In 2003, when we sequenced the human genome, we thought we would have the answer to treat many diseases. But the reality is far from that, because in addition to our genes, our environment and lifestyle could have a significant role in developing many major diseases.
在 2003 年, 當我們為人類的基因組定序時, 我們以為會找到許多疾病的治療方法。 但實際情形卻遠非如此, 因為除了我們的基因之外, 生活環境和生活作息 也是引發重大疾病的關鍵因素。
One example is fatty liver disease, which is affecting over 20 percent of the population globally, and it has no treatment and leads to liver cancer or liver failure. So sequencing DNA alone doesn't give us enough information to find effective therapeutics.
以脂肪肝疾病爲例, 全球超過 20% 的人口 受此疾病影響, 目前沒有任何治療方法 而且最後可發展為肝癌, 或是肝臟衰竭。 所以只靠基因定序 並不能給我們足夠的訊息, 找出有效的治療方法。
On the bright side, there are many other molecules in our body. In fact, there are over 100,000 metabolites. Metabolites are any molecule that is supersmall in their size. Known examples are glucose, fructose, fats, cholesterol -- things we hear all the time. Metabolites are involved in our metabolism. They are also downstream of DNA, so they carry information from both our genes as well as lifestyle. Understanding metabolites is essential to find treatments for many diseases.
好消息是,我們身體裡 還有許多其他的分子, 事實上,我們身體 有超過十萬的代謝物。 代謝物是體積超級小的分子, 已知的例子包括, 葡萄糖、果糖、脂肪、膽固醇—— 我們時常聽到的這些東西。 代謝物會參與新陳代謝活動, 它們也是 DNA 的後段, 所以它們帶著基因訊息 也透露出我們的生活作息。 要找出許多疾病的治療方法 就有必要瞭解代謝物,
I've always wanted to treat patients. Despite that, 15 years ago, I left medical school, as I missed mathematics. Soon after, I found the coolest thing: I can use mathematics to study medicine. Since then, I've been developing algorithms to analyze biological data. So, it sounded easy: let's collect data from all the metabolites in our body, develop mathematical models to describe how they are changed in a disease and intervene in those changes to treat them.
我一直都想要醫治好病人, 但是十五年前, 因爲傾心於數學而離開了醫學院。 不久,我發現最酷的事情是: 我可以用數學來研究醫學, 從那時起,我就一直開發 演算法用來分析生物數據。 這聽起來很簡單: 我們收集身體中所有代謝物的數據, 然後開發數學模型來描述 它們在疾病中如何變化, 並且干預這些變化來進行治療。
Then I realized why no one has done this before: it's extremely difficult.
然後,我明白為什麼之前 沒有人做過這件事了: 因為這實在太困難了。
(Laughter)
(笑聲)
There are many metabolites in our body. Each one is different from the other one. For some metabolites, we can measure their molecular mass using mass spectrometry instruments. But because there could be, like, 10 molecules with the exact same mass, we don't know exactly what they are, and if you want to clearly identify all of them, you have to do more experiments, which could take decades and billions of dollars.
我們身體中有太多代謝物了, 每一個都不盡相同。 針對一些代謝物, 我們能夠用質譜儀 來測量它們的分子量。 但是具有完全相同的 分子量可能有十種之多, 所以無法知道它們確切是什麼東西, 假如要清楚辨識所有代謝物, 必須要做更多的實驗, 那有可能要花上數十年的時間, 還要耗費幾十億美元。
So we developed an artificial intelligence, or AI, platform, to do that. We leveraged the growth of biological data and built a database of any existing information about metabolites and their interactions with other molecules. We combined all this data as a meganetwork. Then, from tissues or blood of patients, we measure masses of metabolites and find the masses that are changed in a disease. But, as I mentioned earlier, we don't know exactly what they are. A molecular mass of 180 could be either the glucose, galactose or fructose. They all have the exact same mass but different functions in our body. Our AI algorithm considered all these ambiguities. It then mined that meganetwork to find how those metabolic masses are connected to each other that result in disease. And because of the way they are connected, then we are able to infer what each metabolite mass is, like that 180 could be glucose here, and, more importantly, to discover how changes in glucose and other metabolites lead to a disease. This novel understanding of disease mechanisms then enable us to discover effective therapeutics to target that.
因此,我們開發了一種 人工智慧來做這事。 我們利用生物數據的增長, 然後建立一個資料庫 裡面有代謝物的相關訊息, 包含代謝物與其他分子 相互作用的訊息, 我們把所有數據組合成一個巨大網絡。 接著,從患者的器官組織或是血液中, 我們測量到代謝物的分子量, 並且尋找因疾病 而產生變化的代謝物質量。 但是,正如我稍早提過, 我們無法確切知道它們是什麼, 分子量為 180 可能是葡萄糖, 不然就是半乳糖或是果糖, 它們都擁有相同的質量, 但在身體中有著不同的功能。 我們的人工智慧演算考慮到 這些含糊不清的情形, 它會在巨大網絡中挖掘數據, 找出那些代謝物如何相互連結, 才會導致疾病的發生。 而且因為它們連接的方式, 我們得以推斷出 每個代謝物的分子量是多少。 在這裡,分子量 180 的可能是葡萄糖。 而且更重要的是, 發現葡萄糖和其他代謝物的變化 如何引發疾病。 這種針對疾病機制的新穎理解, 讓我們能夠針對疾病 找出有效的治療方法。
So we formed a start-up company to bring this technology to the market and impact people's lives. Now my team and I at ReviveMed are working to discover therapeutics for major diseases that metabolites are key drivers for, like fatty liver disease, because it is caused by accumulation of fats, which are types of metabolites in the liver. As I mentioned earlier, it's a huge epidemic with no treatment.
所以我們成立了一家新創公司 將這項技術帶入市場, 對大家的生活帶來正面影響, 現在我和團隊 在 ReviveMed 生技公司 正利用代謝物 努力尋找重大疾病的療法, 像是脂肪肝疾病, 這是由於脂肪的堆積引起。 而脂肪是肝臟中 不同類型的代謝物組成, 我稍早提到這種重大疾病 目前沒有任何治療方式,
And fatty liver disease is just one example. Moving forward, we are going to tackle hundreds of other diseases with no treatment. And by collecting more and more data about metabolites and understanding how changes in metabolites leads to developing diseases, our algorithms will get smarter and smarter to discover the right therapeutics for the right patients. And we will get closer to reach our vision of saving lives with every line of code.
脂肪肝疾病只是其中一個例子, 我們接著要解決 其他數百種目前尚無治療方式的疾病。 藉著搜集更多的代謝物數據, 並且瞭解這些代謝物的變化 如何引發疾病。 我們的演算法會變得愈來愈聰明, 幫助病患找出正確的治療方法。 而且我們能夠利用每條基因碼 一步步達成拯救生命的願景。
Thank you.
謝謝大家。
(Applause)
(掌聲)