Machine learning approaches for biomechanical and bioelectrical regulation in developmental tissue engineering

Abstract Developmental tissue engineering is increasingly guided by the principles of morphogenesis, cellular self-organization, and dynamic microenvironmental regulation, moving beyond static scaffold design and towards adaptive, development-inspired strategies. Integrating insights from developmental biology has revealed new structural-functional relationships and more robust tissue maturation pathways, thereby unlocking biofabrication strategies that harness intrinsic biological regulatory mechanisms rather than imposing static architectures on engineered tissue. This review examines machine learning (ML) applications to tissue engineering within a developmental context, emphasizing how bioelectric, biomechanical, and morphogenic cues influence cell fate, tissue organization, and adaptive growth. We highlight how data-driven and physics-based models, surrogate modeling, and generative design can integrate complex biological data, simulate evolving microenvironments, and guide experimental biofabrication. Despite these advances, significant challenges remain, such as the integration of heterogeneous and multiscale biological data, limitations in model interpretability and generalizability, and ethical and regulatory considerations regarding data use and artificial intelligence (AI)-guided decision-making in biofabrication applications. Through the coupling of developmental principles with computational tools, ML-driven tissue engineering is nevertheless well positioned to enable more predictive, adaptive, and reproducible paradigms for creating functional living systems.