Human1 is the newest, most advanced, and highest quality genome-scale model for human metabolism (stock photo/Colourbox)

New metabolic model predicts features of human diseases

Wednesday 22 Apr 20

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Bernhard Palsson
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DTU Biosustain
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About Human1

Human1 is the newest, most advanced, and highest quality genome-scale model for human metabolism. The model consolidates decades of biochemical and modelling research into a high-quality resource with over 13,000 biochemical reactions, 4,100 metabolites, and 3,500 genes comprising human metabolism. Unlike previous human models, Human1, was developed entirely in a public online repository that tracks all changes to the model. 

The project was led by Professor Jens Nielsen with a group of researchers in the Department of Biology and Biological Engineering at Chalmers, in collaboration with the Human Protein Atlas (HPA) and National Bioinformatics Infrastructure Sweden (NBIS). The work was funded by the Knut and Alice Wallenberg Foundation.

By linking thousands of cellular tell-tale molecules, the metabolic model Human1 can assist researchers in developing diagnostic tools and drugs against diseases such as obesity, various cancers, type 2 diabetes and Alzheimer’s.

How do you predict what happens inside a cell that turns malignant when thousands of biological reactions may affect the cell’s behaviour? This compares to looking at a highway in rush hour trying to predict what will happen if roadblocks are set up in certain places. To make such predictions, you need extensive knowledge about (cell) behaviour and massive computational power and knowhow.

 

This is exactly what the Human1 genome-scale metabolic model is all about: Predicting cellular metabolic behaviour using computational modelling. A study about this modelling work and its results was recently published in Science Signaling

 

“Reconstruction of human metabolic networks has been on-going since 2007. They have grown in scope and capabilities over time. Human1 sets a new standard on both fronts and should expand the range of the uses of reconstruction for metabolic research in health and disease,” says co-author Professor Bernhard Palsson, CEO at The Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain) at Technical University of Denmark.

Metabolism is all chemical reactions necessary to maintain and sustain life. Other metabolic models have previously been developed, but many faced constrains regarding reproducibility and uncertainties within predictions.

“The primary aim of this framework is to ensure transparency and reproducibility and to provide a system through which others in the modelling community can contribute and collaborate in real time,says first-author Jonathan Robinson, Researcher in the Computational Systems Biology Infrastructure at the Department of Biology and Biological Engineering in a press release from Chalmers University of Technology.

In order to predict what will happen inside a sick cell, the model compares metabolism inside healthy and malignant tissues and cells. For instance, the model can compare cancer metabolism to healthy metabolism even when normal tissue was not collected from the patient along with cancer tissue. Also, the model allows for comparison of metabolism of acute myeloid leukemia to that of healthy blood. In total, the scientists constructed 53 healthy tissue metabolic models and 33 cancer metabolic models from tissue models including brain, liver, kidney, stomach and colon.

 

“Human1 will transform the way in which scientists develop and apply models to study human health and disease”, says Project Leader Jens Nielsen, Professor in Systems and Synthetic Biology at the Department of Biology and Biological Engineering at Chalmers University of Technology, Sweden. Jens Nielsen is also CEO of BioInnovation Institute and Scientific Director at DTU Biosustain.

 

The integration revealed metabolic differences of clinical relevance, such as potential drug targets for cancers of the liver and blood. Furthermore, Human1 was demonstrated to predict the effect of gene disruptions with substantially greater accuracy than previous human models.

Press release from Chalmers University of Technology

 

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24 NOVEMBER 2020