CoChat
Sign Up
Cited by 35Gut microbiota and healthMicrobial Metabolic Engineering and BioproductionBioinformatics and Genomic Networks

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Metabolic modeling with Big Data and the gut microbiome

Open access
Sung, Jaeyun, Hale, Vanessa, Merkel, Annette C., Kim, Pan-Jun, Chia, Nicholas·2016·Applied & Translational Genomics
DOI 10.1016/j.atg.2016.02.001PMC PMC5025471
Open this paper in CoChat — free accountOpen PDF

Abstract

The recent advances in high-throughput omics technologies have enabled researchers to explore the intricacies of the human microbiome. On the clinical front, the gut microbial community has been the focus of many biomarker-discovery studies. While the recent deluge of high-throughput data in microbiome research has been vastly informative and groundbreaking, we have yet to capture the full potential of omics-based approaches. Realizing the promise of multi-omics data will require integration of disparate omics data, as well as a biologically relevant, mechanistic framework - or metabolic model - on which to overlay these data. Also, a new paradigm for metabolic model evaluation is necessary. Herein, we outline the need for multi-omics data integration, as well as the accompanying challenges. Furthermore, we present a framework for characterizing the ecology of the gut microbiome based on metabolic network modeling.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.atg.2016.02.001

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Metabolic models of the microbiome have yielded understanding in microbe-microbe interactions and microbe-environment interactions.

    “Metabolic models of the microbiome, often utilizing our growing capacity to produce, process and handle big data, have yielded invaluable understanding in microbe-microbe interactions and microbe-environment interactions (Magnusdottir and Thiele, 2018;Sung et al., 2016).”

    From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Sung et al., 2016

    The Introduction (page unavailable) says metabolic models help resolve relationships between microbes and may predict effects of ecosystem-wide perturbations. The section on Metabolic models for Big Data synthesis says community models map microbial interactions. But the paper frames much of the understanding of microbe–environment interactions as potential rather than something already yielded, so the claim overstates that part. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 51

  1. Abubucker, 2012, Metabolic reconstruction for metagenomic data and its application to the human microbiome, PLoS Comput. Biol., 8, 10.1371/journal.pcbi.1002358 · DOI
  2. Alivisatos, 2015, A unified initiative to harness Earth's microbiomes, Science, 350, 507, 10.1126/science.aac8480 · DOI
  3. Bauer, 2015, Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires, Microbiome, 3, 55, 10.1186/s40168-015-0121-6 · DOI
  4. Benedict, 2014, Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models, PLoS Comput. Biol., 10, 10.1371/journal.pcbi.1003882 · DOI
  5. Borenstein, 2012, Computational systems biology and in silico modeling of the human microbiome, Brief. Bioinform., 10.1093/bib/bbs022 · DOI
  6. Bultman, 2013, Emerging roles of the microbiome in cancer, Carcinogenesis
  7. Chen, 2016, Impact of demographics on human gut microbial diversity in a US Midwest population, Peer J., 4, 10.7717/peerj.1514 · DOI
  8. Chia, 2006, A practical approach to significance assessment in alignment with gaps, J. Comput. Biol., 13, 429, 10.1089/cmb.2006.13.429 · DOI
  9. Collins, 2003, The Human Genome Project: lessons from large-scale biology, Science, 300, 286, 10.1126/science.1084564 · DOI · Paper page
  10. Costa, 2013, Big data in biomedicine, Drug Discov. Today, 00, 1
  11. David, 2014, Diet rapidly and reproducibly alters the human gut microbiome, Nature, 505, 559, 10.1038/nature12820 · DOI · Paper page
  12. De Filippo, 2010, Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa, Proc. Natl. Acad. Sci., 107, 14691, 10.1073/pnas.1005963107 · DOI · Paper page
  13. de Vos, 2012, Role of the intestinal microbiome in health and disease: from correlation to causation, Nutr. Rev., 70, S45, 10.1111/j.1753-4887.2012.00505.x · DOI · Paper page
  14. Dräger, 2014, Improving collaboration by standardization efforts in systems biology, Front Bioeng. Biotechnol., 2, 61, 10.3389/fbioe.2014.00061 · DOI
  15. Dubilier, 2015, Microbiology: create a global microbiome effort, Nature, 526, 631, 10.1038/526631a · DOI
  16. Fondi, 2015, Multi-omics and metabolic modelling pipelines: challenges and tools for systems microbiology, Microbiol. Res., 171, 52, 10.1016/j.micres.2015.01.003 · DOI
  17. Franzosa, 2015, Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling, Nat. Rev. Microbiol., 13, 360, 10.1038/nrmicro3451 · DOI
  18. Franzosa, 2014, Relating the metatranscriptome and metagenome of the human gut, Proc. Natl. Acad. Sci. U. S. A., 111, E2329, 10.1073/pnas.1319284111 · DOI
  19. Haiser, 2013, Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta, Science (New York, N.Y.), 341, 295, 10.1126/science.1235872 · DOI · Paper page
  20. Heinken, 2015, Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework, Gut Microbes, 6, 120, 10.1080/19490976.2015.1023494 · DOI
  21. Heinken, 2013, Systems-level characterization of a host–microbe metabolic symbiosis in the mammalian gut, Gut Microbes, 4, 28, 10.4161/gmic.22370 · DOI
  22. Henry, 2009, iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations, Genome Biol., 10, R69, 10.1186/gb-2009-10-6-r69 · DOI
  23. Imam, 2015, Data-driven integration of genome-scale regulatory and metabolic network models, Front. Microbiol., 6, 409, 10.3389/fmicb.2015.00409 · DOI
  24. Jeraldo, 2015, Draft genome sequences of 24 microbial strains assembled from direct sequencing from 4 stool samples, Genome Announc., 3, 10.1128/genomeA.00526-15 · DOI
  25. Kashyap, 2013, Complex interactions among diet, gastrointestinal transit, and gut microbiota in humanized mice, Gastroenterology, 144, 967, 10.1053/j.gastro.2013.01.047 · DOI · Paper page
  26. Klitgord, 2010, Environments that induce synthetic microbial ecosystems, PLoS Comput. Biol., 6, 10.1371/journal.pcbi.1001002 · DOI
  27. Lachin, 1981, Introduction to sample size determination and power analysis for clinical trials, Control. Clin. Trials, 2, 93, 10.1016/0197-2456(81)90001-5 · DOI · Paper page
  28. Lagier, 2015, The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota, Clin. Microbiol. Rev., 28, 237, 10.1128/CMR.00014-14 · DOI
  29. Lander, 2011, Initial impact of the sequencing of the human genome, Nature, 470, 187, 10.1038/nature09792 · DOI · Paper page
  30. Langille, 2013, Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences, Nat. Biotechnol., 31, 814, 10.1038/nbt.2676 · DOI · Paper page
  31. Lee, 2012, Improving metabolic flux predictions using absolute gene expression data, BMC Syst. Biol., 6, 1, 10.1186/1752-0509-6-73 · DOI
  32. Lee, 2014, Gut microbiota-generated metabolites in animal health and disease, Nat. Chem. Biol., 10, 416, 10.1038/nchembio.1535 · DOI · Paper page
  33. Levy, 2015, NetCooperate: a network-based tool for inferring host–microbe and microbe–microbe cooperation, BMC Bioinf., 16, 164, 10.1186/s12859-015-0588-y · DOI
  34. Louis, 2014, The gut microbiota, bacterial metabolites and colorectal cancer, Nat. Rev. Microbiol., 12, 661, 10.1038/nrmicro3344 · DOI
  35. McNulty, 2011, The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins, Sci. Transl. Med., 3, 10.1126/scitranslmed.3002701 · DOI
  36. Methé, 2012, A framework for human microbiome research, Nature, 486, 215, 10.1038/nature11209 · DOI · Paper page
  37. Nell, 2010, The impact of the microbiota on the pathogenesis of IBD: lessons from mouse infection models, Nat. Rev. Microbiol., 8, 564, 10.1038/nrmicro2403 · DOI
  38. Proulx, 2005, Network thinking in ecology and evolution, Trends Ecol. Evol., 20, 345, 10.1016/j.tree.2005.04.004 · DOI
  39. Qin, 2010, A human gut microbial gene catalogue established by metagenomic sequencing, Nature, 464, 59, 10.1038/nature08821 · DOI · Paper page
  40. Reed, 2006, Towards multidimensional genome annotation, Nat. Rev. Genet., 7, 130, 10.1038/nrg1769 · DOI
  41. Samal, 2011, Randomizing genome-scale metabolic networks, PLoS ONE, 6, 10.1371/journal.pone.0022295 · DOI
  42. Sinha, 2015, Collecting fecal samples for microbiome analyses in epidemiology studies, Cancer Epidemiol. Biomark. Prev.
  43. The Human Microbiome Project, C., 2012, Structure, function and diversity of the healthy human microbiome, Nature, 486, 207, 10.1038/nature11234 · DOI · Paper page
  44. Töpfer, 2015, Integration of metabolomics data into metabolic networks, Front Plant Sci., 6, 49
  45. Turnbaugh, 2009, The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice, Sci. Transl. Med., 1, 10.1126/scitranslmed.3000322 · DOI · Paper page
  46. Wang, 2013, Metagenomic sequencing reveals microbiota and its functional potential associated with periodontal disease, Sci. Rep., 3, 1843, 10.1038/srep01843 · DOI
  47. Wang, 2013, Whole-genome DNA/RNA sequencing identifies truncating mutations in RBCK1 in a novel Mendelian disease with neuromuscular and cardiac involvement, Genome Med., 5, 67-67, 10.1186/gm471 · DOI
  48. Yugi, 2014, Reconstruction of insulin signal flow from phosphoproteome and metabolome data, Cell Rep., 8, 1171, 10.1016/j.celrep.2014.07.021 · DOI
  49. Zackular, 2013, The gut microbiome modulates colon tumorigenesis, mBio, 4, 13, 10.1128/mBio.00692-13 · DOI
  50. Zambirinis, 2014, Pancreatic cancer, inflammation and microbiome, Cancer J., 20, 195, 10.1097/PPO.0000000000000045 · DOI
  51. Zelezniak, 2015, Metabolic dependencies drive species co-occurrence in diverse microbial communities, Proc. Natl. Acad. Sci., 112, 6449, 10.1073/pnas.1421834112 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Sung, J., Hale, V., Merkel, A. C., Kim, P.-J., & Chia, N.
Affiliations
  • Asia Pacific Center for Theoretical Physics (KR)
  • Mayo Clinic (US)
  • Pohang University of Science and Technology (KR)
Corresponding author
Chia, Nicholas
ORCID
Venue
Applied & Translational Genomics
Publisher
Elsevier BV
Published
2016-09
Volume
10
Pages
10-15
ISSN
2212-0661 (ISSN-L)
DOI
10.1016/j.atg.2016.02.001
PMC
PMC5025471
OpenAlex
W2269779036
Cited by
35
References
8
Citations by year
2026: 1 · 2024: 1 · 2023: 1 · 2022: 5 · 2021: 4 · 2020: 2 · 2019: 10 · 2018: 3 · 2017: 5 · 2016: 3
FWCI
0.63
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied & Translational Genomics (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • Mayo Clinic
  • National Institutes of Health (R01CA179243)
  • National Research Foundation of Korea (2015R1C1A1A02037045)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
gut microbiota, metabolic modeling, multi-omics integration, metabolic network model, model evaluation, biomarker discovery, microbial ecology, high-throughput omics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review