There's more to a model than code: understanding and formalizing in silico modeling experience

Abstract

Mapping biology into computation has both a domain specific aspect -- biological theory -- and a methodological aspect -- model development. Computational modelers have implicit knowledge that guides modeling in many ways but this knowledge is rarely communicated. We review the challenge of biological complexity and current practices in modeling genetic regulatory networks with the aim of understanding characteristics of the in silico modeling process and proposing directions for future improvements. Specifically, we contend that the modeling of complex biological systems can be made more efficient and more effective by the use of structured methodologies incorporating experience about modeling algorithms and implementation. We suggest that an appropriate formalism is Complex Systems Patterns, adopted from Design Patterns in software engineering. First steps towards building community resources for such patterns are described.

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