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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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Systematically gap-filling the genome-scale metabolic model of CHO cells. Biotechnol Lett 2020; 43:73-87. [PMID: 33040240 DOI: 10.1007/s10529-020-03021-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/03/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. RESULTS We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. CONCLUSIONS The present CHO model is an important step towards more complete metabolic models of CHO cells.
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Artificial cells containing sustainable energy conversion engines. Emerg Top Life Sci 2019; 3:573-578. [DOI: 10.1042/etls20190103] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 11/17/2022]
Abstract
Living cells naturally maintain a variety of metabolic reactions via energy conversion mechanisms that are coupled to proton transfer across cell membranes, thereby producing energy-rich compounds. Until now, researchers have been unable to maintain continuous biochemical reactions in artificially engineered cells, mainly due to the lack of mechanisms that generate energy-rich resources, such as adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide (NADH). If these metabolic activities in artificial cells are to be sustained, reliable energy transduction strategies must be realized. In this perspective, this article discusses the development of an artificially engineered cell containing a sustainable energy conversion process.
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Griesemer M, Kimbrel JA, Zhou CE, Navid A, D'haeseleer P. Combining multiple functional annotation tools increases coverage of metabolic annotation. BMC Genomics 2018; 19:948. [PMID: 30567498 PMCID: PMC6299973 DOI: 10.1186/s12864-018-5221-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/05/2018] [Indexed: 12/15/2022] Open
Abstract
Background Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30–50% of genes without functional annotation, severely limiting our knowledge of the “parts lists” that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. Results Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3–8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. Conclusions Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways. Electronic supplementary material The online version of this article (10.1186/s12864-018-5221-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marc Griesemer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Carol E Zhou
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Ali Navid
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA
| | - Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA. .,Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, 94551, USA.
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Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018; 46:249-260. [PMID: 29588387 PMCID: PMC5906707 DOI: 10.1042/bst20170268] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/01/2018] [Accepted: 03/02/2018] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.
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The spatial and metabolic basis of colony size variation. ISME JOURNAL 2018; 12:669-680. [PMID: 29367665 PMCID: PMC5864198 DOI: 10.1038/s41396-017-0038-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 08/11/2017] [Accepted: 08/14/2017] [Indexed: 11/15/2022]
Abstract
Spatial structure impacts microbial growth and interactions, with ecological and evolutionary consequences. It is therefore important to quantitatively understand how spatial proximity affects interactions in different environments. We tested how proximity influences colony size when either Escherichia coli or Salmonella enterica are grown on various carbon sources. The importance of colony location changed with species and carbon source. Spatially explicit, genome-scale metabolic modeling recapitulated observed colony size variation. Competitors that determine territory size, according to Voronoi diagrams, were the most important drivers of variation in colony size. However, the relative importance of different competitors changed through time. Further, the effect of location increased when colonies took up resources quickly relative to the diffusion of limiting resources. These analyses made it apparent that the importance of location was smaller than expected for experiments with S. enterica growing on glucose. The accumulation of toxic byproducts appeared to limit the growth of large colonies and reduced variation in colony size. Our work provides an experimentally and theoretically grounded understanding of how location interacts with metabolism and diffusion to influence microbial interactions.
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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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Krumholz EW, Libourel IGL. Thermodynamic Constraints Improve Metabolic Networks. Biophys J 2017; 113:679-689. [PMID: 28793222 DOI: 10.1016/j.bpj.2017.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 05/24/2017] [Accepted: 06/02/2017] [Indexed: 10/19/2022] Open
Abstract
In pursuit of establishing a realistic metabolic phenotypic space, the reversibility of reactions is thermodynamically constrained in modern metabolic networks. The reversibility constraints follow from heuristic thermodynamic poise approximations that take anticipated cellular metabolite concentration ranges into account. Because constraints reduce the feasible space, draft metabolic network reconstructions may need more extensive reconciliation, and a larger number of genes may become essential. Notwithstanding ubiquitous application, the effect of reversibility constraints on the predictive capabilities of metabolic networks has not been investigated in detail. Instead, work has focused on the implementation and validation of the thermodynamic poise calculation itself. With the advance of fast linear programming-based network reconciliation, the effects of reversibility constraints on network reconciliation and gene essentiality predictions have become feasible and are the subject of this study. Networks with thermodynamically informed reversibility constraints outperformed gene essentiality predictions compared to networks that were constrained with randomly shuffled constraints. Unconstrained networks predicted gene essentiality as accurately as thermodynamically constrained networks, but predicted substantially fewer essential genes. Networks that were reconciled with sequence similarity data and strongly enforced reversibility constraints outperformed all other networks. We conclude that metabolic network analysis confirmed the validity of the thermodynamic constraints, and that thermodynamic poise information is actionable during network reconciliation.
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Affiliation(s)
- Elias W Krumholz
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota
| | - Igor G L Libourel
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota; Biotechnology Institute, University of Minnesota, Saint Paul, Minnesota; Genome Craft, Saint Paul, Minnesota.
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Hosseini Z, Marashi SA. Discovering missing reactions of metabolic networks by using gene co-expression data. Sci Rep 2017; 7:41774. [PMID: 28150713 PMCID: PMC5288723 DOI: 10.1038/srep41774] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 12/30/2016] [Indexed: 11/23/2022] Open
Abstract
Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.
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Affiliation(s)
- Zhaleh Hosseini
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of science, University of Tehran, Tehran, Iran
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Biggs MB, Papin JA. Metabolic network-guided binning of metagenomic sequence fragments. Bioinformatics 2016; 32:867-74. [PMID: 26568626 PMCID: PMC6169484 DOI: 10.1093/bioinformatics/btv671] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 10/16/2015] [Accepted: 11/09/2015] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Most microbes on Earth have never been grown in a laboratory, and can only be studied through DNA sequences. Environmental DNA sequence samples are complex mixtures of fragments from many different species, often unknown. There is a pressing need for methods that can reliably reconstruct genomes from complex metagenomic samples in order to address questions in ecology, bioremediation, and human health. RESULTS We present the SOrting by NEtwork Completion (SONEC) approach for assigning reactions to incomplete metabolic networks based on a metabolite connectivity score. We successfully demonstrate proof of concept in a set of 100 genome-scale metabolic network reconstructions, and delineate the variables that impact reaction assignment accuracy. We further demonstrate the integration of SONEC with existing approaches (such as cross-sample scaffold abundance profile clustering) on a set of 94 metagenomic samples from the Human Microbiome Project. We show that not only does SONEC aid in reconstructing species-level genomes, but it also improves functional predictions made with the resulting metabolic networks. AVAILABILITY AND IMPLEMENTATION The datasets and code presented in this work are available at: https://bitbucket.org/mattbiggs/sorting_by_network_completion/ CONTACT papin@virginia.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthew B Biggs
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22903 USA
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Sarge S, Haase I, Illarionov B, Laudert D, Hohmann HP, Bacher A, Fischer M. Catalysis of an Essential Step in Vitamin B2Biosynthesis by a Consortium of Broad Spectrum Hydrolases. Chembiochem 2015; 16:2466-9. [DOI: 10.1002/cbic.201500352] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Sonja Sarge
- Hamburg School of Food Science; Institute of Food Chemistry; University of Hamburg; Grindelallee 117 20146 Hamburg Germany
| | - Ilka Haase
- Hamburg School of Food Science; Institute of Food Chemistry; University of Hamburg; Grindelallee 117 20146 Hamburg Germany
| | - Boris Illarionov
- Hamburg School of Food Science; Institute of Food Chemistry; University of Hamburg; Grindelallee 117 20146 Hamburg Germany
| | - Dietmar Laudert
- DSM Nutritional Products; P. O. Box 2676 4002 Basel Switzerland
| | | | - Adelbert Bacher
- Hamburg School of Food Science; Institute of Food Chemistry; University of Hamburg; Grindelallee 117 20146 Hamburg Germany
| | - Markus Fischer
- Hamburg School of Food Science; Institute of Food Chemistry; University of Hamburg; Grindelallee 117 20146 Hamburg Germany
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