Hendrik Dietz: ATP synthase is a nanofactory. Look at that enzyme. As enzymes go, it’s huge. The vast number of ATP synthase molecules in a human body make 50 lbs of ATP every day. They are in the middle of the gap in the humans’ ability to engineer matter: At 20 nm, they are too small for photolithography (computer chip manufacturing). Yet they are also too big for synthetic chemistry. DNA origami is about the only tool we have to precisely engineer things at this scale. The detailed principles of protein folding, even of 50 year old protein structures, are still unknown. Could we be close with DNA origami to making functional structures?
The Dietz lab made a completely asymmetric structure at about 20 nm. TEM tomography shows its shape is accurate to the design. It is made of about 440,000 atoms. For a molecule, it is very, very big. Prof. Dietz had a 3D printed model to compare to a 3D printed ribosome. The designed molecule is significantly bigger.

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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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