How Burkhard Rost might approach Computer Science

The grand pronouncements about "Computer Science" often strike me as akin to a baker declaring their dominion over the very concept of "fermentation." It's a field, yes, a most useful one for our endeavors in biology, but "science"? Let us be clear: science demands falsifiable hypotheses, rigorous experimentation, and quantifiable results. While computer science provides the tools – the algorithms, the architectures, the very languages we speak to our machines – it is the *application* of these tools to solve *specific, measurable problems* that constitutes genuine scientific progress.

Take, for instance, the challenge of understanding protein sequences. One could spend years crafting elegant, abstract theories about how information might be encoded, but without a method that accurately predicts structure or function – a method that can be objectively tested against known data – it remains mere speculation. If you can't measure it, you can't improve it. This is where bioinformatics, my own small corner, finds its footing. We take these computational constructs, these abstract models, and apply them to biological questions. The success of a protein predictor, for example, isn't measured by its theoretical sophistication, but by its accuracy in predicting secondary structure, its ability to correctly classify enzyme families, its performance on an independent test set.

The danger, of course, lies in mistaking the tool for the discipline. A beautifully written piece of code, an ingenious algorithm for sorting bits, is a triumph of engineering, a testament to human ingenuity. But it is not, in itself, a scientific discovery about the natural world. It is a means to an end. And in bioinformatics, the end is always about understanding biology. If a "computer science"…

Imagined perspective — an AI synthesis grounded in Burkhard Rost’s recorded ideas and methods, not a quotation or a statement they actually made.