For many experimental biologists, the field of computational biology is a vast and unexplored terrain. Sure, there is curiosity about this exotic land, especially now with the increasing overlap between computational biology and the rediscovered systems biology. But, when asked to take the opportunity to embark on an expedition to explore computational biology, most are not interested.
It is not that they do not appreciate what the field has to offer. It is more that the community has had success through its traditional laboratory techniques and corresponding tried-and-tested analysis tools. Trained from their university days to utilize tools such as Microsoft Excel to digest data sets generated from Western blots and such, most experimental scientists cannot find strong reasons to get into a whole new field.
Furthermore, there is a distinct disconnect brought on by computational tools. Computational biology, which is a convergence of three distinct concentrations — biology, computer science and mathematics — often utilizes tools that require programming experience and is thus favored by biological scientists with solid foundations in some aspect of programming. Experimental biologists not trained to approach science from a programming perspective often find computational tools tasking, requiring precious time to learn and master. Additionally, translating laboratory data into the unfamiliar and unfriendly languages used by computational tools (e.g., ODEs, scripts, etc) is a tricky hill to surmount.
For computational biology to be properly integrated into the mainstream scientific process, there need to be better ways to merge the existing synergy between current biology and the new computational arm. Learning new techniques and transforming schools of thought requires time, which many scientists do not have. Perhaps then, the best way to better integrate computational biology with traditional laboratory approaches is to incorporate some computational curricula at an earlier stage in a scientist’s education. This can be done, for example, during the days of undergraduate studies, when no particular methodology has been engraved into the routine and the fertile mind is in the stage of gaining an understanding of the many scientific fields.