
A Computer Scientist's guide to cell biology (2007) [pdf] - nafizh
http://www.cs.cmu.edu/~wcohen/GuideToBiology-sampleChapter-release1.4.pdf
======
alexholehouse
This is very nicely writen, and looks like an extremely useful resource for
those interested in entering the bioscience field. I would offer one word of
caution, which I think is especially pertinent to those coming from a CS
background (and, I should add, not something this book particularly does, as
far as I have read).

Historically and practically, biology as a field has often represented
processes and events in an highly linearized and well defined fashion. This is
extremely attractive for a number of reasons. As one key example, we (as
humans) remember through narrative, and so the construction of a Rube Goldberg
type description of a process is often a useful technique for easy recall of
complicated information ("A hits B which winds up C and switches on D __etc
__... ").

The other reason is that many of the experiments would really imply a linear
pathway if that were all one look for. There __is __often a clear-cut
progression of information signals or metabolic intermediates moving from one
state to another state through well defined intermediates, such that if that
were what you were looking for, you 'd find it.

In this representation, many biological processes are highly analogous to
computer programs which perform some task - you have an input, and through
functional manipulation generate an output.

The realty, as has been uncovered in the last 15-20 years, is that most of
these processes and events are not linear pathways. They are wildly
heterogenous networks that integrate information spanning a range of temporal
and spatial scales. The non-equilibrium nature of the cell means that, to a
certain extent, everything is coupled to everything else through interactions
where the associated coupling coefficients are also dependent on everything
else.

The reason I bring this up is that I think it's very temping to find analogies
between CS and biology (DNA = hard drive, RNA = memory etc). The problem with
this is that we (again, as humans) implemented the underlying computer
architecture, while we're only scratching the surface of biological
complexity. By prescribing that some mapping of CS-to-biology exists we risk
convincing ourselves that we understand the biology better than we do, or
making assumptions regarding how the biology may or may not work.

Clearly, this kind of description can be used early on, but its important to
recognize that these analogies should be viewed as broad-brush stroke
descriptors and not functional ones.

~~~
chairleader
+1 This is a nice reminder about how "organic" this domain is. I agree, even
if I read this cover to cover, I wouldn't be ready to nail a bioinformatics
job interview.

As an armchair biologist (among other things,) this paper is a great next-
level-of detail from the pop-sci knowledge that "DNA is the Program." Indeed,
Wikipedia's illustration of the workings of a ribosome takes steps towards
your point, @alexholehouse. It's jagged and sloppy, and while it appears
clock-like in its machinations, one must immediately ask how that could be
anything but an oversimplification. Is this the workings of the computer that
interprets DNA's "program?"

[https://en.wikipedia.org/wiki/Ribosome#/media/File:Protein_t...](https://en.wikipedia.org/wiki/Ribosome#/media/File:Protein_translation.gif)

A final point, it's sobering to watch this and think that every movement of
these proteins represents at least one doctoral thesis' worth of work.
Although we have amazing ways of seeing these microscopic actions at play, we
don't exactly have debuggers, REPLs or profilers that let us observe cells
unaffected. Messy stuff.

------
emcq
If you like this book you will probably also like the gold standard for
cellular biology: Molecular Biology of the Cell
([http://www.amazon.com/Molecular-Biology-Cell-5th-
Edition/dp/...](http://www.amazon.com/Molecular-Biology-Cell-5th-
Edition/dp/0815341059)).

It costs more but it's worth it. It's a deeply informative book that covers a
large spectrum of topics that you can read without much background knowledge
in biology. It's the same book your doctor or bioinformatics professional
probably used in school learning about cellular biology.

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nsajko
Does something something (except marketing) make this book specially relevant
for computer scientists? I mean, better than ordinary biology courses,
wikipedia, etc.

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kris-s
I work at a biotech company and what I've read so far has been a very well
written overview. Really great writeup.

~~~
doomrobo
I've currently taken a very strong interest in biotech but my majors don't
really give me much time to study chemistry or biology in any significant way.
What is the percent composition of R&D in biotech in terms of CS and bio? Is
there little enough bio that I could get away with self-teaching it?

~~~
kris-s
Depends on the company and what their product/service is. You might need
varying levels of knowledge. I'd say you totally could self-teach it for most
roles.

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dekhn
This is not bad. Probably should hand one of these to every computer scientist
I work with who is contemplating biological problems.

I think having it focus on cell biology, rather than just the information
systems, is worthwhile.

