Steve McKeever
ABSTRACT
In this paper we discuss two projects looking at applying advanced abstraction mechanisms from software engineering to the field of physiological modelling. We focus on two abstraction mechanisms commonly found in modern object-oriented programming languages: generics and inheritance. Generics allows classes to take other classes as parameters, allowing common behaviour to be described with particularities abstracted away. We demonstrate this technique on an example from heart modelling. Inheritance allows one to reuse code and to establish a subtype of an existing object. We focus on the benefits reaped from inheritance where this property enables run-time substitutability. This technique is demonstrated within the context of multi-scale tumour modelling. Finally, we look at how combining both techniques enables greater modularity and the construction of a model driven framework for the rapid creation and extension of families of biological models.
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Index Terms
- Abstraction in physiological modelling languages
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Reviews
Mariam Kiran
Biological modeling has been extensively studied in various ways. Systems Biology Markup Language (SBML) and other languages have been developed specifically to model biological systems and present notations that describe them more effectively. This paper presents two case studies that merge software engineering with the modeling of biological systems. The authors present a very interesting object-oriented approach to modeling and simulation, with class diagrams and inheritance discussions. The model is presented well, so software engineers, and perhaps even biologists, should be able to replicate the experiments. However, apart from the basic experiment, the idea presented is itself very valuable. Using inheritance allows a generic cell model to be created that can then be inherited into all other cells and specialized, into heart or lung tissue cells for example. This valuable technique will make it easier to write technical models and also quickens the process of simulation studies. There are, however, some drawbacks to this approach. Most notably, it would be difficult to track errors (if researchers are lucky enough to find them in the simulation), and general testing could be cumbersome. The paper is well written, and presents a good approach to modeling biological simulations. The authors make the case for more and closer collaborations between biologists and computer scientists to develop correct and efficient models for research. Online Computing Reviews Service
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