Boada Yadira, Reynoso-Meza Gilberto, Picó Jesús, Vignoni Alejandro
Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Valencia, Spain.
Industrial and Systems Engineering Graduate Program (PPGEPS), Pontificial Catholic University of Parana (PUCPR), Curitiba, Brazil.
BMC Syst Biol. 2016 Mar 11;10:27. doi: 10.1186/s12918-016-0269-0.
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.
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.
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.
基于模型的设计在合成生物学中起着基础性作用。利用模块性，即使用生物部件并将它们相互连接以构建新的和更复杂的生物电路，是关键问题之一。在这种情况下，数学模型已被用于生成对所设计装置行为的预测。设计者不仅希望在确定了电路的所有组件后能够预测其行为，还希望在其生物部件的设计和选择方面得到帮助，即为实验实施提供指导方针。这等同于获得模型参数的合适值，因为电路行为是由模型结构和参数调整之间的相互作用产生的。然而，为所涉及部件的参数确定精确值并不是一种现实的方法。不确定性在生物学中无处不在，生物部件的表征也不例外。此外，所设计电路的期望动态行为通常是由几个要优化的目标之间的权衡产生的。
我们提出使用多目标优化调整框架来获得基于模型的一组指导方针，用于选择构建具有期望行为的生物装置所需的动力学参数。设计标准编码在目标的制定和优化问题本身中。结果，一方面，设计者获得了导致预定义电路行为的电路参数值的定性区域/区间；另一方面，他在实施过程中获得了有用的指导信息。选择这些参数以便能够在湿实验室中有效地对其进行调整，即它们是有效的生物调整旋钮。为了展示所提出的方法，该方法应用于一个著名生物电路的设计：一个显示自适应行为的遗传非相干前馈电路。
所提出的多目标优化设计框架能够提供有效的指导方针来调整生物参数，以实现期望的电路行为。此外，很容易分析上下文对要设计的合成装置的影响。也就是说，可以分析下游负载的存在如何影响所设计电路的性能，并将其考虑在内。
