Bioinformatics: Where code meets biology

Alright, how long do I got? 29, 28, 27. Yeah, it's just, I'm a little bit nervous, that's all. Alright, alright, I'll get into it. Okay, so here at NutraMe, we operate at the center of this very poorly drawn Venn diagram. We've got biology, nutrition, and data science. And what do we do? Well, we combine them in a bioinformatics fashion. And in the middle here, this is where we would be. This is us, Nutrime. I even draw that poorly. So, what Nutrime does is we take your DNA, we run data science on it, and we figure out a nutrition plan that's specifically tailored to you in a form of precision medicine. And that's it. NutriMe, hey, you should be excited about this. This is some pretty amazing stuff. I mean, if you can do what you can say you do. Yeah, you're right. Shouldn't be nervous. You should be excited. This is my project. This is bioinformatics. This is health crossed with data science crossed with everything I'm into. So yeah, at NutriMe, we do, we do take your DNA. We do take your food profile. We do create you a specifically tailored nutrition plan to you. Much better. Just next time, 10% less infomercial-y. Yeah, you're right. I'll work on that for next time. Alright, I'm not quite sure what happened there, but I'm excited. Why? Because today is the day. Today is the day. What? What day? The day I'm going to start getting into the bioinformatics specialization on Coursera. And PS, big shout out to Coursera, they're sponsoring this video. I use Coursera almost every day. I've used it for the machine learning course, deeplearning.ai course, learning how to learn course, and now bioinformatics. Now why bioinformatics is because nothing excites me more. If you've seen my other videos, you probably, you probably know I'm into, I'm into health. Nothing excites— where you going? Why did I try to move that while talking? Nothing excites me more than the crossover of health and technology, and that's what bioinformatics is. So I'm going to get into— get into— start the 7-day free trial and see how it goes. I mean, the whole course goes for like 11 months, so I'm not entirely sure if I'm going to do the whole thing, but I think it's good to, to try things out, see if you like it, see if you like the teaching style. And today's Sunday, so we're going to do a a Sunday study session, which is 4 Pomodoros. But anyway, I'm gonna get into it, and then after I've done those 4 Pomodoros, I'll share with you what I learn, and we'll try and figure out— hopefully it introduces what bioinformatics is, and then I can teach you. Alexa, stop the timer. What a Sunday session. Now, I've probably done a bit more than 4 Pomodoros and I've taken plenty of notes on the course so far. There is a book to go with it and that's what I found out most of the course is based around. It's going through chapters of the book. But of course, because I started today, I haven't got the book yet. Now, some initial thoughts, very good. Fascinating, exciting, but hard. But that's a good thing, right? Because if you're learning something new, by definition it's going to be hard. So there was a whole bunch of concepts around DNA and then different ways of programming in Python. I had to brush up on some of my Python skills, right? A lot of searching on Stack Overflow. And what I did find out about 3/4 of the way through, through some of the coding exercises, was that there's a beginner versus not beginner track. And the not beginner one is someone for someone who's got a bit more experience in Python programming, so I probably would have started there anyway. And the absolute beginner one is more of a step-by-step approach to Python programming for biology. Frankly, bioinformatics, and I mean, we haven't even discussed what bioinformatics is. There's lots of formal definitions on the internet. You can go to Wikipedia, you could Google what is bioinformatics, but to me, I'm thinking of it as the combination between the language of life, right, so DNA, meets the language of nature, which is math. So combining those two together, look, you can, you could say it's, it's data science meets biology, it's math, statistics, computation meets biology. There's a whole different way to think about it, but my concept of it is the language of life meets the language of nature. Now even more important than what is bioinformatics is is why is it important? And I've thought of a few examples just in, just in my day-to-day life of where bioinformatics could come into play. Possibly the biggest example or the biggest reason to be interested in bioinformatics is the concept of personalized medicine. Now, this is going to be a high-level approach, so if you have any, any better examples, I'd love to hear it in the comments below. So this is just how I'm thinking about it after just being introduced to the the fundamentals of bioinformatics. Let's take the concept of medication, right? So my dad has Parkinson's, Alzheimer's, and dementia, so a plethora of cognitive diseases, and takes medication daily that has been prescribed to him by a doctor. However, as great as the medication is and as much good as it does, I'm just thinking about how it could be done better, right? Like, could Could my dad have his genome sequenced and then potentially have a drug tailored to his genetic makeup, right? So, that's the concept of personalized medicine. Or a step further is that when you visit the doctor, how about the doctor, if they have a history of or they have some data of your genome that was sequenced at birth or something of the like. And now, of course, there's going to be ethical and privacy issues around this, but this is just what I'm thinking about at the moment. And they could use that information to help treat you better. And another example is the concept of nutrigenomics. So, personalized medicine, the next step in my eyes would be personalized nutrition, or maybe that's one and they're one and the same. But right now, as I've got a background in nutrition, right, and it's hard for me to recommend a food that someone should eat because everyone's so different. But if we could put some data behind, or some quantitative data behind, your DNA, your genetic makeup, the DNA of your microbiome, so your gut bacteria, the DNA that's in the food that you eat, and create a system that feeds itself or gives information back on how these systems are interacting with each other, we could potentially figure out a way to to have a tailored, much like a tailored drug, it would be a tailored diet for each individual. And then you could take it a step further and do the same with exercise. Should you be doing this type of workout? Should you be, if you incorporate fitness data and then you incorporate the data of your genome, maybe you shouldn't be doing sprints every second day. Maybe you should be doing longer forms of exercise. These are the kinds of things that I'm thinking about that are in my head after being introduced to to bioinformatics. That's enough talking, let's check out some actual code. Now as different as we all are, we have one thing in common, and that's about 3 billion or so of these little letters here. Now what are these letters? Well, they're the nucleotides that make up DNA. So all of our DNA is just 3 billion lines, and I mean it's much more complex than this, I'm simplifying it with letters on a page. Is just a combination of the letters A, C, G, and T. Now, they're nucleotides or nucleic acids. So, we got A is for Adenosine, T is for Thymine, C is for Cytosine, and G is for Guanine. Now, the difference between you and I is about 20 to 30 million. So, just under, just over half, between half a percent and 1% of the 3 billion or so in every cell. Now, we've got a little code, a little toy problem here. So we got, say we have this sequence, it's about 500 or so of these A, C, Gs and Ts. And then we have this sequence down the bottom, right? And that's, that's pretty hard to decipher, but let's just say this is a sequence we've isolated and we know that this one is the start of a particular protein that we're after. Why? Because maybe that protein does good things in the body and we want to isolate where it starts So we can pull it out, make more of it, and then say, for example, that, that protein helped to fight cancer. We want to put more of that back in the body. So that's one example of how you could potentially use bioinformatics to find— first of all, find this sequence and then figure out how to pull it out. So what, what can we do here? Well, this pattern count is going to just simply count how many times this occurs in this sequence here. Put that in. So 18. So in that one sequence, we've got a potential for 18 versions of that good protein we were talking about. And now again, this is a very, very simple example. There's a few more here that are a part of the bioinformatics course, which I'd highly recommend checking out if you want to get into this stuff. I'm certainly going to be continuing with it. And to finish up this video, I just want to give a big, big thank you to Coursera for sponsoring this. I'm I love the platform and I love even more that, that we all have access to some of the most incredible, incredible health content out there and it's constantly being updated. I mean, they just, they just released a whole bunch of new stuff for 2019. So, so be sure to check it out. If you want the bioinformatics course that I'm, I'm into, there'll be a link below. Otherwise Coursera have a whole bunch of stuff on everything we've mentioned here, programming, statistics, math, health, you name it, it's probably there. But now you've heard a little bit about what bioinformatics is and some ways that it could potentially be used for. What's something that you would use it for? Leave a comment below. Would you, do you think the, is NutraMe a good idea? If you were future Dan, would you invest? Would you be a fan? Would you use the product? I probably would. But if you've got something, leave it below. I'll respond to it all. I gotta go, I gotta go. Speaking of, Speaking of nutrition, I gotta go make dinner. But otherwise, thank you so much for watching. We'll see you next week, hey? And as always, keep learning. Yeah, look, I don't know where the wink came from, but you know the drill.