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Occhipinti A, Eyassu F, Rahman TJ, Rahman PKSM, Angione C. In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production. PeerJ 2018; 6:e6046. [PMID: 30588397 PMCID: PMC6301282 DOI: 10.7717/peerj.6046] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 10/30/2018] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications. METHODS We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane. RESULTS We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida. CONCLUSIONS We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.
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Affiliation(s)
- Annalisa Occhipinti
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Filmon Eyassu
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
| | - Thahira J. Rahman
- Technology Futures Institute, School of Science, Engineering and Design, Teesside University, Middlesbrough, UK
| | - Pattanathu K. S. M. Rahman
- Technology Futures Institute, School of Science, Engineering and Design, Teesside University, Middlesbrough, UK
- Institute of Biological and Biomedical Sciences, School of Biological Sciences, University of Portsmouth, Portsmouth, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK
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Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries. Curr Opin Biotechnol 2017; 51:103-108. [PMID: 29278837 DOI: 10.1016/j.copbio.2017.12.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 12/18/2022]
Abstract
With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.
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Oyetunde T, Zhang M, Chen Y, Tang Y, Lo C. BoostGAPFILL: improving the fidelity of metabolic network reconstructions through integrated constraint and pattern-based methods. Bioinformatics 2017; 33:608-611. [PMID: 27797784 DOI: 10.1093/bioinformatics/btw684] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/25/2016] [Indexed: 11/13/2022] Open
Abstract
Motivation Metabolic network reconstructions are often incomplete. Constraint-based and pattern-based methodologies have been used for automated gap filling of these networks, each with its own strengths and weaknesses. Moreover, since validation of hypotheses made by gap filling tools require experimentation, it is challenging to benchmark performance and make improvements other than that related to speed and scalability. Results We present BoostGAPFILL, an open source tool that leverages both constraint-based and machine learning methodologies for hypotheses generation in gap filling and metabolic model refinement. BoostGAPFILL uses metabolite patterns in the incomplete network captured using a matrix factorization formulation to constrain the set of reactions used to fill gaps in a metabolic network. We formulate a testing framework based on the available metabolic reconstructions and demonstrate the superiority of BoostGAPFILL to state-of-the-art gap filling tools. We randomly delete a number of reactions from a metabolic network and rate the different algorithms on their ability to both predict the deleted reactions from a universal set and to fill gaps. For most metabolic network reconstructions tested, BoostGAPFILL shows above 60% precision and recall, which is more than twice that of other existing tools. Availability and Implementation MATLAB open source implementation ( https://github.com/Tolutola/BoostGAPFILL ). Contacts toyetunde@wustl.edu or muhan@wustl.edu . Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Muhan Zhang
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University, Saint Louis, MO 63130, USA
| | - Yinjie Tang
- Department of Energy, Environmental and Chemical Engineering
| | - Cynthia Lo
- Department of Energy, Environmental and Chemical Engineering
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Fröhlich F, Kaltenbacher B, Theis FJ, Hasenauer J. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks. PLoS Comput Biol 2017; 13:e1005331. [PMID: 28114351 PMCID: PMC5256869 DOI: 10.1371/journal.pcbi.1005331] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 12/20/2016] [Indexed: 01/06/2023] Open
Abstract
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. In this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.
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Affiliation(s)
- Fabian Fröhlich
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | | | - Fabian J. Theis
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany
- * E-mail:
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Sarajlić A, Malod-Dognin N, Yaveroğlu ÖN, Pržulj N. Graphlet-based Characterization of Directed Networks. Sci Rep 2016; 6:35098. [PMID: 27734973 PMCID: PMC5062067 DOI: 10.1038/srep35098] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 09/26/2016] [Indexed: 01/22/2023] Open
Abstract
We are flooded with large-scale, dynamic, directed, networked data. Analyses requiring exact comparisons between networks are computationally intractable, so new methodologies are sought. To analyse directed networks, we extend graphlets (small induced sub-graphs) and their degrees to directed data. Using these directed graphlets, we generalise state-of-the-art network distance measures (RGF, GDDA and GCD) to directed networks and show their superiority for comparing directed networks. Also, we extend the canonical correlation analysis framework that enables uncovering the relationships between the wiring patterns around nodes in a directed network and their expert annotations. On directed World Trade Networks (WTNs), our methodology allows uncovering the core-broker-periphery structure of the WTN, predicting the economic attributes of a country, such as its gross domestic product, from its wiring patterns in the WTN for up-to ten years in the future. It does so by enabling us to track the dynamics of a country's positioning in the WTN over years. On directed metabolic networks, our framework yields insights into preservation of enzyme function from the network wiring patterns rather than from sequence data. Overall, our methodology enables advanced analyses of directed networked data from any area of science, allowing domain-specific interpretation of a directed network's topology.
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Affiliation(s)
- Anida Sarajlić
- Department of Computing, Imperial College London, SW7 2AZ London, UK
| | - Noël Malod-Dognin
- Department of Computer Science, University College London, WC1E 6BT London, UK
| | | | - Nataša Pržulj
- Department of Computer Science, University College London, WC1E 6BT London, UK
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van Heck RGA, Ganter M, Martins dos Santos VAP, Stelling J. Efficient Reconstruction of Predictive Consensus Metabolic Network Models. PLoS Comput Biol 2016; 12:e1005085. [PMID: 27563720 PMCID: PMC5001716 DOI: 10.1371/journal.pcbi.1005085] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 07/29/2016] [Indexed: 01/08/2023] Open
Abstract
Understanding cellular function requires accurate, comprehensive representations of metabolism. Genome-scale, constraint-based metabolic models (GSMs) provide such representations, but their usability is often hampered by inconsistencies at various levels, in particular for concurrent models. COMMGEN, our tool for COnsensus Metabolic Model GENeration, automatically identifies inconsistencies between concurrent models and semi-automatically resolves them, thereby contributing to consolidate knowledge of metabolic function. Tests of COMMGEN for four organisms showed that automatically generated consensus models were predictive and that they substantially increased coherence of knowledge representation. COMMGEN ought to be particularly useful for complex scenarios in which manual curation does not scale, such as for eukaryotic organisms, microbial communities, and host-pathogen interactions.
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Affiliation(s)
- Ruben G. A. van Heck
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
| | - Mathias Ganter
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
| | - Vitor A. P. Martins dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University, Wageningen, The Netherlands
- LifeGlimmer GmbH, Berlin, Germany
- * E-mail: (VAPMdS); (JS)
| | - Joerg Stelling
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
- * E-mail: (VAPMdS); (JS)
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Ryu JY, Kim HU, Lee SY. Reconstruction of genome-scale human metabolic models using omics data. Integr Biol (Camb) 2016; 7:859-68. [PMID: 25730289 DOI: 10.1039/c5ib00002e] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.
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Affiliation(s)
- Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
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Moretti S, Martin O, Van Du Tran T, Bridge A, Morgat A, Pagni M. MetaNetX/MNXref--reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res 2015; 44:D523-6. [PMID: 26527720 PMCID: PMC4702813 DOI: 10.1093/nar/gkv1117] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 10/11/2015] [Indexed: 12/02/2022] Open
Abstract
MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments—namely MNXref—and proteins. The MetaNetX.org website (http://www.metanetx.org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available.
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Affiliation(s)
- Sébastien Moretti
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland Department of Ecology and Evolution, Biophore, Evolutionary Bioinformatics group, University of Lausanne, Lausanne 1015, Switzerland
| | - Olivier Martin
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - T Van Du Tran
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Alan Bridge
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva 1206, Switzerland
| | - Anne Morgat
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva 1206, Switzerland Equipe ERABLE, INRIA Grenoble Rhône-Alpes, Montbonnot Saint-Martin 38330, France
| | - Marco Pagni
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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Carrera J, Covert MW. Why Build Whole-Cell Models? Trends Cell Biol 2015; 25:719-722. [PMID: 26471224 DOI: 10.1016/j.tcb.2015.09.004] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 09/11/2015] [Accepted: 09/14/2015] [Indexed: 10/22/2022]
Abstract
Our ability to build computational models that account for all known gene functions in a cell has increased dramatically. But why build whole-cell models, and how can they best be used? In this forum, we enumerate several areas in which whole-cell modeling can significantly impact research and technology.
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Affiliation(s)
- Javier Carrera
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305-4125, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA 94305-4125, USA.
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