Maths becomes biology's magic number
bbc.co.uk
bbc.co.uk
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.
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.
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.
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.
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
> 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.
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.
Also, when you say "in all possible universes", do you actually mean "in all models of mathematical theory"?
Biology and medicine often get besmirched by 'hard' science people and mathematicians, but you never see any of those besmirchers actually come up with pragmatic solutions to the problems biologists face.
You may be interested to know that all statistical methods originate in pure math.
The "mathematicians don't do anything real" meme is even more inane than the accusation that biological science can't be rigorous.
You may be interested to know that my point is that you don't need mathematical proofs to have a pragmatic, beneficial effect - and while the OP is sitting there feeling smug, the others at that dinner table are actually out there reducing pain and disorder in the people around them. Some medical tools are completely impossible without maths - MRIs are the poster child for this - but feeling superior because "my field uses proofs and yours doesn't" is just obnoxious. This vid link is about teachers rather than medicos, but it's the same sitation; a response to the smug one denigrating the others at the dinner table, because those others don't match up to a very narrow standard: https://www.youtube.com/watch?v=RxsOVK4syxU
Besides, as the saying goes: "In theory, there is no difference between practice and theory. In practice, there is".
MRIs fall into the field of electrical engineering, and EEs generally don't understand the math they use. e.g. when working with medical imaging the radon transform is used, which would require a post graduate mathematical education to understand.
That's why it annoys me when the math/physics types think that if they don't make it in academia they can "fall back on engineering", even though they're studying something completely different.
By the time you get to 4th year math, you will be able to prove things about the existence and uniqueness of the Fourier transform. That's one way to understand it. On the other hand, you use Fourier transform (more specifically, the frequency domain), from first year EE - you can see the frequency spectrum on an oscilloscope. That's another way of understanding the Fourier transform.
Maths itself may be pure and correct, but it isn't the maths being smug at the dinner party :)
I work with health analytics and knowing both worlds is unquestionably helpful.
Interestingly, I've noticed several PhDs in Physics or EE change their careers to study biology using their math background.
But the general point does hold that yes, a high level of math or CS training is advantageous for anyone moving into a career in the life sciences, since it appears that that's where much of the foreseeable growth (in careers and research funding) seems to be.
It's also been said by many that it's easier to learn some biology after training rigorously in CS/math, rather than the other way around. Dudley Herschbach (a Nobel-prize winning chemist) once said to me that his one piece of advice for young researchers would be simply, "Learn as much math as you can."
I agree largely with @GarrisonPrime's thoughts regarding the systematic nature of biological systems. I also share the same sense of trying to understand the logic and intuition of the physiology I learned in the first two years of medical school while many of my peers were just trying to guzzle and regurgitate. I have to admit that I also fell into that mode as well at times, just due to the volume of material that was expected. But that's for another time.
I'm currently taking a year to work on research that is in systems biology/bioinformatics. While there are many things that I like about it, and I'm grateful that it presents an opportunity for me to continue learning about computer science, machine learning (been really getting into learning about Bayesian analysis this year) and biology I have to admit that this article sounds like it was written from the same vantage point that I stood on a couple years ago as I just started getting into this area of research.
The technology we have today to probe cellular systems is amazing and was literally the stuff of science fiction some 20 years ago, but it's not without its faults. This line from the article especially rang true to how I feel these days:
"But there's a problem. The vast data sets that give bioinformatics its power are also its Achilles heel."
The problem is that the systems biologists and bioinformaticists are most interested in are dynamic with complex regulatory systems that we don't have ways of measuring and most methods of measurement either completely destroy the system or alter its dynamics. In addition, it's akin to taking a snapshot of how the system is behaving at one instance in time or condition. Yet many times we are asked to use that information in a way that's akin to trying to describe the dynamics of an entire motion picture from 2 or 3 photos. And those photos are greyscale. Take for example mRNA-sequencing, a type of data that I work with frequently. It's trying to measure the amount of gene product that a cell or cells have at one point in time (basically trying to get a measure of how much geneX the cell is trying to produce). While it is an interesting measure and can give some insight into how the the cell may be adapting to different conditions, those measurements alone tell us almost nothing about the regulation behind those differences, which is the thing we really want to understand. It's a bit like seeing oil on top of water and then trying to infer the complex dynamics of geophysics that are occurring on the ocean floor. Not saying that it's not useful at all, and can help direct your attention to the next interesting thing, but I think that many people overestimate how informative the data is. And then there are still a lot of technical issues but that is a discussion for another day.
The other main point I want to make is that for all the data you think we have now about these biological systems, it's like a snowflake on top of the iceberg. Even many of these large consortium projects like ENCODE have relatively small amounts of information if you want to learn about some transcription factor or cell type that isn't one of the top 10 most well known or studied. And how many of those datasets out there are really lacking in good quality control, and then there is the politics of sharing data in an academic/research environment that is so competitive getting a job (that you will have to continue to work like a madman/woman at) is like winning the lottery.
OK, I don't want this to descend into a full blown rant. Main points - it's still really exciting, and it's a great time to have intersecting interests in medicine, math and computer science. Just that the tech we have to work with right now is still a bit nascent and expensive. I think there will be a point where systems bio and machine learning will revolutionize how we understand biology. We're just not quite there yet.
On a side note - where I do see a lot of potential right now where computer science and machine learning can start to make an impact is more on the clinical side of medicine and using ML to learn from the vast stores of EMR data. But that's also another discussion for another day. If you read this far, here is a smiley face, and have a nice weekend :)