Human milk (HM) is a “live tissue”: its composition changes over time including diurnal fluctuations, answering the evolving needs of the infant and programming infants' “biological clock” and physiology. Despite its extraordinary importance, little is known about the chronobiology and system biology of HM, the maternal factors that impact this biology, and the mechanisms that underlie its contribution to normal infant development. It is also unknown how infant input (via suckling) influences HM biology, a crucial question given the almost universal practice of feeding expressed (ie: pumped) HM. As such, our over-arching hypothesis is that HM is an ecological system that harbors distinct diurnal and longitudinal variation that is impacted by both maternal fixed and modifiable factors, and by infant input, resulting in different HM biological dynamics depending on if the infant is primarily fed at-the-breast vs expressed HM. Our interdisciplinary team of HM researchers and computational biologists will collect daily longitudinal and diurnal HM samples, and extensive metadata from 120 mothers exclusively feeding HM to their healthy infants between 1-4 months postpartum: 60 mothers primarily feeding ATB (ATB group) and 60 mothers exclusively expressing HM (Express group). Multi-omics analytical platforms will be used to characterize HM hormones (insulin, leptin, cortisol), macronutrients, microbiota, oligosaccharides, cytokines, and immunoglobulins to study HM as an ecological system. This novel high- resolution sampling combined with cutting-edge analytical and modeling techniques power these aims: 1) Detect temporal changes in HM composition diurnally and longitudinally and compare these trajectories between ATB vs express groups. Time-series models and state-of-the-art analytic approaches will be utilized to infer latent HM dynamics and identify temporal trajectories, for the first time, identifying longitudinal and 24-hour temporal patterns in HM composition. We will then determine how these trajectories differ when infant “biofeedback” into the HM system is lacking by comparing trajectories between ATB vs Express Groups. 2) Identify maternal determinants of HM composition and temporal patterns. In addition to sociodemographic and health factors, mobile phone apps and wearable devices will be utilized to capture granular data on HM expression patterns, maternal diet, stress, physical activity and sleep patterns. These will be linked with “clades” of HM components that co-vary together over time via Bayesian supervised machine learning algorithms. The algorithm will distinguish if relationships between maternal factors and HM composition and dynamics differs between ATB vs Express groups. This work will elucidate mechanisms underlying HM contribution to infant health and development, allowing for optimized feeding practices for infants fed ATB or expressed HM, and premature infants dependent on expressed HM. This study also represents...