Re: FW: [Fis] The Molecule as Text

> So, I need now to think about (and attend to these discussions on) how a
> formalism, say category theory, can deal with the embodiment of
> biological matter in the world�.

Category theory, information theory and statistics are all based on a
characterization of the world in terms of two concepts:

* Objects (A, B, C,...)
* Morphisms (for any pair of objects X, Y, there may be zero or more
morphisms between X and Y - let me call them "connections")

Can we slice and dice the world up into objects and connections between
them?

State space is the notion of slicing and dicing any "situation" or
"state" into a particular object. Dynamics is a morphism of changes and
transitions between states.

==

Kevin's notion of translation is a very powerful one. Indeed, we are
translating (or approximating) something mysterious and vague into the
terms of something known and precise. We're filtering perception into
objects and morphisms. We're filtering all that we can intuitively
understand into the mechanistic template of objects and morphisms. The
whole enterprise of science seems to be to map everything one can wonder
about into objects and morphisms.

Now consider the bubbling foam in a quiet bend of a river. Where are the
objects and morphisms? Consider the orbit of a planet. Where are the
objects and morphisms? Consider a colony of bacteria. Where are the
objects and morphisms? Consider a poem. Where are the objects and morphisms?

==

I think we should relax these bonds. Sure, everything can be chopped up.
  If you manage to chop things up into discrete events, you can apply
Shannon's information theory. If you chop things up into differentiable
functions, you can apply calculus. But this is all known. What FIS can
do is question the underlying representations, to propose a new way of
describing that helps communicate the mystique of, say, a turbulent flow.

There is an interesting toy, ParticleSuck
http://www.experimentalgameplay.com/dl.php?gn=00_Gravity/ParticleSuck.zip
  (the description is at
http://www.experimentalgameplay.com/Prototypes/00_Gravity/ParticleSuck_postmortem.rtf)
Play with it. Think how you could communicate what's happening without
having to work with 1000 particles? We seem to be able to understand the
flow, comprehend it, but not communicate it.

Is there a similar example in bionetworks?

Once it can be communicated in some way, it is easy to work it out and
build the symbol-wrangling toolbox of mathematics around it. But I might
be overly idealistic about our ability to break free of object/relation
shackles.

==

Maybe it's also better to speak of models (theories) and data than of
information. Information is the amount of "surprise" that some data
yields with respect to a model. The reason for the chaos boom in physics
is that some patterns of pendular/orbital dynamics were still surprising
with respect to Newton's model. The model is good when no data surprises
us.

In that sense, the information is easy once we figure out what the model
is and what the data is.

I can see that information is understood in a somewhat different way
sometimes. For example, Jerry wrote "How does an enzyme know how to
inform a chemical change?" To avoid antropomorphisms: how can we predict
the catalytic action or inaction of an enzyme?

"From the perspective of information, how is it possible that
bionetworks construct the informed flows of electrical current flow and
how is it related metabolic flows" -- In other words, can we apply the
metaphor of a flow to bionetworks?

"what is the nature of the code that an enzyme contains such that it
conducts an informed catalytic process?" -- Can we employ the metaphor
of natural language to the enzyme's code?

I find this list of open challenges very interesting, but I don't have
the "data" to be able to perform the translation. I haven't been looking
at the cells, measuring and experimenting long enough to even consider
the possibility of being able to do the translation. And I won't even
start with the shackles of translating the infinite complexity of the
cell into a data set.

-- 
dr. Aleks Jakulin
http://kt.ijs.si/aleks/
Department of Knowledge Technologies,
Jozef Stefan Institute, Ljubljana, Slovenia.
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