A twofold usage of an agent-based model of vascular adaptation to design clinical experiments

Stefano Casarin, Scott A. Berceli, Marc Garbey

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Several computational models of Vein Graft Bypass (VGB) adaptation have been developed in order to improve the surgical outcome and they all share a common property: their accuracy relies on a winning choice of their driving coefficients which are best to be retrieved from experimental data. Since experiments are time-consuming and resources-demanding, the golden standard is to know in advance which measures need to be retrieved on the experimental table and out of how many samples. Accordingly, our goal is to build a computational framework able to pre-design an effective experimental structure to optimize the computational models setup. Our hypothesis is that an Agent-Based Model (ABM) developed by our group is comparable enough to a true set of experiments to be used to generate reliable virtual experimental data. Thanks to a twofold usage of our ABM, we created a filter to be posed before the real experiment in order to drive its optimal design. This work is the natural continuation of a previous study from our group [1], where the attention was posed on simple single-cellular events models. With this new version we focused on more complex models with the purpose of verifying that the complexity of the experimental setup grows proportionally with the accuracy of the model itself.

Original languageEnglish (US)
Pages (from-to)59-69
Number of pages11
JournalJournal of Computational Science
Volume29
DOIs
StatePublished - Nov 2018

Keywords

  • Agent-based model
  • Experiment planning
  • Virtual dataset

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Modeling and Simulation

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