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Section Robustness of Ecosystems first introduces how robustness is understood and estimated in Ecology.
Here I suggest that transitions in network controllability – specifically in the topology of controls – help to explain how robustness and evolvability are balanced during evolution.
It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve.
In the following, in addition of describing the key elements of our approach, we also present how robustness tests are performed at the controller interface by mutating the inputs provided by the probes.
After showing how robustness measures can be obtained for a wide variety of processors, we then illustrate a specific application that can be applied to the area of parameter estimation.
There are many well-studied examples of robust systems in biology, and for the advancement of synthetic biology, particularly in performance-critical applications, fundamental understanding of how robustness is both achieved and maintained is very important.
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Perhaps it's to do with the way the denim moulds to our bodies over time, and how its robustness means jeans get more wear than our other clothes.
In order to do this the following questions need to be addressed: How is robustness defined?
Second, we investigate how the robustness of the detector can be improved by temporal analysis in Section 5.2.
Here, we use a computational model with lattice proteins to demonstrate how this robustness can result from population dynamics during the evolutionary process.
Three case studies further illustrate how a robustness study design for a biopharmaceutical DP balances experimental complexity, statistical power, scientific understanding, and risk assessment to provide the desired product and process knowledge.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com