We would like to invite you to take a look at the just-released special collection of the PLoS Computational Biology titled: "Predictive modeling in biology and medicine: Digital twins and multi-scale modeling" and co-edited by Amber Smith, Reinhard Laubenbacher, Roeland M.H. Merks and Mark Alber:

https://collections.plos.org/collection/predictive-modelling-biology-and-medicine/

The first article in the collection is the Perspective/Introduction to the collection:

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014349

Over the last 10 years, it has been shown that multi-scale modeling approaches combined with machine learning provide a powerful method for developing robust predictive models for studying many biomedical processes and exploring massive data sets. Recently, the concept of digital twins has gained increasing attention in the biology, biomedicine, and healthcare communities. This collection explores the construction of digital twins and multi-scale models as they relate to predictive biology and medicine. It also provides novel tools for   next generation of patient-centered care.

The concept of digital twins originated in work at NASA and is broadly used in industry and other fields, such as city planning. In the biological context, 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 and 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.