Next: Artificial Immune Systems, Previous: Artificial Neural Networks, Up: Other Biologically-Inspired Approaches
Molecular computing is an endeavor to exploit the computational abilities of biological systems using non-classical substrates, mainly biological molecules themselves. Like in Monod, binding — also known as molecular recognition — is a fundamental principle of molecular computing:
“Ignoring for the present the question as to whether proteins are the ultimate optimal mechanism or whether nature (and evolution) simply used what was available, it should be pointed out that a very important aspect is the dependence of biological systems for their “information processing” capabilities on what is known as molecular recognition. Molecules bind weakly with other molecules... This recognition is, at base, a quantum effect and is one of the mechanisms by which parallelism is introduced into the system.” [Sienko et al. 2003, p. xv]The mention of quantum physics is meaningful. There is much discussion and controversy as to whether the physical substrate of computation is relevant. Without a doubt, quantum effects play a significant role in the microbiology of the cell, as in molecular recognition above. Whether these effects or other heretofore unknown effects are essential is what's at issue. There are positions in molecular computing on either side of the fence. Some simply advocate that the “independence of a specific representation makes it possible to use new concepts for the computational process, based on real elements such as chemical reactions and quantum mechanical devices, or on virtual elements such as cellular automata and populations of artificial genes” [Gramss et al. 1998, p. 1], while others claim that “it is the physical characteristics of material systems — whether they be relatively simple chemical systems or material in biological cells — that allow highly complex information processing to occur” [Sienko et al. 2003, p. xii]. But the issue is irrelevant for our purposes
At the very least, it is recognized that the substrate plays a significant role in the performance of the computations:
“Biological systems in nature are clearly highly evolvable. In principle, it should be possible to use a structurally programmable machine to simulate the structure-function plasticity that allows for this evolvability. ... But this comes at a computational cost; the computational work required to simulate plastic structure-function relations puts a severe practical limit on the degree of evolvability that can be retained.” [Sienko et al. 2003, p. 5]And also:
“Enzymes, as catalysts, are thermodynamically reversible; their pattern-recognition work is free, driven only by the heat bath.” [Sienko et al. 2003, p. 11]As we have already mentioned, Monod is not meant as a production-grade system to do computations. Hopefully the shortcomings — including the performance ones — will leave some wiggle room for exploration.
Monod does not advocate substrate independence even while the project consists in a simulation on traditional computing hardware. The main goal is the isolation and exploration of certain computational principles that may yet play a role in traditional Turing / von Neumann machines, as explained in Goals earlier. Hence, much of the biological inspiration that applies to molecular computation can be used for the Monod project as well.