A Novel Network Approach to Identify Sample-Specific Context-Informed Metabolic Signatures During Developmental Processes.

Metabolism plays an essential role in cellular processes: development, growth, differentiation, and determination of cell identity. Understanding how metabolic processes dynamically change across cell types, stages, and environmental conditions is crucial for studying developmental biology, aging, and disease progression. Genomewide metabolic models (GEMs) are a powerful network-based tool for studying these processes by integrating omics data to model context-specific metabolism. However, current approaches, such as Flux Balance Analysis (FBA), have limitations in addressing the dynamic nature of metabolism across developmental stages at a sample-specific resolution. To address this, we introduce a novel network-based method for analyzing cell and stage specific metabolic flow using directed and weighted metabolic networks that account for sample-specific transcriptomic data. We apply this method to study ovarian follicle development, providing a deeper understanding of intracellular metabolic processes, identifying key metabolites, enzymes, and potential markers for follicular maturation, important for IVF. By incorporating biologically meaningful data, this approach bridges the gap between theoretical metabolic network models (GEMs) and experimental observations, offering a systems-level view of metabolic dynamics in developmental and understudied contexts.