Team:ETH Zurich/human/overview/question


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Complexity does not have a unique and absolute definition. It is highly relative and variable. However, we encounter it everyday. It has been strikingly apparent throughout our iGEM experience. Neil Johnson suggested that "Even among scientists, there is no unique definition of Complexity. Instead, the scientific notion of Complexity – and hence of a Complex System – has traditionally been conveyed using particular examples of real-world systems which scientists believe to be complex." Here are some illustrations of complexity we came across during the course of our project.

  • We aim to generate a pattern using bacteria on a grid based on a simple rule. In our particular case, we want to see triangles pop up. This property is called emergence. It is typical for complex objects. The most fundamental part of our project is intimately linked with complexity.
  • We work with living cells, namely bacteria. Biology often gives unpredictable results, coming from the very core essence of life. Uncertainty is also one of the main property of complexity.
  • Moreover, while designing our system, we tried to cut down to the most simple functions. We simplified. The simplification process negates the intrinsic complex properties of the whole. It sets boundaries to possible interactions.

The main characteristics of complexity can be retrieved in Neil Johnson's work. He defines complexity as the "study of the phenomena which emerge from a collection of interacting objects." In simpler words, complexity is something with many parts. Emergence and uncertainty can not be dissociated from it. Relationships between subparts are keys to understand better why a complex whole does not correspond to the sum of its parts.

Scientists, in particular biologists, are confronted to complexity in research. How does one deal with complexity?

Should scientists consider that subparts of a complex entity are mixed in a both ordered and unorganized way, accept uncertainty, and try to take it into account? Or should they consider that parts are strictly ordered, and that complexity arises from simple parts by following rules?

The first approach is needed to take into account uncertainty of intrinsic complexity of the parts we consider and of the environment. The second approach is necessary to understand the parts better in order to be able to predict results.