Multi-criteria optimization-based tuning of synthetic genetic circuits

Model based design plays a fundamental role in the new era of synthetic biology where complexity comes to stay. Modularity, i.e. using biological parts and interconnecting them to build new and more complex devices is the way to go. In this context, mathematical models have been used to generate predictions of the behavior of the designed device.

Now, designers not only want the ability of behavior prediction but also want aids and help on the design and selection of biological parts.  However, using a model to determine crisp values for parameters of the involved parts is not a realistic approach, since uncertainties are ubiquitous to biology, and the characterization of biological parts is not exempt of it.

Here, we study the possibility of a multi objective optimization framework to get a model based set of guidelines for the selection of biological parts parameters to build a given device. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, the designer obtains qualitative regions/intervals of parameters giving rise to the predefined behavior.

A natural property of the framework is that is easy to analyze the impact of context on the synthetic devices to be designed, by just incorporating some information on the relationship of the device with the context. In general, this means we only need to know where we are connecting our device. Including this information in the optimization problem, we obtain a qualitative region of parameters taking into account the effect of the context on our device. Then, we can find a larger region of parameters if we allow for a set of desirable output performances, obtaining more robust guidelines for the design of synthetic devices.

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