• Dynamics, Thermodynamics and Information Processing in Chemical Networks, 13-16 June 2017, Complex Systems and Statistical Mechanics Group, University of Luxembourg. Organized by Massimiliano Esposito and Matteo Polettini.

They write, “The idea of the workshop is to bring in contact a small number of high-profile research groups working at the frontier between physics and biochemistry, with particular emphasis on the role of Chemical Networks.”

I’m looking forward to this, in part because there will be a mix of speakers I’ve met, speakers I know but haven’t met, and speakers I don’t know yet. I feel like reminiscing a bit, and I hope you’ll forgive me these reminiscences, since if you try the links you’ll get an introduction to the interface between computation and chemical reaction networks.

]]>• Blake Pollard, Compositional frameworks for open systems, 17 November 2016.

]]>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).

]]>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.

• Matteo Polettini, Gregory Bulnes Cuetara and Massimiliano Esposito, Conservation laws and symmetries in stochastic thermodynamics, *Phys. Rev. E* **94** (2016), 052117.

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

]]>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).

• 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.

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!

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