Abstract
This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data-driven techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection.
Highlights
This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine
The artificial pancreas is an example of a medical digital twin, in analogy to a common strategy in industry
The global model state is the repository for all data describing the state of the simulated model at a given point in time, including any information about the underlying physical structure, if included, and variable states of all computational models in the modules
Summary
This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. Design patterns common to “traditional” model implementations impair the development of integrative digital twins Some of these patterns include the following: 1) lack of transparency in the implementation of computational models, 2) intertwined component models and simulation processes dependent on each other, 3) use of incompatible data structures and computer languages, 4) brittle architectures that do not accommodate extensions of a model, and 5) software environments that do not support distributed collaboration. Solutions to these challenges are still largely lacking, in biomedicine (9). We demonstrate this design approach and its advantages by applying it to the published model in ref. 10 of the early immune response to a respiratory infection by the fungus Aspergillus fumigatus, Significance
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