There is much interest from the bioengineering healthcare community worldwide in the concept of a whole-body "digital twin" computational model that can be personalised and linked with data from clinical imaging and hospital-based functional measurements, as well as from a variety of wearable, implantable or home-based devices. To be effective this is going to require a new much more comprehensive and integrative approach to computational physiology. Models of subcellular biology that can take advantage of tissue biomarkers (blood, urine, etc) must be linked with measurements of genotype and included in models of higher-level physiological function at the tissue and organ scale. Surrogate (via machine learning) organ models must be included in organ systems and integrated into whole-body models that include autonomic neural and endocrine control. This talk will discuss the mathematical framework for an algorithmic energy-based approach to multiscale computational physiology that lends itself to crowd-sourcing the international effort needed to tackle the demanding requirements of a "digital" or "virtual" twin for healthcare.
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