Scientists have created an AI tool that produces intricate models of cellular metabolism, facilitating a better understanding of cellular operations.
Grasping how cells break down nutrients and generate energy, known as metabolism, is crucial in the field of biology. However, the task of analyzing the extensive data regarding cellular functions to uncover metabolic states is quite complicated.
Contemporary biology generates substantial datasets relating to various cellular functions, sometimes referred to as “omics” datasets. These datasets shed light on different aspects of cell function, like gene expression and protein levels. Yet, the integration and interpretation of these datasets to comprehend metabolic processes is difficult.
Kinetic models serve as a solution to this complexity by providing mathematical frameworks that illustrate cellular metabolism. They function like detailed maps explaining how molecules interact and transform within a cell, showing how substances are converted into energy and other products over time. This aids scientists in uncovering the biochemical mechanisms that support cellular metabolism. Nevertheless, creating kinetic models proves to be a formidable challenge because of the difficulty in identifying the parameters that govern cellular processes.
A research team led by Ljubisa Miskovic and Vassily Hatzimanikatis at EPFL has developed RENAISSANCE, an AI-driven tool that streamlines the process of crafting kinetic models. RENAISSANCE integrates diverse types of cellular data to accurately portray metabolic states, simplifying the understanding of cellular functions. This tool represents a significant breakthrough in computational biology, paving the way for innovative research and developments in health and biotechnology.
The researchers utilized RENAISSANCE to construct kinetic models that precisely represented the metabolic activities of Escherichia coli. The tool successfully created models that aligned with observed metabolic behaviors from experiments, simulating how the bacteria would modify their metabolism over time in a bioreactor.
Additionally, the kinetic models demonstrated robustness, retaining stability even when faced with changes in genetic and environmental conditions. This suggests that the models can reliably forecast the cellular reactions to various scenarios, enhancing their practical application for research and in industrial settings.
Miskovic states, “Despite the improvements in omics methodologies, inadequate data coverage continues to be a significant hurdle. For example, metabolomics and proteomics can only identify and measure a limited range of metabolites and proteins. Modeling approaches that incorporate and harmonize omics data from multiple sources can counteract this limitation and improve system comprehension. By merging omics data with other relevant insights, such as extracellular medium composition, physicochemical properties, and expert knowledge, RENAISSANCE enables us to accurately assess unknown intracellular metabolic states, including metabolic fluxes and metabolite levels.”
The capacity of RENAISSANCE to accurately model cellular metabolism has substantial implications, serving as a powerful resource for investigating metabolic alterations, whether caused by diseases or other factors, and supporting the creation of new treatments and biotechnologies. Its user-friendly nature and efficiency will allow a wider range of researchers in both academia and industry to effectively apply kinetic models and promote collaboration.
