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From Matter to SelfOrganizing Life 
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#1
Oct513, 08:29 PM

PF Gold
P: 4,292

The title is the name of a Science book review on the following book:
From Strange Simplicity to Complex Familiarity, by Manfred Eigen, published back in April. The review was published in the current issue of Science, but it's behind a paywall. It's interesting though, if you're at a university you might be able to access it through the library. http://www.sciencemag.org/content/342/6154/39.summary Has anyone here read the book? An excerpt from the review: (6) B. Sinervo, C. M. Lively, Nature 380, 240 (1996) The five chapters of the book are: 1) Matter and Energy 2) Energy and Entropy 3) Entropy and Information 4) Information and Complexity 5) Complexity and SelfOrganisation 


#3
Oct613, 12:07 AM

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Why is it interesting? It just says one has to pay attention to dynamics, and not just optimize blindly.



#4
Oct613, 07:53 AM

PF Gold
P: 4,292

From Matter to SelfOrganizing Life
I think it says not to optimize at all, really, and it doesn't just say what not to do, it suggests that gametheoretic perspective is a more appropriate conceptual toolbag. This is interesting to me, in particular, because I use genetic algorithms to realize unknown physiological parameters and I've been struggling with the contradiction of searching for a single optimum in a diverse landscape.



#5
Oct613, 11:30 AM

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More appropriate than what? Isn't "evolutionary game theory" the new name for the same old thing  dynamics. In a particular form of evolutionary dynamics  eg. frequency dependent fitness  it is conceptually analogous to game theory. But it is basic and not new.
http://www.life.umd.edu/classroom/zo...8ESS/ess.html http://www.biology.arizona.edu/bioma...tions/fds.html http://www.sparknotes.com/biology/an...ogy/terms.html More generally, what is the "environment"? In thermodynamics we know the division is arbitrary. So in evolution the "environment" of an organism includes other species. If the environment changes  including the frequency of other species  the fitness of the organism can change. 


#6
Oct613, 11:41 AM

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Here, for example, is a discussion of related concepts  "Nash equilibrium" and "Evolutionarily stable strategy"  from two different fields.
http://en.wikipedia.org/wiki/Evoluti...table_strategy What's new then? If you look at what's taught in undergraduate biology http://www.life.umd.edu/classroom/zo...8ESS/ess.html, there are the assumptions of infinite populations and interactions between all pairs (dynamics on a fully connected graph). The recent work, including that by Nowak who coauthored the review, is the extension to finite N and graphs that are not fully connected. There are effects that are not obvious even if one knows the result for large N and full connectivity. 


#7
Oct613, 12:17 PM

PF Gold
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#8
Oct613, 12:19 PM

PF Gold
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#9
Oct613, 12:25 PM

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There is a state in all of this, it's truly (stochastic) dynamics. Most of the mathematics is Markov chains. Here's Taylor, Fudenberg, Sasaki and Nowak's review http://www.ped.fas.harvard.edu/peopl...owak/BMB04.pdf, where the derivation from dynamics is clear. They review the classic results, then go on to the finite N results which are new. I've never read (too dense) http://ptp.oxfordjournals.org/conten....full.pdf+html , but it is a quite famous article about (I think) the statistical lattice version of the LotkaVolterra equations you mention. BTW, if you think I'm agressive, sorry, but here's my confession: what on earth is an experimental neurobiologist like me doing with this? It showed up in a model of the neuromuscular junction http://www.plosbiology.org/article/i...l.pbio.1001352 (see Fig 7). 


#10
Oct613, 02:21 PM

PF Gold
P: 4,292

I don't have a great idea of the of the atmosphere to judge the global novelty of literature. I know that optimization approaches are commonplace in physiological modelling and I go a different route and this was something that articulated my feelings on estimating parameters to make model match observation.
I've heard that when a spine makes transcription requests from the soma (like via TIPS after microtubial invasion or other BDNF pathways) that the contents of the package ( presumably things like psd95 carried by motor proteins) can be hijacked by spines closer to the soma as they pass by. I guess that the paper you mention contributes to the same idea of neural darwinism with respect to synapses. I can imagine a high level experimentalist (like a neuropsychologist or someone looking at systems development) might use this to explain system behavior in networks. 


#11
Oct613, 02:54 PM

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As far as I know, if the model landscape has many local minima, which is usually the case with nonlinear systems of high dimension, there is no "general solution". The field of "deep neural nets" was stuck for many years for that reason, but around 2006 Hinton and Salakhutdinov found a trick to initialise the parameters so that the final optimzation could be done in decent time and without getting stuck. For models with few parameters and which are analytically tractable or for which you have enough computer power to cover space by brute force, the general hope is to solve for qualitative differences is different parts of parameters space, so that one can get a prediction at least about the general region one is in  when possible, this is the best since it does not assume that only one set of parameters are consistent with the data. I've seen http://www.ncbi.nlm.nih.gov/pubmed/23864375 and http://www.ncbi.nlm.nih.gov/pubmed/23629582 , but IIRC, your models involve voltagedependent conductances etc, which I'm least familiar with how to fit, and which are usually considered difficult. 


#12
Oct613, 06:00 PM

PF Gold
P: 4,292

Probably not even truly novel in my application; I read the suggestion of using genetic algorithms in a paper written by Wulfram Gerstner reporting on the results of an INCF modelling competition and of course, multiple models comes from Eve Marder's "Multiple Models..." paper (though I couldn't even tell you if her paper was "novel", it's not even really the kind of judgment I care about, I guess; I learned something from it).
Yes, conductance models have a lot of degeneracy, probably because of all the hyperbolic tangent functions (or Boltzmann functions if you like). "Fitting them" is not enough, it's not unique. You also have to see how, for a particular fit, different qualitative/emergent changes arise from small changes in the parameters. So probably some application from perturbation theory will become an important part of analyzing the dynamic "traits" of a particular parameter distribution. 


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