Much of our pursuit of scientific knowledge is based on building new scientific models to explain data derived from detailed experimental study.
Scientific models are hypotheses set forth to explain experimental observations.
These models can range from physical laws that explain the motion of planetary bodies to explanations of human immunological response to antigens. In the course of building and testing these models, however, we are often faced with experimental results that disagree with the model's predictions. I call this discrepancy a data-model conflict. Understanding the cause of data-model conflicts is crucial to building better scientific models. In this proposal, I look at data-model conflicts that arise in bioinformatics.