12 research outputs found
CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology
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).EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to S. G. O and J. R), BBSRC (BRIC2.2 to S. G. O. and N. K. H. S.), Synthetic Biology Research Initiative Cambridge (SynBioFund to D. D., A. C. C. and J. M. L. D.
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Research data supporting "A Tool for Exploiting Complex Adaptive Evolution to Optimise Protocols for Biological Experiments"
CamOptimus is a tool for applying Genetic Algorithm (GA) to solve multi-parametric optimisation problems and Symbolic Regression (SR) to obtain models using the data generated during optimisation procedure to investigate the effect of individual parameters on the system of interest. The source code for the compiled software, and the Graphical User Interface (GUI) of the application are available under free licensing (GNU General Public License v3.0). The user manual is supplied in the compressed folder.
: access to the files for this software has been restricted as they are out of date. The software is available on Github, where updated documentation and new releases are available. .The University of Cambridge Synthetic Biology Research Initiative
[SynBio Fund: CamOptimus] to DD, ACC, JML