[Odenwaelder]

In biology, the problem space is extremely vast and very hard to understand. I have a PhD in biology and find computer science relatively logical and simple. You can just look things up. Looking things up for a specific topic is not even easy in biology.

[esel2k]

Funny how you find comp science is simple and logic. Of course on a if else method level yes. But how to have a running system, dependencies, fail safe operations all coupled with tousand / millions lines of code is easily more complicated than a simply thesis about a mode of action of a protein.

I think we need to stop this useless debate as clearly one side has often no clue about the other. This behavior has led to massive failure in my career to successfully launch IVD devices or have a successfully bioinfo software for physicians - talking from experience here.

[goatlover]

The point is that a computer system can be entirely understood from the chips on up. Sure, it gets complex with millions of LOC. But we humans designed computers and wrote the code. Biology is not like that, and there is lack of a completed understanding form the proteins on up. So you get the complexity amplified on all fronts.

[randcraw]

Biological signals are very noisy. By contrast, CS signals are much cleaner. Only when CS delves into large volumes of social data do signals complicate. But even then, the cooked events of digital data is much cleaner than the raw signals inherent in bio-data. The complexity, interdependency, nonlinearity, and unknowns in bio variables are frequently overwhelming even for experts.

Success in biological research is driven by the scientist's ability to sort wheat from all this chaff, and it's acquired by gathering the data themselves hands-on in wet labs. A master biologist has learned how to navigate that space experimentally and analytically using techniques they've mastered just well enough to see over the noise.

As someone with degrees in both bio and CS and 15 years of work that crosses the boundary between them, I'm decidedly more in awe of those who have mastered biology.

[perl4ever]

What do you mean "can"? Nobody ever does, so I think it is valid to say that it "cannot". Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.

I think that you and a lot of people in this thread don't understand the impossibility of understanding millions of lines of code. Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?

[goatlover]

> Do you really think "computer geeks" just hold them all in their heads, and then assume that since they can do that, it must not be difficult?

No, but I think a group of geeks could. However a group of biologists cannot explain a biological system in full. When I say a computer system can be understood in full, I mean that human knowledge encompasses the working and programming of the machine, not that one person could know every detail. This is not the case for biology, as there's plenty we don't know.

> Otherwise, you could just as well say that a biological system can be understood from the atoms on up, since quantum theory provides a complete understanding.

It really doesn't provide a complete understanding for biology anymore than it does for sociology. It's not possible for humans to reductively explain such fields in terms of physics. No one can even prove this is possible. But it's not a complete understanding for physics either, since there's Relativity and questions about quantum gravity and dark energy.

[perl4ever]

"You can just look things up"

Not only is documentation pretty scarce these days, but good information on the interactions between pieces of software is much scarcer, due to the exponential number of ways that it can interact.

[vbordo]

> Looking things up for a specific topic is not even easy in biology

How do you find info on bio topics right now?

[mattkrause]

Finding the actual info is not terribly difficult: Google Scholar and Pubmed, maybe with some of the arxivs.

The trick is interpreting it and putting it into context. A paper will report the results of one specific experiment, and it’s very rarely exactly what you want. Understanding how a result will generalize to other conditions is tricky, even for experts: there are tons of weird feedback loops, unusual dynamics, and other traps for the unwary (plus badly designed experiments and the occasional legit Type I error). For example, doubling the amount of a substance almost never doubles its effect, and in some cases, the effects aren’t even monotonic: ~75% alcohol, for example, is a much better disinfectant than 50 or 100%.

With time—-and lots of paper-reading, you do eventually develop a sense for what factors might matter and how you could check.

[Odenwaelder]

Depends on how deep you want to go. You need to find out what people are working on, and you do this by searching on PubMed and talking to people working in that field.

[vbordo]

This sounds similar to what my friends in academia describe while exploring their fields. They build an impressive repository of knowledge regarding who's working on different topics/subfields over time. This may be one advantage of pursuing a PhD. Would be cool to see someone make the process of acclimating to a new field more accessible.