1887

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).

Loading

Article metrics loading...

/content/journal/micro/10.1099/mic.0.000477
2017-06-21
2019-10-20
Loading full text...

Full text loading...

/deliver/fulltext/micro/163/6/829.html?itemId=/content/journal/micro/10.1099/mic.0.000477&mimeType=html&fmt=ahah

References

  1. Ravasi P, Peiru S, Gramajo H, Menzella HG. Design and testing of a synthetic biology framework for genetic engineering of Corynebacterium glutamicum. Microb Cell Fact 2012;11:147 [CrossRef][PubMed]
    [Google Scholar]
  2. Kim H, Yoo SJ, Kang HA. Yeast synthetic biology for the production of recombinant therapeutic proteins. FEMS Yeast Res 2015;15:1–16 [CrossRef][PubMed]
    [Google Scholar]
  3. Rosano GL, Ceccarelli EA. Recombinant protein expression in microbial systems. Front Microbiol 2014;5:341 [CrossRef][PubMed]
    [Google Scholar]
  4. Yang E, Lozano AC, Ravikumar P. Elementary estimators for high-dimensional linear regression. Proc 31 St Int Conf Mach Learn 2014;32:388–396
    [Google Scholar]
  5. Weuster-Botz D. Experimental design for fermentation media development: statistical design or global random search?. J Biosci Bioeng 2000;90:473–483 [CrossRef][PubMed]
    [Google Scholar]
  6. McCall J. Genetic algorithms for modelling and optimisation. J Comput Appl Math 2005;184:205–222 [CrossRef]
    [Google Scholar]
  7. Nagata Y, Chu KH. Optimization of a fermentation medium using neural networks and genetic algorithms. Biotechnol Lett 2003;25:1837–1842 [CrossRef][PubMed]
    [Google Scholar]
  8. Sarma MVRK, Sahai V, Bisaria VS. Genetic algorithm-based medium optimization for enhanced production of fluorescent pseudomonad R81 and siderophore. Biochem Eng J 2009;47:100–108 [CrossRef]
    [Google Scholar]
  9. Camacho-Rodríguez J, Cerón-García MC, Fernández-Sevilla JM, Molina-Grima E. Genetic algorithm for the medium optimization of the microalga Nannochloropsis gaditana cultured to aquaculture. Bioresour Technol 2015;177:102–109 [CrossRef][PubMed]
    [Google Scholar]
  10. Shieh H-J, Peralta RC. Optimal system design of in-situ bioremediation using parallel recombinative simulated annealing. In: Ground Water: An Endangered Resource, Proceedings of Theme C, Water for a Changing Global Community, 27th Annual Congress of the International Association of Hydrologic Research 1997; pp.95–100http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=2057&context=cee_facpub [accessed 30 March 2016]
    [Google Scholar]
  11. van Batenburg FH, Gultyaev AP, Pleij CW. An APL-programmed genetic algorithm for the prediction of RNA secondary structure. J Theor Biol 1995;174:269–280 [CrossRef][PubMed]
    [Google Scholar]
  12. Notredame C, Higgins DG. SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res 1996;24:1515–1524 [CrossRef][PubMed]
    [Google Scholar]
  13. Gondro C, Kinghorn BP. A simple genetic algorithm for multiple sequence alignment. Genet Mol Res 2007;6:964-82[PubMed]
    [Google Scholar]
  14. Kumita JR, Johnson RJ, Alcocer MJ, Dumoulin M, Holmqvist F et al. Impact of the native-state stability of human lysozyme variants on protein secretion by Pichia pastoris. FEBS J 2006;273:711–720 [CrossRef][PubMed]
    [Google Scholar]
  15. Hesketh AR, Castrillo JI, Sawyer T, Archer DB, Oliver SG. Investigating the physiological response of Pichia (Komagataella) pastoris GS115 to the heterologous expression of misfolded proteins using chemostat cultures. Appl Microbiol Biotechnol 2013;97:9747–9762 [CrossRef][PubMed]
    [Google Scholar]
  16. Helal R, Melzig MF, Melzig MF. Determination of lysozyme activity by a fluorescence technique in comparison with the classical turbidity assay. Pharmazie 2008;63:415–419[PubMed]
    [Google Scholar]
  17. Dikicioglu D, Kırdar B, Oliver SG. Biomass composition: the "elephant in the room" of metabolic modelling. Metabolomics 2015;11:1690–1701 [CrossRef][PubMed]
    [Google Scholar]
  18. Prielhofer R, Maurer M, Klein J, Wenger J, Kiziak C et al. Induction without methanol: novel regulated promoters enable high-level expression in Pichia pastoris. Microb Cell Fact 2013;12:5 [CrossRef][PubMed]
    [Google Scholar]
  19. Mattanovich D, Graf A, Stadlmann J, Dragosits M, Redl A et al. Genome, secretome and glucose transport highlight unique features of the protein production host Pichia pastoris. Microb Cell Fact 2009;8:29 [CrossRef][PubMed]
    [Google Scholar]
  20. Cos O, Resina D, Ferrer P, Montesinos JL, Valero F. Heterologous production of Rhizopus oryzae lipase in Pichia pastoris using the alcohol oxidase and formaldehyde dehydrogenase promoters in batch and fed-batch cultures. Biochem Eng J 2005;26:86–94 [CrossRef]
    [Google Scholar]
  21. Gach JS, Maurer M, Hahn R, Gasser B, Mattanovich D et al. High level expression of a promising anti-idiotypic antibody fragment vaccine against HIV-1 in Pichia pastoris. J Biotechnol 2007;128:735–746 [CrossRef][PubMed]
    [Google Scholar]
  22. Niu H, Jost L, Pirlot N, Sassi H, Daukandt M et al. A quantitative study of methanol/sorbitol co-feeding process of a Pichia pastoris Mut+/pAOX1-lacZ strain. Microb Cell Fact 2013;12:33 [CrossRef][PubMed]
    [Google Scholar]
  23. Goldberg ED. Genetic Algorithms in Search, Optimization and Machine Learning Addison-Wesley Longman Publishing Co. Inc; 1989
    [Google Scholar]
  24. Searson DP. GPTIPS 2: an open-source software platform for symbolic data mining. In: Handbook of Genetic Programming Applications Chapter 22 2015
    [Google Scholar]
  25. Searson D, Leahy D, Willis M. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proc Int Multi Conf Eng Comput Sci 2010;1:77–80
    [Google Scholar]
  26. Demain AL, Vaishnav P. Production of recombinant proteins by microbes and higher organisms. Biotechnol Adv 2009;27:297–306 [CrossRef][PubMed]
    [Google Scholar]
  27. Flagfeldt DB, Siewers V, Huang L, Nielsen J. Characterization of chromosomal integration sites for heterologous gene expression in Saccharomyces cerevisiae. Yeast 2009;26:545–551 [CrossRef][PubMed]
    [Google Scholar]
  28. Alander JT. On optimal population size of genetic algorithms. In: Computer Systems and Software Engineering Proceedings 1992; pp.65–70
    [Google Scholar]
  29. Roeva O, Fidanova S, Paprzycki M. Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. Fed Conf Comput Sci Inf Syst 2013;371–376
    [Google Scholar]
  30. Malhotra R, Singh N, Singh Y. Genetic algorithms: concepts, design for optimization of process controllers. Comput Inf Sci 2011;4:39–54 [CrossRef]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/micro/10.1099/mic.0.000477
Loading
/content/journal/micro/10.1099/mic.0.000477
Loading

Data & Media loading...

Supplements

Supplementary File 1

PDF

Supplementary File 2

MOVIE

Supplementary File 3

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error