I disagree, however, with your last paragraph - in particular the notion of computational biologists as tool builders. You're absolutely right that lab biologists need to get with the times and become more computer-literate and data-literate. It's not the responsibility of computational biologists to shepherd them though, they have their own research to do. I think the divide that you point to exists largely because the field is still predominantly occupied by either old school wet bench biologists who won't learn new things (and more disturbingly don't think their grad students need to learn any either), and computer scientists / statisticians who migrated into the field from elsewhere some decades ago and still retain a sense of being outsiders. The next generations of biologists, however, should increasingly resemble a true merging of these two groups.
No tool can compensate for an inability to understand statistically valid experimental design or how to manipulate genomic data, and a laboratory biologist who generates this data without being able to do these things isn't much of a biologist. Likewise a computational biologist that doesn't have those important domain-specific insights might not actually be much of a biologist either.
I also find sometimes the inverse is true: computational biologists who don't know any biology. I think there is work on both sides to bridge the gap.
Absolutely.
But, as my dissertation adviser pointed out, "[apathy], you have to remember that Really Stupid People get PhDs all the time. Don't let me down."
Having students (particularly mathematicians) has reminded me that only derivation and demonstration really count in science. All the rest is artifice.
The number one thing that most biologists need (that most scientists need) is a basic understanding of statistics when doing experimental design. Sure, you can get this by having a computational biologist on staff, always timidly raising their voice to get you to add replicates, for fuck's sake. But - how about learning this stuff yourself, so your experiments (which rely deeply on statistical reasoning) aren't shit to begin with?
My goal as a computational biologist isn't to do work for or correct biologists who don't understand statistics, it's to get biologists tools to do this kind of work easily on their own and bridge the gap in understanding. At the end of the day, computational biologists can't have the important domain-specific insights; biologists need to start understanding the datasets better themselves so they can apply their domain knowledge to it, and computational biologists need to be making tools to help them get there. Interdisciplinary work succeeds best by constructing interdisciplinarians who have all of the skills.