Cankorur-Cetinkaya Ayca, Dias Joao M L, Kludas Jana, Slater Nigel K H, Rousu Juho, Oliver Stephen G, Dikicioglu Duygu
Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.
Microbiology (Reading). 2017 Jun;163(6):829-839. doi: 10.1099/mic.0.000477. Epub 2017 Jun 21.
Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).
多种相互作用的因素会影响合成生物学项目中工程生物系统的性能。这些生物系统的复杂性意味着实验设计通常应被视为一个多参数优化问题。然而，现有的方法要么不切实际，因为要进行的实验数量会出现组合爆炸式增长，要么由于缺乏公开可用的、用户友好的软件，大多数实验人员无法使用。尽管进化算法可作为优化实验设计的替代方法，但缺乏易于使用的软件再次限制了其仅被专业人员使用。此外，缺乏进一步研究关键因素及其相互作用的辅助方法，阻碍了对生物技术系统的全面分析和利用。我们解决了这些问题，并在此提供一个易于使用且免费的图形用户界面，使广大实验生物学家能够运用复杂的进化算法来优化他们的实验设计。我们的方法利用遗传算法来发现包含参数最优组合的子空间，并利用符号回归构建一个模型，以评估实验对每个研究参数的敏感性。我们通过一个优化生物活性人类蛋白质微生物生产培养条件的例子来证明该方法的实用性。CamOptimus可通过以下链接获取：(https://doi.org/10.17863/CAM.10257)。
