E. coli bacterium model of super industrial efficiency

February 5th, 2009 - 5:55 pm ICT by IANS  

Washington, Feb 5 (IANS) E. coli bacterium, one of the best-studied single-celled organisms around, is a master of industrial efficiency. This bacterium can be thought of as a factory with just one product - itself. It exists to make copies of itself and its business plan is to make them at the lowest possible cost, with the greatest possible efficiency. Efficiency, in the case of a bacterium, can be defined by the energy and resources it uses to maintain its plant and produce new cells versus the time it expends on the task.

A mathematical model developed at the Weizmann Institute in Rehovot, Israel has revealed how such single-celled organisms regulate their activities for maximum efficiency.

Tsvi Tlusty and research student Arbel Tadmor of the Physics of Complex Systems Department at Weizmann developed a mathematical model for evaluating the efficiency of these microscopic production plants.

Their model uses only five remarkably simple equations to check the efficiency of these complex factory systems, said a Weizmann release.

The equations look at two components of the protein production process:

Ribosomes - the machinery in which proteins are produced; and RNA polymerase - an enzyme that copies the genetic code for protein production onto strands of messenger RNA for further translation into proteins.

The theoretical model was tested in real bacteria. Do bacteria ‘weigh’ the costs of constructing and maintaining their protein production machinery against the gains to be had from being able to produce more proteins in less time? What happens when a critical piece of equipment is in short supply, say a main ribosome protein?

Tlusty and Tadmor found that their model was able to accurately predict how an E. coli would change its production strategy to maximise efficiency following disruptions in the work flow caused by experimental changes to genes with important cellular functions.

These findings recently appeared in the online journal PLoS Computational Biology.

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