期刊
ACS SYNTHETIC BIOLOGY
卷 6, 期 7, 页码 1180-1193出版社
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.6b00306
关键词
Pareto optimality; global optimization; multiobjective; mixed-integer; motifs; design principles; regulatory networks
资金
- Spanish MINECO (European Regional Development Fund) project SYNBIOFACTORY [DPI2014-55276-C5-2-R]
In this work we consider Pareto optimality for automated design in synthetic biology. We present a generalized framework based on a mixed-integer dynamic optimization formulation that, given design specifications, allows the computation of Pareto optimal sets of designs, that is, the set of best trade-offs for the metrics of interest. We show how this framework can be used for (i) forward design, that is, finding the Pareto optimal set of synthetic designs for implementation, and (ii) reverse design, that is, analyzing and inferring motifs and/or design principles of gene regulatory networks from the Pareto set of optimal circuits. Finally, we illustrate the capabilities and performance of this framework considering four case studies. In the first problem we consider the forward design of an oscillator. In the remaining problems, we illustrate how to apply the reverse design approach to find motifs for stripe formation, rapid adaption, and fold-change detection, respectively.
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