Hierarchical semantic composition of biosimulation models using bond graphs

Abstract

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

A semantics, energy-based approach to automate biomodel composition

“…As an improvement to our previous approach [ 28 ], the present framework overcame the aforementioned limitations:…”

“…In this approach all or a group of the bond graph components in the modules are mergeable. In a ‘black box’ composition approach in contrast, only the components predefined as inputs or outputs are accessible [ 28 , 52 ]. In coupling biological models, all entities are mergeable, hence, we found the ‘white box’ configuration more compatible with our model composition method.…”

“…An automated model composition approach significantly assists researchers in creating large-scale models from existing modules [ 27 ]. Shahidi et al [ 28 ] introduced a general hierarchical model composition method by encoding bond graph modules in CellML and constructing a complex model using the SemGen merger tool [ 29 ]. The SemGen merger tool uses the biological semantics of the components in models to identify and interpret them unambiguously.…”

Modular assembly of dynamic models in systems biology

“…As demonstrated in Appendix B of S1 Text, this approach can be extended to use white-box modules with flexible interfaces. Furthermore, the merging of models can be automated through the PLOS COMPUTATIONAL BIOLOGY use of semantic annotations, and bond graphs have shown great potential in this space due to their biophysical detail [70].…”

Modular assembly of dynamic models in systems biology

“…As demonstrated in Appendix B of S1 Text, this approach can be extended to use white-box modules with flexible interfaces. Furthermore, the merging of models can be automated through the use of semantic annotations, and bond graphs have shown great potential in this space due to their biophysical detail [60].…”