Biological problems are usually complex due to their multi-parametric nature and to the fact that these parameters are often interdependent. A commonly employed approach in attacking such problems relies on the use of background knowledge, or informed guesswork, to prioritise these parameters. For novel systems there may be insufficient background knowledge to enable successful prioritisation. Moreover, identifying and testing the effect of individual parameters is often an ineffective strategy because it ignores the interactive effects of mutually dependent parameters.
CamOptimus developed a hybrid approach to solve multi-parametric experimental design problems and to develop a simple-to-use and freely available graphical user interface (GUI) to empower a wider audience of experimental biologists to employ GA in solving their optimisation problems.
Post Doctoral Research Associate, Department of Biochemistry
Bioinformatician, Research Associate, Department of Haematology and Sanger Institute
Principal scientist, PhD, ZuvaSyntha Ltd