Information Processing and Biology

santa_fe_institute

The Santa Fe Institute, in New Mexico, is a place for studying complex systems. I’ve never been there! Next week I’ll go there to give a colloquium on network theory, and also to participate in this workshop:

Statistical Mechanics, Information Processing and Biology, November 16–18, Santa Fe Institute. Organized by David Krakauer, Michael Lachmann, Manfred Laubichler, Peter Stadler, and David Wolpert.

Abstract. This workshop will address a fundamental question in theoretical biology: Does the relationship between statistical physics and the need of biological systems to process information underpin some of their deepest features? It recognizes that a core feature of biological systems is that they acquire, store and process information (i.e., perform computation). However to manipulate information in this way they require a steady flux of free energy from their environments. These two, inter-related attributes of biological systems are often taken for granted; they are not part of standard analyses of either the homeostasis or the evolution of biological systems. In this workshop we aim to fill in this major gap in our understanding of biological systems, by gaining deeper insight in the relation between the need for biological systems to process information and the free energy they need to pay for that processing.

The goal of this workshop is to address these issues by focusing on a set three specific questions: 1) How has the fraction of free energy flux on earth that is used by biological computation changed with time? 2) What is the free energy cost of biological computation or functioning? 3) What is the free energy cost of the evolution of biological computation or functioning? In all of these cases we are interested in the fundamental limits that the laws of physics impose on various aspects of living systems as expressed by these three questions.

I think it’s not open to the public, but I will try to blog about it. The speakers include a lot of experts on information theory, statistical mechanics, and biology. Here they are:

Wednesday November 16: Chris Jarzynski, Seth Lloyd, Artemy Kolchinski, John Baez, Manfred Laubichler, Harold de Vladar, Sonja Prohaska, Chris Kempes.

Thursday November 17: Phil Ball, Matina C. Donaldson-Matasci, Sebastian Deffner, David Wolpert, Daniel Polani, Christoph Flamm, Massimiliano Esposito, Hildegard Meyer-Ortmanns, Blake Pollard, Mikhail Prokopenko, Peter Stadler, Ben Machta.

Friday November 18: Jim Crutchfield, Sara Walker, Hyunju Kim, Takahiro Sagawa, Michael Lachmann, Wojciech Zurek, Christian Van den Broeck, Susanne Still, Chris Stephens.

20 Responses to Information Processing and Biology

  1. How did I miss hearing about this until now? It would be awesome if they were able to livestream or otherwise audio record it. It’s workshops like this where I wish people had Livescribe.com Pulse pens for taking notes with embedded audio for sharing/podcasting after-the-fact.

    • John Baez says:

      This workshop is ‘by invitation only’, not open to the public, so they aren’t going around advertising it. I will try to blog about it, but I will probably be more focused on interacting with people than taking notes: if I get too carried away with blogging I wind up not interacting with people enough. You can look at the Santa Fe Institute website and see whether “workshops” tend to be recorded. I have the vague feeling they’re not.

      • arch1 says:

        I hope you talk w/ Seth Lloyd. In “Programming the Universe” Lloyd recalls making an incorrect statement about QM in one of his first discussions w/ Gell-Mann. “’No,’ said Gell-Mann, his voice getting louder. ‘No!’ Putting his forehead down on the table where we were sitting, he began pounding the table with his fists. ‘No! No! No! No!! No!!’ Here, I thought, was someone I could work with.”

      • John Baez says:

        I’ve never met Seth Lloyd, so I’m looking forward to it. Maybe I should emulate Gell-Mann, so he decides he can work with me.

        One of the people I’m really eager to meet is Christopher Jarzynski, who discovered the wonderful Jarzynski equality, one of the few generally true equations in nonequlibrium statistical mechanics.

        • arch1 says:

          And they are paying you to do this :-) Thanks for letting us participate vicariously John.

        • John Baez says:

          Sure! This week I’ve been working my butt off trying to prepare two good talks while teaching three courses and grading 107 calculus midterms and 6 real analysis qualifier exams, so I don’t feel too guilty about getting a free trip to Santa Fe next week to talk to lots of good scientists.

  2. It would be great to have information from the workshop !

  3. Extremely interesting questions. It would be good to know what the speakers have to say.

  4. www,arxiv.org/abs/1104.2854 may be related

  5. Daniel Mahler says:

    Nice! What does it take to get admission to something like this?

  6. This is my talk for the Santa Fe Institute workshop on Statistical Mechanics, Information Processing and Biology:

    Algorithmic thermodynamics.

    It’s about the link between computation and entropy. I take the idea of a Turing machine for granted, but starting with that I explain recursive functions, the Church-Turing thesis, Kolomogorov complexity, the relation between Kolmogorov complexity and Shannon entropy, the uncomputability of Kolmogorov complexity, the ‘complexity barrier’, Levin’s computable version of complexity, and finally my work with Mike Stay on algorithmic thermodynamics.

    For more details, read our paper:

    • John Baez and Mike Stay, Algorithmic thermodynamics, Math. Struct. Comp. Sci. 22 (2012), 771-787.

    or these blog articles:

    Algorithmic thermodynamics (part 1).

    Algorithmic thermodynamics (part 2).

    They all emphasize slightly different aspects!

  7. John Baez says:

    Amazing connections between category theory and chemistry in Christoph Flamm’s talk. For example:

    • Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler, Inferring chemical reaction patterns using rule composition in graph grammars.

    Abstract. Modeling molecules as undirected graphs and chemical reactions as graph rewriting operations is a natural and convenient approach tom odeling chemistry. Graph grammar rules are most naturally employed to model elementary reactions like merging, splitting, and isomerisation of molecules. It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reactions into a single composite chemical reaction. We use a generic approach for composing graph grammar rules to define a chemically useful rule compositions. We iteratively apply these rule compositions to elementary transformations in order to automatically infer complex transformation patterns. This is useful for instance to understand the net effect of complex catalytic cycles such as the Formose reaction. The automatically inferred graph grammar rule is a generic representative that also covers the overall reaction pattern of the Formose cycle, namely two carbonyl groups that can react with a bound glycolaldehyde to a second glycolaldehyde. Rule composition also can be used to study polymerization reactions as well as more complicated iterative reaction schemes. Terpenes and the polyketides, for instance, form two naturally occurring classes of compounds of utmost pharmaceutical interest that can be understood as “generalized polymers” consisting of five-carbon (isoprene) and two-carbon units, respectively.

    or this:

    • Jakob Lykke Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler, 50 shades of rule composition: from chemical reactions to higher levels of abstraction, in Formal Methods in Macro-Biology, Springer Lecture Notes in Computer Science 838, pp. 1177–135. (Not open-access!)

    Graph rewriting has been applied quite successfully to model chemical and biological systems at different levels of abstraction. A particularly powerful feature of rule-based models that are rigorously grounded in category theory, is, that they admit a well-defined notion of rule composition, hence, provide their users with an intrinsic mechanism for compressing trajectories and coarse grained representations of dynamical aspects. The same formal framework, however, also allows the detailed analysis of transitions in which the final and initial states are known, but the detailed stepwise mechanism remains hidden. To demonstrate the general principle we consider here how rule composition is used to determine accurate atom maps for complex enzyme reactions. This problem not only exemplifies the paradigm but is also of considerable practical importance for many down-stream analyses of metabolic networks and it is a necessary prerequisite for predicting atom traces for the analysis of isotope labelling experiments.

  8. John Baez says:

    Chris Kempes’ talk, full of good stuff, ended with this fun rough calculation comparing the efficiency of the biosphere and the efficiency of the best human supercomputers:

    Biosphere: 4 × 10-18 joules per bit operation.

    Supercomputer: 5 × 10-13 joules per bit operation.

    Here the only ‘bit operations’ in the biosphere that are being counted are those that form proteins out of amino acids.

    This may be from here:

    • C. P. Kempes, L. Wang, J. P. Amend, J. Doyle and T. Hoehler, Evolutionary transitions and shifts in physiological tradeoffs, International Society for Microbial Ecology Journal (2016).

  9. John Baez says:

    Massimiliano Esposito is giving a great talk on stochastic thermodynamics and chemical reaction networks, based in part on this:

    • Massimiliano Esposito and Riccardo Rao, Nonequilibrium thermodynamics of chemical reaction networks: wisdom from stochastic thermodynamics.

  10. […] at the Santa Fe Institute we’re having a workshop on Statistical Physics, Information Processing and Biology. Unfortunately the talks are not being videotaped, so it’s up to me to spread the news of what’s going on.

    Two days ago, Jarzynski gave an incredibly clear hour-long tutorial on this subject, starting with the basics of thermodynamics and zipping forward to modern work. With his permission, you can see the slides here:

    • Christopher Jarzynkski, A brief introduction to the delights of non-equilibrium statistical physics.

    Also try this review article:

    • Christopher Jarzynski, Equalities and inequalities: irreversibility and the Second Law of thermodynamics at the nanoscale, Séminaire Poincaré XV Le Temps (2010), 77–102.

  11. I just skimmed a paper on arxiv on a related topic—energy use by ‘cloud computing’. it said by 2040 100% of electrcity generation by humans if present rate of increase continues will go to power the internet. they were developing an algorithm which would reduce the use tho it would take a bit longer. sortuh like ‘slow food’ or slow computing’.

    the papers by Esposito are on track, but as noted in their references these ideas go back to 2003 and even before that. (when i was in college i asked some profs about papers like these i had seen in the library—they din’t know what i was talking about).

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