New model decodes relationship between metabolic mechanisms and genes

The field of life sciences has witnessed a growing association with information technology in the past 20 years.

Image Credit: MIKHAIL GRACHIKOV / Shutterstock.com

A major driver behind this association is the requirement to process and incorporate huge volumes of data from various fields, such as biochemistry, physiology, cell and molecular biology, and genetics, to gain a better insight into biological processes, systems, as well as whole organisms.

The issue is that compiling data from various interconnected biological networks across different strata of biological analysis (for example, biochemical versus genetic) has been found to be highly challenging.

The complexity and sheer volume of data across various fields is hard to standardize and process, and have partially induced the proliferation of different fields of “omics” (for example, metabolomics, genomics, proteomics, transcriptomics, etc.), the aim of which is to define and measure pools of biological molecules in a way that associates with their function and structure in an organism.

By developing genome-scale metabolic models, or GEMs, the researchers have effectively addressed the problem in the context of genes and metabolism analysis. These are computer models made from biochemical and genetic data, and link genes with the cell’s metabolic pathways.

GEMs are quickly becoming a standard tool for scientists.

They are powerful tools for integrating experimental data for a specific physiology and building context-specific models that can identify changes in the metabolism of diseased cells, such as cancer cells.”

Maria Masid, PhD Student, Lab of Vassily Hatzimanikatis, EPFL

Working to further streamline the GEMs, Masid with her collaborators have currently published an article in the Nature Communications journal that introduces a unique mathematical method to study human metabolism by decreasing the complexities of the human genome-scale GEMs by simply targeting specific parts of metabolism, while reducing the data loss from the other metabolic pathways.

The analysis of cell metabolism is highly applicable because metabolic changes have been identified as a sign of various human diseases, such as Alzheimer’s disease, cardiovascular diseases, cancer, diabetes, and obesity. Thus, understanding the links between genes and metabolic mechanisms can direct the discovery of novel drugs as well as the development of advanced therapies.

The scientists called their technique redHUMAN, and have explained it as “a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology.”

The redHUMAN technique produces reduced-size metabolic models that include the target pathways as well as the metabolic routes needed to examine biomass synthesis and nutrient metabolism, all this considering the cell’s bioenergetics.

By doing so, the new redHUMAN model ensures the reliability of its predictions, resolving a great barrier of the present GEMs.

By combining these metabolic models with gene-expression data, we can identify functional changes that cannot be extracted directly from the data; we can also formulate hypotheses to guide experimental design.”

Maria Masid, PhD Student, Lab of Vassily Hatzimanikatis, EPFL