This helps scientists understand the biochemical processes underpinning cellular metabolism.
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30 Aug, 2024
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Understanding how cells process nutrients and produce energy, known as metabolism, is crucial in the field of biology. However, analyzing the vast amounts of data on cellular processes to determine metabolic states is an incredibly intricate task.
The field of modern biology is constantly generating large datasets on various cellular activities. These “omics” datasets offer invaluable insights into different cellular functions, including gene activity and protein levels. Yet, the challenge lies in effectively integrating and deciphering these complex datasets to gain a comprehensive understanding of cell metabolism.
Kinetic models are vital for unraveling the complexity of cellular metabolism. They provide intricate mathematical representations of molecular interactions and transformations within cells, offering valuable insights into how substances are converted into energy and other products over time. However, the creation of kinetic models has posed considerable challenges due to the complexities of determining the parameters governing cellular processes.
However, a groundbreaking development has emerged from the research efforts led by Ljubisa Miskovic and Vassily Hatzimanikatis at EPFL. They have pioneered RENAISSANCE, an AI-based tool that simplifies the creation of kinetic models like never before. By integrating diverse cellular data, RENAISSANCE accurately portrays metabolic states, facilitating a deeper understanding of cellular function. This revolutionary advancement in computational biology holds immense promise, heralding new frontiers in health and biotechnology research and innovation.
The researchers leveraged the power of RENAISSANCE to craft kinetic models that perfectly mirrored Escherichia coli’s metabolic behavior. The tool adeptly generated models that precisely replicated observed metabolic behaviors, offering a window into how the bacteria would dynamically adapt their metabolism within a bioreactor.
In addition, these kinetic models exhibited remarkable resilience, maintaining stability in the face of genetic and environmental disruptions. This resilience signifies the models’ capacity to reliably forecast cellular responses to diverse scenarios, thereby significantly amplifying their practical value in both research and industrial settings.
“Despite advancements in omics techniques, inadequate data coverage remains a persistent challenge,” says Miskovic. “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. 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 transformative capabilities of RENAISSANCE in accurately modeling cellular metabolism hold immense promise, serving as a potent tool for investigating metabolic shifts induced by both disease and other factors. Its user-friendly interface and efficiency will democratize the utilization of kinetic models, empowering a wider spectrum of researchers in academia and industry to drive impactful collaborations and spearhead advancements in treatments and biotechnologies.
Journal reference:
- Subham Choudhury, Bharath Narayanan, Michael Moret, Vassily Hatzimanikatis & Ljubisa Miskovic. Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states. Nature Catalysis, 2024; DOI: 10.1038/s41929-024-01220-6
