Molecular Computing [Electronic resources] نسخه متنی

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Molecular Computing [Electronic resources] - نسخه متنی

Tanya Sienko

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1.4 Pertinent Molecular Properties

The trade-off principle asserts that systems with nonprogrammable structure-function relations are capable of implementing transforms that are too complex to embody in general-purpose (programmable) architectures. The physical dynamics of such systems, suitably interpreted, effectuates the computation. Conceivably many types of physical dynamics could be utilized in this manner. Macromolecules afford a particularly powerful combination of properties (see table 1.1).

















































Table 1.1: Computationally important properties of macromolecules

Property


Draws on


Confers





Folded shape


Long flexible chains, weak bonding, rotation around single bonds


Specificity, self-assembly


Conformational dynamics


Folded shape


Milieu sensitivity, allosteric control


Well-defined ground state


Individual molecules (not statistical ensembles)


Precisely duplicatable nonlinearity, specific shape


Brownian motion


Specific shape, low mass, heat bath


Cost-free search


High evolvability


Combinatorial variety, high dimensionality


Diverse repertoire of specialized functions


Specificity with speed


Defined shape, Brownian motion


Low dissipation pattern recognition


Supramolecular structure


Self-assembly, free energy minimization


Rich, extended 3-D architecture


Diverse specificities


Building block principle, heat bath, folded shape


Heterogeneous organization, dynamic complexity







The main property is folded shape. This requires long, nonconjugated polymers (because rotation around single bonds is necessary). Carbon, the atom of life, supports section 1.3, there is an intimate connection between evolvability and complexity. If protein folding could be described by an extremely compressed program, therefore a simple process from the algorithmic complexity point of view, then the structure-function relations would approach programmability and would be fragile. Most mutations would be cataclysmic. Evolutionary considerations thus imply that folding and (chemical) complex formation are complex processes in the algorithmic sense. At the same time, the introduction of redundant amino acids in the sequence and the utilization of amino acids with high replaceability serve to buffer the effect of mutation on conformational features critical for function (Conrad and Volkenstein 1981).

Sometimes the argument is put forward that biological molecules are insufficiently reliable for computing. The opposite is actually the case. Single molecules have definite ground states, as opposed to the macroscopic switches from which conventional computers are built. The latter are built from statistical aggregates of particles and are therefore subject to erosion. The reliability issue is rather subtle, because it is clear that with solid-state components, it is possible to perform many repetitive operations and to do so rapidly. But if we want to build a reliable information processing system out of nonlinear base components, the capability for reproducing the nonlinearity in a highly precise manner is absolutely critical. This is infeasible with conventional electronic or other macroscopic components, simply because it is impossible to exactly duplicate a statistical aggregate of particles, let alone preserve their nonlinear characteristics on an operational time scale. The discrete amino acid sequences that determine the function of proteins can be precisely specified. This is sufficient, at least for a large class of sequences, to uniquely determine the folded shape and the set of available conformational states. The shape (or conformation), of course, changes when the protein interacts with its environment, but the existence of a ground state and, more generally, discrete energy levels confer precision that is unobtainable with macroscopic processing elements.

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