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Kim H, Smith HB, Mathis C, Raymond J, Walker SI. Universal scaling across biochemical networks on Earth. SCIENCE ADVANCES 2019; 5:eaau0149. [PMID: 30746442 PMCID: PMC6357746 DOI: 10.1126/sciadv.aau0149] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 12/06/2018] [Indexed: 06/09/2023]
Abstract
The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.
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Affiliation(s)
- Hyunju Kim
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
| | - Harrison B. Smith
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
| | - Cole Mathis
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Jason Raymond
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
| | - Sara I. Walker
- Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA
- School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
- ASU-SFI Center for Biosocial Complex Systems, Tempe, AZ, USA
- Blue Marble Space Institute of Science, Seattle, WA, USA
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Schwahn K, Beleggia R, Omranian N, Nikoloski Z. Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data. FRONTIERS IN PLANT SCIENCE 2017; 8:2152. [PMID: 29326746 PMCID: PMC5741659 DOI: 10.3389/fpls.2017.02152] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Accepted: 12/05/2017] [Indexed: 06/07/2023]
Abstract
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.
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Affiliation(s)
- Kevin Schwahn
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Romina Beleggia
- Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria, Centro di Ricerca per la Cerealicoltura e le Colture Industriali (CREA-CI), Foggia, Italy
| | - Nooshin Omranian
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
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Güell O, Sagués F, Serrano MÁ. Detecting the Significant Flux Backbone of Escherichia coli metabolism. FEBS Lett 2017; 591:1437-1451. [PMID: 28391640 DOI: 10.1002/1873-3468.12650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 03/20/2017] [Accepted: 04/01/2017] [Indexed: 01/25/2023]
Abstract
The heterogeneity of computationally predicted reaction fluxes in metabolic networks within a single flux state can be exploited to detect their significant flux backbone. Here, we disclose the backbone of Escherichia coli, and compare it with the backbones of other bacteria. We find that, in general, the core of the backbones is mainly composed of reactions in energy metabolism corresponding to ancient pathways. In E. coli, the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism. Moreover, the consideration of different media leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful to trace simultaneously both its evolution and adaptation fingerprints.
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Affiliation(s)
- Oriol Güell
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona, Spain
| | - Francesc Sagués
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona, Spain
| | - M Ángeles Serrano
- Department de Física de la Matèria Condensada, Universitat de Barcelona, Spain.,University of Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Spain.,ICREA, Barcelona, Spain
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Basler G, Nikoloski Z, Larhlimi A, Barabási AL, Liu YY. Control of fluxes in metabolic networks. Genome Res 2016; 26:956-68. [PMID: 27197218 PMCID: PMC4937563 DOI: 10.1101/gr.202648.115] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 05/18/2016] [Indexed: 01/09/2023]
Abstract
Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
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Affiliation(s)
- Georg Basler
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, USA; Department of Environmental Protection, Estación Experimental del Zaidín CSIC, Granada, 18008 Spain
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, Potsdam, 14476 Germany
| | - Abdelhalim Larhlimi
- Laboratoire d'Informatique de Nantes Atlantique, Université de Nantes, Nantes, 44322 France
| | - Albert-László Barabási
- Center for Complex Network Research and Departments of Physics, Computer Science, and Biology, Northeastern University, Boston, Massachusetts 02115, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02215, USA
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Shellman ER, Burant CF, Schnell S. Network motifs provide signatures that characterize metabolism. MOLECULAR BIOSYSTEMS 2013; 9:352-60. [PMID: 23287894 PMCID: PMC3619197 DOI: 10.1039/c2mb25346a] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Motifs are repeating patterns that determine the local properties of networks. In this work, we characterized all 3-node motifs using enzyme commission numbers of the International Union of Biochemistry and Molecular Biology to show that motif abundance is related to biochemical function. Further, we present a comparative analysis of motif distributions in the metabolic networks of 21 species across six kingdoms of life. We found the distribution of motif abundances to be similar between species, but unique across cellular organelles. Finally, we show that motifs are able to capture inter-species differences in metabolic networks and that molecular differences between some biological species are reflected by the distribution of motif abundances in metabolic networks.
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Affiliation(s)
- Erin R. Shellman
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, USA
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, USA
| | - Santiago Schnell
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, USA
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
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McCloskey D, Palsson BØ, Feist AM. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 2013; 9:661. [PMID: 23632383 PMCID: PMC3658273 DOI: 10.1038/msb.2013.18] [Citation(s) in RCA: 229] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Accepted: 03/11/2013] [Indexed: 02/07/2023] Open
Abstract
The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy.
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Affiliation(s)
- Douglas McCloskey
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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Beber ME, Hütt MT. How do production systems in biological cells maintain their function in changing environments? LOGISTICS RESEARCH 2012. [DOI: 10.1007/s12159-012-0090-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics. ENTROPY 2012. [DOI: 10.3390/e14060993] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Basler G, Grimbs S, Nikoloski Z. Optimizing metabolic pathways by screening for feasible synthetic reactions. Biosystems 2012; 109:186-91. [PMID: 22575307 DOI: 10.1016/j.biosystems.2012.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2011] [Revised: 03/23/2012] [Accepted: 04/23/2012] [Indexed: 11/18/2022]
Abstract
BACKGROUND Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze. RESULTS Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coli, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis. CONCLUSIONS While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.
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Affiliation(s)
- Georg Basler
- Max-Planck-Institute for Molecular Plant Physiology, 14476 Potsdam, Germany.
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