- Alessio Alexiadis: School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom. Electronic address: a.alexiadis@bham.ac.uk.
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.
METHOD: Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorithm) are combined to achieve an in-silico model with the ability of self-learning and replicating feedback loops occurring in human physiology. Computational particles, used in Discrete Multiphysics to model biological systems, are associated to (computational) neurons: Reinforcement Learning trains these neurons to behave like they would in real biological systems.
RESULTS: As benchmark/validation, we use the case of peristalsis in the oesophagus. Results show that the in-silico model effectively learns by itself how to propel the bolus in the oesophagus.
CONCLUSIONS: The combination of first principles modelling (e.g. multiphysics) and machine learning (e.g. Reinforcement Learning) represents a new powerful tool for in-silico modelling of human physiology. Biological feedback loops occurring, for instance, in peristaltic or metachronal motion, which until now could not be accounted for in in-silico models, can be tackled by the proposed technique.