Computational Biology:学際的で統合的な科学

For many years, Biology, in general, was a discipline considered to be similar to library 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 分子生物学 and in allied areas of biological research, has made Biology diversified. It is no longer a library science. Also, the need for interdisciplinary research has become more prominent. This is evident,specifically in Computational Biology and バイオインフォマティクス, 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.

計算生物学とバイオインフォマティクスは、様々なバックグラウンドを持つ科学者が専門知識を駆使して素晴らしい結果を出すことができる分野の一つです。.次の言葉は、学際的で統合的な研究の利点を雄弁に物語っています。

One of the most fascinating issues we’ve encountered is the notably different ways of thinking that typically characterize biologists and computer scientists.生物学者は、知識を収集し、自分の仕事を物語るように説明することが多く、結論を導き、モデルを構築しようと努力し、生物の世界では規則と同様に例外もよくあることだと理解しています。これを、ルールと最適化を目標とする論理とプロセスを重視するコンピュータサイエンティストと比較すると、ミスコミュニケーションの可能性が出てきます。この2つのグループは、同じ問題を与えられても、異なる質問をし、異なる詳細を拾い、異なるメタファーを使って問題を説明し、異なる前提で状況に臨みます。

何から始めればいいのか？

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 [3]. For example, dynamic programming, intended for finding the shortest path, was successfully applied 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 search 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.

If you don’t like meeting people, then following Twitter, research blogs, and joining discussion forums are the best alternatives.

すべてのことに精通する必要はありません。むしろ、別の目的のために作られたツールやリソース、手法を意識して、それを自分のニーズに合わせて変更することが目的です。例えば、遺伝的アルゴリズム（GA）は、生物学的に観察される組み換え現象にヒントを得ています。そのため、GAベースの技術は最も最適化され、非常に人気があります。また、GAベースの分子ドッキング手法は、計算生物学やバイオインフォマティクス、特に創薬の分野でも同じように人気があります。

The potential of using statistics, mathematics, computer science and signal 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.

今こそ、統合的な科学の時代です。