| On 2015-04-22 at 14:00:00 (Brussels Time) |
Abstract
In life science research, most of the experimental design and product optimisation are based on the individual and collective knowledge of the researchers involved in the project development, through the information acquired by the literature and the personal experience in the field. For some chemical phenomena at both molecular and cellular level, experimental design may greatly improve the efficiency of the targeted experiment, but on the other hand, it may lead to the lack of exploration of some experimental conditions that are actually determining new discoveries. Usually, experimenters in chemical and life sciences design experiments by changing one parameter at time, screening few parameters and levels due to time and budget constraints, and due to the need of gaining knowledge on small set of experimental outcomes at time. The use of statistical analysis if often skipped in material synthesis and design, and usually limited to ANOVA data treatment after biological assays. Particular focus will be given on how and why these research fields needs such kind of "experience-based" product optimisation, compared to other technological fields. Practical examples on how we are designing experiments and optimise products in both material synthesis (i.e. production of carbon nanotubes) and biochemical characterizations (i.e. migration of cancer cells) will be proposed for the discussion.
Keywords
closed-loop optimization, real-world optimization, nanomaterials, biochemical applications
