I just came back from ICSB 2013, the leading international conference on systems biology (short write-up here). During the conference Bernhard Palsson gave a great talk, which he ended by promoting a view that (I suspect) is widely held among computational and theoretical biologists but rarely vocalized: most high-impact journals require that novel predictions are experimentally validated before they are deemed worthy for publication, by which point they cease to be novel predictions. Why not allow scientists to publish predictions by themselves?
I recently had the pleasure of attending the 14th International Conference on Systems Biology in Copenhagen. It was a five-day, multi-track bonanza, a strong sign of the field’s continued vibrancy. The keynotes were generally excellent, and while I cannot help but feel a little dismayed by the incrementalism that is inherent to scientific research and that is on display in conferences, the forest view was encouraging and hopeful. This is one of the most exciting fields of science today.
Last week I attended a talk at MIT by Michael Deem, a professor at Rice University who has done some very interesting work on the emergence of modularity in evolution. This is a topic that I have long thought about, as it seems that modularity is intrinsic to many biological phenomena, and it also seems that modular systems would by construction have certain internal structure that can be exploited in computational modeling. My thinking on the topic has been crude and qualitative, and so it was with some delight that I discovered Michael’s work in this area, as his group has placed this problem on very firm quantitative footing. The talk was thought provoking, and left me with genuinely new insights, something that happens with only a small, small fraction of the talks I attend.
I recently had the fortune of attending the 10th Community Assessment of protein Structure Prediction, or CASP, as it is affectionately known. CASP is a competition of sorts that happens once every two years to ascertain the progress made in computationally predicting protein structure. It is a blind experiment, where the structures to be predicted are unknown beforehand, and thus serves as a unbiased test of the predictive power of current computational methods. It is in many ways a model that the rest of computational biology ought (and is starting) to follow.