Researchers at EPFL have developed an innovative AI-based tool that streamlines the creation of kinetic models, revolutionizing the way we understand cellular functions. This tool maps metabolic states by integrating various types of biological data, making it significantly easier to decipher the complex processes that govern cell behavior.
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Understanding how cells process nutrients and produce energy—collectively known as metabolism—is a fundamental aspect of biology. However, analyzing the vast amounts of data generated by modern biological research to determine metabolic states presents a significant challenge.
Contemporary biology produces large "omics" datasets that capture various aspects of cellular activity, such as gene expression and protein levels. While these datasets offer valuable insights into cellular functions, integrating them to gain a comprehensive understanding of cell metabolism is complex.
Kinetic models provide a powerful solution to this complexity by offering mathematical representations of cellular metabolism. These models serve as detailed maps, illustrating how molecules interact and transform within a cell, ultimately showing how substances are converted into energy and other products over time. Such models are crucial for deciphering the biochemical processes that drive cellular metabolism. However, developing kinetic models is challenging, primarily due to the difficulty in determining the parameters that govern these cellular processes.
A research team led by Ljubisa Miskovic and Vassily Hatzimanikatis at EPFL has developed RENAISSANCE, an AI-based tool designed to simplify the creation of kinetic models. RENAISSANCE integrates various types of cellular data to accurately represent metabolic states, thereby making it easier to understand cellular functions. This tool marks a significant advancement in computational biology, paving the way for new research and innovation in health and biotechnology.
The team successfully applied RENAISSANCE to create kinetic models that accurately reflected the metabolic behavior of Escherichia coli. The tool generated models that closely matched experimentally observed metabolic patterns, simulating how the bacteria would adjust their metabolism over time in a bioreactor.
Moreover, these kinetic models demonstrated robustness, maintaining stability even when faced with genetic modifications and changes in environmental conditions. This robustness suggests that the models can reliably predict cellular responses to various scenarios, enhancing their utility 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.”
RENAISSANCE's capability to accurately model cellular metabolism holds profound implications for both research and industry. This powerful tool facilitates the study of metabolic changes, whether those changes are naturally occurring or disease-induced, and it plays a crucial role in the development of new treatments and biotechnologies.
Its user-friendly design and efficiency make it accessible to a broader range of researchers, enabling effective use of kinetic models across various fields. Moreover, RENAISSANCE's versatility is expected to foster greater collaboration between academia and industry.
Journal Reference:
Choudhury, S., et al. (2024) Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states. Nature Catalysis. doi.org/10.1038/s41929-024-01220-6.