Macromolecular intelligence in microorganisms

Biochemistry and molecular biology have been focusing on the structural, catalytic, and regulatory properties of individual macromolecules from the perspective of clarifying the mechanisms of metabolism and gene expression. Complete genomes of 'primitive' living organisms seem to be substantially larger than necessary for metabolism and gene expression alone. This is in line with the findings of silent phenotypes for supposedly important genes, apparent redundancy of functions, and variegated networks of signal transduction and transcription factors. Here we propose that evolutionary optimization has been much more intensive than to lead to the bare minima necessary for autonomous life. Much more complex organisms prevail. Much of this complexity arises in the nonlinear interactions between cellular macromolecules and in subtle differences between paralogs (isoenzymes). The complexity can only be understood when analyzed quantitatively, for which quantitative experimentation is needed in living systems that are as simple and manipulatable as possible, yet complex in the above sense. We illustrate this for the glutamine synthetase cascade in Escherichia coli. By reviewing recent molecular findings, we show that this cascade is much more complex than necessary for simple regulation of ammonia assimilation. Simulations suggest that the function of this complexity may lie in quasi-intelligent behavior, including conditioning and learning.