Introduction

A model of a complex biological system is often computed through an integrative method, for instance, Bayesian metamodeling, which is a divide-and-conquer modeling approach that aims to integrate heterogeneous input models into a metamodel and update knowledge. While Bayesian metamodeling can in principle be used to couple any set of input models, there are several remaining challenges. Here, we want to overcome some challenges when applying Bayesian metamodeling for more models. Selected solutions for each challenge are packed together as a new framework for practical applications of Bayesian metamodeling. More elaborated descriptions can be found in our manuscript.