In the second category, issues related to implementing the model arise. Often such issues involve the actual code used to implement the model. Computational models are, in essence, large computer programs; issues of software development come to the fore. As the desire for and utility of computational modeling increase, the needs for software are growing rather than diminishing as hardware becomes more capable. On the other hand, progress in software development and engineering over the last several decades has not been nearly as dramatic as progress in hardware capability, and there appears to be no magic bullets on the horizon that will revolutionize the software development process.

This is not to say that good software engineering does not or should not play a role in the development of computational models. Indeed, the Biomedical Information Science and Technology Initiative (BISTI) Planning Workshop of January 15-16, 2003, explicitly recommended that NIH require grant applications, proposing research in bioinformatics or computational biology to adopt as appropriate, accepted practices of software engineering.126Section 4.5 describes some of the elements of good software engineering in the context of tool development, and the same considerations apply to model development.

A second important challenge as large simulation models become more prevalent is a standard specification language to unambiguously specify the model, its parameters, annotations, and even the means by which it is to be scored against data. The challenge will be to provide a language flexible enough to capture all interesting biological processes and incorporate models at different levels of abstraction and in different mathematical paradigms, including stochastic differential, partial differential, algebraic, and discrete equations. It may prove necessary to develop a set of nested languages—for example, a language that specifies the biological process at a very high level and a linked language that specifies the mathematical representation of each process. There are some current attempts at these languages based on the XML framework. SBML and CellML are attempts in this direction.

Finally, many biological modeling applications involve a problem space that is not well understood and may even be intended to explore queries that are not well formulated. Thus, there is a high premium on reducing the labor and time involved to produce an application that does something useful. In this context, technologies for “rapid prototyping” of biological models have considerable interest.127




Note, however, that in the rapid prototyping process often used to create commercial applications, there is a dialogue between developer and user that reveals what the user would find valuable: once the developer knows what the user really wants, the software development effort is straightforward. By contrast, in biological applications, it is nature that determines the appropriate structuring and formulation of a problem, and a problem cannot be structured in a certain way simply because it is convenient to do so. Thus, technologies for the rapid prototyping of biological models must afford the ability to rearrange model components and connections between components with ease.

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