
Maths becomes biology's magic number - nmstoker
http://www.bbc.co.uk/news/science-environment-37630414
======
GarrisonPrime
As a physician who was a mathematics major in undergrad, I completely agree.

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.

~~~
qud
How did you transition from a Maths/Programming undergrad to doing medicine?
I'm doing Computer engineering and plan to do go into medicine later, any
tips?

~~~
evoloution
If I were you: 1) I would go work in a computational biology lab, get some
papers out, Cancer research is always a step ahead but there a lot of good
projects in Neuroscience too (immunology could also work). Go to the best lab
you have around, most PIs know that it is hard to find good programmers
because they mostly go to the big tech companies or startups. Make sure you
don't do wet lab at this step it is going to be a waste of your time since you
will not have time to do anything meaningful. 2) With the papers apply to
medicine or preferably to MD/PhD program, if you get into MD/PhD program you
will have time to learn some real science too. In any case if you are the
ambitious type of person when doing step 1 try not to get stuck working as a
bioinformatician: lower salaries than tech although better than biologists,
you will hit the ladder cap faster, very few transcend to become leaders and
command multidisciplinary groups. Good luck!

------
inopinatus
Mathematics can spoil you in some ways. It's all very well to show that your
longitudinal treatment study rejected a null hypothesis at p of 0.05 or
better. It's another to prove that a theorem is true in all possible
universes. I'm married to a doctor and we have medical/science friends so I
regularly have the pleasure of wielding my mathematician's overbearing sense
of superiority when it comes to standards of proof.

~~~
catnaroek
Science doesn't have "standards of proof" because it doesn't have proofs in
the first place, nor is it supposed to.

Also, when you say "in all possible universes", do you actually mean "in all
models of mathematical theory"?

~~~
inopinatus
That is my cue in this particular dinner party to accuse you of being a
philosopher, or worse, a lawyer, and flourish the quip about the wastepaper
basket.

------
davidf18
I'm a physician with BS EE/CS. The problem solving skills and the view of
systems certainly helped understanding biology/medicine mechanistically. I
actually keep track of doctors with undergrad degrees in math/engineering
because I've noticed we do see medicine differently than our colleagues who
did not have this training.

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.

~~~
llamaz
What fields of medicine do you think are particularly suited to the mindsets
of EE/CS/Math people? I would think "memorisers" would be best at general
practitice.

------
hyperion2010
Not directly related to medicine, but I advise every high schooler and
undergrad that I mentor to take as much math and/or computer science as they
can if they are interested in biology or neuroscience (or to major in
physics). There are not going to be PhD level jobs given out for graduate work
that was essentially being a lab tech for 6 years, no matter how much cheap
labor current professors need/want. That said, I also tell them that if they
want to be successful they need to read and understand most of Molecular
Biology of the Cell because it is the foundation for understanding the
fundamental parts of biological systems.

------
bitL
After graduating in CS with honors, I intentionally enrolled into two best
nation-wide universities and cherry picked the most difficult theoretical
courses in order to get a math boost - now it seems to be paying off with the
possibilities still open in front of me, capability to learn even bleeding
edge concepts whereas observing most of my friends getting stuck in old things
that are rapidly being phased out. It seems like you need to study hard every
single day in our business; I guess the same is coming to all of them soon.

~~~
tmptmp
That's great, can you list the subjects and brief contents?

------
pak
Well, if you want to become a doctor, at least in the US, knowing some biology
will certainly help you on the first step of licensing exams... :-)

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."

------
Xcelerate
Mathematics and computer science are slowly taking over every other field. I
would hazard a guess that it's just a matter of time before psychology meets
the same fate. The goal would be to develop predictive models that can solve
personal problems — depression, addiction, anxiety, etc. Eventually, machine
learning will be able to do this better than people (exactly _how much_ time
until this occurs is hotly contested though).

~~~
qud
It already is! Cognitive psychology (currently the largest field in
psychology) has strong connections with Neuroscience and in many cases
Computational Neuroscience, which is just engineering and mathematical
modelling of the brain.

------
M00n_Sl47r
If that point about datageddon is true (strikes me as logical but im no data
scientist), it seems that learning algorithms that can deduce the importance
of datapoints based on a large but fuzzy dataset would be of significant
importance.

------
hilop
I studied math in college in the 1990s. An NSF official came to our school and
told us to get into computational biology.

------
themantalope
I'm currently a medical student, I double majored in mathematics and
biochemistry, and fell in love with programming and computer science after
doing a physics research internship during undergrad (living with an MIT
computer scientist helped too haha).

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 :)

------
3pt14159
What's also interesting is that it seems the inverse is also true for
computing. The human brain is a very cool processor and I think in the coming
decades it's not super far fetched to see synapses arise as synthetic
computers much like the inverse has been true for the past couple decades.

