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Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes

Nature Computational Science volume 1, pages 348–361 (2021)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Type 1 diabetes (T1D) mellitus is a systemic disease triggered by a local autoimmune inflammatory reaction in insulin-producing cells that induce organ-wide, long-term metabolic effects. Mathematical modeling of the whole-body regulatory bihormonal system has helped to identify therapeutic interventions but is limited to a coarse-grained representation of metabolism. To extend the depiction of T1D, we developed a whole-body model of organ-specific regulation and metabolism that highlighted chronic inflammation as a hallmark of the disease, identified processes related to neurodegenerative disorders and suggested calcium channel blockers as adjuvants for diabetes control. In addition, whole-body modeling of a patient population allowed for the assessment of between-individual variability to insulin and suggested that peripheral glucose levels are degenerate biomarkers of the internal metabolic state. Taken together, the organ-resolved, dynamic modeling approach enables modeling and simulation of metabolic disease at greater levels of coverage and precision and the generation of hypothesis from a molecular level up to the population level.

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Fig. 1: Applied constraints from GIM model to WBM.
Fig. 2: Whole-body reaction flux dynamics classify T1D and healthy states.
Fig. 3: Multiscale whole-body model identified disrupted metabolic processes in T1D mellitus.
Fig. 4: Insulin metabolic indirect effects assessed by the probability density estimates of the reaction flux values.
Fig. 5: Interindividual variability in insulin response is reflected in key pathways of metabolism.
Fig. 6: Intraindividual variability is assessed through a sensitivity analysis of the integrated model.

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Data availability

The male whole-body metabolic model10 is available at the Virtual Metabolic Human48 database (http://vmh.life), the glucose–insulin model6 is available at https://github.com/Open-Systems-Pharmacology/Glucose-Insulin-Model. Source data for Figs. 2–6 are provided with this paper and in Zenodo64.

Code availability

All related code is available at https://github.com/ThieleLab/dwbm and in Zenodo64.

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Acknowledgements

We acknowledge Bayer Technology Services for providing an academic version of the PK-SIM/MOBI software suite and the source file for the GIM model, K. Yugi from the University of Tokyo for providing the insulin model, and the Molecular Systems Physiology lab members at the University of Luxembourg for reviewing the manuscript and valuable discussions. Vector elements for Figs. 1, 4 and 6 were taken from vecteezy.com and freepik.com. This study was funded by Luxembourg National Research Fund (FNR) through the ATTRACT programme grant (FNR/A12/01, awarded to I.T.) and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 757922, awarded to I.T.). The funders had no role in study design, data collection, data analysis, or interpretation.

Author information

Authors and Affiliations

  1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Marouen Ben Guebila

  2. School of Medicine, National University of Ireland, Galway, Ireland

    Ines Thiele

  3. Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Galway, Ireland

    Ines Thiele

  4. APC Microbiome, Cork, Ireland

    Ines Thiele

  5. Ryan Institute, National University of Ireland, Galway, Ireland

    Ines Thiele

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  1. Marouen Ben Guebila
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  2. Ines Thiele
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Contributions

M.B.G. and I.T. conceived the study. M.B.G. developed the framework and carried out the simulations and analysis. M.B.G. and I.T. wrote and edited the manuscript.

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Correspondence to Ines Thiele.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Thomas Eissing, Laurence Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Methods, figures and tables.

Source data

Source Data Fig. 2

GIM simulation results from 0 to 600 min at 5 min intervals for 14 glucose and insulin challenges.

Source Data Fig. 3

Minimum and maximum bounds of healthy and T1D whole-body model with irreversible reactions.

Source Data Fig. 4

FVA solutions for the T1D model.

Source Data Fig. 5

Simulation results of 30 patients + 1 average patient for 600 min at 5 min time step.

Source Data Fig. 6

CRONICS simulation results for 30 patients from 16 min after insulin injection to 600 min at a time step of 2.5 min.

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Ben Guebila, M., Thiele, I. Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes. Nat Comput Sci 1, 348–361 (2021). https://doi.org/10.1038/s43588-021-00074-3

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