Abstract
BackgroundOne of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. While circuits in Biology are complex and subject to natural tradeoffs, most synthetic circuits are simple in terms of the number of regulatory regions, and have been designed to meet a single design criterion.ResultsIn this contribution we introduce a multiobjective formulation for the design of biocircuits. We set up the basis for an advanced optimization tool for the modular and systematic design of biocircuits capable of handling high levels of complexity and multiple design criteria. Our methodology combines the efficiency of global Mixed Integer Nonlinear Programming solvers with multiobjective optimization techniques. Through a number of examples we show the capability of the method to generate non intuitive designs with a desired functionality setting up a priori the desired level of complexity.ConclusionsThe methodology presented here can be used for biocircuit design and also to explore and identify different design principles for synthetic gene circuits. The presence of more than one competing objective provides a realistic design setting where every solution represents an optimal trade-off between different criteria.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0113-3) contains supplementary material, which is available to authorized users.
Highlights
One of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity
A hallmark of Synthetic Biology is, quoting Arkin, the ambition to formalize the process of designing cellular systems in the way that traditional engineering disciplines have formalized design and manufacture, so that complex behaviours can be achieved for practical ends [1]
The design of biocircuits can be formulated as a Mixed Integer Nonlinear Programming Problem [17,18], where the model structure can be encoded by integer variables and the constraints are the dynamics of the system in form of ordinary differential equations (ODE)
Summary
One of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. A hallmark of Synthetic Biology is, quoting Arkin, the ambition to formalize the process of designing cellular systems in the way that traditional engineering disciplines have formalized design and manufacture, so that complex behaviours can be achieved for practical ends [1]. In formalizing the design process, as it is the case in more traditional engineering disciplines, mathematical modeling and optimization play a central role. One of the challenges of the second wave in progress is the integration of modules to create circuits of increasing complexity [5]. As reported by Purnick and Weiss [5], the level of complexity achieved
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