Researchers at Chalmers University of Technology have developed a human metabolic model, Human1, which enables integrative analysis of human biological data and simulation of metabolite flow through the reaction network. The model can be used to predict metabolic behaviour in cells, which can help researchers identify novel metabolic markers or drug targets for many diseases, such as cancer, type 2 diabetes, and Alzheimer’s disease.
“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, about the model that was recently published in in Science Signaling.
Metabolism is the network of chemical reactions providing cells with the building blocks and energy necessary to sustain life. Studying the individual components of human metabolism and how they function as part of a connected system is therefore critical to improving health and treating disease. To study such a complex system, computational tools such as genome-scale metabolic models have been developed.
Human1 − highest quality genome-scale model
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 primary aim of this framework is to ensure transparency and reproducibility,” explains co-author Jonathan Robinson, Researcher in the Computational Systems Biology Infrastructure at the Department of Biology and Biological Engineering, “and to provide a system through which others in the modelling community can contribute and collaborate in real time.”
In the study, the researchers integrated Human1 with gene expression data from hundreds of different tumour and healthy tissue cell types. 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.
“An advancement in the area of human metabolic modelling”
A major limitation for human metabolic models has been the difficulty in simulating realistic reaction rates due to the infeasibility of obtaining the necessary measurements. However, the authors demonstrated that applying an enzyme-limitation framework to Human1 enabled the prediction of realistic growth and metabolite exchange rates without requiring these difficult measurements.
“This is a considerable advancement in the area of human metabolic modelling,” says Jens Nielsen.
“The framework now unlocks many powerful approaches that have typically only been feasible for studying microbes and it will enable a wide use of the model for studying metabolic diseases.”
Metabolic Atlas provides maps for metabolic pathways
In parallel with Human1, the researchers developed Metabolic Atlas, an online resource to explore and visualise the model. The website provides 2D and 3D maps for different cellular compartments and metabolic pathways, and links content to other biochemical databases.
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.
Read the article in Science Signaling
CHALMERS UNIVERSITY OF TECHNOLOGY