Self-Adaptive Simulation Models: A Case Study in Cell Biology
Abstract
Artificial intelligence is transforming science into a highly automated process, accelerating the pace of scientific discovery. We argue that automatically adapting simulation models to new data, knowledge about mechanisms, or research questions plays a key role in this change of paradigm. To this end, we explore the use of the MAPE-K (Monitor, Analyze, Plan, and Execute, over a shared Knowledge base) framework from self-adaptive software systems to realize a self-adaptive simulation model. Such a model will preserve its currency, reveal new insights, and provide enhanced predictions as well as effective feedback to the observed system. As a case study, we use a cell-biological model of glucose-stimulated insulin secretion. We conclude with a discussion relating our approach to digital twins and current developments in inferring simulation models from data that opens up questions to be pursued in future research.
2025
Pia Wilsdorf, Philipp Henning, Justin N. Kreikemeyer, Marcel Kliefoth, Simone Baltrusch, Adelinde M. Uhrmacher. In 29th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), September 17-19, Prague, Czech Republic. IEEE, 1-8.
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