> 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.
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.
This so much:
Cookbook approach to statistics without insight leads to terrible results, much data effectively thrown away - very costly data $millions worth.
I have some complex medical conditions and it is just terrible to see highly trained specialists who are nothing more than cookbook executors. Once you are off the beaten path they are at a complete loss.
The rampant statistical illiteracy in the medical world from general practitioners to researchers make this era of big data a huge risk.
<Person I know> is a molecular biologist and jokes that her medical practitioner friends know almost nothing about how the human body works.
> Memorisation
> Cookbooks
> Linear regression, maybe
Exceptions: lab/field work aren't covered by math class (do these as your minor and senior year) , and theoretical physics/chem do the math -- but you are better off doing a year of math and a year of accelerated whatever, than doing intro whatever and then advanced whatever.
I've given dozens of presentations to my fellow hospital docs, and by far the most attended and appreciated are those dealing with proper use and interpretation of statistics.
My transition was easy. The required prerequisite college courses for admission to med school (two semesters biology, two physical chemistry, two organic chemistry, two physics, one basic calculus) were more than sufficient to get me through my med school courses. I took a few extras too (anatomy/physiology, molecular biology, and genetics) but I feel those were overkill and might not have been necessary. At least they were fun.
I think the biggest part though was that I sought out some experiences in the medical field. This demonstrated to the admissions committees that my career goals weren't a lark and I knew what I was getting into. I did a 6-week summer mini-med-school camp, shadowed physicians in the ER a couple of times, and volunteered in a medical records department for a semester. When I didn't get into med school the first time, I spent the interim year getting my Certified Nursing Assistant license and working in an assisted living facility and a hospital's locked psychiatric ward. Fun times.
Good luck.
I took Physical Chemistry which I am told is looked at by medical admissions offices and something which standard applicants would have a hard time with but those with math/engineering backgrounds should do well in.
The GP is implicitly comparing the impact a math curriculum had on their reasoning with the impact that a biology curriculum might have had. But n=1 for the math curriculum (perhaps the beneficial traits are endogenous) and n=0 for the biology curriculum.
An astonishing amount of biology is rather mechanistic, systematic, and logical. I feel my math (and minor programming) experience trained my brain in such a way as to understand complex biological interactions more intuitively.
I'm not bragging here, but while I was actually understanding the theories behind what we were learning, on a fundamental systemic basis, many of my classmates were essentially relying on memorization. They passed the exams fine, and make okay doctors, but they can't explain things to patients well and their research ideas are very limited and safe.