背景:基于模型的设计在合成生物学中起着基本作用。利用模块化,即使用生物部件并将它们互连以构建新的和更复杂的生物回路是关键问题之一。在这种情况下,数学模型已被用于生成所设计设备的行为的预测。设计人员不仅希望在确定所有组件后能够预测电路行为,而且还有助于其生物部分的设计和选择,即为实验实施提供指导。这等于获得模型参数的适当值,因为电路行为是由模型结构和参数调整之间的相互作用引起的。然而,确定所涉及零件的参数的清晰值不是现实的方法。不确定性在生物学中无处不在,生物部分的表征也不能免除。此外,所设计的电路所需的动力学行为通常是由要优化的几个目标之间的权衡得出的。
结果:我们建议使用多目标优化调整框架来获得一组基于模型的指南,用于选择构建具有所需行为的生物设备所需的动力学参数。设计标准被编码在目标和优化问题本身的制定中。因此,一方面,设计者获得产生预定义电路行为的电路参数的值的定性区域/间隔;另一方面,他在实施过程中获得有用的信息作为指导。这些参数的选择,以便他们可以有效地调整在湿实验室,即它们是有效的生物调谐旋钮。为了展示拟议的方法,该方法被应用于一个众所周知的生物电路的设计:一个遗传不相干的前馈电路显示自适应行为。
结论:提出的多目标优化设计框架能够提供有效的准则来调节生物参数,从而实现所需的电路行为。此外,这是很容易分析上下文对合成设备设计的影响。也就是说,可以分析下游负载的存在如何影响设计电路的性能,并考虑到这一点。
BACKGROUND: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.
RESULTS: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.
CONCLUSIONS: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.