Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Figure 1.. Synergy of mechanistic and statistical (data-driven) models. Reproduced from Corral-Acero et al. [12] under the Creative Commons Attribution License 4.0.

Figure 2.. Hierarchy of multi-scale cardiac electrophysiology models ranging from ion channels (A) via integrated cell (B) and tissue level models (C) to the body surface and electrocardiogram (D). The simulation system allows one to investigate what-if scenarios by changing the input parameters of the model (top row) and analysing the effect on simulation outputs on numerous scales (bottom row) in comparison with wet lab and clinical data. Adapted with permission from [20].

Figure 3.. Digital twin workflow. A baseline model builds the basis for the digital twin. Often, it is a bottom-up mechanistic model, informed by biophysical first principles and population-level knowledge. Anatomical and functional personalization are performed based on individual patient measurements (e.g. computed tomography or magnetic resonance imaging for anatomical twinning and ECG for functional twinning). The parametrized digital twin can then be used in computational simulations to make predictions regarding personal risk or support decisions regarding optimal therapy for personalized medicine. The digital twin should be updated continuously when new measurements are available and refined by comparing predictions with real-world outcomes. Adapted with permission from [25].

Figure 4.. Computational M&S approaches. Representing an individual as well as possible with a computational digital twin model requires both anatomical and functional personalization (A). Once a digital twin is established, different interventions can be evaluated in silico, for example to support precision medicine (B). If anatomical and functional envelopes are continuous and limited to biologically relevant ranges, arbitrary numbers of new virtual subjects (digital chimeras) can be sampled from this space, representative of a population but not of a specific individual in this population (C). With a whole cohort of digital twins, digital chimeras, or a mix thereof in silico clinical trials can be conducted, evaluating the effects of specific interventions consistently applied to the entire virtual study population (D). Figure inspired by Zeike A. Taylor, University of Leeds.