Modern times

It turns out that we should learn something in terms of economic efficiency from Escherichia coli bacteria

In business administration schools, students are taught how to achieve higher profits: reduce costs, increase revenues, and optimize the work processes in the factory as much as possible. At first hearing it sounds simple, but efficiency has many faces, and the road to it is winding and slippery. Bonuses for employees, for example, are an expense that can be easily avoided, but they stimulate employees and thus optimize work processes. Is the additional income that will be created as a result of these incentives greater than the cost of the bonuses? The answer to this question is not so simple. There are already hundreds of models
Mathematicians who are trying to deal with it, and offer a suitable formula for predicting the result according to the various starting situations, the nature of the organization, the socio-economic context, and more. And this is just one of the hundreds of questions that a financial business manager asks himself every day. In short, there is nothing to talk about, business management is no picnic.
The person who can offer interesting insights in the field of economic efficiency is none other than the bacterium Escherichia coli, one of the most studied unicellular organisms. In a way, this bacterium can be seen as a sort of industrial plant. The production system of this bacterium is aimed at only one product: it seeks to reproduce itself, that is, to produce another factory. And like any good business manager, he wants to get maximum output at minimum cost. The cost in this case is measured by the amount of energy and other resources that the bacterium needs to spend to produce the various components of its body, and the energy required to assemble them and create a new, independent bacterium. The yield is measured during the time required for the production of a new bacterium. The combination between these two factors (time and resources) is the efficiency rate of the "plant".

Dr. Zvi Telusti and research student Arbel Tadmor, from the Department of Physics of Complex Systems at the Weizmann Institute of Science, developed a mathematical model that describes the bacterium's production system and examines its effectiveness. The model, described
Recently in an article published by the scientists in the online journal PloS Computational Biology, they manage to describe this complex micro-plant using five incredibly simple mathematical equations. The first examines the process in which the bacterium produces ribosomes, the intracellular organelles through which the cell produces proteins (unlike most human factories, the bacterial factory produces its own machines and devices). Naturally, the second equation describes how these ribosomes produce the other proteins that make up the cell. The third equation focuses on the production process of the enzyme by means of which the cell translates the genetic information contained in the DNA, and thereby produces single-stranded messenger RNA molecules, which carry the genetic information from the cell nucleus to the ribosome. In a certain sense, this enzyme (called RNA polymerase) can be seen as a kind of "production manager" that uses the production plan stored in DNA to manage the production of proteins in the necessary quantities and at the desired rate. Some of the RNA that the polymerase produces is used to build the ribosomes, so the production manager is himself, in fact, a kind of machine. The fourth and fifth equations describe how the bacterium wisely divides the ribosomes (the "machines") and the polymerases (the "production managers") between the different tasks of protein production, ribosome production, and polymerase production. These five simple equations make it possible to calculate and predict the bacteria's reproduction rate - the "bottom line" by which its effectiveness is measured.
The model was tested against experiments in which the reproduction rate of Escherichia coli bacteria was measured, and then the changes that occurred in the reproduction rate were observed as a result of a series of genetic changes made in these bacteria (the change was manifested in the addition or deletion of various genes responsible for the production of some central component in the bacterial cell, or playing a central role in. For example, the gene responsible for the main part of the ribosome structure). As a result of the genetic changes, the bacteria changed their "production strategy", with the aim of achieving the best possible production performance in the given situation imposed on them. The model was able to accurately predict the reactions of the bacteria. For example, the bacterium can "decide" how many ribosomes to produce. Apparently, one should aim for as many ribosomes as possible, because the more ribosomes there are, the more proteins he will be able to produce in less time. But the production of the ribosome and its maintenance cost quite a bit in terms of energy. The model found that the optimal number of genes encoding the production of the ribosome is seven, as indeed exists in nature. Furthermore, in those that contained nine copies of the ribosome gene, or for example only five copies, the culture efficiency was measured to be lower compared to bacteria with seven copies. In other words, evolution "plans" the plant to be the most efficient under the given conditions.

the degree of efficiency

Henry Ford once said that the production process of one "Model T" car included no less than 7,882 different operations. In his autobiography, he divides these actions into groups: 949 actions could only be performed by extremely strong and healthy men, 3,338 actions could be performed by "normal" men, and the rest, light crafts, could also be performed by women and children.
Ford was not satisfied with this division, which is at the base of the concept of "normality" that characterized industrial organizations in the middle of the twentieth century (and also inspired the film "Modern Times" by Charlie Chaplin). He also inspected, classified and distributed the light crafts. This is how he was able to match the cheapest and most efficient worker to each position in his factories. He found, for example, that amputees can perform 670 operations on the Model T production line, amputees can perform 2,637 operations, amputees can perform two operations, amputees can perform 715 operations, and blind people can perform ten operations.