Biomedical Engineering Reference
In-Depth Information
the weak relationships are formed using a number of general interface conditions that
allow for reuse of the coupling method in a number of different physical situations.
The final key feature for multi-scale, multi-physics problems in OpenCMISS is
the use of CellML and FieldML. CellML models allow for the physics at a small
spatial scale to be abstracted and viewed as a single point model within another
model at a larger spatial scale. OpenCMISS also allows for CellML models to be
evaluated and integrated at a different temporal scale than other models. Further
spatial and temporal scales can be incorporated into OpenCMISS problems using
hierarchical field concepts from FieldML which allow data to be viewed at different
levels.
8.13 Physiome standards based multiscale modelling
In an illustrative example demonstrating the utility and capabilities of the Physiome-
style modelling paradigm described in the previous sections, we present here a renal
nephron modelling portal (Nickerson et al., 2011). This portal (Fig. 8.9; and available
at www.abi.auckland.ac.nz/nephron) provides an interface for browsing a collection
of related models of the renal nephron spanning the spatial scales from individual
transport pathways through to the whole nephron. The renal nephron is the primary
functional unit of the kidney, with approximately one million nephrons in a human
kidney.
The renal nephron portal uses CellML to encode cellular and subcellular models
and the models are available from the CellML model repository (see models.cellml.
org/exposure/4f9 for an example collection). The renal nephron is a 1-dimensional
model in which we have described the distribution of cell types. This allows the por-
tal to present the user with a graphical rendering of the nephron from which they
are able to select the different anatomical segments and be presented with informa-
tion related to that segment, such as the various cell models in the database for that
segment. From a given cell model, the user is then able to investigate the various
transport proteins expressed in that cell type and their representation in the various
cellular models. These relationships are currently captured statically in the portal us-
ing unique integer identifiers. We plan to replace these static relationships with dy-
namic database queries using more advanced ontological annotation. In this manner
we are able to extend the portal to dynamically incorporate new models and simula-
tion results added to the various model repositories and data sources. Similarly, by
simply providing a different base model with associated annotations the portal can
be utilized by any Physiome standards based modelling project or organ system. For
example, an annotated heart model and web service access to related data sources
would enable the portal presented in Fig. 8.9 to be dynamically presented to cardiac
Physiome users.
