Understanding metabolism in cells is crucial in biology, but analyzing the vast amount of data on cellular processes to determine metabolic states is challenging. Modern biology generates large datasets on various cellular activities known as “omics” datasets, providing insights into gene activity and protein levels. However, integrating and making sense of these datasets to understand cell metabolism is complex. Kinetic models offer a way to decode this complexity by providing mathematical representations of cellular metabolism, acting as detailed maps that describe how molecules interact and transform within a cell, providing insight into biochemical processes underpinning cellular metabolism.
In a significant advancement in computational biology, a team of researchers led by Ljubisa Miskovic and Vassily Hatzimanikatis at EPFL has developed RENAISSANCE, an AI-based tool that simplifies the creation of kinetic models. By combining various types of cellular data, RENAISSANCE accurately depicts metabolic states, making it easier to understand how cells function. This tool aims to address the challenge of determining the parameters that control cellular processes, making the development of kinetic models more accessible and efficient. RENAISSANCE opens new avenues for research and innovation in health and biotechnology by providing a powerful tool for studying metabolic changes and aiding in the development of new treatments and biotechnologies.
Using RENAISSANCE, the researchers were able 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. These kinetics models were robust, maintaining stability even when subjected to genetic and environmental condition perturbations. This indicates 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 remains a persistent challenge in understanding cellular metabolism. For example, metabolomics and proteomics can only detect and quantify 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. RENAISSANCE addresses this challenge by combining omics data with other relevant information such as extracellular medium content, physicochemical data, and expert knowledge, allowing for the accurate quantification of unknown intracellular metabolic states including metabolic fluxes and metabolite concentrations.
RENAISSANCE’s ability to accurately model cellular metabolism has significant implications, offering a powerful tool for studying metabolic changes induced by disease or other factors and aiding in the development of new 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, fostering collaboration and advancing research in the field. By simplifying the creation of kinetic models and providing a comprehensive understanding of cellular metabolism, RENAISSANCE has the potential to revolutionize how researchers approach metabolism in cells and develop new therapies and biotechnologies.