We plant two non interacting zygotes. As the system evolves, the two CA start interacting,  i = {1.6 , 1.9} . At the age of 100 days they die. The system does not interact with the environment. It is isolated.  Now we ask at what age is it healthiest?  Are young CA healthier than old, or perhaps it is the other way around?

An isolated does not reveal its health. In order to find what it is,  we have to  challenge it, and its  response will reveal its health. The challenge here, is injury. An injured system is not isolated any more, since the hazard originates in the environment. Injury is inflicted  at rising times,   and system  response monitored. During injury one bit is turned to zero. Only CA-1 will be injured, while CA-2 will be left untouched. The effect of an injury is described in chapters 4 and  24.

The image depicts an isolated system. Below it, the system injured at t = 2 and t = 24. The CA do not resemble the isolated pair. Following each injury both CA change their structure.

The figure below depicts cell count and production as a function of injury time. Injury hits CA-1 . Initially its size and cell production are smaller than those of CA-2. Later on the trend reverses. While CA-1 thrives, CA-2 suffers and its production declines, despite not being injured!

The first graph depicts cell production  by the system. In the young system production suffers. For injury days 15 and on it oscillates within a fixed region.  Production fraction = Production / Cell content, continuously declines. The system needs more and more cells (= resources)  in order to maintain production. A 2 day old system responds the best, and is also the most efficient (= f - 2).

Population study

The curves depict the outcome of an experiment made on a two CA system. However, they may be interpreted also in a different way. The experiment might be performed on a group of 40 CA systems, each injured at a different time. In this context the curves stand for frequency distributions or response spectra of a CA group to different challenges. Such an interpretation will be applied in the next chapter.

Health is system performance following injury.

In the present experiment health is defined as performance of an injured system. Performance in an isolated system does not indicate its health, since it is not injured. Its production is marked by the horizontal line in the production figure. It is lower than the production by most cases, and so is its production fraction.

It is the response to a challenge which reveals the health of a system. If the system has enough resources to maintain production or even improve it, it is healthy. The production fraction graph indicates that  the system's response was generally better than that of an isolated system. It is  best at injury time = 2 (= age of two days) and declines thereafter. The system is thus healthiest when young, and with time its health declines.

The response to a challenge  is systemic

It is striking that CA-2 which is not directly hit, also responds to injury.  Injury is strictly localized. One bit of CA-1 is replaced by a zero, and both CA respond. The response is systemic. This is true also for our organism. Since all processes interact, even a localized injury invokes a systemic response. This is also the main message of this site. A tumor may appear as localized, yet is part of a systemic response. From its very beginning,  cancer is a systemic disease.

The response to a challenge is a solution.

A challenge perturbs the system, and its subsequent configuration  is a solution to this particular challenge . It is  unpredictable, and therefore creative.  If performance improves, the solution is healthier (= more optimal), and vice versa.  The great secret of our WOB is that it always comes up with  the best solution (= healthiest) to any perturbation

WOB computer.

The objective of the present study may now be stated more rigorously: Given a set of CA, how to set their impacts on each other so that their solution be optimal.

Set up
injuryrange=1; injurytime = *; effect[1,2, 1.6, sa[[1]]] effect[2,1, 1.9, sa[[2]]]

Further reading:

What is health   
Health measure:

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