A digital twin is a computer-based model that is kept connected to what is being modeled. A digital twin of a biological system, whether a specific animal or patient, a specific corn field, or the population of a village, is a computational model of some aspect of the system that it is calibrated dynamically to evolve together with the system, linking the physical and the digital twin. The digital twin can then be used to identify interventions or features of interest of the physical twin. This approach to “personalized” biology has tremendous potential for biotechnology, ecology, and healthcare in the future, to name just a few application areas.

This special collection in PLOS Computational Biology contains papers submitted by the plenary and invited speakers at the conference Predictive Modeling in Biology and Medicine (with several additional contributions by other authors). The conference was organized by the Interdisciplinary Center for Data-driven Modeling in Biology (ICDMB) and held at University of California, Riverside, from November 17^th to 19^th, 2023. It was partially supported by the National Science Foundation (NSF DMS 2331170) as well as by UCR's Department of Mathematics, CNAS and Office of Research, Innovation, and Economic  Development. The collected papers explore the construction of digital twins as they relate to predictive biology and medicine, providing novel tools for next generation of patient-centered care.

The conference focused on the progress in multi-scale mathematical and computational modeling of biological systems, on translational model predictions in biology and medicine, and on data-driven and machine learning methodologies in biology and medicine. The conference brought together researchers at different stages of their career to exchange ideas and novel approaches, as well as to promote interdisciplinary collaborations.