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2. Identification of metabolites reproducibly associated with Parkinson’s Disease via meta-analysis and computational modelling

Author

Xi Luo, Yanjun Liu, Alexander Balck, Christine Klein, and Ronan M. T. Fleming

Subjects
Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Many studies have reported metabolomic analysis of different bio-specimens from Parkinson’s disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson’s disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.

Published
2024
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5. DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations

Author

Heinken, Almut, Magnúsdóttir, Stefanía, Fleming, Ronan M. T., and Thiele, Ines

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Molecular Networks
Abstract

Motivation: Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools typically do not take species-specific experimental data and manually refined genome annotations into account. Results: We developed DEMETER, a COBRA Toolbox extension that enables the efficient simultaneous refinement of thousands of draft genome-scale reconstructions while ensuring adherence to the quality standards in the field, agreement with available experimental data, and refinement of pathways based on manually refined genome annotations. Availability: DEMETER and tutorials are available at https://github.com/opencobra/cobratoolbox., Comment: 6 pages, 1 Figure

Published
2021

6. Omics data integration suggests a potential idiopathic Parkinson’s disease signature

Author

Alise Zagare, German Preciat, Sarah. L. Nickels, Xi Luo, Anna S. Monzel, Gemma Gomez-Giro, Graham Robertson, Christian Jaeger, Jafar Sharif, Haruhiko Koseki, Nico J. Diederich, Enrico Glaab, Ronan M. T. Fleming, and Jens C. Schwamborn

Subjects
Biology (General), QH301-705.5
Abstract

Abstract The vast majority of Parkinson’s disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson’s disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

Published
2023
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7. Finding Zeros of H\'{o}lder Metrically Subregular Mappings via Globally Convergent Levenberg-Marquardt Methods

Author

Ahookhosh, Masoud, Fleming, Ronan M. T., and Vuong, Phan T.

Subjects
Mathematics - Optimization and Control, Quantitative Biology - Molecular Networks, 90C26, 68Q25, 65K05
Abstract

We present two globally convergent Levenberg-Marquardt methods for finding zeros of H\"{o}lder metrically subregular mappings that may have non-isolated zeros. The first method unifies the Levenberg- Marquardt direction and an Armijo-type line search, while the second incorporates this direction with a nonmonotone trust-region technique. For both methods, we prove the global convergence to a first-order stationary point of the associated merit function. Furthermore, the worst-case global complexity of these methods are provided, indicating that an approximate stationary point can be computed in at most $\mathcal{O}(\varepsilon^{-2})$ function and gradient evaluations, for an accuracy parameter $\varepsilon>0$. We also study the conditions for the proposed methods to converge to a zero of the associated mappings. Computing a moiety conserved steady state for biochemical reaction networks can be cast as the problem of finding a zero of a H\"{o}lder metrically subregular mapping. We report encouraging numerical results for finding a zero of such mappings derived from real-world biological data, which supports our theoretical foundations., Comment: 28 pages, 3 figures

Published
2018

8. ARTENOLIS: Automated Reproducibility and Testing Environment for Licensed Software

Author

Heirendt, Laurent, Arreckx, Sylvain, Trefois, Christophe, Yarosz, Yohan, Vyas, Maharshi, Satagopam, Venkata P., Schneider, Reinhard, Thiele, Ines, and Fleming, Ronan M. T.

Subjects
Computer Science - Software Engineering
Abstract

Motivation: Automatically testing changes to code is an essential feature of continuous integration. For open-source code, without licensed dependencies, a variety of continuous integration services exist. The COnstraint-Based Reconstruction and Analysis (COBRA) Toolbox is a suite of open-source code for computational modelling with dependencies on licensed software. A novel automated framework of continuous integration in a semi-licensed environment is required for the development of the COBRA Toolbox and related tools of the COBRA community. Results: ARTENOLIS is a general-purpose infrastructure software application that implements continuous integration for open-source software with licensed dependencies. It uses a master-slave framework, tests code on multiple operating systems, and multiple versions of licensed software dependencies. ARTENOLIS ensures the stability, integrity, and cross-platform compatibility of code in the COBRA Toolbox and related tools. Availability and Implementation: The continuous integration server, core of the reproducibility and testing infrastructure, can be freely accessed under artenolis.lcsb.uni.lu. The continuous integration framework code is located in the /.ci directory and at the root of the repository freely available under github.com/opencobra/cobratoolbox.

Published
2017

9. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, Assal, Diana C. El, Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Guebila, Marouen Ben, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., Assal, Lemmer P. El, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Artacho, Francisco J. Aragón, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, and Fleming, Ronan M. T.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods.

Published
2017

10. Local convergence of the Levenberg-Marquardt method under H\'{o}lder metric subregularity

Author

Ahookhosh, Masoud, Artacho, Francisco J. Aragón, Fleming, Ronan M. T., and Vuong, Phan T.

Subjects
Quantitative Biology - Molecular Networks, Mathematics - Optimization and Control, 65K05, 65K10, 90C26, 92C42
Abstract

We describe and analyse Levenberg-Marquardt methods for solving systems of nonlinear equations. More specifically, we propose an adaptive formula for the Levenberg-Marquardt parameter and analyse the local convergence of the method under H\"{o}lder metric subregularity of the function defining the equation and H\"older continuity of its gradient mapping. Further, we analyse the local convergence of the method under the additional assumption that the \L{}ojasiewicz gradient inequality holds. We finally report encouraging numerical results confirming the theoretical findings for the problem of computing moiety conserved steady states in biochemical reaction networks. This problem can be cast as finding a solution of a system of nonlinear equations, where the associated mapping satisfies the \L{}ojasiewicz gradient inequality assumption., Comment: 30 pages, 10 figures

Published
2017

11. Integration of proteomic data with genome‐scale metabolic models: A methodological overview.

Author

Zare, Farid and Fleming, Ronan M. T.

Abstract

The integration of proteomics data with constraint‐based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome‐level phenomena and functional adaptations. Integrating a generic genome‐scale model with information on proteins enables generation of a context‐specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome‐scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade‐off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions. [ABSTRACT FROM AUTHOR]

Published
2024
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12. DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia

Author

Heirendt, Laurent, Fleming, Ronan M. T., and Thiele, Ines

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Motivation: Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. Results: DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. Availability: The code and benchmark data are freely available on http://github.com/opencobra/COBRA.jl. The documentation can be found at http://opencobra.github.io/COBRA.jl, Comment: 2 pages, 1 figure. Supplementary Material: 1 page

Published
2016

13. MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

Author

Aurich, Maike K., Fleming, Ronan M. T., and Thiele, Ines

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Cell Behavior
Abstract

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Our previous work revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. Through this work, which consists of a protocol, a toolbox, and tutorials of two use cases, we make our methods available to the broader scientific community. The protocol describes, in a step-wise manner, the workflow of data integration and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, this protocol constitutes a comprehensive guide to the intra-model analysis of extracellular metabolomic data and a resource offering a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

Published
2016

14. ReconMap: An interactive visualisation of human metabolism

Author

Noronha, Alberto, Danielsdóttir, Anna Dröfn, Jóhannsson, Freyr, Jónsdóttir, Soffia, Jarlsson, Sindri, Gunnarsson, Jón Pétur, Brynjólfsson, Sigurður, Gawron, Piotr, Schneider, Reinhard, Thiele, Ines, and Fleming, Ronan M. T.

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods
Abstract

A genome-scale reconstruction of human metabolism, Recon 2, is available but no interface exists to interactively visualise its content integrated with omics data and simulation results. We manually drew a comprehensive map, ReconMap 2.0, that is consistent with the content of Recon 2. We present it within a web interface that allows content query, visualization of custom datasets and submission of feedback to manual curators. ReconMap can be accessed via http://vmh.uni.lu, with network export in a Systems Biology Graphical Notation compliant format. A Constraint-Based Reconstruction and Analysis (COBRA) Toolbox extension to interact with ReconMap is available via https://github.com/opencobra/cobratoolbox., Comment: 3 pages, 1 figure, submitted

Published
2016

15. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Author

Ma, Ding, Yang, Laurence, Fleming, Ronan M. T., Thiele, Ines, Palsson, Bernhard O., and Saunders, Michael A.

Subjects
Quantitative Biology - Molecular Networks, 90C05, 90C06, 90C90, 92-08
Abstract

Constraint-Based Reconstruction and Analysis (COBRA) is currently the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers are extremely slow and hence not practical for ME models that currently have 70,000 constraints and variables and will grow larger. We have developed a quadruple-precision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves efficiency and reliability for ME models. DQQ enables extensive use of large, multiscale, linear and nonlinear models in systems biology and many other applications., Comment: 14 pages, 1 figures

Published
2016

16. Identification of conserved moieties in metabolic networks by graph theoretical analysis of atom transition networks

Author

Haraldsdóttir, Hulda S. and Fleming, Ronan M. T.

Subjects
Quantitative Biology - Molecular Networks
Abstract

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties., Comment: 28 pages, 11 figures

Published
2016
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17. Conditions for duality between fluxes and concentrations in biochemical networks

Author

Fleming, Ronan M. T., Vlassis, Nikos, Thiele, Ines, and Saunders, Michael A.

Subjects
Quantitative Biology - Molecular Networks
Abstract

Mathematical and computational modelling of biochemical networks is often done in terms of either the concentrations of molecular species or the fluxes of biochemical reactions. When is mathematical modelling from either perspective equivalent to the other? Mathematical duality translates concepts, theorems or mathematical structures into other concepts, theorems or structures, in a one-to-one manner. We present a novel stoichiometric condition that is necessary and sufficient for duality between unidirectional fluxes and concentrations. Our numerical experiments, with computational models derived from a range of genome-scale biochemical networks, suggest that this flux-concentration duality is a pervasive property of biochemical networks. We also provide a combinatorial characterisation that is sufficient to ensure flux-concentration duality. That is, for every two disjoint sets of molecular species, there is at least one reaction complex that involves species from only one of the two sets. When unidirectional fluxes and molecular species concentrations are dual vectors, this implies that the behaviour of the corresponding biochemical network can be described entirely in terms of either concentrations or unidirectional fluxes.

Published
2015

18. Accelerating the DC algorithm for smooth functions

Author

Artacho, Francisco J. Aragón, Fleming, Ronan M. T., and Vuong, Phan T.

Subjects
Mathematics - Optimization and Control, Quantitative Biology - Molecular Networks, 65K05, 65K10, 90C26, 92C42
Abstract

We introduce two new algorithms to minimise smooth difference of convex (DC) functions that accelerate the convergence of the classical DC algorithm (DCA). We prove that the point computed by DCA can be used to define a descent direction for the objective function evaluated at this point. Our algorithms are based on a combination of DCA together with a line search step that uses this descent direction. Convergence of the algorithms is proved and the rate of convergence is analysed under the Lojasiewicz property of the objective function. We apply our algorithms to a class of smooth DC programs arising in the study of biochemical reaction networks, where the objective function is real analytic and thus satisfies the Lojasiewicz property. Numerical tests on various biochemical models clearly show that our algorithms outperforms DCA, being on average more than four times faster in both computational time and the number of iterations. Numerical experiments show that the algorithms are globally convergent to a non-equilibrium steady state of various biochemical networks, with only chemically consistent restrictions on the network topology.

Published
2015
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19. The Physics of the B Factories

Author

Bevan, A. J., Golob, B., Mannel, Th., Prell, S., Yabsley, B. D., Abe, K., Aihara, H., Anulli, F., Arnaud, N., Aushev, T., Beneke, M., Beringer, J., Bianchi, F., Bigi, I. I., Bona, M., Brambilla, N., rodzicka, J. B, Chang, P., Charles, M. J., Cheng, C. H., Cheng, H. -Y., Chistov, R., Colangelo, P., Coleman, J. P., Drutskoy, A., Druzhinin, V. P., Eidelman, S., Eigen, G., Eisner, A. M., Faccini, R., Flood, K. T ., Gambino, P., Gaz, A., Gradl, W., Hayashii, H., Higuchi, T., Hulsbergen, W. D., Hurth, T., Iijima, T., Itoh, R., Jackson, P. D., Kass, R., Kolomensky, Yu. G., Kou, E., Križan, P., Kronfeld, A., Kumano, S., Kwon, Y. J., Latham, T. E., Leith, D. W. G. S., Lüth, V., Martinez-Vidal, F., Meadows, B. T., Mussa, R., Nakao, M., Nishida, S., Ocariz, J., Olsen, S. L., Pakhlov, P., Pakhlova, G., Palano, A., Pich, A., Playfer, S., Poluektov, A., Porter, F. C., Robertson, S. H., Roney, J. M., Roodman, A., Sakai, Y., Schwanda, C., Schwartz, A. J., Seidl, R., Sekula, S. J., Steinhauser, M., Sumisawa, K., Swanson, E. S., Tackmann, F., Trabelsi, K., Uehara, S., Uno, S., van der Water, R., Vasseur, G., Verkerke, W., Waldi, R., Wang, M. Z., Wilson, F. F., Zupan, J., Zupanc, A., Adachi, I., Albert, J., Banerjee, Sw., Bellis, M., Ben-Haim, E., Biassoni, P., Cahn, R. N., Cartaro, C., Chauveau, J., Chen, C., Chiang, C. C., Cowan, R., Dalseno, J., Davier, M., Davies, C., Dingfelder, J. C., nard, B. Eche, Epifanov, D., Fulsom, B. G., Gabareen, A. M., Gary, J. W., Godang, R., Graham, M. T., Hafner, A., Hamilton, B., Hartmann, T., Hayasaka, K., Hearty, C., Iwasaki, Y., Khodjamirian, A., Kusaka, A., Kuzmin, A., Lafferty, G. D., Lazzaro, A., Li, J., Lindemann, D., Long, O., Lusiani, A., Marchiori, G., Martinelli, M., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Muller, D. R., Nakazawa, H., Ongmongkolkul, P., Pacetti, S., Palombo, F., Pedlar, T. K., Piilonen, L. E., Pilloni, A., Poireau, V., Prothmann, K., Pulliam, T., Rama, M., Ratcliff, B. N., Roudeau, P., Schrenk, S., Schroeder, T., Schubert, K. R., Shen, C. P., Shwartz, B., Soffer, A., Solodov, E. P., Somov, A., Starič, M., Stracka, S., Telnov, A. V., Todyshev, K. Yu., Tsuboyama, T., Uglov, T., Vinokurova, A., Walsh, J. J., Watanabe, Y., Won, E., Wormser, G., Wright, D. H., Ye, S., Zhang, C. C., Abachi, S., Abashian, A., Abe, N., Abe, R., Abe, T., Abrams, G. S., Adam, I., Adamczyk, K., Adametz, A., Adye, T., Agarwal, A., Ahmed, H., Ahmed, M., Ahmed, S., Ahn, B. S., Ahn, H. S., Aitchison, I. J. R., Akai, K., Akar, S., Akatsu, M., Akemoto, M., Akhmetshin, R., Akre, R., Alam, M. S., Albert, J. N., Aleksan, R., Alexander, J. P., Alimonti, G., Allen, M. T., Allison, J., Allmendinger, T., Alsmiller, J. R. G., Altenburg, D., Alwyn, K. E., An, Q., Anderson, J., Andreassen, R., Andreotti, D., Andreotti, M., Andress, J. C., Angelini, C., Anipko, D., Anjomshoaa, A., Anthony, P. L., Antillon, E. A., Antonioli, E., Aoki, K., Arguin, J. F., Arinstein, K., Arisaka, K., Asai, K., Asai, M., Asano, Y., Asgeirsson, D. J., Asner, D. M., Aso, T., Aspinwall, M. L., Aston, D., Atmacan, H., Aubert, B., Aulchenko, V., Ayad, R., Azemoon, T., Aziz, T., Azzolini, V., Azzopardi, D. E., Baak, M. A., Back, J. J., Bagnasco, S., Bahinipati, S., Bailey, D. S., Bailey, S., Bailly, P., van Bakel, N., Bakich, A. M., Bala, A., Balagura, V., Baldini-Ferroli, R., Ban, Y., Banas, E., Band, H. R., Banerjee, S., Baracchini, E., Barate, R., Barberio, E., Barbero, M., Bard, D. J., Barillari, T., Barlow, N. R., Barlow, R. J., Barrett, M., Bartel, W., Bartelt, J., Bartoldus, R., Batignani, G., Battaglia, M., Bauer, J. M., Bay, A., Beaulieu, M., Bechtle, P., Beck, T. W., Becker, J., Becla, J., Bedny, I., Behari, S., Behera, P. K., Behn, E., Behr, L., Beigbeder, C., Beiline, D., Bell, R., Bellini, F., Bellodi, G., Belous, K., Benayoun, M., Benelli, G., Benitez, J. F., Benkebil, M., Berger, N., Bernabeu, J., Bernard, D., Bernet, R., Bernlochner, F. U., Berryhill, J. W., Bertsche, K., Besson, P., Best, D. S., Bettarini, S., Bettoni, D., Bhardwaj, V., Bhimji, W., Bhuyan, B., Biagini, M. E., Biasini, M., van Bibber, K., Biesiada, J., Bingham, I., Bionta, R. M., Bischofberger, M., Bitenc, U., Bizjak, I., Blanc, F., Blaylock, G., Blinov, V. E., Bloom, E., Bloom, P. C., Blount, N. L., Blouw, J., Bly, M., Blyth, S., Boeheim, C. T., Bomben, M., Bondar, A., Bondioli, M., Bonneaud, G. R., Bonvicini, G., Booke, M., Booth, J., Borean, C., Borgland, A. W., Borsato, E., Bosi, F., Bosisio, L., Botov, A. A., Bougher, J., Bouldin, K., Bourgeois, P., Boutigny, D., Bowerman, D. A., Boyarski, A. M., Boyce, R. F., Boyd, J. T., Bozek, A., Bozzi, C., Bračko, M., Brandenburg, G., Brandt, T., Brau, B., Brau, J., Breon, A. B., Breton, D., Brew, C., Briand, H., Bright-Thomas, P. G., Brigljević, V., Britton, D. I., Brochard, F., Broomer, B., Brose, J., Browder, T. E., Brown, C. L., Brown, C. M., Brown, D. N., Browne, M., Bruinsma, M., Brunet, S., Bucci, F., Buchanan, C., Buchmueller, O. L., Bünger, C., Bugg, W., Bukin, A. D., Bula, R., Bulten, H., Burchat, P. R., Burgess, W., Burke, J. P., Button-Shafer, J., Buzykaev, A. R., Buzzo, A., Cai, Y., Calabrese, R., Calcaterra, A., Calderini, G., Camanzi, B., Campagna, E., Campagnari, C., Capra, R., Carassiti, V., Carpinelli, M., Carroll, M., Casarosa, G., Casey, B. C. K., Cason, N. M., Castelli, G., Cavallo, N., Cavoto, G., Cecchi, A., Cenci, R., Cerizza, G., Cervelli, A., Ceseracciu, A., Chai, X., Chaisanguanthum, K. S., Chang, M. C., Chang, Y. H., Chang, Y. W., Chao, D. S., Chao, M., Chao, Y., Charles, E., Chavez, C. A., Cheaib, R., Chekelian, V., Chen, A., Chen, E., Chen, G. P., Chen, H. F., Chen, J. -H., Chen, J. C., Chen, K. F., Chen, P., Chen, S., Chen, W. T., Chen, X., Chen, X. R., Chen, Y. Q., Cheng, B., Cheon, B. G., Chevalier, N., Chia, Y. M., Chidzik, S., Chilikin, K., Chistiakova, M. V., Cizeron, R., Cho, I. S., Cho, K., Chobanova, V., Choi, H. H. F., Choi, K. S., Choi, S. K., Choi, Y., Choi, Y. K., Christ, S., Chu, P. H., Chun, S., Chuvikov, A., Cibinetto, G., Cinabro, D., Clark, A. R., Clark, P. J., Clarke, C. K., Claus, R., Claxton, B., Clifton, Z. C., Cochran, J., Cohen-Tanugi, J., Cohn, H., Colberg, T., Cole, S., Colecchia, F., Condurache, C., Contri, R., Convert, P., Convery, M. R., Cooke, P., Copty, N., Cormack, C. M., Corso, F. Dal, Corwin, L. A., Cossutti, F., Cote, D., Ramusino, A. Cotta, Cottingham, W. N., Couderc, F., Coupal, D. P., Covarelli, R., Cowan, G., Craddock, W. W., Crane, G., Crawley, H. B., Cremaldi, L., Crescente, A., Cristinziani, M., Crnkovic, J., Crosetti, G., Cuhadar-Donszelmann, T., Cunha, A., Curry, S., D'Orazio, A., Dû, S., Dahlinger, G., Dahmes, B., Dallapiccola, C., Danielson, N., Danilov, M., Das, A., Dash, M., Dasu, S., Datta, M., Daudo, F., Dauncey, P. D., David, P., Davis, C. L., Day, C. 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F., Susaki, Y., Sutcliffe, P., Suzuki, A., Suzuki, J., Suzuki, J. I., Suzuki, K., Suzuki, S., Suzuki, S. Y., Swain, J. E., Swain, S. K., T'Jampens, S., Tabata, M., Tackmann, K., Tajima, H., Tajima, O., Takahashi, K., Takahashi, S., Takahashi, T., Takasaki, F., Takayama, T., Takita, M., Tamai, K., Tamponi, U., Tamura, N., Tan, N., Tan, P., Tanabe, K., Tanabe, T., Tanaka, H. A., Tanaka, J., Tanaka, M., Tanaka, S., Tanaka, Y., Tanida, K., Taniguchi, N., Taras, P., Tasneem, N., Tatishvili, G., Tatomi, T., Tawada, M., Taylor, F., Taylor, G. N., Taylor, G. P., Telnov, V. I., Teodorescu, L., Ter-Antonyan, R., Teramoto, Y., Teytelman, D., Thérin, G., Thiebaux, Ch., Thiessen, D., Thomas, E. W., Thompson, J. M., Thorne, F., Tian, X. C., Tibbetts, M., Tikhomirov, I., Tinslay, J. S., Tiozzo, G., Tisserand, V., Tocut, V., Toki, W. H., Tomassini, E. W., Tomoto, M., Tomura, T., Torassa, E., Torrence, E., Tosi, S., Touramanis, C., Toussaint, J. C., Tovey, S. N., Trapani, P. P., Treadwell, E., Triggiani, G., Trincaz-Duvoid, S., Trischuk, W., Troost, D., Trunov, A., Tsai, K. L., Tsai, Y. T., Tsujita, Y., Tsukada, K., Tsukamoto, T., Tuggle, J. M., Tumanov, A., Tung, Y. W., Turnbull, L., Turner, J., Turri, M., Uchida, K., Uchida, M., Uchida, Y., Ueki, M., Ueno, K., Ujiie, N., Ulmer, K. A., Unno, Y., Urquijo, P., Ushiroda, Y., Usov, Y., Usseglio, M., Usuki, Y., Uwer, U., Va'vra, J., Vahsen, S. E., Vaitsas, G., Valassi, A., Vallazza, E., Vallereau, A., Vanhoefer, P., van Hoek, W. C., Van Hulse, C., van Winkle, D., Varner, G., Varnes, E. W., Varvell, K. E., Vasileiadis, G., Velikzhanin, Y. S., Verderi, M., Versillé, S., Vervink, K., Viaud, B., Vidal, P. B., Villa, S., Villanueva-Perez, P., Vinograd, E. L., Vitale, L., Vitug, G. M., Voß, C., Voci, C., Voena, C., Volk, A., von Wimmersperg-Toeller, J. H., Vorobyev, V., Vossen, A., Vuagnin, G., Vuosalo, C. O., Wacker, K., Wagner, A. P., Wagner, D. L., Wagner, G., Wagner, M. N., Wagner, S. R., Wagoner, D. E., Walker, D., Walkowiak, W., Wallom, D., Wang, C. C., Wang, C. H., Wang, J., Wang, J. G., Wang, K., Wang, L., Wang, L. L., Wang, P., Wang, T. J., Wang, W. F., Wang, X. L., Wang, Y. F., Wappler, F. R., Watanabe, M., Watson, A. T., Watson, J. E., Watson, N. K., Watt, M., Weatherall, J. H., Weaver, M., Weber, T., Wedd, R., Wei, J. T., Weidemann, A. W., Weinstein, A. J. R., Wenzel, W. A., West, C. A., West, C. G., West, T. J., White, E., White, R. M., Wicht, J., Widhalm, L., Wiechczynski, J., Wienands, U., Wilden, L., Wilder, M., Williams, D. C., Williams, G., Williams, J. C., Williams, K. M., Williams, M. I., Willocq, S. Y., Wilson, J. R., Wilson, M. G., Wilson, R. J., Winklmeier, F., Winstrom, L. O., Winter, M. A., Wisniewski, W. J., Wittgen, M., Wittlin, J., Wittmer, W., Wixted, R., Woch, A., Wogsland, B. J., Wong, Q. K., Wray, B. C., Wren, A. C., Wright, D. M., Wu, C. H., Wu, J., Wu, S. L., Wulsin, H. W., Xella, S. M., Xie, Q. L., Xie, Y., Xu, Z. Z., Yèche, Ch., Yamada, Y., Yamaga, M., Yamaguchi, A., Yamaguchi, H., Yamaki, T., Yamamoto, H., Yamamoto, N., Yamamoto, R. K., Yamamoto, S., Yamanaka, T., Yamaoka, H., Yamaoka, J., Yamaoka, Y., Yamashita, Y., Yamauchi, M., Yan, D. S., Yan, Y., Yanai, H., Yanaka, S., Yang, H., Yang, R., Yang, S., Yarritu, A. K., Yashchenko, S., Yashima, J., Yasin, Z., Yasu, Y., Ye, S. W., Yeh, P., Yi, J. I., Yi, K., Yi, M., Yin, Z. W., Ying, J., Yocky, G., Yokoyama, K., Yokoyama, M., Yokoyama, T., Yoshida, K., Yoshida, M., Yoshimura, Y., Young, C. C., Yu, C. X., Yu, Z., Yuan, C. Z., Yuan, Y., Yumiceva, F. X., Yusa, Y., Yushkov, A. N., Yuta, H., Zacek, V., Zain, S. B., Zallo, A., Zambito, S., Zander, D., Zang, S. L., Zanin, D., Zaslavsky, B. G., Zeng, Q. L., Zghiche, A., Zhang, B., Zhang, J., Zhang, L., Zhang, L. M., Zhang, S. Q., Zhang, Z. P., Zhao, H. W., Zhao, M., Zhao, Z. G., Zheng, Y., Zheng, Y. H., Zheng, Z. P., Zhilich, V., Zhou, P., Zhu, R. Y., Zhu, Y. S., Zhu, Z. M., Zhulanov, V., Ziegler, T., Ziegler, V., Zioulas, G., Zisman, M., Zito, M., Zürcher, D., Zwahlen, N., Zyukova, O., Živko, T., and Žontar, D.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C. Please note that version 3 on the archive is the auxiliary version of the Physics of the B Factories book. This uses the notation alpha, beta, gamma for the angles of the Unitarity Triangle. The nominal version uses the notation phi_1, phi_2 and phi_3. Please cite this work as Eur. Phys. J. C74 (2014) 3026., Comment: 928 pages, version 3 (arXiv:1406.6311v3) corresponds to the alpha, beta, gamma version of the book, the other versions use the phi1, phi2, phi3 notation

Published
2014
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21. Mass conserved elementary kinetics is sufficient for the existence of a non-equilibrium steady state concentration

Author

Fleming, Ronan M. T. and Thiele, Ines

Subjects
Quantitative Biology - Molecular Networks
Abstract

Living systems are forced away from thermodynamic equilibrium by exchange of mass and energy with their environment. In order to model a biochemical reaction network in a non-equilibrium state one requires a mathematical formulation to mimic this forcing. We provide a general formulation to force an arbitrary large kinetic model in a manner that is still consistent with the existence of a non-equilibrium steady state. We can guarantee the existence of a non-equilibrium steady state assuming only two conditions; that every reaction is mass balanced and that continuous kinetic reaction rate laws never lead to a negative molecule concentration. These conditions can be verified in polynomial time and are flexible enough to permit one to force a system away from equilibrium. In an expository biochemical example we show how a reversible, mass balanced perpetual reaction, with thermodynamically infeasible kinetic parameters, can be used to perpetually force a kinetic model of anaerobic glycolysis in a manner consistent with the existence of a steady state. Easily testable existence conditions are foundational for efforts to reliably compute non-equilibrium steady states in genome-scale biochemical kinetic models., Comment: 11 pages, 2 figures (v2 is now placed in proper context of the excellent 1962 paper by James Wei entitled "Axiomatic treatment of chemical reaction systems". In addition, section 4, on "Utility of steady state existence theorem" has been expanded.)

Published
2011

22. Existence of Positive Steady States for Mass Conserving and Mass-Action Chemical Reaction Networks with a Single Terminal-Linkage Class

Author

Akle, Santiago, Dalal, Onkar, Fleming, Ronan M. T., Saunders, Michael, Taheri, Nicole, and Ye, Yinyu

Subjects
Quantitative Biology - Molecular Networks, Mathematics - Optimization and Control
Abstract

We establish that mass conserving single terminal-linkage networks of chemical reactions admit positive steady states regardless of network deficiency and the choice of reaction rate constants. This result holds for closed systems without material exchange across the boundary, as well as for open systems with material exchange at rates that satisfy a simple sufficient and necessary condition. Our proof uses a fixed point of a novel convex optimization formulation to find the steady state behavior of chemical reaction networks that satisfy the law of mass-action kinetics. A fixed point iteration can be used to compute these steady states, and we show that it converges for weakly reversible homogeneous systems. We report the results of our algorithm on numerical experiments., Comment: 17 pages, 7 images

Published
2011

23. A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks

Author

Fleming, Ronan M. T., Maes, Christopher M., Saunders, Michael A., Ye, Yinyu, and Palsson, Bernhard Ø.

Subjects
Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods
Abstract

We derive a convex optimization problem on a steady-state nonequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen's theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed., Comment: 17 pages, 1 figure

Published
2011

25. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease

Author

Almut Heinken, Dmitry A. Ravcheev, Federico Baldini, Laurent Heirendt, Ronan M. T. Fleming, and Ines Thiele

Subjects
Gut microbiome, Bile acids, Host-microbe interactions, Metabolism, Genome-scale reconstruction, Constraint-based modeling, Microbial ecology, QR100-130
Abstract

Abstract Background The human gut microbiome performs important functions in human health and disease. A classic example for host-gut microbial co-metabolism is host biosynthesis of primary bile acids and their subsequent deconjugation and transformation by the gut microbiome. To understand these system-level host-microbe interactions, a mechanistic, multi-scale computational systems biology approach that integrates the different types of omic data is needed. Here, we use a systematic workflow to computationally model bile acid metabolism in gut microbes and microbial communities. Results Therefore, we first performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes and expanded 232 curated genome-scale microbial metabolic reconstructions with the corresponding reactions (available at https://vmh.life). We then predicted the bile acid biotransformation potential of each microbe and in combination with other microbes. We found that each microbe could produce maximally six of the 13 secondary bile acids in silico, while microbial pairs could produce up to 12 bile acids, suggesting bile acid biotransformation being a microbial community task. To investigate the metabolic potential of a given microbiome, publicly available metagenomics data from healthy Western individuals, as well as inflammatory bowel disease patients and healthy controls, were mapped onto the genomes of the reconstructed strains. We constructed for each individual a large-scale personalized microbial community model that takes into account strain-level abundances. Using flux balance analysis, we found considerable variation in the potential to deconjugate and transform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric inflammatory bowel disease patients were significantly depleted in their bile acid production potential compared with that of controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between inflammatory bowel disease patients and controls. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. Conclusions This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between the host and gut microbiomes in health and diseases states. Our models and tools are freely available to the scientific community.

Published
2019
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26. Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer

Author

Nicolas Sompairac, Jennifer Modamio, Emmanuel Barillot, Ronan M. T. Fleming, Andrei Zinovyev, and Inna Kuperstein

Subjects
Signalling, Metabolism, Networks, Comprehensive map, Systems biology, Cancer, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease. Results We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration (https://navicell.curie.fr/pages/maps_ReconMap%202.html). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified. Conclusions As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.

Published
2019
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27. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Author

Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, El Assal, Diana C., Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Ben Guebila, Marouen, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., El Assal, Lemmer P., Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Aragón Artacho, Francisco J., Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, and Fleming, Ronan M. T.

Published
2019
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28. Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP)

Author

Andrea Sturchio, Luca Marsili, Joaquin A. Vizcarra, Alok K. Dwivedi, Marcelo A. Kauffman, Andrew P. Duker, Peixin Lu, Michael W. Pauciulo, Benjamin D. Wissel, Emily J. Hill, Benjamin Stecher, Elizabeth G. Keeling, Achala S. Vagal, Lily Wang, David B. Haslam, Matthew J. Robson, Caroline M. Tanner, Daniel W. Hagey, Samir El Andaloussi, Kariem Ezzat, Ronan M. T. Fleming, Long J. Lu, Max A. Little, and Alberto J. Espay

Subjects
biomarkers, Parkinson’s disease, Alzheimer’s disease, neurodegeneration, cohort, drug repurposing, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Ongoing biomarker development programs have been designed to identify serologic or imaging signatures of clinico-pathologic entities, assuming distinct biological boundaries between them. Identified putative biomarkers have exhibited large variability and inconsistency between cohorts, and remain inadequate for selecting suitable recipients for potential disease-modifying interventions. We launched the Cincinnati Cohort Biomarker Program (CCBP) as a population-based, phenotype-agnostic longitudinal study. While patients affected by a wide range of neurodegenerative disorders will be deeply phenotyped using clinical, imaging, and mobile health technologies, analyses will not be anchored on phenotypic clusters but on bioassays of to-be-repurposed medications as well as on genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, and pharmacogenomics analyses blinded to phenotypic data. Unique features of this cohort study include (1) a reverse biology-to-phenotype direction of biomarker development in which clinical, imaging, and mobile health technologies are subordinate to biological signals of interest; (2) hypothesis free, causally- and data driven-based analyses; (3) inclusive recruitment of patients with neurodegenerative disorders beyond clinical criteria-meeting patients with Parkinson’s and Alzheimer’s diseases, and (4) a large number of longitudinally followed participants. The parallel development of serum bioassays will be aimed at linking biologically suitable subjects to already available drugs with repurposing potential in future proof-of-concept adaptive clinical trials. Although many challenges are anticipated, including the unclear pathogenic relevance of identifiable biological signals and the possibility that some signals of importance may not yet be measurable with current technologies, this cohort study abandons the anchoring role of clinico-pathologic criteria in favor of biomarker-driven disease subtyping to facilitate future biosubtype-specific disease-modifying therapeutic efforts.

Published
2020
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31. Embryonic development of selectively vulnerable neurons in Parkinson’s disease

Author

Miguel A. P. Oliveira, Rudi Balling, Marten P. Smidt, and Ronan M. T. Fleming

Subjects
Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract A specific set of brainstem nuclei are susceptible to degeneration in Parkinson’s disease. We hypothesise that neuronal vulnerability reflects shared phenotypic characteristics that confer selective vulnerability to degeneration. Neuronal phenotypic specification is mainly the cumulative result of a transcriptional regulatory program that is active during the development. By manual curation of the developmental biology literature, we comprehensively reconstructed an anatomically resolved cellular developmental lineage for the adult neurons in five brainstem regions that are selectively vulnerable to degeneration in prodromal or early Parkinson’s disease. We synthesised the literature on transcription factors that are required to be active, or required to be inactive, in the development of each of these five brainstem regions, and at least two differentially vulnerable nuclei within each region. Certain transcription factors, e.g., Ascl1 and Lmx1b, seem to be required for specification of many brainstem regions that are susceptible to degeneration in early Parkinson’s disease. Some transcription factors can even distinguish between differentially vulnerable nuclei within the same brain region, e.g., Pitx3 is required for specification of the substantia nigra pars compacta, but not the ventral tegmental area. We do not suggest that Parkinson’s disease is a developmental disorder. In contrast, we consider identification of shared developmental trajectories as part of a broader effort to identify the molecular mechanisms that underlie the phenotypic features that are shared by selectively vulnerable neurons. Systematic in vivo assessment of fate determining transcription factors should be completed for all neuronal populations vulnerable to degeneration in early Parkinson’s disease.

Published
2017
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32. Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

Author

German A. Preciat Gonzalez, Lemmer R. P. El Assal, Alberto Noronha, Ines Thiele, Hulda S. Haraldsdóttir, and Ronan M. T. Fleming

Subjects
Atom mapping, Metabolic network reconstruction, Automation, RDT, DREAM, AutoMapper, Information technology, T58.5-58.64, Chemistry, QD1-999
Abstract

Abstract The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

Published
2017
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37. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

Author

Almut Heinken, Johannes Hertel, Geeta Acharya, Dmitry A. Ravcheev, Malgorzata Nyga, Onyedika Emmanuel Okpala, Marcus Hogan, Stefanía Magnúsdóttir, Filippo Martinelli, Bram Nap, German Preciat, Janaka N. Edirisinghe, Christopher S. Henry, Ronan M. T. Fleming, and Ines Thiele

Subjects
Biomedical Engineering, Molecular Medicine, Bioengineering, Applied Microbiology and Biotechnology, Biotechnology
Abstract

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host–microbiome metabolic interactions.

Published
2023

38. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O., Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S. Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Published
2020
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40. A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines.

Author

Maike K Aurich, Ronan M T Fleming, and Ines Thiele

Subjects
Biology (General), QH301-705.5
Abstract

The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells.

Published
2017
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41. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper. MEMOTE for standardized genome-scale metabolic model testing (http://hdl.handle.net/10261/230245) Nature Biotechnology, Volume 38, Issue 3, Pages 272 - 276, 1 March 2020

Published
2020

42. MetaboAnnotator: an efficient toolbox to annotate metabolites in genome-scale metabolic reconstructions

Author

Ines Thiele, German Preciat, and Ronan M T Fleming

Subjects
Statistics and Probability, Computational Mathematics, Genome, Databases, Factual, Computational Theory and Mathematics, Metabolomics, Molecular Biology, Biochemistry, Metabolic Networks and Pathways, Software, Computer Science Applications
Abstract

Motivation Genome-scale metabolic reconstructions have been assembled for thousands of organisms using a wide range of tools. However, metabolite annotations, required to compare and link metabolites between reconstructions, remain incomplete. Here, we aim to further extend metabolite annotation coverage using various databases and chemoinformatic approaches. Results We developed a COBRA toolbox extension, deemed MetaboAnnotator, which facilitates the comprehensive annotation of metabolites with database independent and dependent identifiers, obtains molecular structure files, and calculates metabolite formula and charge at pH 7.2. The resulting metabolite annotations allow for subsequent cross-mapping between reconstructions and mapping of, e.g., metabolomic data. Availability and implementation MetaboAnnotator and tutorials are freely available at https://github.com/opencobra. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2022

45. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks.

Author

Hulda S Haraldsdóttir and Ronan M T Fleming

Subjects
Biology (General), QH301-705.5
Abstract

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.

Published
2016
Full Text
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46. MEMOTE for standardized genome-scale metabolic model testing

Author

Kiran Raosaheb Patil, Jens Nielsen, Vassily Hatzimanikatis, Hyun Uk Kim, Nathan D. Price, Edda Klipp, Parizad Babaei, Lars K. Nielsen, Moritz Emanuel Beber, Sang Yup Lee, Radhakrishnan Mahadevan, Meiyappan Lakshmanan, Lars M. Blank, Jon Olav Vik, Steffen Klamt, Nikolaus Sonnenschein, Saeed Shoaie, Bernhard O. Palsson, Georgios Fengos, Christian Diener, Christopher S. Henry, Andreas Dräger, Janaka N. Edirisinghe, Daniel Machado, Beatriz García-Jiménez, Osbaldo Resendis-Antonio, Hongwu Ma, Peter J. Schaap, Dong-Yup Lee, Wout van Helvoirt, José P. Faria, Judith A. H. Wodke, Adam M. Feist, Siddharth Chauhan, Isabel Rocha, Henning Hermjakob, Qianqian Yuan, Brett G. Olivier, Rahuman S. Malik Sheriff, Markus J. Herrgård, Frank Bergmann, Adil Mardinoglu, Anne Richelle, Filipe Liu, Joana C. Xavier, Maksim Zakhartsev, Paulo Vilaça, Cheng Zhang, Ronan M. T. Fleming, Birgitta E. Ebert, Gregory L. Medlock, Ali Kaafarani, Nathan E. Lewis, Mark G. Poolman, Intawat Nookaew, Jonathan M. Monk, Jason A. Papin, Benjamin Sanchez, Christian Lieven, Matthias König, Juan Nogales, Paulo Maia, Sunjae Lee, Jasper J. Koehorst, Meriç Ataman, Jennifer A. Bartell, Bas Teusink, Kevin Correia, Zachary A. King, Systems Bioinformatics, AIMMS, Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), and Universidade do Minho

Subjects
endocrine system diseases, Applied Microbiology and Biotechnology, Biochemistry, Workflow, German, 0302 clinical medicine, Bioinformatics: 475 [VDP], Computational models, Systems and Synthetic Biology, Grand Challenges, media_common, 0303 health sciences, Systeem en Synthetische Biologie, Genome, Health technology, Publisher Correction, language, ddc:660, Molecular Medicine, Bioinformatikk: 475 [VDP], Systems biology, Administration (government), Metabolic Networks and Pathways, Biotechnology, reconstruction, media_common.quotation_subject, Biomedical Engineering, Library science, Bioengineering, Models, Biological, Biokjemi, 03 medical and health sciences, Excellence, Correspondence, media_common.cataloged_instance, Life Science, European union, 030304 developmental biology, VLAG, Science & Technology, Biochemical networks, fungi, Systembiologi, Computational Biology, Molecular Sequence Annotation, language.human_language, Alliance, Information and Communications Technology, 030217 neurology & neurosurgery, Software
Abstract

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-y, Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality., We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503)., info:eu-repo/semantics/publishedVersion

Published
2020

49. XomicsToModel: Omics data integration and generation of thermodynamically consistent metabolic models

Author

Agnieszka B. Wegrzyn, Ines Thiele, Ronan M. T. Fleming, German Preciat, and Thomas Hankemeier

Subjects
Constraint (information theory), Metabolomics, Computer science, Experimental data, Context (language use), computer.software_genre, Biological system, computer, Protocol (object-oriented programming), Pipeline (software), Flux (metabolism), Data integration
Abstract

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, including modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in a given context. However, existing model extraction algorithms are unable to ensure that the context-specific model is thermodynamically feasible. This protocol introducesXomicsToModel, a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. TheXomicsToModelpipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model, but it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. With all input data fully prepared, algorithmic completion of the pipeline takes ~10 min, however manual review of intermediate results may also be required, e.g., when inconsistent input data lead to an infeasible model.

Published
2021

50. Neuronal hyperactivity in a LRRK2-G2019S cellular model of Parkinson’s Disease

Author

Mahsa Moein, Sarah Louise Nickels, Vanden Berghe P, Lucumi Moreno E, Ronan M. T. Fleming, Alexander Skupin, Jens Christian Schwamborn, Khalid I. W. Kane, and Siham Hachi

Subjects
Mutation, Parkinson's disease, Neurite, chemistry.chemical_element, Biology, Calcium, medicine.disease_cause, medicine.disease, LRRK2, nervous system diseases, Cell biology, chemistry, medicine, Premovement neuronal activity, Cellular model, Induced pluripotent stem cell
Abstract

Monogenic Parkinson’s Disease can be caused by a mutation in the leucine-rich repeat kinase 2 (LRRK2) gene, causing a late-onset autosomal dominant inherited form of Parkinson’s Disease. The function of the LRRK2 gene is incompletely understood, but several in vitro studies have reported that LRRK2-G2019S mutations affect neurite branching, calcium homeostasis and mitochondrial function, but thus far, there have been no reports of effects on electrophysiological activity. We assessed the neuronal activity of induced pluripotent stem cell derived neurons from Parkinson’s Disease patients with LRRK2-G2019S mutations and isogenic controls. Neuronal activity of spontaneously firing neuronal populations was recorded with a fluorescent calcium-sensitive dye (Fluo-4) and analysed with a novel image analysis pipeline that combined semi-automated neuronal segmentation and quantification of calcium transient properties. Compared with controls, LRRK2-G2019S mutants have shortened inter-spike intervals and an increased rate of spontaneous calcium transient induction.

Published
2021

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