An apology of molecular computers and answers to critics

This is how a molecular computer would look like, if seen with a magically powerful microscope. It is a single molecule which interacts randomly with other molecules, called “enzymes”, invisible in this animation.


There is no control over the order of the chemical reactions. This is the idea, to compute without control.

The way it works is like this: whenever a reaction happens, this creates the conditions for the next reaction to happen.

There is no need to use a supercomputer to model such a molecule, nor is it reasonable to try, because of big number of the atoms.

It is enough instead to find real molecular assemblies for nodes, ports and bonds, figured here by colored circles and lines.

The only computations needed are those for simulating the family of rewrites – chemical reactions. Every such rewrite involves up to 4 nodes, therefore the computational task is handy.

Verify once that the rewrites are well done, independently of the situation where you want to apply them, that is all.

Once such molecular compounds are found, the next task is to figure out how to build (by chemical reactions) such molecules.

But once one succeeds to build one molecule, the rest is left to Nature way of computing: random, local, asynchronous.

From this stage there is no need to simulate huge molecules in order to know they work. That is something given by the chemlambda formalism.

It is so simple: translate the rewrites into real chemistry, they are easy, then let go the unneeded control from that point on.

This animation is a screencast of a part of the article Molecular computers
and everything can be validated (i.e. verified by your own) by using the chemlambda repository

Now I’ll pass to a list of critics which, faced with the evidence, they look uninformed:
1. Chemlambda is one of those rewriting systems everybody knows. Ignorant claim, while it is true that some rewrites appear all over the place, from string theory to knot theory to category theory to geometry of interaction, the family of graphs considered is not the same, because those graphs are purely combinatorial object and they don’t need a global embedding, like all other formalism do, in a schematic space-time. Moreover, the choice of the rewrites is such that the system works only by local rewriting and no global control on the cascade of rewrites. No other formalism from the family does that.

2.  Is well known that all this is already done in the category theory treatment of lambda calculus.

False, if one really reads what they do in category theory with lambda calculus, then one figures quick that they can’t do much for untyped lambda beta calculus, that is without eta reduction. This is mentioned explicitly in Barendregt, for example, but the hype around categories and lambda calculus is so pervasive that people believe more than what actually is.

3.  Chemical computing is old stuff: DNA computing, membrane computing, the chemical abstract machine, algorithmic chemistry.

Just because it is chemical computing, it does not mean that it is in the family mentioned.

The first name of chemlambda was “chemical concrete machine” and there there are comparison with the chemical abstract machine
(btw I see that some people discover now “catalysts” without credits in the written papers)
The cham is a formalism working with multisets of molecules, not with individual ones, and the computation is done by what corresponds to lab operation (splitting a solution in two, heating, cooling, etc)
The membrane computing work is done around membranes which enclose containers of multisets of molecules, the membrane themselves being some abstract concepts, of a global nature, whil ein reality, as well as in chemlambda, everything is a molecule. Membranes exist in reality but they are made of many molecular compounds.
DNA computing is an amazing research subject, which may be related to chemlambda if there is a realization of chemlambda nodes, ports and bonds, but not otherwise, because there is not, up to my knowledge, any model in DNA computing with the properties: individual molecules, random reactions, not lab operations.
Algorithmic chemistry is indeed very much related to chemlambda, by the fact that it proposes a chemical view on lambda calculus. But from this great insight, the paths are very different. In algorithmic chemistry the application operation from lambda calculus represents a chemical reaction and the lambda abstraction signals a reactive site. In chemlambda the application and lambda abstraction corresponds to atoms of molecules. Besides, chemlambda is not restricted to lambda calculus, only some of the chemlambda molecules can be put in relation with lambda terms, but even for those, the reactions they enter don’t guarantee that the result is a molecule for a lambda term.

Conclusion: if you are a chemist, consider chemlambda, there is nothing like it already proposed. The new idea is to let control go and instead chain the randomly appearing reactions by their spatial patterns, not by lab operations, nor by impossibly sophisticated simulations.
Even if in reality there would be more constraints (coming from the real spatial shapes of the molecules constructed from these fundamental bricks) this would only influence the weights of the random encounters with the enzymes, thus not modifying the basic formalism.
And if it works in reality, even for only situations where there are cascades of tens of reactions, not hundreds or thousands, even that would be a tremendous advance in chemical computing, when compared with the old idea of copying boolean gates and silicon computers circuits.


Appeared also in the chemlambda collection microblog


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