doi.org/10.1142/s0219265911002964.

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Metabolic networks are composed of several functional modules, reproducing metabolic pathways and describing the entire cellular metabolism of an organism. In the last decade, an enormous interest has grown for the study of tolerance to errors and attacks in metabolic networks. Studies on their robustness have suggested that metabolic networks are tolerant to errors, but very vulnerable to targeted attacks against highly connected nodes. However, many findings on metabolic networks suggest that the above classification is too simple and imprecise, since hub node attacks can be by-passed if alternative metabolic paths can be exploited. On the contrary, non-hub nodes attacks can affect cell survival when the node is the only path within a functional module. In this paper an integrated approach for metabolic networks robustness analysis is presented. With more details, statistical, topological, and functional analysis are used together to evaluate metabolic network behavior under normal operation conditions and under random or targeted attacks. Two real biological metabolic networks have been used to test the effectiveness of the proposed approach.

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Noda-Garcia, Lianet, Wolfram Liebermeister, and Dan S. Tawfik. "Metabolite–Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks." Annual Review of Biochemistry 87, no. 1 (June 20, 2018): 187–216. http://dx.doi.org/10.1146/annurev-biochem-062917-012023.

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How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite–enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.

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Jevremovic, Dimitrije, Cong T. Trinh, Friedrich Srienc, and Daniel Boley. "On Algebraic Properties of Extreme Pathways in Metabolic Networks." Journal of Computational Biology 17, no. 2 (February 2010): 107–19. http://dx.doi.org/10.1089/cmb.2009.0020.

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Kaddurah-Daouk, R., H. Zhu, S. Sharma, M. Bogdanov, S. G. Rozen, W. Matson, N. O. Oki, et al. "Alterations in metabolic pathways and networks in Alzheimer’s disease." Translational Psychiatry 3, no. 4 (April 2013): e244-e244. http://dx.doi.org/10.1038/tp.2013.18.

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Blair, Rachael Hageman, Daniel J. Kliebenstein, and Gary A. Churchill. "What Can Causal Networks Tell Us about Metabolic Pathways?" PLoS Computational Biology 8, no. 4 (April 5, 2012): e1002458. http://dx.doi.org/10.1371/journal.pcbi.1002458.

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Akutsu, T., S. Miyano, and S. Kuhara. "Inferring qualitative relations in genetic networks and metabolic pathways." Bioinformatics 16, no. 8 (August 1, 2000): 727–34. http://dx.doi.org/10.1093/bioinformatics/16.8.727.

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Folch-Fortuny, A., M. Tortajada, J. M. Prats-Montalbán, F. Llaneras, J. Picó, and A. Ferrer. "MCR-ALS on metabolic networks: Obtaining more meaningful pathways." Chemometrics and Intelligent Laboratory Systems 142 (March 2015): 293–303. http://dx.doi.org/10.1016/j.chemolab.2014.10.004.

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Gómez-Vela, Francisco, and Norberto Díaz-Díaz. "Gene Network Biological Validity Based on Gene-Gene Interaction Relevance." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/540679.

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In recent years, gene networks have become one of the most useful tools for modeling biological processes. Many inference gene network algorithms have been developed as techniques for extracting knowledge from gene expression data. Ensuring the reliability of the inferred gene relationships is a crucial task in any study in order to prove that the algorithms used are precise. Usually, this validation process can be carried out using prior biological knowledge. The metabolic pathways stored in KEGG are one of the most widely used knowledgeable sources for analyzing relationships between genes. This paper introduces a new methodology, GeneNetVal, to assess the biological validity of gene networks based on the relevance of the gene-gene interactions stored in KEGG metabolic pathways. Hence, a complete KEGG pathway conversion into a gene association network and a new matching distance based on gene-gene interaction relevance are proposed. The performance of GeneNetVal was established with three different experiments. Firstly, our proposal is tested in a comparative ROC analysis. Secondly, a randomness study is presented to show the behavior of GeneNetVal when the noise is increased in the input network. Finally, the ability of GeneNetVal to detect biological functionality of the network is shown.

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Mulvihill, Melinda M., and Daniel K. Nomura. "Metabolomic strategies to map functions of metabolic pathways." American Journal of Physiology-Endocrinology and Metabolism 307, no. 3 (August 1, 2014): E237—E244. http://dx.doi.org/10.1152/ajpendo.00228.2014.

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Genome sequencing efforts have revealed a strikingly large number of unannotated and uncharacterized genes that fall into metabolic enzymes classes, likely indicating that our current knowledge of biochemical pathways in normal physiology, let alone in disease states, remains largely incomplete. This realization presents a daunting challenge for post-genomic-era scientists in deciphering the biochemical and (patho)physiological roles of these enzymes and their metabolites and metabolic networks. This is further complicated by many recent studies showing a rewiring of normal metabolic networks in disease states to give rise to unique pathophysiological functions of enzymes, metabolites, and metabolic pathways. This review focuses on recent discoveries made using metabolic mapping technologies to uncover novel pathways and metabolite-mediated posttranslational modifications and epigenetic alterations and their impact on physiology and disease.

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Ralser, Markus. "An appeal to magic? The discovery of a non-enzymatic metabolism and its role in the origins of life." Biochemical Journal 475, no. 16 (August 29, 2018): 2577–92. http://dx.doi.org/10.1042/bcj20160866.

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Until recently, prebiotic precursors to metabolic pathways were not known. In parallel, chemistry achieved the synthesis of amino acids and nucleotides only in reaction sequences that do not resemble metabolic pathways, and by using condition step changes, incompatible with enzyme evolution. As a consequence, it was frequently assumed that the topological organisation of the metabolic pathway has formed in a Darwinian process. The situation changed with the discovery of a non-enzymatic glycolysis and pentose phosphate pathway. The suite of metabolism-like reactions is promoted by a metal cation, (Fe(II)), abundant in Archean sediment, and requires no condition step changes. Knowledge about metabolism-like reaction topologies has accumulated since, and supports non-enzymatic origins of gluconeogenesis, the S-adenosylmethionine pathway, the Krebs cycle, as well as CO2 fixation. It now feels that it is only a question of time until essential parts of metabolism can be replicated non-enzymatically. Here, I review the ‘accidents’ that led to the discovery of the non-enzymatic glycolysis, and on the example of a chemical network based on hydrogen cyanide, I provide reasoning why metabolism-like non-enzymatic reaction topologies may have been missed for a long time. Finally, I discuss that, on the basis of non-enzymatic metabolism-like networks, one can elaborate stepwise scenarios for the origin of metabolic pathways, a situation that increasingly renders the origins of metabolism a tangible problem.

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Pfau, Thomas, Mafalda Galhardo, Jake Lin, and Thomas Sauter. "IDARE2—Simultaneous Visualisation of Multiomics Data in Cytoscape." Metabolites 11, no. 5 (May 6, 2021): 300. http://dx.doi.org/10.3390/metabo11050300.

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Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.

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Tan, Cheng, Xiaoyang Liu, and Jiajun Chen. "Microarray Analysis of the Molecular Mechanism Involved in Parkinson’s Disease." Parkinson's Disease 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/1590465.

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Purpose. This study aimed to investigate the underlying molecular mechanisms of Parkinson’s disease (PD) by bioinformatics.Methods. Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed.Results. Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1) was the hub node in the PPI network; hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain-containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD.Conclusions. “rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD.ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development.

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Gerlee, P., T. Lundh, B. Zhang, and A. R. A. Anderson. "Gene divergence and pathway duplication in the metabolic network of yeast and digital organisms." Journal of The Royal Society Interface 6, no. 41 (March 18, 2009): 1233–45. http://dx.doi.org/10.1098/rsif.2008.0514.

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We have studied the metabolic gene–function network in yeast and digital organisms evolved in the artificial life platform A vida . The gene–function network is a bipartite network in which a link exists between a gene and a function (pathway) if that function depends on that gene, and can also be viewed as a decomposition of the more traditional functional gene networks, where two genes are linked if they share any function. We show that the gene–function network exhibits two distinct degree distributions: the gene degree distribution is scale-free while the pathway distribution is exponential. This is true for both yeast and digital organisms, which suggests that this is a general property of evolving systems, and we propose that the scale-free gene degree distribution is due to pathway duplication, i.e. the development of a new pathway where the original function is still retained. Pathway duplication would serve as preferential attachment for the genes, and the experiments with A vida revealed precisely this; genes involved in many pathways are more likely to increase their connectivity. Measuring the overlap between different pathways, in terms of the genes that constitute them, showed that pathway duplication also is a likely mechanism in yeast evolution. This analysis sheds new light on the evolution of genes and functionality, and suggests that function duplication could be an important mechanism in evolution.

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Rezvan, Abolfazl, and Changiz Eslahchi. "Comparison of different approaches for identifying subnetworks in metabolic networks." Journal of Bioinformatics and Computational Biology 15, no. 06 (December 2017): 1750025. http://dx.doi.org/10.1142/s0219720017500251.

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A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera and Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera and Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected.

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Raine, D. J. "Network structure of metabolic pathways." Journal of Biological Physics and Chemistry 1, no. 2 (December 30, 2001): 89–94. http://dx.doi.org/10.4024/10ra01a.01.02.

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Clark, Teresa J., Longyun Guo, John Morgan, and Jorg Schwender. "Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models." Annual Review of Plant Biology 71, no. 1 (April 29, 2020): 303–26. http://dx.doi.org/10.1146/annurev-arplant-050718-100221.

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Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

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Oyarzún, Diego A., and Guy-Bart V. Stan. "Synthetic gene circuits for metabolic control: design trade-offs and constraints." Journal of The Royal Society Interface 10, no. 78 (January 6, 2013): 20120671. http://dx.doi.org/10.1098/rsif.2012.0671.

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A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.

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CHEN, JING, YANRUI DING, and WENBO XU. "COMPARATIVE ANALYSIS OF METABOLIC NETWORKS IN MESOPHILIC AND THERMOPHILIC ARCHAEA METHANOGENS BASED ON MODULARITY." Journal of Biological Systems 21, no. 02 (May 27, 2013): 1350015. http://dx.doi.org/10.1142/s0218339013500150.

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Metabolic networks are useful representations of the metabolic capabilities of cells. A comparison of metabolic networks across species is essential to better understand how evolutionary pressures shape these networks. By comparing the set of reactions that are expected to occur in an organism with the set of reactions in reference metabolic pathways, it is possible to infer the main metabolic functions of an organism. In this paper, the metabolic networks of the mesophilic archaeon Methanosarcina acetivorans and the thermophilic archaeon Methanopyrus kandleri have been reconstructed based on the KEGG LIGAND database, followed by four topological statistical analyses of the nodes in the two networks to compare their metabolic networks. The values of average degree and characteristic path length are very small but clustering coefficient is relatively large. The results show that the complete metabolic networks of M. acetivorans and M. kandleri possessed "small-world" network properties. Then we used Girvan–Newman modular algorithm to identify hub modules and compared hub modules with non-hub modules, respectively. The results show that M. kandleri metabolic network has a better modular organization than the M. acetivorans network. M. acetivorans includes 39 modules, 25 modules of them are independent, and 15 modules are functionally pure. On the other hand, M. kandleri includes 30 modules. Among them, there are 20 independent modules, and 14 of them are functionally pure. These results further indicated that the present approach for identifying modules yields modules that have biologically significant functions. We also identified hub modules of the metabolic networks and found that these hub modules are carbohydrate metabolism and amino acid metabolism. The conclusions obtained from such studies provide a broad overview of the similarities and differences between organism's metabolic networks. These will be very helpful for further research on thermostability of methanogens.

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Caminiti, Silvia P., Marco Tettamanti, Arianna Sala, Luca Presotto, Sandro Iannaccone, Stefano F. Cappa, Giuseppe Magnani, and Daniela Perani. "Metabolic connectomics targeting brain pathology in dementia with Lewy bodies." Journal of Cerebral Blood Flow & Metabolism 37, no. 4 (July 21, 2016): 1311–25. http://dx.doi.org/10.1177/0271678x16654497.

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Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak’s early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment.

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Lisec, Jan, Dennis Kobelt, Wolfgang Walther, Margarita Mokrizkij, Carsten Grötzinger, Carsten Jaeger, Katharina Baum, et al. "Systematic Identification of MACC1-Driven Metabolic Networks in Colorectal Cancer." Cancers 13, no. 5 (February 26, 2021): 978. http://dx.doi.org/10.3390/cancers13050978.

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MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and show the comprehensive analysis of MACC1-driven metabolic networks. We analyzed concentration-dependent changes in nutrient use, nutrient depletion, metabolic tracing employing 13C-labeled substrates, and in vivo studies. We found that MACC1 permits numerous effects on cancer metabolism. Most of those effects increased nutrient uptake. Furthermore, MACC1 alters metabolic pathways by affecting metabolite production or turnover from metabolic substrates. MACC1 supports use of glucose, glutamine and pyruvate via their increased depletion or altered distribution within metabolic pathways. In summary, we demonstrate that MACC1 is an important regulator of metabolism in cancer cells.

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Yeung, Matthew, Ines Thiele, and Bernard Ø. Palsson. "Estimation of the number of extreme pathways for metabolic networks." BMC Bioinformatics 8, no. 1 (2007): 363. http://dx.doi.org/10.1186/1471-2105-8-363.

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Hoffmann, R., M. Krallinger, E. Andres, J. Tamames, C. Blaschke, and A. Valencia. "Text Mining for Metabolic Pathways, Signaling Cascades, and Protein Networks." Science Signaling 2005, no. 283 (May 3, 2005): pe21. http://dx.doi.org/10.1126/stke.2832005pe21.

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Moulin, Cecile, Laurent Tournier, and Sabine Peres. "Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics." Processes 9, no. 10 (September 23, 2021): 1701. http://dx.doi.org/10.3390/pr9101701.

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To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.

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Guo, Zi-Han, Lei Chen, and Xian Zhao. "A Network Integration Method for Deciphering the Types of Metabolic Pathway of Chemicals with Heterogeneous Information." Combinatorial Chemistry & High Throughput Screening 21, no. 9 (January 21, 2019): 670–80. http://dx.doi.org/10.2174/1386207322666181206112641.

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Aim and Objective: A metabolic pathway is an important type of biological pathway, which is composed of a series of chemical reactions. It provides essential molecules and energies for living organisms. To date, several metabolic pathways have been uncovered. However, their completeness is still on the way. A number of prediction methods have been built to assign chemicals into certain metabolic pathway, which can further be used to predict novel latent chemicals for a given metabolic pathway. However, they did not make use of chemical properties in a system level to construct prediction models. Method: In this study, we applied a network integration method, which can extract topological features from different chemical networks, representing chemical associations from their different properties, and fused several high-dimension vector representations into a low-dimension vector representation for each chemical. The compact vector representations were fed into the Support Vector Machine (SVM) to construct the prediction model. To tackle the problem that one chemical can participate in more than one pathway type, we construct an SVM-based binary prediction model for each pathway type to determine whether a given chemical can participate in the pathway type. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) was adopted to weaken the influence of imbalanced dataset. Results and Conclusion: Each binary model gave a quite good performance and was superior to the classic prediction model, indicating that the proposed models can be useful tools for integrating heterogeneous information to assign chemicals into correct metabolic pathways.

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De Las Morenas Mateos, Carlos, and Rafael Lahoz-Beltra. "Application of Graph Theory and Automata Modeling for the Study of the Evolution of Metabolic Pathways with Glycolysis and Krebs Cycle as Case Studies." Computation 11, no. 6 (May 28, 2023): 107. http://dx.doi.org/10.3390/computation11060107.

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Today, graph theory represents one of the most important modeling techniques in biology. One of the most important applications is in the study of metabolic networks. During metabolism, a set of sequential biochemical reactions takes place, which convert one or more molecules into one or more final products. In a biochemical reaction, the transformation of one metabolite into the next requires a class of proteins called enzymes that are responsible for catalyzing the reaction. Whether by applying differential equations or automata theory, it is not easy to explain how the evolution of metabolic networks could have taken place within living organisms. Obviously, in the past, the assembly of biochemical reactions into a metabolic network depended on the independent evolution of the enzymes involved in the isolated biochemical reactions. In this work, a simulation model is presented where enzymes are modeled as automata, and their evolution is simulated with a genetic algorithm. This protocol is applied to the evolution of glycolysis and the Krebs cycle, two of the most important metabolic networks for the survival of organisms. The results obtained show how Darwinian evolution is able to optimize a biological network, such as in the case of glycolysis and Krebs metabolic networks.

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Lin, Chuwei, Aneirin Alan Lott, Wei Zhu, Craig P. Dufresne, and Sixue Chen. "Mitogen-Activated Protein Kinase 4-Regulated Metabolic Networks." International Journal of Molecular Sciences 23, no. 2 (January 14, 2022): 880. http://dx.doi.org/10.3390/ijms23020880.

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Mitogen-activated protein kinase 4 (MPK4) was first identified as a negative regulator of systemic acquired resistance. It is also an important kinase involved in many other biological processes in plants, including cytokinesis, reproduction, and photosynthesis. Arabidopsis thaliana mpk4 mutant is dwarf and sterile. Previous omics studies including genomics, transcriptomics, and proteomics have revealed new functions of MPK4 in different biological processes. However, due to challenges in metabolomics, no study has touched upon the metabolomic profiles of the mpk4 mutant. What metabolites and metabolic pathways are potentially regulated by MPK4 are not known. Metabolites are crucial components of plants, and they play important roles in plant growth and development, signaling, and defense. Here we used targeted and untargeted metabolomics to profile metabolites in the wild type and the mpk4 mutant. We found that in addition to the jasmonic acid and salicylic acid pathways, MPK4 is involved in polyamine synthesis and photosynthesis. In addition, we also conducted label-free proteomics of the two genotypes. The integration of metabolomics and proteomics data allows for an insight into the metabolomic networks that are potentially regulated by MPK4.

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Patrick, Ryan M., Xing-Qi Huang, Natalia Dudareva, and Ying Li. "Dynamic histone acetylation in floral volatile synthesis and emission in petunia flowers." Journal of Experimental Botany 72, no. 10 (February 19, 2021): 3704–22. http://dx.doi.org/10.1093/jxb/erab072.

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Abstract Biosynthesis of secondary metabolites relies on primary metabolic pathways to provide precursors, energy, and cofactors, thus requiring coordinated regulation of primary and secondary metabolic networks. However, to date, it remains largely unknown how this coordination is achieved. Using Petunia hybrida flowers, which emit high levels of phenylpropanoid/benzenoid volatile organic compounds (VOCs), we uncovered genome-wide dynamic deposition of histone H3 lysine 9 acetylation (H3K9ac) during anthesis as an underlying mechanism to coordinate primary and secondary metabolic networks. The observed epigenome reprogramming is accompanied by transcriptional activation at gene loci involved in primary metabolic pathways that provide precursor phenylalanine, as well as secondary metabolic pathways to produce volatile compounds. We also observed transcriptional repression among genes involved in alternative phenylpropanoid branches that compete for metabolic precursors. We show that GNAT family histone acetyltransferase(s) (HATs) are required for the expression of genes involved in VOC biosynthesis and emission, by using chemical inhibitors of HATs, and by knocking down a specific HAT gene, ELP3, through transient RNAi. Together, our study supports that regulatory mechanisms at chromatin level may play an essential role in activating primary and secondary metabolic pathways to regulate VOC synthesis in petunia flowers.

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Zhao, Shanguang, Selina Khoo, Siew-Cheok Ng, and Aiping Chi. "Brain Functional Network and Amino Acid Metabolism Association in Females with Subclinical Depression." International Journal of Environmental Research and Public Health 19, no. 6 (March 11, 2022): 3321. http://dx.doi.org/10.3390/ijerph19063321.

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This study aimed to investigate the association between complex brain functional networks and the metabolites in urine in subclinical depression. Electroencephalography (EEG) signals were recorded from 78 female college students, including 40 with subclinical depression (ScD) and 38 healthy controls (HC). The phase delay index was utilized to construct functional connectivity networks and quantify the topological properties of brain networks using graph theory. Meanwhile, the urine of all participants was collected for non-targeted LC-MS metabolic analysis to screen differential metabolites. The global efficiency was significantly increased in the α-2, β-1, and β-2 bands, while the characteristic path length of β-1 and β-2 and the clustering coefficient of β-2 were decreased in the ScD group. The severity of depression was negatively correlated with the level of cortisone (p = 0.016, r = −0.40). The metabolic pathways, including phenylalanine metabolism, phenylalanine tyrosine tryptophan biosynthesis, and nitrogen metabolism, were disturbed in the ScD group. The three metabolic pathways were negatively correlated (p = 0.014, r = −0.493) with the global efficiency of the brain network of the β-2 band, whereas they were positively correlated (p = 0.014, r = 0.493) with the characteristic path length of the β-2 band. They were mainly associated with low levels of L-phenylalanine, and the highest correlation sparsity was 0.11. The disturbance of phenylalanine metabolism and the phenylalanine, tryptophan, tyrosine biosynthesis pathways cause depressive symptoms and changes in functional brain networks. The decrease in the L-phenylalanine level may be related to the randomization trend of the β-1 frequency brain functional network.

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Chen, Gen Lang, Li Feng An, and Xiao Gang Jin. "An Urban Traffic Network Analysis Method Based on EPs." Key Engineering Materials 460-461 (January 2011): 142–47. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.142.

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Urban traffic networks (UT-Nets) and cellular metabolic networks (C-Nets) have many common functional characteristics and internal mechanisms. Based on the similarity and traffic conservation, we use extreme pathways (EPs) to analysis the state of UT-Nets. Experiments showed the EPs method for cell metabolism also can be used in urban traffic network analysis after defining some indicators.

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Kan, N. E., Z. V. Khachatryan, V. V. Chagovets, N. L. Starodubtseva, E. Yu Amiraslanov, V. L. Tyutyunnik, N. A. Lomova, and V. E. Frankevich. "Analysis of metabolic pathways in intrauterine growth restriction." Biomeditsinskaya Khimiya 66, no. 2 (2020): 174–80. http://dx.doi.org/10.18097/pbmc20206602174.

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Objective was to analyze metabolic pathways based on a study of the metabolomic profile of pregnant women with intrauterine growth restriction. The metabolic profile of pregnant women with fetal growth restriction has been analyzed using liquid chromatography-mass spectrometry. At the second stage pathways were identified using SMPDB and MetaboAnalyst databases to clarify the relationship between metabolites. Biological networks allow to determine the effect of proteins on the metabolic pathways involved in pathogenesis of IUGR and determine the epigenetic mechanisms of its formation.

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Noree, Chalongrat, Kyle Begovich, Dane Samilo, Risa Broyer, Elena Monfort, and James E. Wilhelm. "A quantitative screen for metabolic enzyme structures reveals patterns of assembly across the yeast metabolic network." Molecular Biology of the Cell 30, no. 21 (October 1, 2019): 2721–36. http://dx.doi.org/10.1091/mbc.e19-04-0224.

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Despite the proliferation of proteins that can form filaments or phase-separated condensates, it remains unclear how this behavior is distributed over biological networks. We have found that 60 of the 440 yeast metabolic enzymes robustly form structures, including 10 that assemble within mitochondria. Additionally, the ability to assemble is enriched at branch points on several metabolic pathways. The assembly of enzymes at the first branch point in de novo purine biosynthesis is coordinated, hierarchical, and based on their position within the pathway, while the enzymes at the second branch point are recruited to RNA stress granules. Consistent with distinct classes of structures being deployed at different control points in a pathway, we find that the first enzyme in the pathway, PRPP synthetase, forms evolutionarily conserved filaments that are sequestered in the nucleus in higher eukaryotes. These findings provide a roadmap for identifying additional conserved features of metabolic regulation by condensates/filaments.

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Mattei, Gianluca, Zhuohui Gan, Matteo Ramazzotti, Bernhard O. Palsson, and Daniel C. Zielinski. "Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways." Metabolites 13, no. 11 (November 3, 2023): 1127. http://dx.doi.org/10.3390/metabo13111127.

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Pathway analysis is ubiquitous in biological data analysis due to the ability to integrate small simultaneous changes in functionally related components. While pathways are often defined based on either manual curation or network topological properties, an attractive alternative is to generate pathways around specific functions, in which metabolism can be defined as the production and consumption of specific metabolites. In this work, we present an algorithm, termed MetPath, that calculates pathways for condition-specific production and consumption of specific metabolites. We demonstrate that these pathways have several useful properties. Pathways calculated in this manner (1) take into account the condition-specific metabolic role of a gene product, (2) are localized around defined metabolic functions, and (3) quantitatively weigh the importance of expression to a function based on the flux contribution of the gene product. We demonstrate how these pathways elucidate network interactions between genes across different growth conditions and between cell types. Furthermore, the calculated pathways compare favorably to manually curated pathways in predicting the expression correlation between genes. To facilitate the use of these pathways, we have generated a large compendium of pathways under different growth conditions for E. coli. The MetPath algorithm provides a useful tool for metabolic network-based statistical analyses of high-throughput data.

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Sambamoorthy, Gayathri, and Karthik Raman. "MinReact: a systematic approach for identifying minimal metabolic networks." Bioinformatics 36, no. 15 (May 14, 2020): 4309–15. http://dx.doi.org/10.1093/bioinformatics/btaa497.

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Abstract Motivation Genome-scale metabolic models are widely constructed and studied for understanding various design principles underlying metabolism, predominantly redundancy. Metabolic networks are highly redundant and it is possible to minimize the metabolic networks into smaller networks that retain the functionality of the original network. Results Here, we establish a new method, MinReact that systematically removes reactions from a given network to identify minimal reactome(s). We show that our method identifies smaller minimal reactomes than existing methods and also scales well to larger metabolic networks. Notably, our method exploits known aspects of network structure and redundancy to identify multiple minimal metabolic networks. We illustrate the utility of MinReact by identifying multiple minimal networks for 77 organisms from the BiGG database. We show that these multiple minimal reactomes arise due to the presence of compensatory reactions/pathways. We further employed MinReact for a case study to identify the minimal reactomes of different organisms in both glucose and xylose minimal environments. Identification of minimal reactomes of these different organisms elucidate that they exhibit varying levels of redundancy. A comparison of the minimal reactomes on glucose and xylose illustrates that the differences in the reactions required to sustain growth on either medium. Overall, our algorithm provides a rapid and reliable way to identify minimal subsets of reactions that are essential for survival, in a systematic manner. Availability and implementation Algorithm is available from https://github.com/RamanLab/MinReact. Supplementary information Supplementary data are available at Bioinformatics online.

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Journal articles on the topic 'Metabolic Networks and Pathways'

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