Do modelers have low self esteem?

When was the last time an experimental biologist experienced a manuscript rejection because the work in question didn't include modeling? As a modeler working in biology, I tend to be hesitant about trying to report work that doesn't include new data (from a collaborator), because it doesn't usually go well. Modeling without experimentation tends to be held in low regard, especially among modelers, which is a tragic irony. If physicists of the early 20th century had the same attitude as many of today's modelers and experimental biologists, "Zur Elektrodynamik bewegter Körper" would not have been published without its author first doing experiments to confirm his ideas or him finding an experimental collaborator to generate confirmatory data. I don't think that modelers should take a favorable view of every modeling study they come across but I wonder if we need to be more supportive of each other and allow more room for independence from collaborations with experimentalists. If a modeling study is based on reasonable assumptions and performed with care and it produces at least one non-obvious testable prediction, why should it not be reported immediately? It seems that some of us might be concerned that such reports will be ignored or that such reports are too untrustworthy, given all the complexities and ambiguities. It's true that models need to be tested, but it seems unlikely that someone able to build and analyze a model will also be the best person to test the model or to have a circle of friends that includes this special person. Indeed, I think the requirement to publish with data has led some modelers to produce predictions that are, let's say, "obvious," because this is the type of prediction can be confirmed easily. Let's be rigorous, but to a reasonable standard. Let's also be bold. Many experimental results turn out to be misinterpreted, or plain wrong. It's OK for models to be wrong too. Biological systems are complicated. We need models to guide our study of these systems. Most of the work being done in biology today is being performed without models. Until experimentalists start chiding each other for failing to leverage the powerful reasoning aids that models are, it makes little sense for modelers to criticize each other for work that doesn't include generation of new data.