For many years, Biology, in general, was a discipline considered to be similar to ライブラリ sciences, due to the practice of collecting specimens and samples and cataloging them. (I made a herbarium for my high school project.) However, since the 1970s, the advancements in molecular biology and in allied 地域 of biological research, has made Biology diversified. It is no longer a library サイエンス. Also, the need for interdisciplinary research has become more prominent. This is evident,specifically in Computational Biology and Bioinformatics, with scientists from diverse background expertise, working on a common problem. In the current scenario, with the advent of newer technologies and techniques, interdisciplinary and integrative scientific research skills are in high demand.
私たちが遭遇した最も興味深い問題の一つは、生物学者とコンピュータ科学者の間に顕著に見られる考え方の違いです。. The biologist gathers knowledge, will often describe his or her work as if telling a story, strives to draw conclusions and construct モデル, and appreciates that exceptions are just as common as rules in our biological 世界. Compare this to the logic and プロセス-oriented computer scientist, for whom rules and optimization are the goals, and you have the potential for miscommunication. The two groups, given the same problem, will ask different questions, pick up on different details, use different metaphors to describe the problem, and come into the situation with different assumptions.
コンピュータ・バイオロジーでは 生物学的な問題を解決するために意図されていない、あるいは発明されていないアルゴリズムの実装に成功し、開発されたツールによってこの分野は大きく発展した . For example, dynamic programming, intended for finding the shortest path, was successfully 応用 for aligning sequences (both global and local alignment). An extension of the same is BLAST, a popular and essential tool for biologists to identify homologs for a given sequence. Thus, knowledge of algorithms and updating one with variants of the algorithms is essential for a computational biologist.
If you are a biologist, having the time tested routine laboratory work, would make you ask the question “I really don’t have time for this!”. And, you are right. But, think it in this way, the field of Computational Biology and Bioinformatics, was developed and nurtured by pioneers were physicists, biologists, chemists, statisticians, etc. Going out of the comfort zone, and listening to researchers from other areas over coffee or a drink is an excellent way to think out of the box. Conferences are a mine field, in this respect. Rather than listening to someone talking about their research (assuming that the research majorly overlaps your focused area, and most likely you have heard their talk on a different occasion), which will eventually be read by me in a few months; one can 探索 for talks that have very less to do with your research. Such opportunities provide brainstorming ideas to implement techniques from other fields to your own research, more specifically Computational Biology and Bioinformatics.
The potential of using statistics, mathematics, computer science and 信号 processing in biology is immense. The key to develop an integrative research is communication. Communication with colleagues from other departments is the key. Also, a knack for looking out where the field is moving towards helps. Some interdisciplinary research in computational biology yielding groundbreaking results will be in discussed in subsequent posts.