Bioinformatician here. All my formal training is in biology, but I've spent most of my research hours learning CS, statistics, etc, rather than biology.
> An astonishing amount of biology is rather mechanistic, systematic, and logical.
I disagree vehemently. Or, to be more precise, it is very likely that this is true in reality, but in terms of the dominant way it's viewed in both clinical and research practice, it's very much not.
The average paper's findings are qualitative, not quantitative, in nature. Of course, numbers, statistics, etc, will be used -- but they will be used to get p-values to support a qualitative conclusion, rather than to work towards a quantitative, systematic understanding of biology.
There are exceptions, of course -- a decent amount of biochemistry is quantitative enough -- but when you get to topics like cell signalling, "cell biology", etc, it is almost never quantitative.
I have a favorite question I like to use in seminars when someone is presenting findings along the lines of "protein A regulates protein B". The question is: "Among the various regulators of B, how important is A? Is A a key regulator of B, or simply one of many?". Or, even more simply, "What is the most important regulator of B?" I invariably get blank looks, as if this is an unreasonable or impossible question to answer. Of course it is, as long as your models are qualitative. The point of the question is to get the speaker to think about that, but so far, no luck.
I didn't meant to imply biological systems were simple A-->B boxes or machines with perfectly predictable actions and reactions. I worded my comment poorly.
You are quite right that it's a dynamic, multi-faceted reality. I think the blank looks are saying that it's unreasonable to ask because it's impossible to know; all they're doing is trying to tease out at least one connection. Hopefully, none of them seriously think they've hit the nail on the head and have found the one true mechanistic answer.
Well yes, if they thought this, they would likely be wrong. But that isn't what I was suggesting. The answer I would be looking for would be something like "A accounts for approximately 20%/50%/90% of the variance of B in context C". Clearly many or most biological entities have more than one input and output.
My problem isn't that there's complexity, it's that we don't/can't/don't even try to quantify and robustly model it. The reason why I feel it is such a problem is that without quantification of effect sizes, (and obviously these effect sizes would be probabilistic and perhaps context-dependent), we cannot really predict much of anything.
Another way of making this point is to consider the sort of pathway or signal transduction diagram you often see in Nature Reviews, etc. Then imagine for a moment that you are looking at a electric circuit diagram instead of a biological diagram. You have a resistor here connected to that capacitor there, etc...
What's missing? Resistances. Capacitances. More generally, numbers or functions specifying the behavior of the nodes and edges conditional on their inputs. Such an electric circuit diagram would be almost entirely useless to a person who wanted to predict the behavior of the circuit under different conditions.
I guess I just object to the statement (and you are far from the only one to have implied it, if indeed that's part of what you were saying) that we have a mechanistic understanding of X, if we cannot predict the behavior of X conditional on perturbations, even approximately.
For me, this was and is a major stumbling block in my ongoing journey to learn biology. I am often shown such diagrams and told "this is how it works", as if these diagrams had any explanatory or predictive power. I am perpetually confused about how and why people think they do.
N.B. I admit they have SOME explanatory/predictive power, but it seems to me to be very close to zero. The only remotely quantitative aspects of these diagrams are the stimulatory/inhibitory "arrowheads", indicating a positive or negative correlation, and missing edges, indicating presumed conditional independence. These diagrams also lack any temporal information.
> An astonishing amount of biology is rather mechanistic, systematic, and logical.
I disagree vehemently. Or, to be more precise, it is very likely that this is true in reality, but in terms of the dominant way it's viewed in both clinical and research practice, it's very much not.
The average paper's findings are qualitative, not quantitative, in nature. Of course, numbers, statistics, etc, will be used -- but they will be used to get p-values to support a qualitative conclusion, rather than to work towards a quantitative, systematic understanding of biology.
There are exceptions, of course -- a decent amount of biochemistry is quantitative enough -- but when you get to topics like cell signalling, "cell biology", etc, it is almost never quantitative.
I have a favorite question I like to use in seminars when someone is presenting findings along the lines of "protein A regulates protein B". The question is: "Among the various regulators of B, how important is A? Is A a key regulator of B, or simply one of many?". Or, even more simply, "What is the most important regulator of B?" I invariably get blank looks, as if this is an unreasonable or impossible question to answer. Of course it is, as long as your models are qualitative. The point of the question is to get the speaker to think about that, but so far, no luck.