@@philipm3173 nah... It's just a lot of theory and for some people like me it's kinda hard to remember facts and retain it after the exam, while for subjects like maths or physics it's kinda easy once you get the logic. I'm not saying nobody should learn biology or anything... I have huge respect for people who are able to learn biology
Yes! Finally someone explains the mathematics behind biochemical processes. The cell doesn't have a mind that conscientiously tells it what to do. It's the concentration of the molecules themselves that regulates their own production and this is the mechanism. Beautiful video. Keep 'em coming.
the mind also doesn't have a mind of its own, its just information acting on itself, probably, that's the motif that all layers of complexity bellow it uses, to the quantum particle level.
Yes! Knowing can also inspire awe just as much as mystery. I hate it when people mystify things and impose their own interpretations (such as "consciousness" which isn't even well defined) on phenomena and totally dismiss logical, physical mechanisms behind these phenomena.
Actually cells also adapts based on its surroundings and environment. For example if stomach is starting to fill with becteria E. Coli the stomach cells began to change to intestinal cells because E. Coli inhibits acid production and nutralizes it with urease . So stomach cells began to adapt and changed to intestinal cells as an adaptation. This process is called "intestinal metaplasia". There is also "esophageal metaplasia" with similar anaolgy . Cells are as smart as you :).
@@monad_tcp yes but that is not related to how stuff on molecular level work. Before our body or us decide to do anything, information must first go to central nervous system, that is the brain, and then the brain is like a function where it decide what to do with those information. Just like u said, information acting on itself. But stuff on molecules level doesnt have a central nervous system, there is no info acting on itself. It just happen because that is it physical property. But the way we learn at school is that it was taught like biochemical actually have a brain and know what to do
A set of chemical and enzymatic processes, mixed to maintain an optimal operating temperature for the chemical and enzymatic processes, among other things.
It's not just 1 codebase, we have several services in the background, the main executable (the DNA of our cells) and the power support service (mitochondrial DNA). But when it is installed, it unzips several services running at the same time: The antivirus (anti viral, anti bacterial, memory cells, etc...). Digestive system, Kernel, and a long ETC... So the DNA is the program but compressed in an installable file (Conception), and for it to work it must go through a long process of decompression and assembly of the hardware that takes 9 months.
Well said! Actually, it does not only take 9 months… It takes about 30 YEARS to complete all its assigned tasks and result in a fully mature human being, where the body and brain development has reached its all completeness and fullness. Indeed, praise God the Father for his Superior Architectural Wisdom!
Very well explained! Do you guys happen to know a book that would explain more concepts in a similar fashion (metaphors), something like biology for software engineers? God is a programmer.
I understand what you are going for, but i disagree that the maths explanation is required to understand why a protein would repress its own production. without a direct feedback loop, the cell would have to sense the amount of protein it produced with another system. this sensor system would also be a protein and its production would need to be sensed and controlled as well. for the sake of accuracy, each protein would need its own sensor protein which would require its own DNA to produce as well as control mechanisms to produce enough to repress the sensed protein by enough. in addition, this sensor would need to its consumption to be sensed as well as its production. the problem is clearly one of complexity, which leads to multiple failure points. the simple answer is that the sensor and inhibitor comes with the protein, so theyre produced simultaneously and in correct proportions. the ability of a protein to inhibit its own production negates the need for a separate sensor protein. this makes the negative feedback loop of "negative autoregulation" to be the most efficient control mechanism within a single cell. when we are talking about cell-cell contact or hormonal activation, the sensor (receptor) often triggers a number of responses, some of which are usually to downregulate multiple initial signals, sometimes including the receptor itself, depending on the frequency of the signal. looking at this problem through the same equations would yield different outcomes, however, the biological difference between communication and signalling is quite clear. I think its a bit inaccurate to say that the mathematical description of the problem is the only way to intuit the solution, as the biology of the issue is quite self explanatory and the solution still requires a biological understanding of what is occuring.
This has got to be the best video on biology I've ever seen. Superb quality (both in animation and in script writing), sleek style; fellow commenters, we are witnessing the birth of a giant.
Here before this blows up 👀 A shame I'm still not even good at basic calculus, but the previous video's notion that molecule interactions act as logic gates was wildly valuable already!
As wannabe mathematical biologist, yest, there's a strong connection between maths and biology and not only in modelling with ODEs! Simetries, shapes, groups, graphs and trees, information ... awesome!
I really enjoyed this, we need more animated biology channels out there! As an educational biology video creator myself, I understand how much effort must have been put into this. Liked and subscribed, always enjoy supporting fellow small creators :)
That's why in biology, especially molecular biology, there is A LOT of mathematics. However it's rarely overly complex. In 99% of cases it's basic school mathematics. Elementary school to basic analysis. Plus imaginary numbers maybe, but that's pretty rare and also not very hard. Many things can be predicted and that's also why in silico research (computer based research) works. Look up "bioinformatics" if you're interested in that (you can also study it, which I do right now). And writing code for that is not overly hard! If you know about if/else, for-loops and arrays/lists/collections you can recreate many systems. But it doesn't stop there, you can also dive deeper, depending on _what_ you want to do. But that obviously doesn't mean that everything is trivial. You need a lot of knowledge about the system in order to do effective research.
ironically, this simple equation in this video is already more complex than any math on economics class, its funny they try to model a complex system using linear equations. I refuse to acknowledge economics as a real science, I'm recreating my own mathematical models and simulations starting from scratch. someone might have done it, but mainstream economics is pure bullshit, and I really want to understand how to complex system works, not what the mainstream status quo think how it works.
This video was a very good introduction to the concept of computational biology. Your decisions of when to introduce what vocabulary were all well conceived and executed. Keep going. You are doing a wonderful thing here
There is a section that a little bit confusing: at 8:00 you are talking about changing X, but diagram shows changing of b, then you are talking about changing b, but showing changing n - had to rewatch it to understand what is going on. Also it is hard to hear you at 9:54 when music is starting to play too loud (it was a problem in previous video too) Otherwise it is a great video, thank you very much, hope to see more!
Tremendous content. You’re still in the algorithmic lag phase before you get some exponential growth. As a molecular biologist trying to learn how to love math, I loved this video and immediately subscribed. Have been going along a learning chain on this topic for the past few days(months to years really) and the algorithm has been central to finding new topics and information. That just means your channel is being recognized and recommended to ideal viewers by the algo. Definitely checking out the rest of your videos and super happy I got this video in my feed. (Long comment for algorithm luck)
@@justindie7543 definitely open to collaboration across any and all avenues. I’m yet to enter my PhD but definitely headed in that direction. Would love to chat about things and hear your experience thus far in your doctorate!
Wow such amazing content, as a biologist I always love to have a deeper understanding by knowing the math behind the different biological actions. So insightful 👍👍👍
I'm glad i found this channel the content you making is unique and very informative & worth a sub. A small suggestion share your channel of reddit programming subreddit and Hacker News you will get more people who interested in watching this amazing content
Super rare channel I’m glad popped up. Amazing super summary of the central dogma Btw. It’s very rare to find someone who can explain something in a polymath type of way 🙏 I love the black box summary you give thank you
"Negative autoregulation." You mean an "initializer function?" It's not hard to find computer programs have procedures that perform one task while also setting a flag to prevent it from being run again. The biological negative autoregulation is obviously more fluid and analog, but it's also pretty clear that they effectively accomplish the same objective, setting up a state and then doing nothing. If the regulators get lost somehow, it's basically like the initialized flag getting unset meaning the procedure can run again.
damn I've always wondered if DNA and life itself uses a 'programing language' so to speak, it really is an interesting topic! I really hope you make more videos like this
There are some very serious problems with this video particularly with the overuse of the the "cell = computer/machine" metaphor. I've made about why the cell is not a machine here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-jPhvic-eqbc.html In short, the argument is as follows: The genome is very much not a blueprint for the organism, nor is it the OS of living systems. The proteins that get produced from it often have many unpredictable functions, interacting with all sorts of partners in all sorts of ways. Hence, we cannot draw out circuit diagrams and model the cell completely with DEs. It just doesn't work and is not an accurate depiction of what is going on. Any possible 'circuit' represents only one of an essentially infinite number of ways proteins could interact. Before you make any more videos on this topic I would urge you to read this paper by Dan Nicholson (particularly sections 3 and 5): philpapers.org/archive/NICITC.pdf. This video is well-produced and the animations are nice. But we have to stop this machine concept of the cell spreading any more than it already has. It is not consistent with the complex reality of life.
Alright, I’ll look into your video and I’ll prepare a proper response in the next video. I highly appreciate the well thought out comment and criticism!
@@Nanorooms Thanks for taking the criticism well! Honestly this channel is very cool, and there is indeed plenty of maths to be done in biology. Just need to make sure it is based on good conceptual grounds that reflect what we know empirically about how cells behave :)
Yo the video you linked is fucking awesome. Well I guess you made it. Amazing. I love that you're taking this angle on the subject. Not further spreading overly complicated explanations of a subject as many academics believe their duty to be, but instead digging to the bottom of a subject by reading diverse sources of information and really pondering the truth of even basic (generally accepted) facts about the material, and spreading those challenging ideas in a well communicated fashion. Kudos.
Really good video. I am very new to mathematical biology. However, with this particular example, you have done a really good job in making me understand the importance of mathematics in biology. Thanks!
Great man, I was always facinated about biology. Mainly my questions were why does a zygote give birth to different type of cells when cell theory says cells can only produce their own kind of cells, and how cells communicated; how does body know to produce this many cells etc. I was able to find the answer to first one. Your video gives the answer to the latter. I am now doing engineering but your video showed me how I can integrate these two fields. Thanks mate, expecting wonderful things from you.
Great video! You might already be planning to do this but adding time delay to this equation and the subsequent oscillatory behaviour could be a good addition to this series!
Beautiful animation, beautiful topic, beautiful explanation. 1000 subs is a shame, but thankfully the algorithm seems to have started doing its job. Cheers!
Oh boy, just the topic for a youtube channel that I want to see. If you're ever interested in proof reading or interviews or writers or whatnot, I'm part of a whole network of labs of epigeneticists trying to bring math models in our study of gene regulation.
If you’re not comfortable with sharing your contact information here, this channel’s email is nanoroomsatbottom@gmail.com. I would love to get in touch!
A series! Subscribed. Fan't wait to you get to morphospace and the electrical interactions ect between cells as that's the next level of abstraction above protein regulation and still you're not even to brains yet
SEMA students (especially computing): 4:11 Negative auto-regulation (NAR) simplest network motif, how it works (4:21 a protein can be its own repressor) should be enough to make should give a straightforward notion of
I am a med student and you killed it dude. You are super smart. Wishing you very good luck going somewhere with this research. I am probably gonna use some parts of this video in my next presentation. Hehe
A little rushed through the last third of the video or so, but otherwise a good explanation of these phenomenon with very expressive graphics. Good stuff! I would suggest pacing things out just a little more carefully and letting the natural rhythm of your thoughts breathe in the future.
You are my favourite channel right now in the whole platform. If I tell you that creation is a massive massive computer and there is a way to hack it up would you believe me??? Please keep uploading. You have an amazing content.
Specifically if it's a square matrix that when multiplied by itself gives a negative scalar matrix (or something similar in the case of spirals). But of course something that squares to a negative scalar should be very familiar, it's an imaginary number. As it turns out, every complex number can be represented as a matrix, specifically a rotation matrix. Because rotations _are_ exponentials. Hyperbolic rotations are also exponentials, though they're easier to see with matrices since split-complex numbers aren't as well known. Values that square to 0 (matrix or otherwise) can also be exponentiated into something that behaves like a rotation, though most wouldn't call it one. They'd instead call it a translation.
People have been so focused on aerospace and engineering and computer science for the future, which they're right to do, because those fields are booming, but over the next few decades, molecular biology, biotechnology, biomolecular sciences etc. are gonna go CRAZY. Nanomedicine is just the beginning.
6:30 Funny you should mention a lump model. Those are often used in electronic to approximate a circuit with composite characteristics. like a Head phone speaker of a piezo electric crystal mic.
mate your videos are really nice and they showcase the similarity we can exploit between genetics and software. BUT please normalize the sound of the videos. I have to turn up the volume a lot to hear you. You can do this in audacity, in the effects tab!
hey, i think you would benefit a lot from control theory! i'm an ee student who's interested in robotic controls, and this is very similar to what we deal with in our classes and workshop.
I think the problem of activity given concentrations is kinda easy in the sense of math. The big problem is: given a sequence of nucleotides, what are the proteins it can produce? Obviously are sequence of transciption start with MET, but after the transcription, what is the correct folding of the protein and how interact with other tissue or structure? Untill now, genetist need to switch on and off a gen to speculate what the protein code by that gene is doing
