Alessandra Carbone talked about protein evolution. I was pleasantly surprised to find a protein evolution project represented here. I gather it was an informatics approach to finding physical points of contact in a globular protein. I missed some of this one, alas, so perhaps I misrepresented it in my mind.

Moya Chen talked about parallel computation using self-assembly. I gather that there is such a thing as a nu-bot system? It can grow into different shapes and lines and has more functions than just bind or not-bind. The nu-bots can push each other and move. There are complex movement rules. The animations are really cool. They show how these little robots could grow into deterministic shapes. They have implemented things like a sorting algorithm which is pretty clever. I am not clear how it could be implemented experimentally.

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Constantine Evans from Eric Winfree’s lab talked about theoretical and experimental tile assembly results. The optimal use of the sequences of the sticky ends was important. There are only so many short sequences that are appropriate for sticky ends. You run out of unstructured (linear) combinations of ATGC if you can only have an 8-10 base sequence. If you had infinite, specific partner sequences, you could program tiles to assemble anything, deterministically, just by making each tile a new pixel that only binds its neighbors in an array. But we do not have infinite sequence space. That is why biology does not specify “cell X, Y, Z becomes muscle. Cell X+1, Y+1, Z+1 becomes bone.” In biological development, a cell follows a contingency tree according to the genome and the local stimulus to generate a complex shape. Self assembly might be approached the same way.

Tiles can do that. They can follow simple rules like cellular automata. But errors also propagate. How does one avoid locking in an error? Some sequences are better than others. Different pairs have different sensitivities to sequence issues. This can be used to derive rules for what sticky end pairs are best in a given design. Basically, this is careful analysis of sequence binding and misbinding energy for every sticky end in the system. Careful analysis of misbinding off-rates gives a much better simulation of errors and allows the designer to avoid them.

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The last few presentations were about state-changing tiles. Most self-assembly simply takes passive objects that are thermodynamically predisposed to bind each other. Under the right circumstances, these objects will fall into the most favorable position by any of a multitude of routes. The “programming” is in designing the lowest energy state. DNA origami falls into this category. Even algorithmic self assembly like the “counter” I referenced in the last post are just falling into thermodynamic wells that are generated in sequence as the tiles assemble. The tiles don’t “know” that they are bound or not.

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Please excuse any typographical issues as this is going up right after lunch. I’ll proofread and remove this message later.

The morning session was mostly computational/simulation studies. Ibáňez talked about threshold networks and how they can be used to simulate complex reactions and biological regulation.

Hajiaghayi and Doty talked about stochastic and differential equation based simulators. The limitations of using a formal language to describe reactions and reaction networks are that it makes assumptions about concentrations and that it is limited to rather small numbers of molecules. The advantage is that the reaction (number of molecules of each chemical species) can be highly precisely defined in time. I have always taken an ODE approach; the stochastic nature of this approach makes me nervous. Both approaches are reasonable and highlight a fundamental, interesting distinction that all nano-scientists need to make: deal with such large numbers of molecules that you can treat them as a fungible quantity, or deal with small numbers of molecules on a discrete basis.

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Today is day one of the International Conference on DNA Computing and Molecular Programming (DNA19). The first day is a “tutorial session” which is a conference feature that new to me. I like the idea, though: catch everyone up and get on the same page for the rest of the conference. The sessions were oriented to reviews of the field and introductions to new tools. Prof. Kurt Gothelf discussed primarily structure. Most of this is fairly familiar to me: DNA has been used to build increasingly complex physical and computational designs over the last 10-15 years.

DNA origami was very popular in the scientific media for a while. It has expanded into moving/actuated structures and 3 dimensions. It is starting to go from intellectual curiosity to functional nano-objects. I’m confident we’ll talk more about the functions of these constructs this week, but for today it was essentially a single slide about nanopores.

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