I agree with your overall point and particularly the first part. Computational biologists and laboratory biologists are both biologists, just with different to

I agree with your overall point and particularly the first part. Computational biologists and laboratory biologists are both biologists, just with different tool sets. 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.