New AI Tool Deciphers Cellular Metabolism

Biology depends on humans' ability to comprehend metabolism, the process by which cells use resources and create energy. To ascertain metabolic states, however, requires a complicated analysis of the enormous volumes of data on biological processes.

Modern biology produces large databases of various cellular activities. These "omics" datasets reveal various biological processes, including gene activity and protein levels. Integrating and interpreting these datasets can make it difficult to comprehend cell metabolism.

Kinetic models provide mathematical representations of cellular metabolism, which help to unravel this complexity. They serve as in-depth schematics that illustrate the interactions and transformations that take place between molecules inside a cell, showing the gradual transformation of raw materials into products like energy.

This advances knowledge of the molecular mechanisms behind cellular metabolism. The difficulty in identifying the factors that govern biological activities makes the development of kinetic models difficult, notwithstanding their potential.

RENAISSANCE is an AI-based tool that makes the process of creating kinetic models easier. It was developed by a team of researchers at EPFL, led by Ljubisa Miskovic and Vassily Hatzimanikatis.

Through the correct depiction of metabolic states, RENAISSANCE makes it easier to comprehend how cells function by combining multiple forms of biological data. Among the significant advances in computational biology, RENAISSANCE stands out as paving the way for fresh approaches to biotechnology and health research.

Utilizing RENAISSANCE, the scientists produced kinetic models that faithfully captured the metabolic processes of Escherichia coli. The program accurately represented metabolic characteristics shown in experiments, mimicking the way bacteria would gradually modify their metabolism in a bioreactor.

The kinetics models demonstrated robustness, maintaining stability despite genetic and environmental perturbations. This suggests that the models can reliably predict cellular responses in various scenarios, increasing their practical value in both research and industrial applications.

Despite advancements in omics techniques, inadequate data coverage remains a persistent challenge. For instance, metabolomics and proteomics can detect and quantify only a limited number of metabolites and proteins. Modeling techniques that integrate and reconcile omics data from various sources can compensate for this limitation and enhance systems understanding.”

Ljubisa Miskovic, Swiss Federal Institute of Technology Lausanne

Miskovic said, “By combining omics data and other relevant information, such as extracellular medium content, physicochemical data, and expert knowledge, RENAISSANCE allows us to accurately quantify unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations.”

The accuracy with which RENAISSANCE can model cellular metabolism has important applications; it provides a valuable tool for researching metabolic alterations that arise from disease or not and supports the creation of novel therapeutics and biotechnologies. Its efficiency and convenience of use will make kinetic models more accessible to a wider spectrum of academic and industrial researchers, promoting cooperation.