Sunday, April 17, 2011

You can't optimize what you can't predict

In a post about the relationship between predictive analytics and operations research, Harlan Harris says, "You can't optimize what you can't predict." Predictive analytics is using statistical and machine-learning tools on large data sets to find complex relationships in the data and predict future trends. Operations research is the process of optimizing supply chains and industrial systems.

A synthetic oscillatory network of transcriptional regulators, Elowitz and Leibler, Nature, 1999

It's interesting because the same relationship exists between systems biology and synthetic biology. (At least we hope it does.) That is, understanding, modeling and predicting a system will eventually let you bend it towards your own ends. Same techniques, different domains. Systems biology is essentially predictive analytics on biological data. It hopes to build models and discover principles that will guide synthetic biology, which re-engineers biological systems toward novel and useful functions - everything from cleaning up toxic waste to producing energy. And the process of building entirely new biological processes inevitably feeds back into better understanding of natural biological systems.

It would be a great validation of systems biology methods to do a blind analysis of a synthetic biological circuit. Even better would be to predict the behavior of a synthetic system, then build it and see how well we did. If we do that enough times, we can't help but improve our ability to predict and optimize biological systems.

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