Data-driven biology has become essential in decoding the complexities of regenerative systems. Bioinformatics and computational modeling are accelerating discoveries by integrating genomic, proteomic, and metabolomic datasets to uncover patterns that guide tissue repair, scaffold design, and cell differentiation. Through simulations and predictive modeling, researchers can anticipate cellular responses, optimize scaffold geometry, and personalize therapeutic regimens. These tools also help in reconstructing tissue microenvironments, mapping cell signaling pathways, and identifying gene regulatory networks critical to regeneration. Bioinformatics and computational modeling empower the development of digital twins for patients, supporting virtual testing of treatment strategies and minimizing trial-and-error in lab settings. As machine learning algorithms grow more sophisticated, their synergy with computational biology is opening new frontiers in precision tissue engineering. From predicting graft rejection to identifying ideal stem cell candidates, these technologies are central to the next generation of regenerative interventions.
Title : Eliminating implants infections with nanomedicine: Human results
Thomas J Webster, Interstellar Therapeutics, United States
Title : Graphene, butterfly structures, and stem cells: A revolution in surgical implants
Alexander Seifalian, Nanotechnology & Regenerative Medicine Commercialisation Centre, London NW1 0NH, United Kingdom
Title : Biodistribution and gene targeting in regenerative medicine
Nagy Habib, Imperial College London, United Kingdom
Title : Precision in cartilage repair: Breakthroughs in biofabrication process optimization
Pedro Morouco, Polytechnic of Leiria, Portugal
Title : AI-integrated high-throughput tissue-chip for brain aging
Kunal Mitra, Florida Tech, United States
Title : Assembly and stability of on-chip microvasculature
Kara E McCloskey, University of California, Merced, United States