It is not wrong to state that many sciences have gone through three different stages of scientific perception, which we wish to call the three A’s. They are amazement, abstraction and application. This is no different in biology. After defining the three A’s, we will elaborate this theory using computers as a perfect example. Well, we like to call it the perfect example, as computer hard- and software has in more than one way something to do with synthetic biology. At the end of this chapter we hope to have convinced you that synthetic biology is, however very new, not an unnatural thing to happen to biology.
Defining the three A’s
A little boy is standing in front of the window watching the rain fall down on a stormy afternoon. When his mum enters the room, he shouts: "Look mummy, it’s raining slantingly!" This kid is truly amazed. How is it possible that the rain is not falling straight down, as his marble, pencil or spoon does? Not only little children, but also grown-up people tend to be amazed every now and then. Sometimes this amazement is something of a deeper feeling, like the amazement when seeing a beautiful flower, or the amazement when being confronted with the loss of a close relative. However, man is also sometimes amazed about more technical things. Why does rain fall from the sky? Why do plants bear fruits with different colors? And what is the cause of the death of a person? We see that these questions all search for a reasonable, thought-through explanation for all phenomena that reach our perception.
Why is that little kid, watching the rain fall down on a windy day, amazed by the fact that the rain falls down slantingly? There is no reason to believe that this amazement is a natural reflex, like the one we have when we pull back our hand after touching something very hot. No, the kid is amazed because the perception he has, does not fit with what he is used to see, being things that fall straight to the ground. Statistics, the quantitative and mathematical approach, is crucial in abstracting everything we see in nature. We see that the fruits on one plant have different colors, but only the red ones are edible. Smokers seem to die earlier than non-smokers and rain only falls from the sky when it is very cloudy. We can, using quantitative data, indeed distill a general scheme, a (kind of) theory from these perceptions. “Look mummy, it’s raining slantingly!” “Well, you see son, when the wind is blowing and it is raining at the same time, the wind blows the rain to the side when it is falling down. That is why the rain falls slantingly.”
The little boy has learned that when the wind blows, the rain falls slantingly in the same direction of the blowing of the wind. Clever as he is, he says to his mum: “So mummy, when the rain comes from that direction, I will have to close the window of my room then?” When many scientists search to explain all kinds of phenomena, others try to predict them. This prediction often concerns the behavior of artificial systems. When this artificial system leads to practical benefit, we can truly speak of application. It is the engineer’s view on the world. As this view usually starts from a problem that has to be solved, it is not directly the result of abstraction. However, no engineer can work without these abstractions and theories. We can thus truly say that application is the last and final step in the development of a (sub)science, and that it is indeed even an outcome of the amazement about and abstraction of everything that reaches our perception.