CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Ayca Cankorur-Cetinkaya
  • Joao M. L. Dias
  • Jana Kludas
  • Nigel K. H. Slater
  • Juho Rousu

  • Stephen G. Oliver
  • Duygu Dikicioglu

Research units

  • University of Cambridge
  • Wellcome Trust Sanger Institute
  • Cambridge University Hospitals NHS Foundation Trust

Abstract

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).

Details

Original languageEnglish
Pages (from-to)829-839
Number of pages11
JournalMICROBIOLOGY
Volume163
Issue number6
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Evolutionary algorithms, Experimental design tool, Genetic algorithm, Pichia pastoris, Recombinant protein production, Symbolic regression

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