Researchers at the University of Galway have developed digital babies to gain a better understanding of infants’ health during the critical first 180 days of life. This involved creating 360 advanced computer models that simulate the metabolic processes of each baby, with 26 organs, six cell types, and over 80,000 metabolic reactions. Real-life data from 10,000 newborns was used to personalize and validate the models, allowing for the investigation of individual infant metabolism for precision medicine applications. This groundbreaking work was carried out by a team of scientists from the University of Galway’s Digital Metabolic Twin Centre and Heidelberg University, led by Professor Ines Thiele from APC Microbiome Ireland.
Lead author Elaine Zaunseder from Heidelberg University explained that babies have unique metabolic features that necessitate a specific focus on their energy demands for growth and development. The research team identified these processes and translated them into mathematical concepts for the computational model. By capturing metabolism in an organ-specific manner, the model can simulate the diverse energy requirements in infants compared to adults. Using real breast milk data, the researchers were able to simulate the metabolism throughout a baby’s body, including various organs, over six months. The results showed that the digital babies would grow at the same rate as real-world infants based on their nutrition.
Professor Ines Thiele emphasized the importance of newborn screening programs in detecting metabolic diseases early on to improve infant survival rates and health outcomes. The variability in how these diseases present in babies highlights the need for personalized approaches to disease management. The computational models developed by the research team allow for the investigation of healthy infants’ metabolism as well as those with inherited metabolic diseases detected through newborn screening. These models can predict biomarkers for these diseases and assess metabolic responses to different treatment strategies, demonstrating their potential in clinical settings.
Elaine Zaunseder noted that this work represents a crucial first step in establishing digital metabolic twins for infants, providing a detailed insight into their metabolic processes. These digital twins have the potential to transform pediatric healthcare by enabling tailored disease management based on each infant’s unique metabolic needs. The computational modeling of infant metabolism is described as groundbreaking and seminal, enhancing the understanding of infant metabolism and opening up possibilities to improve the diagnosis and treatment of medical conditions during the early stages of a baby’s life, such as inherited metabolic diseases.
The team’s research focuses on advancing precision medicine through computational modeling, with the goal of improving disease management for infants with metabolic disorders. The personalized nature of the models allows for a detailed investigation of individual infant metabolism, offering insights into disease development and potential treatment strategies. By simulating the metabolism of infants with various diseases, the models can predict biomarkers and assess metabolic responses to different interventions. The development of digital metabolic twins for infants represents a significant advancement in understanding infant metabolism and holds promise for improving pediatric healthcare through personalized disease management strategies tailored to each infant’s unique metabolic profile.
