Name: 生物信息學與計算生物學的區別 (Difference Between Bioinformatics and Computational Biology)
Uploaded: 2025-07-28T16:33:22.000Z
Duration: 9 min 6 s

Many times students come to me asking, sir, what is the difference between bioinformatics and computational biology?

很多時候，學生來問我，先生，生物信息學和計算生物學有什麼區別？

程度副詞

Are they the same or just interchangeable names or is there any real difference?

它們是一樣的，還是隻是名字可以互換，還是有什麼真正的區別？

So, in today's video, we will talk about definition of bioinformatics and computational biology, but we will also dive deep into what is the difference, what are the similarities and can we really call bioinformatics as computational biology or computational biology as bioinformatics.

是以，在今天的視頻中，我們將討論生物信息學和計算生物學的定義，同時還將深入探討兩者的區別和相似之處，以及我們是否真的可以將生物信息學稱為計算生物學，或者將計算生物學稱為生物信息學。

So, we will try to clear the air, but when I was researching on this topic, I found out that many colleges and academics use this term interchangeably without understanding the impact of it.

是以，我們將努力澄清事實，但我在研究這一主題時發現，許多學院和學者在不瞭解其影響的情況下交替使用這一術語。

So, let us start with the definition, which obviously we can say bioinformatics would be interdisciplinary field that combines biology, computer science and statistics to analyze biological data such as DNA sequences and protein structures.

顯然，我們可以說生物信息學是一個跨學科領域，它將生物學、計算機科學和統計學結合起來，對 DNA 序列和蛋白質結構等生物數據進行分析。

So, that is what we can say as bioinformatics.

這就是我們所說的生物信息學。

But now if I have to say computational biology, how do you define computational biology?

但現在如果我要說計算生物學，你如何定義計算生物學？

So, you can call it as a branch of biology that uses computational and mathematical models to study biological processes such as gene regulatory networks and metabolic pathways.

是以，可以將其稱為生物學的一個分支，它使用計算和數學模型來研究基因調控網絡和代謝途徑等生物過程。

So, now the difference if we try to make out from these two definitions itself is it is combining biology, computers and statistics.

是以，如果我們試圖從這兩個定義中找出區別，那就是它結合了生物學、計算機和統計學。

And the other one is using computers to learn the biological process.

另一種是利用計算機學習生物過程。

So, in bioinformatics per se, you are taking the data from biology, putting in computer and then using statistics to analyze.

是以，生物信息學本身就是從生物學中獲取數據，輸入計算機，然後利用統計學進行分析。

In computational biology, you are straightaway going from the computer aspect and mathematical models and then you are going to analyze the data.

在計算生物學中，你直接從計算機方面和數學模型入手，然後對數據進行分析。

But now let us deep dive and look at what can be the applications of bioinformatics.

現在，讓我們深入探討一下生物信息學的應用領域。

So, the applications of bioinformatics is genomics, proteomics and pharmacogenomics.

是以，生物信息學的應用領域包括基因組學、蛋白質組學和藥物基因組學。

Now, genomics is nothing but sequencing and analyzing of genomes to understand genetic variations and their implications in diseases.

現在，基因組學不過是對基因組進行測序和分析，以瞭解基因變異及其對疾病的影響。

Now, proteomics will be studying the structure, functions and integrations or interactions of protein using computational methods.

現在，蛋白質組學將利用計算方法研究蛋白質的結構、功能和整合或相互作用。

And the pharmacogenomics aspect will be identifying genetic factors that influence a patient's response to drug help and helping in personalized medicine.

藥物基因組學方面將確定影響病人對藥物反應的遺傳因素，幫助實現個性化醫療。

Now, these are the more or less the applications of bioinformatics.

現在，這些或多或少都是生物信息學的應用。

But now let us understand what is the application of a computational biologist or biology.

現在，讓我們來了解一下計算生物學家或生物學的應用。

Now, the first one will be gene regulatory framework or gene regulatory network.

現在，第一個將是基因調控框架或基因調控網絡。

We do the modeling of the interactions between genes to understand how they regulate cellular processes.

我們對基因之間的相互作用進行建模，以瞭解它們是如何調控細胞過程的。

Then the second application will be metabolic pathways.

然後，第二個應用將是代謝途徑。

So, we simulate biochemical reactions to study this metabolism of cell and organisms.

是以，我們模擬生化反應來研究細胞和生物體的這種新陳代謝。

So, we are trying to understand how gene is regulating the cellular metabolism.

是以，我們試圖瞭解基因是如何調節細胞新陳代謝的。

Then we are also analyzing how all the metabolism is happening and we try to simulate that in a virtual environment.

然後，我們還分析所有新陳代謝是如何進行的，並嘗試在虛擬環境中進行模擬。

And then the third will be protein folding.

So, we use computational models to understand the 3D structure of protein and how they dock and all that.

是以，我們利用計算模型來了解蛋白質的三維結構以及它們如何對接等。

So, that is where the protein folding mechanism we try to understand under this.

是以，這就是我們試圖理解的蛋白質摺疊機制。

Now, under in bioinformatics, we have various methods which we follow, which will be different than the computational biology.

現在，在生物信息學領域，我們有各種方法，與計算生物學不同。

So, under in bioinformatics, we will do sequence alignment.

是以，在生物信息學中，我們將進行序列比對。

So, we will compare the DNA or protein sequences to identify similarities and infer the evolutionary relationship.

是以，我們要對 DNA 或蛋白質序列進行比較，找出相似之處，推斷進化關係。

Now, we will also do structural modeling.

現在，我們還將進行結構建模。

So, under that we will predict the 3D structure of proteins similar to what we do in computational biology, but there we do a 3D modeling using mathematical models here will be different.

是以，在這種情況下，我們將預測蛋白質的三維結構，這與我們在計算生物學中的做法類似，但在那裡，我們使用數學模型進行三維建模，而在這裡則有所不同。

And we will also learn about the amino acid sequences.

Then the third methods we use is data mining.

So, we extract meaningful patterns and knowledge from biological data sets.

是以，我們從生物數據集中提取有意義的模式和知識。

And of course, under this comes the machine learning where we use algorithms to develop predictive models of biological phenomena.

當然，在機器學習之下，我們使用算法來開發生物現象的預測模型。

But now, if we look at the comparison with computational biology, the number one will be mathematical modeling, like I said in the definition itself.

但現在，如果我們將其與計算生物學進行比較，第一位的將是數學建模，就像我在定義中所說的那樣。

So, here what we are doing, we are formulating the biological process as a mathematical equation to simulate and predict their behavior.

是以，我們在這裡所做的，就是將生物過程編製成數學公式，以模擬和預測它們的行為。

So, we are taking the biological process and trying to create a mathematical equation and we say, if this happens, that happens.

是以，我們正在利用生物過程，並試圖建立一個數學等式，我們說，如果發生這種情況，就會發生那種情況。

So, we try to create an equation, a mathematical equation to predict the next steps.

是以，我們嘗試建立一個等式，一個數學等式來預測下一步。

So, that is the mathematical modeling we do.

So, we study the biological systems as a network to analyze their structure, dynamics and functional properties.

是以，我們將生物系統作為一個網絡來研究，分析其結構、動力學和功能特性。

So, basically, if we see the entire biological system is not a disconnected system, instead it is a connected system.

是以，從根本上說，如果我們看到整個生物系統並不是一個斷開的系統，相反，它是一個連接的系統。

So, we try to analyze that to understand the structure, dynamics and functional properties, how things will happen, if this happens, that happens.

是以，我們試圖對其進行分析，以瞭解其結構、動力學和功能特性，以及如果發生這種情況，會發生什麼情況。

For example, let us say the hormonal structure of the body.

So, if in case you see the female hormones like estrogen, progesterone, how they will act and how it will create the more secretion of a particular hormone.

是以，如果你看到雌激素、孕激素等女性荷爾蒙，它們會如何發揮作用，又會如何產生更多的特定荷爾蒙分泌。

So, understanding all of that and learning that network analysis, that chain analysis we can do.

是以，瞭解所有這些，學習網絡分析和鏈分析，我們就能做到這一點。

So, we use statistical methods to make inferences and draw conclusions from biological data.

是以，我們使用統計方法從生物數據中進行推論並得出結論。

So, this is where, these are the methods we follow in computational biology.

是以，這就是我們在計算生物學中遵循的方法。

But we cannot really put both of them apart.

但是，我們真的不能把它們分開。

Like I said, the body is a connected system, the same way all the sciences are a connected system.

正如我所說，人體是一個相互聯繫的系統，就像所有科學都是一個相互聯繫的系統一樣。

You really cannot remove physics from chemistry, you cannot remove chemistry from biology, you cannot remove biology from bioinformatics, you cannot remove biology from computational biology or computers from computational biology.

你確實無法將物理學從化學中剝離出來，無法將化學從生物學中剝離出來，無法將生物學從生物信息學中剝離出來，無法將生物學從計算生物學中剝離出來，也無法將計算機從計算生物學中剝離出來。

So, there has to be analyzing biological data.

是以，必須對生物數據進行分析。

And what is happening in computational biology?

在計算生物學領域又發生了什麼？

We are developing applications of computational models.

我們正在開發計算模型的應用。

So, we are using mathematical models in computational models to predict the next steps.

是以，我們正在使用計算模型中的數學模型來預測下一步。

And here, we are taking the data from biological side and trying to create the inference.

在這裡，我們從生物方面獲取數據，並試圖建立推論。

So, in both the cases, one is the, we are, bioinformatics is on a biological side is predominant, while in computational biology, the computers and mathematics is predominant.

是以，在這兩種情況下，一種是我們，生物信息學在生物學方面占主導地位，而在計算生物學方面，計算機和數學占主導地位。

So, that is where the difference would be.

And that is where they both complement each other.

而這正是兩者相輔相成的地方。

Because when you combine both, then only you will learn.

因為只有將兩者結合起來，你才能學到東西。

So, more or less, you will have to learn both.

是以，或多或少，你必須同時學習這兩方面的知識。

Question to be asked is, what will be the future of this?

要問的問題是，它的未來會怎樣？

What will be the future of computational biology?

What will be the future of bioinformatics?

So, I have answered this question in multiple details, in many, much more detail in different videos, but just in one line.

是以，我已經在不同的視頻中詳細回答了這個問題，但我只用一句話來回答。

See, advanced data analysis is the future.

瞧，先進的數據分析就是未來。

Biological data is in like petabytes of storage today.

如今，生物數據的存儲量已達 PB 級。

And today, scientists are working on developing more sophisticated algorithms and computational methods for analyzing complex data sets in biology.

如今，科學家們正在努力開發更復雜的算法和計算方法，用於分析生物學中的複雜數據集。

The same with integration of multiomics data sets.

多組學數據集的整合也是如此。

So, we combine the data from genomics, transcriptomics, proteomics, etc., to gain a more comprehensive view of the biological systems.

是以，我們將基因組學、轉錄組學、蛋白質組學等數據結合起來，以獲得對生物系統更全面的瞭解。

So, this is more or less the difference and similarity and the future scope.

所以，這或多或少就是異同和未來的範圍。

Now, many students, I am sure, will ask me, okay, so after my bachelor's in bioinformatics, can I go for MSc Computational Biology?

現在，我相信很多學生會問我，好吧，那麼在生物信息學本科畢業後，我可以去讀計算生物學碩士嗎？

Or after my computational biology, can I do a PhD in this?

或者，在完成計算生物學課程後，我能否攻讀這方面的博士學位？

See, how I see it is, computational biology would be a more advanced format, you can always do it, are more or less the same.

在我看來，計算生物學將是一種更高級的形式，你可以一直這樣做，都差不多。

But at the end of the day, where your career grows is when you learn how to use it for commercial development.

但歸根結底，當你學會如何將其用於商業開發時，你的事業才會得到發展。

If you are an academic guy who will do a PhD and just sit back, relax as a professor, then you are not going into the industry and doing challenging jobs.

如果你是一個學術型的人，只想讀個博士學位，然後安安心心地當個教授，那你就不可能進入行業，從事具有挑戰性的工作。

And I am not saying the professors are not doing, they are doing a challenging job of teaching the students.

我並不是說教授們沒有做，他們在教授學生方面做得很有挑戰性。

But what you studied, applying that in a commercial establishment is totally different than doing research in academia.

但是，將你所學的應用於商業機構，與在學術界從事研究是完全不同的。

But at the same time, you have to understand if you really want to take up that job, then do bioinformatics, then computational biology and go for that.

但與此同時，你必須明白，如果你真的想從事這份工作，那就去做生物信息學、計算生物學，然後去做這個。

And there are jobs in US, Europe and various parts of the world where you can get placed.

在美國、歐洲和世界各地都有工作機會。

And there is a huge demand of bioinformaticians who know computational biology as well.

同時，對懂得計算生物學的生物資訊學家的需求也非常大。

但現在，在哪裡可以學習這些課程呢？

Well, bioinformatics and computational biology, we do train at Biotechnica.

嗯，生物信息學和計算生物學，我們在生物技術展上都有培訓。

You can check all the details at stores.biotechnica.org.

您可以在 stores.biotechnica.org 上查看所有詳細信息。

Thank you so much for watching this video.