Is Computational Biology increasingly—and steadily—progressing toward addressing the mammoth challenge of actually computing biology? That is, have we reached the stage where we do not support biological research but drive it? This question is vitally important for all—young and established computational biologists. Even though forecasting future research can be risky, we still venture to predict that the future will see considerably more research projects drifting toward this ambitious aspiration. Computational Biology is powerful for abstracting signatures of disease, for predicting it, and for proposing medications. It is effective in figuring out disease mechanisms and forceful in bridging experimental disciplines to obtain testable predictions. However, perhaps its biggest challenges lie in putting together the available broad and disparate information, devising tools to efficiently and effectively carry out these tasks while sifting through noise and recognizing cell specificity, and most importantly coming up with sound, coherent, and testable schemes.
Nussinov R, Papin JA (2016) Computing Biology. PLoS Comput Biol 12(7): e1005050. doi:10.1371/journal.pcbi.1005050