Cellular Computation

Shapiro next turns his attention to the manner in which the genome interacts with the rest of the cell to carry out computations. He uses the classic example of the lac operon to draw out several general principles:

  • Weak interactions, specific binding and cooperativity are essential aspects of molecular computations in cells.

  • Repetition in DNA and proteins means that specific logical operations arise through combinations of basic circuit elements (e.g. complex regulatory regions in
  • DNA, intra- and intermolecular interactions between protein domains).

  • Allostery, the fact that binding of one ligand affects binding a distinct ligand, confers communication and processing capabilities on individual molecules so that cellular network nodes act as complex microprocessors.

  • Layering of weak and bfuzzyQ interactions provides overall sharpness to integrated cellular responses (i.e.cells operate by Fuzzy Logic principles; Zadeh, 1975).

  • Cells use chemical symbols to represent physiological information.

  • No separation exists between control molecules and execution molecules, telling us we cannot apply Cartesian dualism models to the E. coli cell, or any other cell.

  • Participation of DNA directly in formation of repression and transcription nucleoprotein complexes suggests that it may also not be useful to apply Turing’s concepts of separate “machine” and “tape” (Turing, 1950) to cellular computations.

Shapiro then notes:

This list indicates that the principles underlying cellular analog computing may well be different from those that operate in electronic digital computers. Such a difference does not invalidate the informatic metaphor. But it does mean that we will have to be careful in applying existing computational models to cells. Combinatorics, fuzzy logic models, and principles learned from linguistics and semiotics may all serve as key guides to a formal description of cellular information-processing networks.

This makes sense, for as also Shapiro notes:

After all, no human contrivance operates with either the degree of complexity, the precision, or the efficiency of living cells.

Thus, as I note in TDM, while the use of design concepts and analogies from engineering and computer science is very useful, we need to remember that if indeed life was designed, we are using more primitive designs to help guide us when considering far more advanced designs.

But here is the take home message.

Genome sequence analysis is one of our most important guides to disentangling how cellular systems operate and how function changes in the course of evolution. Here we find support for some of the general principles deducible from individual cases like lac. In particular, repetition, reuse and combinatorics have proven to be fundamental in protein and whole genome evolution.

Repetition. Reuse. Combinatorics. These themes not only shed light on the computation processes of the cell, but expand into the evolution of the cell (proteins and genome). And of course, these are themes that are very friendly to front-loading.

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2 responses to “Cellular Computation

  1. I have nothing topical to contribute, Mike Gene.

    But I was just rereading this review and thought it might tingle your spidey-sense.

    http://sysbio.harvard.edu/csb/about/pdfs/hsp90_review.pdf

    (And check this out!)

    http://repositories.cdlib.org/cgi/viewcontent.cgi?article=5191&context=postprints

  2. Thanks, Rock! It is very interesting to see how life is smartly built to facilitate the generation of variation.

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