I think what is described here comes down to the fact that we don't have much (any?) _deep_ understanding of biology. The most concrete aspects of biology are observations. For example, anatomy is very well understood because it's essentially observations of structures within living organisms, as field it has been relatively stable for a long time, hence there are well-established methods for teaching anatomy.
There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
When so much of biology is observation without deep insight, it shouldn't be a surprise that biology is difficult to teach, and even more difficult to find beauty in for new students.
I would argue that depends on your definition of deep - we are certainly getting better at developing both genetic and chemical tools that allow us to probe specific pathways/sub-systems of biology, and read out the resulting perturbed phenotype(s).
> There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
I think this is just about the last thing that we will ever solve/figure out.
There are just a mind-boggling number of parameters, feedback loops, dynamic modifications, interactions, etc that are effecting cellular state (let alone organism state) - something that I think many CS oriented folks ignore when talking about "DNA as source code" (perhaps if program behavior depended on the size of indents, font, variable names, how many lines of code you wrote, the proximity in source location of different functions, etc).
> (perhaps if program behavior depended on the size of indents, font, variable names, how many lines of code you wrote, the proximity in source location of different functions, etc)
I think I've seen all of those functionalities implemented in esoteric programming languages! Nice comparison.
What you haven't seen is a CD-ROM sized program with no abstraction, encapsulation, or modularization, all implemented on a language that has all of those.
Oh, and that is being interpreted by more than one incompatible interpreter at the same time.
Yeah, I think programmers would better appreciate the complexity and subtlety of biology much better if they had to evolve their programs rather than code them up explicitly. (I say this as someone with degrees in both subjects.)
You can have what I would consider deep knowledge of a system without the ability to manufacture it or modify it. For instance, we have pretty deep knowledge of how the sun or other stars work, but we can’t even begin to dream about creating one, or controlling one.
In the same way, we know a lot of how biology works. Obviously nowhere near all of it; but we are far beyond just scratching the surface. It just turns out that modifying a working complex system is pretty hard.
> ow the sun or other stars work, but we can’t even begin to dream about creating one
Wolfram didn't answer "how much would a solar mass of hydrogen cost" for me, but it did tell me that the solar mass is 1.988435×10^33 grams, and another search found hydrogen prices [1] in the range of US$ 250 to 1350 per MT ... So just the financing on building another sun is going to be tricky.
[1] I know it's not all hydrogen but we'll burn those bridges when we get to them.
> There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things).
And then a very similar gap between the fundamental units of one small branch of biology, ecology, where the fundamental units are living things and the emergent behavior is everything you see outside your window! We have a lot of math that explains how things act and evolve together and it's all just the tiniest little smidge of what actually happens.
Hard disagree. We understand biology for the most part. The issue is in the exact implementations.
An analogy would be like understanding how a computer works. We know how chips are made, the physics behind them. We know how bits are stored and processing steps are executed. We also know generally how operating systems work. We have the full compiled code as assembly instructions. But we don’t have the source code of the OS. We just use crude tools to figure out The details of the OS and how it works on particular subsystems but because of the crude nature of the tools the knowledge we gain is ambiguous at times.
Having done a lot of biology, I'd disagree that we understand biology.
My background is neuro, so take that into account. But in neuro, we've nearly no idea about the larger parts of how it all works. Sure, yeah, electrically active neurons, we have that down. But the non electrically active parts? I mean, we're still debating about how much of the brain is glia. Like, we can't even agree on how to count. Don't get me started on synapses.
I don't even think we understand software, much less biology :-) We can only hope to understand the pieces that are most relevant to the business domain we're trying to solve (like curing a disease or expanding an online business). The complexity of both types of systems is just increasing exponentially over time, so there's little hope (or even need) to understand the whole thing. The challenge is, of course, to understand what's relevant in the first place.
And just like in software, we can only hope to come with the right levels of abstraction and disregard the irrelevant parts at each level of understanding.
For sure - and I don't think we necessarily have the ANYONE part either :-) The reason for that is folks who build systems often leave, and the details of why or even how they did something leave with them. At some point, there's no one in the company who understands certain things.
And the analogy with biology actually goes even further - just like in software, we "know" the code (DNA), but how does that translate to the behavior of the complex system (and business requirements in software), is lost to time and the sheer complexity of these systems.
So someone builds something and they leave, and you can’t figure out their code? Or more importantly, you’re suggesting it’s impossible to figure out that code by anyone? Seems a stretch don’t you think?
he's not saying it's impossible to figure out some piece of code, but "all" code. There is just too much of it! Going into even something as relatively simple as an operating system, let alone a whole ecosystem with drivers, internet protocols & more would take many lifetimes.
The "anyone" part comes because there are countless parts of those infinately complex systems that have no documentation and no maintainers. They can individually be reverse engineered if it is needed in an individual case, but nobody is going to do that for most of them.
Even in your own example, there’s no ambiguity in the fact that glia play a role is not under question. What percentage, IMO, is just a detail. My analogy still stands I think but I suppose that’s open to interpretation.
One question I implore you to ask yourself is how much of this “we don’t fully understand it” attitude comes from indoctrination of that way of thinking that you need to have to write grants and aggrandize your own research topic. As Sydney Brenner said a long time back, (in the context of mol bio) the fundamentals have been discovered, we can let the Americans figure out the details.
If we really understood biology, or even just precisely what aspects of a given phenomenon we need to investigate and how in order to understand it, we wouldn't have wasted a decade trying to reduce CVD by increasing HDL. Billions of dollars wouldn't have been wasted chasing the wrong mechanistic hypotheses in Alzheimer's treatment. Cancer would have already been sorted.
Since 15 years ago, extracellular vesicles went from particles used to export rubbish from cells, perhaps with some vague immune involvement, to one of the fundamental mechanisms involved in intercellular communication, carrying nucleic acids between cells.
The reality is the more we understand in biology, the larger we realise biology is and the relative amount of information that still needs to be figured out doesn't change much - especially when you look at it in terms of labour required, because what left is progressively less-low hanging fruit.
Biology is incomparable to computers, or to any other man-made machine. In computers the components interact in well-defined separable and independent roles. In a biological organism, all components depend heavily on not just one or two other components, but many. The role we impute for each mechanism often interfere and/or collaborate with other seemingly unrelated mechanisms, often in hierarchical and nonlinear fashion. That's why the function of even simple biological subsystems is so challenging to decipher. Context and interdependency are everywhere. That's why the oxymoronicism of a biologist “fixing a radio” rings so true.
Very much applicable to software as well :-) Modern systems are so complex there're very few people (if at all) who understand everything in them, even though they were man-made over time.
There's a huge gap between the fundamental units of biology (biochemistry) and the resulting emergent behaviour (living things). We don't have a good bottom-up system to predict the emergent behaviour so we're mostly left with observing from the top down and poking/prodding sub-systems, hoping to gain some insight.
When so much of biology is observation without deep insight, it shouldn't be a surprise that biology is difficult to teach, and even more difficult to find beauty in for new students.