Understanding how cells process nutrients and produce energy — collectively often known as metabolism — is important in biology. Nonetheless, analyzing the vast amounts of knowledge on cellular processes to find out metabolic states is a posh task.
Modern biology generates large datasets on various cellular activities. These “omics” datasets provide insights into different cellular functions, equivalent to gene activity and protein levels. Nonetheless, integrating and making sense of those datasets to grasp cell metabolism is difficult.
Kinetic models offer a method to decode this complexity by providing mathematical representations of cellular metabolism. They act as detailed maps that describe how molecules interact and transform inside a cell, depicting how substances are converted into energy and other products over time. This helps scientists understand the biochemical processes underpinning cellular metabolism. Despite their potential, developing kinetic models is difficult because of the problem in determining the parameters that control cellular processes.
A team of researchers led by Ljubisa Miskovic and Vassily Hatzimanikatis at EPFL has now created RENAISSANCE, an AI-based tool that simplifies the creation of kinetic models. RENAISSANCE combines various kinds of cellular data to accurately depict metabolic states, making it easier to grasp how cells function. RENAISSANCE stands out as a significant advancement in computational biology, opening recent avenues for research and innovation in health and biotechnology.
The researchers used RENAISSANCE to create kinetic models that accurately reflected Escherichia coli’s metabolic behavior. The tool successfully generated models that matched experimentally observed metabolic behaviors, simulating how the bacteria would adjust their metabolism over time in a bioreactor.
The kinetics models also proved to be robust, maintaining stability even when subjected to genetic and environmental condition perturbations. This means that the models can reliably predict the cellular response to different scenarios, enhancing their practical utility in research and industrial applications.
“Despite advancements in omics techniques, inadequate data coverage stays a persistent challenge,” says Miskovic. “As an example, metabolomics and proteomics can detect and quantify only a limited variety of metabolites and proteins. Modeling techniques that integrate and reconcile omics data from various sources can compensate for this limitation and enhance systems understanding. By combining omics data and other relevant information, equivalent to extracellular medium content, physicochemical data, and expert knowledge, RENAISSANCE allows us to accurately quantify unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations.”
RENAISSANCE’s ability to accurately model cellular metabolism has significant implications, offering a strong tool for studying metabolic changes whether or not they are induced by disease or not, and aiding in the event of recent treatments and biotechnologies. Its ease of use and efficiency will enable a broader range of researchers in academia and industry to utilize kinetic models effectively and can foster collaboration.
