2,247 results on '"Metabolic Networks"'
Search Results
2. Analysing the Expressiveness of Metabolic Networks Representations
- Author
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García, Irene, Chouaia, Bessem, Llabrés, Mercè, Palmer-Rodríguez, Pere, Simeoni, Marta, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Villani, Marco, editor, Cagnoni, Stefano, editor, and Serra, Roberto, editor
- Published
- 2024
- Full Text
- View/download PDF
3. A complex metabolic network and its biomarkers regulate laccase production in white-rot fungus Cerrena unicolor 87613
- Author
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Long-Bin Zhang, Xiu-Gen Qiu, Ting-Ting Qiu, Zhou Cui, Yan Zheng, and Chun Meng
- Subjects
Laccase production ,White rot fungi ,Cerrena unicolor ,Fructose ,Metabolic networks ,Regulation mechanism ,Microbiology ,QR1-502 - Abstract
Abstract Background White-rot fungi are known to naturally produce high quantities of laccase, which exhibit commendable stability and catalytic efficiency. However, their laccase production does not meet the demands for industrial-scale applications. To address this limitation, it is crucial to optimize the conditions for laccase production. However, the regulatory mechanisms underlying different conditions remain unclear. This knowledge gap hinders the cost-effective application of laccases. Results In this study, we utilized transcriptomic and metabolomic data to investigate a promising laccase producer, Cerrena unicolor 87613, cultivated with fructose as the carbon source. Our comprehensive analysis of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) aimed to identify changes in cellular processes that could affect laccase production. As a result, we discovered a complex metabolic network primarily involving carbon metabolism and amino acid metabolism, which exhibited contrasting changes between transcription and metabolic patterns. Within this network, we identified five biomarkers, including succinate, serine, methionine, glutamate and reduced glutathione, that played crucial roles in co-determining laccase production levels. Conclusions Our study proposed a complex metabolic network and identified key biomarkers that determine the production level of laccase in the commercially promising Cerrena unicolor 87613. These findings not only shed light on the regulatory mechanisms of carbon sources in laccase production, but also provide a theoretical foundation for enhancing laccase production through strategic reprogramming of metabolic pathways, especially related to the citrate cycle and specific amino acid metabolism.
- Published
- 2024
- Full Text
- View/download PDF
4. A complex metabolic network and its biomarkers regulate laccase production in white-rot fungus Cerrena unicolor 87613.
- Author
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Zhang, Long-Bin, Xiu, Xiu-Gen, Qiu, Ting-Ting, Cui, Zhou, Zheng, Yan, and Meng, Chun
- Subjects
- *
LACCASE , *BIOMARKERS , *CARBON metabolism , *AMINO acid metabolism - Abstract
Background: White-rot fungi are known to naturally produce high quantities of laccase, which exhibit commendable stability and catalytic efficiency. However, their laccase production does not meet the demands for industrial-scale applications. To address this limitation, it is crucial to optimize the conditions for laccase production. However, the regulatory mechanisms underlying different conditions remain unclear. This knowledge gap hinders the cost-effective application of laccases. Results: In this study, we utilized transcriptomic and metabolomic data to investigate a promising laccase producer, Cerrena unicolor 87613, cultivated with fructose as the carbon source. Our comprehensive analysis of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) aimed to identify changes in cellular processes that could affect laccase production. As a result, we discovered a complex metabolic network primarily involving carbon metabolism and amino acid metabolism, which exhibited contrasting changes between transcription and metabolic patterns. Within this network, we identified five biomarkers, including succinate, serine, methionine, glutamate and reduced glutathione, that played crucial roles in co-determining laccase production levels. Conclusions: Our study proposed a complex metabolic network and identified key biomarkers that determine the production level of laccase in the commercially promising Cerrena unicolor 87613. These findings not only shed light on the regulatory mechanisms of carbon sources in laccase production, but also provide a theoretical foundation for enhancing laccase production through strategic reprogramming of metabolic pathways, especially related to the citrate cycle and specific amino acid metabolism. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Models and molecular mechanisms for trade‐offs in the context of metabolism.
- Author
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Hashemi, Seirana, Laitinen, Roosa, and Nikoloski, Zoran
- Subjects
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MICROBIAL metabolism , *METABOLIC models , *MICROBIAL communities , *METABOLISM - Abstract
Accumulating evidence for trade‐offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade‐offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade‐offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade‐offs obtained from constraint‐based metabolic modelling, we emphasize the relationship of metabolic trade‐offs to the classical black box Y‐model that provides a conceptual explanation for resource acquisition–allocation trade‐offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade‐offs by relying on the toolbox provided by constraint‐based metabolic modelling for single organisms and microbial communities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Metabolic Perspective on Soybean and Its Potential Impacts on Digital Breeding: An Updated Overview.
- Author
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Mani, Vimalraj, Park, Soyoung, Lee, Kijong, Kim, Jin A., Ha, Kihun, Park, Soo-Kwon, Park, Sewon, Lee, Soo In, Kwon, Soojin, and Lee, Sichul
- Abstract
Owing to its high nutritional content of protein, oil, fatty acids, and sugars, soybean (Glycine max L.), one of the most significant legume crops, is utilized worldwide as food, feed, and fuel for daily life applications. The seeds, leaves, branches, roots, and pods of soybean contain essential bioactive compounds, including flavonoids, isoflavonoids, and other specialized metabolites, that play important roles in plant growth, development, and stress responses. In recent years, significant progress has been made in increasing soybean production. Therefore, here, we summarize the most recent breakthroughs in metabolite profiling and bioactive compound identification in soybeans to inform future digital breeding approaches. In addition to classical metabolite investigations, the discovery of metabolites involved in the dehydration response was made through a recent study that examined the regulatory network of metabolites and plant hormone genes. This review aimed to provide a metabolic perspective on soybeans that will benefit soybean production. The findings of this review will facilitate the development of novel soybean cultivars containing highly valuable metabolites for digital breeding. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Visual analysis of multi-omics data
- Author
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Austin Swart, Ron Caspi, Suzanne Paley, and Peter D. Karp
- Subjects
omics ,omics analysis ,multi-omics ,multi-omics analysis ,metabolic networks ,Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.
- Published
- 2024
- Full Text
- View/download PDF
8. A review of advances in integrating gene regulatory networks and metabolic networks for designing strain optimization
- Author
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Ridho Ananda, Kauthar Mohd Daud, and Suhaila Zainudin
- Subjects
In silico metabolic engineering ,Metabolic networks ,Gene regulatory networks ,Constraint-based modeling ,Strain optimization ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Strain optimization aims to overproduce valuable metabolites by leveraging an understanding of biological systems, including metabolic networks and gene regulatory networks (GRNs). Accordingly, researchers proposed integrating metabolic networks and GRNs to be analyzed simultaneously. The proposed algorithms from 2002 to 2021 were rFBA, SR-FBA, iFBA, PROM, PROM2.0, TIGER, BeReTa, CoRegFlux, IDREAM, TRFBA, OptRAM, TRIMER, and PRIME. Each algorithm has different characteristics. Thus, using the appropriate algorithm for designing strain optimization is essential. Therefore, a critical review was conducted by synthesizing and analyzing the existing algorithms. Five aspects are discussed in this review: the strategic approaches, model of GRNs, source of GRNs, optimization, supplementary methods, and the programming language used. Based on the review, several algorithms were better at modeling integrated regulatory-metabolic networks with high confidence, i.e., PROM, PROM2.0, and TRFBA. A simulation was applied to six strains. The results show that PROM2.0 best predicted the production rate and time complexity. However, the model is heavily influenced by the quality and quantity of the gene expression data. In addition, there are inconsistencies between GRNs and the gene expression data. Thus, this review also discussed future work based on GRNs and gene expression data.
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- 2024
- Full Text
- View/download PDF
9. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models.
- Author
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Habibpour, Mahdis, Razaghi-Moghadam, Zahra, and Nikoloski, Zoran
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METABOLIC models , *ESCHERICHIA coli , *SMALL molecules , *SACCHAROMYCES cerevisiae , *CELL growth - Abstract
Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications. • We provided a classification of constraint-based modeling approaches for prediction MPIs and integration of their effects in large-scale models of metabolism. • We compared the performance of four approaches for prediction of MPIs using GEMs of E. coli and S. cerevisiae , and identified that SIMMER and SCOUR showed the largest macro-averaged F1-score on S. cerevisiae and E. coli , respectively. • Approaches that rely on structural features and easy-to-obtain metabolic phenotypes resulted in more accurate predictions of MPIs, providing the basis of future developments approaches for integrating the effects of MPIs in genome-scale metabolic models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. Microbial Pathway Thermodynamics: Stoichiometric Models Unveil Anabolic and Catabolic Processes.
- Author
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Ebenhöh, Oliver, Ebeling, Josha, Meyer, Ronja, Pohlkotte, Fabian, and Nies, Tim
- Subjects
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METABOLIC regulation , *BIOTECHNOLOGICAL microorganisms , *THERMODYNAMICS , *MICROBIAL metabolism , *METABOLIC models , *ENERGY metabolism - Abstract
The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow for the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genome-scale metabolic model. Interestingly, both approaches are rather successful at characterizing and predicting the expected product yield. Over the years, macrochemical equations especially have been extensively characterized in terms of their thermodynamic properties. However, a common challenge when characterizing microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and we use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. Evolution at the Origins of Life?
- Author
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Schoenmakers, Ludo L. J., Reydon, Thomas A. C., and Kirschning, Andreas
- Subjects
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ORIGIN of life , *PHILOSOPHY of science , *EMPIRICAL research - Abstract
The role of evolutionary theory at the origin of life is an extensively debated topic. The origin and early development of life is usually separated into a prebiotic phase and a protocellular phase, ultimately leading to the Last Universal Common Ancestor. Most likely, the Last Universal Common Ancestor was subject to Darwinian evolution, but the question remains to what extent Darwinian evolution applies to the prebiotic and protocellular phases. In this review, we reflect on the current status of evolutionary theory in origins of life research by bringing together philosophy of science, evolutionary biology, and empirical research in the origins field. We explore the various ways in which evolutionary theory has been extended beyond biology; we look at how these extensions apply to the prebiotic development of (proto)metabolism; and we investigate how the terminology from evolutionary theory is currently being employed in state-of-the-art origins of life research. In doing so, we identify some of the current obstacles to an evolutionary account of the origins of life, as well as open up new avenues of research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
12. Maintaining beneficial alga-associated bacterial communities under heat stress: insights from controlled co-culture experiments using antibiotic-resistant bacterial strains.
- Author
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Karimi, Elham and Dittami, Simon M
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BACTERIAL communities , *BIOTIC communities , *BACTERIAL diversity , *MICROBIAL communities , *HEATING control , *ALGAL communities , *EUKARYOTES , *BROWN algae - Abstract
Brown algae, like many eukaryotes, possess diverse microbial communities. Ectocarpus— a model brown alga—relies on these communities for essential processes, such as growth development. Controlled laboratory systems are needed for functional studies of these algal–bacterial interactions. We selected bacterial strains based on their metabolic networks to provide optimal completion of the algal metabolism, rendered them resistant to two antibiotics, and inoculate them to establish controlled co-cultures with Ectocarpus under continuous antibiotic treatment. We then monitored the stability of the resulting associations under control conditions and heat stress using 16S metabarcoding. Antibiotics strongly reduced bacterial diversity both in terms of taxonomy and predicted metabolic functions. In the inoculated sample, 63%–69% of reads corresponded to the inoculated strains, and the communities remained stable during temperature stress. They also partially restored the predicted metabolic functions of the natural community. Overall, the development of antibiotic-resistant helper cultures offers a promising route to fully controlled laboratory experiments with algae and microbiota and thus represents an important step towards generating experimental evidence for specific host–microbe interactions in the systems studied. Further work will be required to achieve full control and progressively expand our repertoire of helper strains including those currently 'unculturable'. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
13. Shortest Hyperpaths in Directed Hypergraphs for Reaction Pathway Inference.
- Author
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Krieger, Spencer and Kececioglu, John
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HYPERGRAPHS , *LINEAR programming , *CYTOLOGY , *INTEGER programming , *SOURCE code , *COMBINATORIAL optimization - Abstract
Signaling and metabolic pathways, which consist of chains of reactions that produce target molecules from source compounds, are cornerstones of cellular biology. Properly modeling the reaction networks that represent such pathways requires directed hypergraphs, where each molecule or compound maps to a vertex, and each reaction maps to a hyperedge directed from its set of input reactants to its set of output products. Inferring the most likely series of reactions that produces a given set of targets from a given set of sources, where for each reaction its reactants are produced by prior reactions in the series, corresponds to finding a shortest hyperpath in a directed hypergraph, which is NP-complete. We give the first exact algorithm for general shortest hyperpaths that can find provably optimal solutions for large, real-world, reaction networks. In particular, we derive a novel graph-theoretic characterization of hyperpaths, which we leverage in a new integer linear programming formulation of shortest hyperpaths that for the first time handles cycles, and develop a cutting-plane algorithm that can solve this integer linear program to optimality in practice. Through comprehensive experiments over all of the thousands of instances from the standard Reactome and NCI-PID reaction databases, we demonstrate that our cutting-plane algorithm quickly finds an optimal hyperpath—inferring the most likely pathway—with a median running time of under 10 seconds, and a maximum time of less than 30 minutes, even on instances with thousands of reactions. We also explore for the first time how well hyperpaths infer true pathways, and show that shortest hyperpaths accurately recover known pathways, typically with very high precision and recall. Source code implementing our cutting-plane algorithm for shortest hyperpaths is available free for research use in a new tool called Mmunin. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
14. Proteomic analysis to unravel the biochemical mechanisms triggered by Bacillus toyonensis SFC 500-1E under chromium(VI) and phenol stress.
- Author
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Fernandez, Marilina, Callegari, Eduardo A., Paez, María D., González, Paola S., and Agostini, Elizabeth
- Abstract
Bacillus toyonensis SFC 500-1E is a member of the consortium SFC 500-1 able to remove Cr(VI) and simultaneously tolerate high phenol concentrations. In order to elucidate mechanisms utilized by this strain during the bioremediation process, the differential expression pattern of proteins was analyzed when it grew with or without Cr(VI) (10 mg/L) and Cr(VI) + phenol (10 and 300 mg/L), through two complementary proteomic approaches: gel-based (Gel-LC) and gel-free (shotgun) nanoUHPLC-ESI–MS/MS. A total of 400 differentially expressed proteins were identified, out of which 152 proteins were down-regulated under Cr(VI) and 205 up-regulated in the presence of Cr(VI) + phenol, suggesting the extra effort made by the strain to adapt itself and keep growing when phenol was also added. The major metabolic pathways affected include carbohydrate and energetic metabolism, followed by lipid and amino acid metabolism. Particularly interesting were also ABC transporters and the iron-siderophore transporter as well as transcriptional regulators that can bind metals. Stress-associated global response involving the expression of thioredoxins, SOS response, and chaperones appears to be crucial for the survival of this strain under treatment with both contaminants. This research not only provided a deeper understanding of B. toyonensis SFC 500-1E metabolic role in Cr(VI) and phenol bioremediation process but also allowed us to complete an overview of the consortium SFC 500-1 behavior. This may contribute to an improvement in its use as a bioremediation strategy and also provides a baseline for further research. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
15. Transcriptomic and Non-Targeted Metabolomic Analyses Reveal Changes in Metabolic Networks during Leaf Coloration in Cyclocarya paliurus (Batalin) Iljinsk.
- Author
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Sun, Caowen, Fang, Shengzuo, and Shang, Xulan
- Subjects
SECONDARY metabolism ,METABOLOMICS ,METABOLITES ,METABOLISM ,KREBS cycle ,PENTOSE phosphate pathway ,LEAF growth - Abstract
Secondary metabolites in Cyclocarya paliurus (Batalin) Iljinsk. leaves are beneficial for human health. The synthesis and accumulation of secondary metabolites form a complex process that is influenced by the trade-off between primary and secondary metabolism and by the biosynthetic pathways themselves. In this study, we explored the relationship between secondary metabolite accumulation and the activity of metabolic networks in leaves of C. paliurus. Leaves at three different growth stages were subjected to transcriptomic and non-targeted metabolomic analyses. The results revealed that nitrogen assimilation increased and carbon assimilation decreased as leaves matured, and the patterns of secondary metabolite accumulation and gene expression differed among the leaves at different growth stages. Mature green leaves had higher nitrogen assimilation and lower carbon assimilation, which were correlated with variations in secondary metabolite accumulation. As a major source of carbon and nitrogen, glutamine accumulated in the mature green leaves of C. paliurus. The accumulation of glutamine inhibited phenylalanine biosynthesis by modulating the pentose phosphate pathway but promoted acetyl-CoA biosynthesis through the tricarboxylic acid cycle. These changes led to decreased flavonoid contents and increased triterpenoid contents in mature leaves. These metabolomic and transcriptomic data reveal the differential expression of metabolic regulatory networks during three stages of leaf development and highlight the trade-off between primary and secondary metabolism. Our results provide a comprehensive picture of the metabolic pathways that are active in the leaves of C. paliurus at different growth stages. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
16. Metabolic Networks in Parkinson’s Disease
- Author
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Unadkat, Prashin, Niethammer, Martin, Eidelberg, David, Manto, Mario, Series Editor, and Grimaldi, Giuliana, editor
- Published
- 2023
- Full Text
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17. Biological Networks Analysis
- Author
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Najma, Farooqui, Anam, and Ishrat, Romana, editor
- Published
- 2023
- Full Text
- View/download PDF
18. An Efficient Implementation of Flux Variability Analysis for Metabolic Networks
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Galuzzi, Bruno G., Damiani, Chiara, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, De Stefano, Claudio, editor, Fontanella, Francesco, editor, and Vanneschi, Leonardo, editor
- Published
- 2023
- Full Text
- View/download PDF
19. On the geometry of elementary flux modes.
- Author
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Wieder, Frederik, Henk, Martin, and Bockmayr, Alexander
- Abstract
Elementary flux modes (EFMs) play a prominent role in the constraint-based analysis of metabolic networks. They correspond to minimal functional units of the metabolic network at steady-state and as such have been studied for almost 30 years. The set of all EFMs in a metabolic network tends to be very large and may have exponential size in the number of reactions. Hence, there is a need to elucidate the structure of this set. Here we focus on geometric properties of EFMs. We analyze the distribution of EFMs in the face lattice of the steady-state flux cone of the metabolic network and show that EFMs in the relative interior of the cone occur only in very special cases. We introduce the concept of degree of an EFM as a measure how elementary it is and study the decomposition of flux vectors and EFMs depending on their degree. Geometric analysis can help to better understand the structure of the set of EFMs, which is important from both the mathematical and the biological viewpoint. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
20. Principles of metabolome conservation in animals.
- Author
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Liska, Orsolya, Boross, Gábor, Rocabert, Charles, Szappanos, Balázs, Tengölics, Roland, and Papp, Balázs
- Subjects
- *
WILDLIFE conservation , *BIOLOGICAL evolution , *NATURAL selection , *FRUIT flies , *AMINO acid sequence - Abstract
Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides
- Author
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Kim, Joonhoon, Coradetti, Samuel T, Kim, Young-Mo, Gao, Yuqian, Yaegashi, Junko, Zucker, Jeremy D, Munoz, Nathalie, Zink, Erika M, Burnum-Johnson, Kristin E, Baker, Scott E, Simmons, Blake A, Skerker, Jeffrey M, Gladden, John M, and Magnuson, Jon K
- Subjects
Biological Sciences ,Bioinformatics and Computational Biology ,Biomedical and Clinical Sciences ,Industrial Biotechnology ,Medical Biochemistry and Metabolomics ,Biotechnology ,Genetics ,Responsible Consumption and Production ,Rhodosporidium toruloides ,multi-omics ,metabolic networks ,genome-scale models ,lignocellulosic biomass ,Other Biological Sciences ,Biomedical Engineering ,Medical Biotechnology ,Industrial biotechnology ,Medical biotechnology ,Biomedical engineering - Abstract
An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.
- Published
- 2021
22. Microbial Pathway Thermodynamics: Stoichiometric Models Unveil Anabolic and Catabolic Processes
- Author
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Oliver Ebenhöh, Josha Ebeling, Ronja Meyer, Fabian Pohlkotte, and Tim Nies
- Subjects
energy metabolism ,elementary conversion modes ,metabolic networks ,energy converter ,Science - Abstract
The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow for the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genome-scale metabolic model. Interestingly, both approaches are rather successful at characterizing and predicting the expected product yield. Over the years, macrochemical equations especially have been extensively characterized in terms of their thermodynamic properties. However, a common challenge when characterizing microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and we use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions.
- Published
- 2024
- Full Text
- View/download PDF
23. Evolution at the Origins of Life?
- Author
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Ludo L. J. Schoenmakers, Thomas A. C. Reydon, and Andreas Kirschning
- Subjects
origins of life ,evolutionary theory ,generalization ,reduction ,chemical evolution ,metabolic networks ,Science - Abstract
The role of evolutionary theory at the origin of life is an extensively debated topic. The origin and early development of life is usually separated into a prebiotic phase and a protocellular phase, ultimately leading to the Last Universal Common Ancestor. Most likely, the Last Universal Common Ancestor was subject to Darwinian evolution, but the question remains to what extent Darwinian evolution applies to the prebiotic and protocellular phases. In this review, we reflect on the current status of evolutionary theory in origins of life research by bringing together philosophy of science, evolutionary biology, and empirical research in the origins field. We explore the various ways in which evolutionary theory has been extended beyond biology; we look at how these extensions apply to the prebiotic development of (proto)metabolism; and we investigate how the terminology from evolutionary theory is currently being employed in state-of-the-art origins of life research. In doing so, we identify some of the current obstacles to an evolutionary account of the origins of life, as well as open up new avenues of research.
- Published
- 2024
- Full Text
- View/download PDF
24. Sign‐sensitivity of metabolic networks: Which structures determine the sign of the responses.
- Author
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Vassena, Nicola
- Subjects
- *
SENSITIVITY analysis , *STOICHIOMETRY , *METABOLISM , *EQUILIBRIUM - Abstract
Perturbations are ubiquitous in metabolism. A central tool to understand and control their influence on metabolic networks is sensitivity analysis, which investigates how the network responds to external perturbations. We follow here a structural approach: the analysis is based on the network stoichiometry only and it does not require any quantitative knowledge of the reaction rates. We consider perturbations of reaction rates and metabolite concentrations, at equilibrium, and we investigate the responses in the network. For general metabolic systems, this paper focuses on the sign of the responses, that is, whether a response is positive, negative or whether its sign depends on the parameters of the system. In particular, we identify and describe the subnetworks that are the main players in the sign description. These subnetworks are associated to certain kernel vectors of the stoichiometric matrix and are thus independent from the chosen kinetics. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
25. Challenges and advancements in bioprocess intensification of fungal secondary metabolite: kojic acid.
- Author
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Sharma, Sumit, Singh, Shikha, and Sarma, Saurabh Jyoti
- Subjects
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PHENOL oxidase , *ASPERGILLUS flavus , *KOJI , *RENEWABLE natural resources , *CHEMICAL synthesis , *GENE clusters , *AFLATOXINS - Abstract
Kojic acid is a fungal secondary metabolite commonly known as a tyrosinase inhibitor, that acts as a skin-whitening agent. Its applications are widely distributed in the area of cosmetics, medicine, food, and chemical synthesis. Renewable resources are the alternative feedstocks that can fulfill the demand for free sugars which are fermented for the production of kojic acid. This review highlights the current progress and importance of bioprocessing of kojic acid from various types of competitive and non-competitive renewable feedstocks. The bioprocessing advancements, secondary metabolic pathway networks, gene clusters and regulations, strain improvement, and process design have also been discussed. The importance of nitrogen sources, amino acids, ions, agitation, and pH has been summarized. Two fungal species Aspergillus flavus and Aspergillus oryzae are found to be extensively studied for kojic acid production due to their versatile substrate utilization and high titer ability. The potential of A. flavus to be a competitive industrial strain for large-scale production of kojic acid has been studied. [ABSTRACT FROM AUTHOR]
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- 2023
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26. Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study.
- Author
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Li, Yu-Lin, Zheng, Mou-Xiong, Hua, Xu-Yun, Gao, Xin, Wu, Jia-Jia, Shan, Chun-Lei, Zhang, Jun-Peng, Wei, Dong, and Xu, Jian-Guang
- Subjects
- *
LARGE-scale brain networks , *CHINESE people , *DEFAULT mode network , *POSITRON emission tomography - Abstract
The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The 18F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
27. Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN.
- Author
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Tamura, Takeyuki, Muto-fujita, Ai, Tohsato, Yukako, and Kosaka, Tomoyuki
- Subjects
- *
DELETION mutation , *LINEAR programming , *VITAMIN B2 , *PANTOTHENIC acid , *ALGORITHMS , *MINIMAL design - Abstract
Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
28. Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides.
- Author
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Kim, Joonhoon, Coradetti, Samuel T, Kim, Young-Mo, Gao, Yuqian, Yaegashi, Junko, Zucker, Jeremy D, Munoz, Nathalie, Zink, Erika M, Burnum-Johnson, Kristin E, Baker, Scott E, Simmons, Blake A, Skerker, Jeffrey M, Gladden, John M, and Magnuson, Jon K
- Subjects
Rhodosporidium toruloides ,genome-scale models ,lignocellulosic biomass ,metabolic networks ,multi-omics ,Other Biological Sciences ,Biomedical Engineering ,Medical Biotechnology - Abstract
An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.
- Published
- 2020
29. Machine Learning for Metabolic Networks Modelling: A State-of-the-Art Survey
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Biba, Marenglen, Vajjhala, Narasimha Rao, Kacprzyk, Janusz, Series Editor, Roy, Sanjiban Sekhar, editor, and Taguchi, Y.-H., editor
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- 2022
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30. Visualising Metabolic Pathways and Networks: Past, Present, Future
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Schreiber, Falk, Grafahrend-Belau, Eva, Kohlbacher, Oliver, Mi, Huaiyu, Chen, Ming, editor, and Hofestädt, Ralf, editor
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- 2022
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31. Detection of the Inflammatory Bowel Diseases via Machine Learning Methods
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Kim, Elliot, Kouznetsova, Valentina L., Tsigelny, Igor F., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor
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- 2022
- Full Text
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32. Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells
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Bruno G. Galuzzi, Marco Vanoni, and Chiara Damiani
- Subjects
Metabolic networks ,scRNA-seq ,Flux balance analysis ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. Methods We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. Results We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. Conclusions Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.
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- 2022
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- View/download PDF
33. Metabolic modeling of the International Space Station microbiome reveals key microbial interactions
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Rachita K. Kumar, Nitin Kumar Singh, Sanjaay Balakrishnan, Ceth W. Parker, Karthik Raman, and Kasthuri Venkateswaran
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Microbial communities ,Space microbiome ,Community modeling ,Network biology ,Metabolic networks ,Microbial ecology ,QR100-130 - Abstract
Abstract Background Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization. Results Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures. Conclusion Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract
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- 2022
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34. Why do we need to go beyond overall biological variability assessment in metabolomics?
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Boccard, Julien and Rudaz, Serge
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METABOLOMICS ,EXPERIMENTAL design ,METABOLITES ,DATA analysis - Abstract
Unlike other systems such as plants, microorganisms or fungi, human cells are not proficient in eliciting the production of defense compounds in response to external stresses and threats. Human metabolism is essentially based on a set of primary metabolites that participate in the various regulatory events of cells and tissues. The challenge is therefore to maintain homeostasis and allow the survival of the individual through the modulation of existing endogenous metabolic pathways with a relatively stable set of ubiquitous compounds. Since these complex regulatory phenomena are potentially subject to multiple influences, assessing their overall variability, as achieved by most conventional approaches, is not sufficiently informative. The experimental evaluation of several factors acting simultaneously on the metabolome is paramount. Because metabolomics involves the characterization of multivariate metabolic phenotypes, such a methodology requires specific data analysis tools to fully exploit the relevant information considering the different factors, as well as their respective impact on metabolite levels. The investigation of high-dimensional multifactorial data in metabolomics opens new challenges and requires the development of innovative experimental strategies involving structured designs of experiments to assess cause-effect associations and offer deeper insight into relevant biological information. In the future, key outputs should not only consider lists of metabolites, but also include their specific variation related to each effect that can be identified and/or quantified, thus allowing accurate biochemical and functional relationships to be highlighted. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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35. Insights into the potential for mutualistic and harmful host–microbe interactions affecting brown alga freshwater acclimation.
- Author
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KleinJan, Hetty, Frioux, Clémence, Califano, Gianmaria, Aite, Méziane, Fremy, Enora, Karimi, Elham, Corre, Erwan, Wichard, Thomas, Siegel, Anne, Boyen, Catherine, and Dittami, Simon M.
- Subjects
- *
FRESHWATER algae , *ACCLIMATIZATION , *MICROBIAL genes , *COMMUNITIES , *VITAMIN K , *BROWN algae - Abstract
Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater‐tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory‐grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal‐bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal‐bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater‐intolerant algal‐bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater‐intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI‐1, a quorum‐sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behaviour in the intolerant algal‐bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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36. Metagenome
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Parro, Víctor, Gargaud, Muriel, editor, Irvine, William M., editor, Amils, Ricardo, editor, Claeys, Philippe, editor, Cleaves, Henderson James, editor, Gerin, Maryvonne, editor, Rouan, Daniel, editor, Spohn, Tilman, editor, Tirard, Stéphane, editor, and Viso, Michel, editor
- Published
- 2023
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37. Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining
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Barupal, Dinesh Kumar, Gao, Bei, Budczies, Jan, Phinney, Brett S, Perroud, Bertrand, Denkert, Carsten, and Fiehn, Oliver
- Subjects
Genetics ,Biotechnology ,Breast Cancer ,Cancer ,ChemRICH ,candidate gene prioritization ,metabolic networks ,multi-omics ,set-enrichment ,Oncology and Carcinogenesis - Abstract
Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p
- Published
- 2019
38. Development and validation of diagnostic models for immunoglobulin A nephropathy based on gut microbes.
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Yijun Dong, Jiaojiao Chen, Yiding Zhang, Zhihui Wang, Jin Shang, and Zhanzheng Zhao
- Subjects
IGA glomerulonephritis ,RECEIVER operating characteristic curves ,KIDNEY glomerulus diseases ,GUT microbiome ,BLOOD urea nitrogen - Abstract
Background: Immunoglobulin A nephropathy (IgAN) is a highly prevalent glomerular disease. The diagnosis potential of the gut microbiome in IgAN has not been fully evaluated. Gut microbiota, serum metabolites, and clinical phenotype help to further deepen the understanding of IgAN. Patients and methods: Cohort studies were conducted in healthy controls (HC), patients of IgA nephropathy (IgAN) and non-IgA nephropathy (n_IgAN). We used 16S rRNA to measure bacterial flora and non-targeted analysis methods to measure metabolomics; we then compared the differences in the gut microbiota between each group. The random forest method was used to explore the non-invasive diagnostic value of the gut microbiome in IgAN. We also compared serum metabolites and analyzed their correlation with the gut microbiome. Results: The richness and diversity of gut microbiota were significantly different among IgAN, n_IgAN and HC patients. Using a random approach, we constructed the diagnosis model and analysed the differentiation between IgAN and n_IgAN based on gut microbiota. The area under the receiver operating characteristic curve for the diagnosis was 0.9899. The metabolic analysis showed that IgAN patients had significant metabolic differences compared with HCs. In IgAN, catechol, l-tryptophan, (1H-Indol-3-yl)-Nmethylmethanamine, and pimelic acid were found to be enriched. In the correlation analysis, l-tryptophan, blood urea nitrogen and Eubacterium coprostanoligenes were positively correlated with each other. Conclusion: Our study demonstrated changes in the gut microbiota and established models for the non-invasive diagnosis of IgAN from HC and n_IgAN. We further demonstrated a close correlation between the gut flora, metabolites, and clinical phenotypes of IgAN. These findings provide further directions and clues in the study of the mechanism of IgAN. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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39. Idiosyncratic Purifying Selection on Metabolic Enzymes in the Long-Term Evolution Experiment with Escherichia coli.
- Author
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Maddamsetti, Rohan
- Subjects
- *
LONG-Term Evolution (Telecommunications) , *ESCHERICHIA coli , *GAMES , *ENZYMES , *TIME series analysis - Abstract
Bacteria, Archaea, and Eukarya all share a common set of metabolic reactions. This implies that the function and topology of central metabolism has been evolving under purifying selection over deep time. Central metabolism may similarly evolve under purifying selection during long-term evolution experiments, although it is unclear how long such experiments would have to run (decades, centuries, millennia) before signs of purifying selection on metabolism appear. I hypothesized that central and superessential metabolic enzymes would show evidence of purifying selection in the long-term evolution experiment with Escherichia coli (LTEE). I also hypothesized that enzymes that specialize on single substrates would show stronger evidence of purifying selection in the LTEE than generalist enzymes that catalyze multiple reactions. I tested these hypotheses by analyzing metagenomic time series covering 62,750 generations of the LTEE. I find mixed support for these hypotheses, because the observed patterns of purifying selection are idiosyncratic and population-specific. To explain this finding, I propose the Jenga hypothesis, named after a children's game in which blocks are removed from a tower until it falls. The Jenga hypothesis postulates that loss-of-function mutations degrade costly, redundant, and non-essential metabolic functions. Replicate populations can therefore follow idiosyncratic trajectories of lost redundancies, despite purifying selection on overall function. I tested the Jenga hypothesis by simulating the evolution of 1,000 minimal genomes under strong purifying selection. As predicted, the minimal genomes converge to different metabolic networks. Strikingly, the core genes common to all 1,000 minimal genomes show consistent signatures of purifying selection in the LTEE. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
40. Lichens as a repository of bioactive compounds: an open window for green therapy against diverse cancers.
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Dar, Tanvir Ul Hassan, Dar, Sajad Ahmad, Islam, Shahid Ul, Mangral, Zahid Ahmed, Dar, Rubiya, Singh, Bhim Pratap, Verma, Pradeep, and Haque, Shafiul
- Subjects
- *
BIOACTIVE compounds , *LICHENS , *METABOLITES , *CELL cycle , *CELL division - Abstract
Lichens, algae and fungi-based symbiotic associations, are sources of many important secondary metabolites, such as antibiotics, anti-inflammatory, antioxidants, and anticancer agents. Wide range of experiments based on in vivo and in vitro studies revealed that lichens are a rich treasure of anti-cancer compounds. Lichen extracts and isolated lichen compounds can interact with all biological entities currently identified to be responsible for tumor development. The critical ways to control the cancer development include induction of cell cycle arrests, blocking communication of growth factors, activation of anti-tumor immunity, inhibition of tumor-friendly inflammation, inhibition of tumor metastasis, and suppressing chromosome dysfunction. Also, lichen-based compounds induce the killing of cells by the process of apoptosis, autophagy, and necrosis, that inturn positively modulates metabolic networks of cells against uncontrolled cell division. Many lichen-based compounds have proven to possess potential anti-cancer activity against a wide range of cancer cells, either alone or in conjunction with other anti-cancer compounds. This review primarily emphasizes on an updated account of the repository of secondary metabolites reported in lichens. Besides, we discuss the anti-cancer potential and possible mechanism of the most frequently reported secondary metabolites derived from lichens. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. Metabolic network changes during skotomorphogenesis in Arabidopsis thaliana mutant (atdfb‐3).
- Author
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Li, Xingjuan, Meng, Hongyan, Liu, Liqing, Hong, Cuiyun, and Zhang, Chunyi
- Subjects
ORGANIC acids ,CARBON metabolism ,AMINO acids ,FOLIC acid ,FATTY acids ,ADENOSYLMETHIONINE ,ARABIDOPSIS thaliana - Abstract
The metabolic networks underlying skotomorphogenesis in seedlings remain relatively unknown. On the basis of our previous study on the folate metabolism in seedlings grown in darkness, the plastidial folylpolyglutamate synthetase gene (AtDFB) T‐DNA insertion Arabidopsis thaliana mutant (atdfb‐3) was examined. Under the nitrate‐sufficient condition, the mutant exhibited deficient folate metabolism and hypocotyl elongation, which affected skotomorphogenesis. Further analyses revealed changes to multiple intermediate metabolites related to carbon and nitrogen metabolism in the etiolated atdfb‐3 seedlings. Specifically, the sugar, polyol, and fatty acid contents decreased in the atdfb‐3 mutant under the nitrate‐sufficient condition, whereas the abundance of various organic acids and amino acids increased. In response to nitrate‐limited stress, multiple metabolites, including sugars, polyols, fatty acids, organic acids, and amino acids, accumulated more in the mutant than in the wild‐type control. The differences in the contents of multiple metabolites between the atdfb‐3 and wild‐type seedlings decreased following the addition of exogenous 5‐F‐THF under both nitrogen conditions. Additionally, the mutant accumulated high levels of one‐carbon metabolites, such as Cys, S‐adenosylmethionine, and S‐adenosylhomocysteine, under both nitrogen conditions. Thus, our data demonstrated that the perturbed folate metabolism in the atdfb‐3 seedlings, which was caused by the loss‐of‐function mutation to AtDFB, probably altered carbon and nitrogen metabolism, thereby modulating skotomorphogenesis. Furthermore, the study findings provide new evidence of the links among folate metabolism, metabolic networks, and skotomorphogenesis. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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42. Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.
- Author
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Galuzzi, Bruno G., Vanoni, Marco, and Damiani, Chiara
- Subjects
- *
CLUSTER analysis (Statistics) , *CELL analysis , *FEATURE extraction , *RNA sequencing , *BIOMASS energy - Abstract
Background: Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. Methods: We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. Results: We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. Conclusions: Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Relative flux trade-offs and optimization of metabolic network functionalities
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Seirana Hashemi, Zahra Razaghi-Moghadam, Roosa A.E. Laitinen, and Zoran Nikoloski
- Subjects
Trade-offs ,Metabolic networks ,Fluxes ,Overexpression targets ,Growth ,Biotechnology ,TP248.13-248.65 - Abstract
Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth. We then employed FluTOr to identify relative flux trade-offs in the genome-scale metabolic networks of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana. For the metabolic models of E. coli and S. cerevisiae we showed that: (i) the identified relative flux trade-offs depend on the carbon source used and that (ii) reactions that participated in relative trade-offs in both species were implicated in cofactor biosynthesis. In contrast to the two microorganisms, the relative flux trade-offs for the metabolic model of A. thaliana did not depend on the available nitrogen sources, reflecting the differences in the underlying metabolic network as well as the considered environments. Lastly, the established connection between relative flux trade-offs allowed us to identify overexpression targets that can be used to optimize fitness-related traits. Altogether, our computational approach and findings demonstrate how relative flux trade-offs can shape optimization of metabolic tasks, important in biotechnological applications.
- Published
- 2022
- Full Text
- View/download PDF
44. Enhanced degradation of methanethiol via operational pH regulation in biofilm reactor: Insight of sulfur metabolism pathways and microbial adaptation mechanism.
- Author
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Zheng, Xiong, Wang, Yanzhao, Wu, Jing, Wu, Yang, Long, Min, and Chen, Yinguang
- Subjects
- *
SULFUR metabolism , *DESULFURIZATION , *QUORUM sensing , *DNA replication , *ENVIRONMENTAL health - Abstract
[Display omitted] • pH regulation contributed to the transformation of CH 3 SH to SO 4 2- in MABR system. • pH regulation reduced excess secretion of EPS to promote the CH 3 SH transformation. • pH regulation enriched the abundances and activities of sulfur-oxidizing bacteria. • pH unregulation enhanced the microbial adaption capability in harsh environments. • pH regulation enhanced critical gene expressions participated in sulfur metabolism. Methanethiol (CH 3 SH), a typical organic sulfur pollutant, poses significant risks to both human health and ecological stability due to its high toxicity and corrosive properties. Membrane aerated biofilm reactor (MABR) has been regarded as a promising and eco-friendly solution for removing organic sulfur from wastewater. However, the oxidation of CH 3 SH might lead to spontaneous acidification, which might affect microbial metabolic activities to influence the CH 3 SH biotransformation in MABR system. This work demonstrated that operational pH regulation could effectively promote the complete conversion of CH 3 SH to SO 4 2- in MABR, while the transformation efficiency was only 40.6% under uncontrolled pH condition. Meanwhile, operational pH regulation resulted in 35.2% reduction in the excessive secretion of extracellular polymeric substances, thereby building a stable biofilm structure with sufficient oxygen mass transfer to enhance the CH 3 SH transformation. Additionally, operational pH regulation favored the enrichment of sulfur-oxidizing microorganisms (e.g., Rhodanobacter and Rhodobacter), while the uncontrolled pH favored the proliferation of microorganisms in harsh environment (e.g., Chitinophagaceae sp. and Sphingobacteriales sp.). Moreover, operational pH regulation enhanced the expression of critical genes involved in sulfur metabolism (e.g., SELENBP1 and SoxA), accompanied with glycolysis (e.g., ppgK and pfkB) and pyruvate metabolism (e.g., ackA and aceE), which could provide sufficient energy and electron. Conversely, these gene expressions were down-regulated in pH unregulation reactor, while the microbial adaptation mechanism was activated through the enhancement of DNA replication, quorum sensing, and two-component system, enabling resistance to extreme acidic conditions. This study would provide the in-depth understanding of operational pH regulation on organic sulfur degradation and transformation in MABR system and offer novel guidance for organic sulfur removal. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. A review of advances in integrating gene regulatory networks and metabolic networks for designing strain optimization.
- Author
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Ananda, Ridho, Daud, Kauthar Mohd, and Zainudin, Suhaila
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GENE regulatory networks ,TIME complexity ,BIOLOGICAL systems ,GENE expression ,PROGRAMMING languages - Abstract
Strain optimization aims to overproduce valuable metabolites by leveraging an understanding of biological systems, including metabolic networks and gene regulatory networks (GRNs). Accordingly, researchers proposed integrating metabolic networks and GRNs to be analyzed simultaneously. The proposed algorithms from 2002 to 2021 were rFBA, SR-FBA, iFBA, PROM, PROM2.0, TIGER, BeReTa, CoRegFlux, IDREAM, TRFBA, OptRAM, TRIMER, and PRIME. Each algorithm has different characteristics. Thus, using the appropriate algorithm for designing strain optimization is essential. Therefore, a critical review was conducted by synthesizing and analyzing the existing algorithms. Five aspects are discussed in this review: the strategic approaches, model of GRNs, source of GRNs, optimization, supplementary methods, and the programming language used. Based on the review, several algorithms were better at modeling integrated regulatory-metabolic networks with high confidence, i.e., PROM, PROM2.0, and TRFBA. A simulation was applied to six strains. The results show that PROM2.0 best predicted the production rate and time complexity. However, the model is heavily influenced by the quality and quantity of the gene expression data. In addition, there are inconsistencies between GRNs and the gene expression data. Thus, this review also discussed future work based on GRNs and gene expression data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
46. A Framework Based on Metabolic Networks and Biomedical Images Data to Discriminate Glioma Grades
- Author
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Maddalena, Lucia, Granata, Ilaria, Manipur, Ichcha, Manzo, Mario, Guarracino, Mario R., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ye, Xuesong, editor, Soares, Filipe, editor, De Maria, Elisabetta, editor, Gómez Vilda, Pedro, editor, Cabitza, Federico, editor, Fred, Ana, editor, and Gamboa, Hugo, editor
- Published
- 2021
- Full Text
- View/download PDF
47. Machine Learning Strategies to Distinguish Oral Cancer from Periodontitis Using Salivary Metabolites
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Romm, Eden, Li, Jeremy, Kouznetsova, Valentina L., Tsigelny, Igor F., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor
- Published
- 2021
- Full Text
- View/download PDF
48. Omics Data Analysis Tools for Biomarker Discovery and the Tutorial
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Nojima, Yosui, Takeda, Yoshito, Suzuki, Takashi, editor, Poignard, Clair, editor, Chaplain, Mark, editor, and Quaranta, Vito, editor
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- 2021
- Full Text
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49. Of Evolution, Systems and Complexity
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Beslon, Guillaume, Liard, Vincent, Parsons, David P., Rouzaud-Cornabas, Jonathan, and Crombach, Anton, editor
- Published
- 2021
- Full Text
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50. Machine learning methods for detecting structure in metabolic flow networks
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Jay, Maxwell and Lio, Pietro
- Subjects
006.3 ,Linear programming ,optimisation ,machine learning ,decision trees ,metabolic networks ,network structure ,flow networks ,network clustering - Abstract
Metabolic flow networks are large scale, mechanistic biological models with good predictive power. However, even when they provide good predictions, interpreting the meaning of their structure can be very difficult, especially for large networks which model entire organisms. This is an underaddressed problem in general, and the analytic techniques that exist currently are difficult to combine with experimental data. The central hypothesis of this thesis is that statistical analysis of large datasets of simulated metabolic fluxes is an effective way to gain insight into the structure of metabolic networks. These datasets can be either simulated or experimental, allowing insight on real world data while retaining the large sample sizes only easily possible via simulation. This work demonstrates that this approach can yield results in detecting structure in both a population of solutions and in the network itself. This work begins with a taxonomy of sampling methods over metabolic networks, before introducing three case studies, of different sampling strategies. Two of these case studies represent, to my knowledge, the largest datasets of their kind, at around half a million points each. This required the creation of custom software to achieve this in a reasonable time frame, and is necessary due to the high dimensionality of the sample space. Next, a number of techniques are described which operate on smaller datasets. These techniques, focused on pairwise comparison, show what can be achieved with these smaller datasets, and how in these cases, visualisation techniques are applicable which do not have simple analogues with larger datasets. In the next chapter, Similarity Network Fusion is used for the first time to cluster organisms across several levels of biological organisation, resulting in the detection of discrete, quantised biological states in the underlying datasets. This quantisation effect was maintained across both real biological data and Monte-Carlo simulated data, with related underlying biological correlates, implying that this behaviour stems from the network structure itself, rather than from the genetic or regulatory mechanisms that would normally be assumed. Finally, Hierarchical Block Matrices are used as a model of multi-level network structure, by clustering reactions using a variety of distance metrics: first standard network distance measures, then by Local Network Learning, a novel approach of measuring connection strength via the gain in predictive power of each node on its neighbourhood. The clusters uncovered using this approach are validated against pre-existing subsystem labels and found to outperform alternative techniques. Overall this thesis represents a significant new approach to metabolic network structure detection, as both a theoretical framework and as technological tools, which can readily be expanded to cover other classes of multilayer network, an under explored datatype across a wide variety of contexts. In addition to the new techniques for metabolic network structure detection introduced, this research has proved fruitful both in its use in applied biological research and in terms of the software developed, which is experiencing substantial usage.
- Published
- 2018
- Full Text
- View/download PDF
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