DESIGN OF EXPERIMENTS & LABORATORY AUTOMATION
Biological systems are inherently complex, multifactorial systems. There are many interacting factors and non-linear responses. Traditional experimental methods, in which one factor is varied at a time, can be insufficient to understand such complexities. They may simply confirm prior beliefs or generate geographically sensitive conclusions. Design of Experiments is a experimental method that seeks to mitigate these problems through the use of multifactorial experiments coupled with statistical modelling. To facilitate this work we are developing our expertise in small-scale laboratory automation. This permits a greater number of experimental permutations than is possible manually and improves precision.
METABOLIC ENGINEERING FOR SUSTAINABLE CHEMICALS
Autotrophic plants and microbes are capable of extracting energy and inorganic carbon from their environment and using it to build a range of biochemicals required for their growth and development. Many plants and microbes exploit sunlight to fix carbon dioxide, but some bacteria extract electrons from compounds of mineral origin to the same end. Inspired by these process we are interested in building molecular and biochemical routes for carbon fixation to fuels and chemicals.
Biology can respond rapidly and appropriately to a wide range of stimuli, including chemical, biological and physical triggers. Many of these responses are mediated at a molecular level through gene expression networks, metabolic responses and protein-protein interactions. We are interested in reconstructing and repurposing these molecular mechanisms in novel, non-living chassis. The goal is two-fold: to better understand these responses through building, and the development of sensors and multi-responsive, multi-functional materials.
Within an active learning process, participants gain knowledge and understanding as a result of the opportunities and guidance provided by the teacher. Activities challenge participants to think and apply ideas, resulting in deeper understanding and longer-term retention. This philosophy can be seen in our advocacy for student participation in the International Genetically Engineered Machines (iGEM) competition, the continued development of an undergraduate Bioprospecting module, and the use of virtual experiments for teaching statistical Design of Experiments for Biology and Biotechnology.