Researchers develop ‘digital baby’ to improve infant health care

Researchers from the University of Galway have created a digital baby to better understand key health conditions during the first 180 days of an infant’s life.

The research team created 360 advanced computer models that simulate each baby’s unique metabolic processes. The Digital Baby is the first gender-specific, whole-body computational model representing newborn and infant metabolism with 26 organs, six cell types and more than 80,000 metabolic reactions.

Real data from 10,000 newborns, including gender, birth weight and metabolite concentrations, enabled the creation and validation of personalizable models, allowing scientists to explore individual infant metabolism for applications in precision medicine.

The research was carried out by a team of scientists from the Digital Metabolic Twin Centre, University of Galway and the University of Heidelberg, led by Professor Innes Thiele, Principal Investigator at APC Microbiome Ireland.

The team’s research aims to advance precision medicine using computational modelling, and they say computational models of infants are crucial as they will improve our understanding of infant metabolism and create opportunities to improve the diagnosis and treatment of medical conditions early in an infant’s life, such as inherited metabolic diseases.

Lead author Elaine Saunseder from the University of Heidelberg said: “Babies are not just little adults. They have unique metabolic functions that enable them to grow and develop healthily. For example, babies have a high surface area to mass ratio, so they need a lot of energy to regulate their body temperature, but because they are not able to shiver until six months of age, metabolic processes must keep them warm.”

“A key part of this research work was therefore to identify these metabolic processes and translate them into mathematical concepts that can be applied to computational models. We captured metabolism in an organ-specific way, which provides a unique opportunity to model organ-specific energy demands, which differ significantly between infants and adults.”

“Since nutrition is the fuel for metabolism, we can use breast milk data from real newborns in our model to simulate the relevant metabolism of the baby’s entire body, including various organs. Based on nutrition, we simulated the development of a digital baby over a six-month period and showed that it grows at the same rate as a real infant.”

Professor Thiel, research leader on the project, said: “Newborn screening programmes are essential to detect metabolic diseases early and improve infant survival and health. However, the variability we observe in how these diseases present in infants highlights the urgent need for a personalised approach to disease management.”

“Our model allows researchers to investigate the metabolism of healthy infants as well as infants with inherited metabolic diseases, including those investigated in newborn screening. When simulating the metabolism of diseased infants, we showed that the model can predict known biomarkers of these diseases. Furthermore, the model accurately predicted metabolic responses to different treatment strategies, demonstrating its potential in clinical settings.”

“This study is a first step towards establishing an infant’s digital metabolic twin, which would provide a detailed view of an infant’s metabolic processes. Such a digital twin could revolutionize pediatric healthcare by enabling disease management to be tailored to each infant’s individual metabolic needs,” Saunseder added.

The study was published this week. Cell metabolism.

The research was led by the University of Galway and completed as part of a collaboration with the University of Heidelberg, Germany, the Heidelberg Theoretical Institute and Heidelberg University Hospital.