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.
2003. godine, kad smo razložili ljudski genom, mislili smo da ćemo imati odgovor za liječenje mnogih bolesti. No, stvarnost je daleko od toga, jer osim naših gena, naše okruženje i način života mogu imati značajnu ulogu u razvoju mnogih velikih bolesti.
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.
Jedan primjer je bolest masne jetre, koja pogađa preko 20% stanovnika svijeta, i nema joj lijeka, a vodi do raka jetre ili zatajenja jetre. Dakle, sekvenciranje DNK samo po sebi ne daje nam dovoljno informacija za pronalazak učinkovitih terapija.
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.
Dobro je što postoje mnoge druge molekule u našem tijelu. Zaista, postoji preko 100.000 metabolita. Metaboliti su bilo koja molekula supermale veličine. Poznati primjeri su glukoza, fruktoza, masti, kolesterol -- ono o čemu stalno slušamo. Metaboliti su uključeni u naš metabolizam. Oni su na nižoj razini od DNK pa nose informacije iz naših gena, kao i stila života. Razumijevanje metabolita je ključno za pronalazak tretmana za mnoge bolesti.
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.
Oduvijek sam željela liječiti pacijente. Unatoč tome, prije 15 godina, napustila sam medicinsku školu jer mi je nedostajala matematika. Ubrzo potom otkrila sam sjajnu stvar: Mogu koristiti matematiku za studij medicine. Od tada razvijam algoritme za analizu bioloških podataka. Dakle, zvučalo je jednostavno: prikupimo podatke o svim metabolitima u našem tijelu, razvijmo matematičke modele za opisivanje kako se mijenjaju u bolesti i intervenirajmo u te promjene kako bismo ih liječili.
Then I realized why no one has done this before: it's extremely difficult.
Tada sam shvatila zašto to nitko nije učinio prije: to je iznimno teško.
(Laughter)
(Smijeh)
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.
Postoje mnogi metaboliti u našem tijelu. Svaki od njih različit je od onog drugog. Nekim metabolitima možemo mjeriti molekularnu masu instrumentima za spektrometriju mase. No kako bi moglo biti, recimo, 10 molekula s istom masom, ne znamo točno koje su, pa ako ih želite sve jasno identificirati, treba raditi još eksperimenata, što bi moglo trajati desetljećima i stajati milijarde dolara.
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.
Tako smo razvili umjetnu inteligenciju, ili AI, kao platformu koja će to učiniti. Iskoristili smo rast bioloških podataka i izgradili bazu podataka svih postojećih informacija o metabolitima i interakcija njih s drugim molekulama. Povezali smo sve te podatke u megamrežu. Zatim iz tkiva ili krvi bolesnika mjerimo mase metabolita i tražimo one koje se mijenjaju u bolesti. Ali, kao što sam spomenula ranije, ne znamo točno koji su. Molekulska masa 180 može biti glukoza, galaktoza ili fruktoza. Sve one imaju iste mase ali različite funkcije u našem tijelu. Naš AI algoritam uzima u obzir sve te nedorečenosti. Zatim pretražuje tu megamrežu da vidi kako su te metaboličke mase međusobno povezane kad rezultiraju bolešću. I po načinu na koji su povezani, onda možemo zaključiti što je svaka metabolička masa, kao, ovdje bi 180 mogla biti glukoza, i, što je još važnije, otkriti kako promjene u glukozi i drugim metabolitima dovode do bolesti. To novo razumijevanje mehanizama bolesti omogućuje nam zatim otkrivanje učinkovitih ciljanih terapija.
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.
Stoga smo osnovali start-up tvrtku kako bismo tu tehnologiju stavili na tržište i poboljšali živote ljudi. Sada moj tim i ja u ReviveMed radimo na otkrivanju terapija za glavne bolesti kojima su ključni pokretači metaboliti, poput bolesti masne jetre, jer je uzrokovana nakupljanjem masti, koje su vrste metabolita u jetri. Kao što sam spomenula ranije, to je ogromna epidemija bez lijeka.
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.
A bolest masne jetre je samo jedan primjer. Ubuduće ćemo se boriti sa stotinama drugih bolesti za koje nema lijeka. Prikupljanjem sve više podataka o metabolitima i razumijevanjem kako promjene metabolita dovode do razvoja bolesti, naši algoritmi će postajati sve pametniji u otkrivanju pravih terapija za pravog pacijenta. Približit ćemo se ostvarenju naše vizije: spašavanja života sa svakim retkom koda.
Thank you.
Hvala vam.
(Applause)
(Pljesak)