
DNA from hagfish being used to synthesize bulletproof slime - cjfont
http://motherboard.vice.com/read/dna-from-this-ugly-fish-is-being-used-to-synthesize-bulletproof-slime?trk_source=popular
======
saidajigumi
It'll be very interesting to see how efforts to create "biological reactors"
for replicas of natural fibers progresses. Thus far, there have been a number
of successful attempts at synthetic replicas of natural fibers. These
successes have been spawned from chemical vs biological approaches. For
example, rayon[1] was one of the first attempts at a synthetic silk, with
nylon appearing just a few decades later[2], and eventually a number of other
synthetic fibers. While there have been undeniable commercial successes, all
of the pretenders remain worlds worlds apart from the look, feel, and even
function of natural silk.

Likewise, I've heard occasional reports of teams working on synthetic spider
silk[3]. That I've heard, none of these have developed into viable products
much less replicated the properties of the original fiber.

Artifacts like this fabric woven from golden orb spider silk[4] remind me of
tales of early plastic artifacts being collected and valued as rare treasures
(which they were). Now plastics are absolutely ubiquitous. At what point will
we create synthetic fibers that fully replicate or even surpass natural
fibers? Interestingly, while a "plastic treasure" seems like a joke today, I
expect that golden spider silk cloth will remain an impressive object both for
its history and its amazing character as a physical object.

[1] [https://en.wikipedia.org/wiki/Rayon](https://en.wikipedia.org/wiki/Rayon)

[2] [https://en.wikipedia.org/wiki/Nylon](https://en.wikipedia.org/wiki/Nylon)

[3]
[https://en.wikipedia.org/wiki/Spider_silk#Attempts_at_produc...](https://en.wikipedia.org/wiki/Spider_silk#Attempts_at_producing_synthetic_spider_silk)

[4] [http://www.wired.com/2009/09/spider-
silk](http://www.wired.com/2009/09/spider-silk)

~~~
delinquentme
This make me wonder what the functional layers of abstraction IN synthetic
biology are. Currently we think of intra-species gene sequence swapping as an
applicable layer. But It seems the _truly_ functional bits are when we can
biologically encode and express control over organ development. Something like
building a spider with X*N body mass to support Y clusters of silk glands.

~~~
possibilistic
Biological abstraction is simply _not a thing_. You must understand that the
fundamental difference between digital logic, software, etc. and biological
systems, reaction mechanisms, signaling, and the like is not simply about
sorting out how the different things are connected together. The biggest
differentiating factor is by far is how deeply fundamental high-order
nonlinear dynamics and complexity are within biological systems. The topology
is an implementation detail and isn't want causes us problems.

Consider the nature of digital logic for algorithms of a low computational
complexity. There might be a simple state machine, some kind of input, and one
or more terminal states. Let's reduce this even further to the level of logic
gates. NOT, AND, OR, and all the like are extremely simple and trivial to
calculate. As far as the physical implementation of these goes, I have to
admit that my knowledge of EE is fairly poor. I know there are some rather
involved mathematics. AFAIK, however, the ultimate determinate of "on" or
"off" state is a thresholding function. It's really not that bad of a problem.

Now let's consider one of the lowest possible levels of what we would consider
to be a biochemical system: two chemicals involved in a reaction. For our
sake, we would probably make it an acid and base problem, since acid-base
chemistry is prevalent in biochem and it's quite easy at the freshman level.

[http://en.wikipedia.org/wiki/Chemical_equilibrium](http://en.wikipedia.org/wiki/Chemical_equilibrium)

As you can see, the treatment is a little bit more complicated than Boolean
algebra. There are terms for Gibbs free energy, chemical potential,
activities, solubility potentials, etc. to consider for a system sitting in
some glassware. I won't get into the details, but do make note of the math.
Can you think of how to compute these quickly in silico or by hand? Probably
not too hard.

We're just getting started though. Things get rather noisy as we start to
consider more than a few chemicals. As we trend up towards approximating an in
vivo system, we also have to developing a model for interaction. To do that,
we have to understand each component. At both ends, we enter into the realm of
statistical thermodynamics. Here you'll encounter much more complicated models
and concepts such as canonical ensembles:

[http://en.wikipedia.org/wiki/Grand_canonical_ensemble](http://en.wikipedia.org/wiki/Grand_canonical_ensemble)

You know can't simply consider aggregate systems as pairwise interactions or
summations or any other trivial model. For one, there are too many incredibly
localized chemistries within solution that produce rich behaviors. For
example, here's a decent figure that presents a chemical transport process:
[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2980713/figure/f...](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2980713/figure/fig1/)

All of the familiar, easy math typically involves equilibria, and equilibria
systems are nice and easy systems to study. But to be alive is to not be at
equilibrium--one facet of biology is to push against that state as long as
possible. Nonequilibrium systems present further computational and modeling
challenges for us.
[http://en.wikipedia.org/wiki/Boltzmann_equation](http://en.wikipedia.org/wiki/Boltzmann_equation)

I've presented a bunch of math, and I'm not done yet. It's known that
Metabolic pathways, the interplay interacting metabolites, happen to be NP-
hard. Typical of graphs, fairly orthodox. The computational complexity of the
topology isn't really isn't expressive enough to convey the beauty of a graph
edges themselves--that they are in constant flux, connected through a chemical
calculus. Chaotic, transient metastabilities that arise and collapse. Digital
bits blink, but chemicals dance.

Do you know the breadth and depth of this machine? Here's a wonderfully
illustrative map of the known metabolic pathways--at a glance it'll give you
an instant perspective if you didn't already have context:
[http://www.cc.gatech.edu/~turk/bio_sim/articles/metabolic_pa...](http://www.cc.gatech.edu/~turk/bio_sim/articles/metabolic_pathways.png)
(When observing the forest, be careful not to forget what the trees actually
represent. There is unfathomable complexity to be found in the small
interactions.)

It probably dawns on you now that we're _not going to live to see the
answers_. I've only really introduced metabolites here. Gene regulation,
proteins... I never even mentioned the word "cell". That's far too much.

If it doesn't make sense why bioengineering is slow-paced, then I did a poor
job at conveying how hard the problem space is. Yes, we can make certain gene
products and achieve simple little results that still dazzle us. It's not
quick, it's leagues away from what we want, and it's definitely not abstractly
or generically portable. (I don't refer to the assays/methods.)

I suspect the only way to enter into the biological renaissance requires that
we get better at everything else: math, low-level logic, algorithms. I imagine
we'll have to find ingenious new ways of greatly contracting the problem
space, reducing dimensionality, operating over compositions of subgraphs, etc.
in order to deal with computing the complicated nonlinear dynamics at
interaction level. Integration into an acceptable holistic model is mind-
boggling. How to do this and get meaningful results, I have no idea. Whatever
far future tools that might be effectively applied to this will appear to have
bent the Matrix.

Thinking of today's "big data", I can't help but feel dwarfed at a truly
astronomical scale. The depth of ignorance we'll eventually have to cross in
order to solve the problems presented by a single cell... it puts everything
into perspective.

Sorry for the long post. I hope I illustrated that the core tendencies of
electrical engineering and computer science towards simplification and logic
reduction are simply not very amenable to bioengineering. You can't simply
tweak one thing or edit the binary. Everything touches everything, and you
can't adjust the weights and levers without understanding the ultimate runtime
behavior. You already know that line from Jurassic Park about the weather...

~~~
robin_reala
Thanks for the chart. That really hammered the complexity home to me.

------
jfb
I have been made deeply uncomfortable by hagfish since reading Cruz Smith's
_Polar Star_ [1] in high school, where they feature in a minor but
electrically memorable plot point.

[1]
[https://en.wikipedia.org/wiki/Polar_Star_(novel)](https://en.wikipedia.org/wiki/Polar_Star_\(novel\))

------
gourneau
Hey y'all hackers. If any of you want to work on this type of science in the
bay area, my startup Synthego wants to talk to you. Contact me at
josh@synthego.com if you are interested.

------
chrisbennet
"Try on this dress honey."

"Ooooh, is this silk?"

"Um, not exactly..."

