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Because Darwinian evolution through natural selection is the fundamental element unifying all biological organisms, we propose that our metric of complexity is potentially a more relevant metric than others, based on the count of artificially defined set of objects.
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To circumvent this problem, we have developed a metric of biological complexity termed phenotypic complexity (ne).
Essentially, this is a top-down metric of organismal complexity that we term phenotypic complexity.
The second is to substitute (5) for a simpler similarity metric of lower complexity.
We have focused on an elegant and relatively simple metric of genome complexity: sequence space coverage.
Moreover, the use of very simple organisms such as viruses is useful for gaining insight into metrics of complexity, as for such simple organisms, gene number is likely to be a very good correlate of organismal complexity, and this should be reflected by the metric.
Taking one's cue from other scientific disciplines, a metric for complexity based on the performance of the load paths through the structural scheme is proposed.
However, to test this hypothesis a quantitative metric of overall geomorphic complexity is needed.
This could be considered as a metric of the synthetic complexity of these molecules or a measure of the functional utility of having many stereocenters in precise orientations in a given molecule.
These concepts will then allow us to arrive at a metric for capsid complexity (hexamer complexity), which is useful in explaining and predicting various structural and evolutionary properties of the capsid.
Results suggest therefore that the TINVOK metric, for classes comparable in terms of complexity (based on the performed univariate clustering), is the least volatile.
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