Bt Resistance Management: Software for Research, Education, and Outreach

1. Crops that have been genetically modified to protect against insect pests, such as those expressing Bt toxins, have gained tremendous economic and societal importance. These biotechnology products improve the stability of agricultural production through highly effective crop protection while reducing the use of traditional pesticides, fuel, and water by farmers in the US and internationally. Significantly, Bt technology has improved economic outcomes for growers. However, the durability of any given Bt toxin as a protective agent is shortened by the evolution of resistance in the target pest population. Strategies for delaying the evolution of resistance, called insect resistance mangement (IRM), need to be paired intelligently with the crop type, the climate, the target pest, and local agricultural practices. Research into IRM increasingly relies on sophisticated mathematical models that allow many numerical experiments to be done in a short amount of time. The complexity of these models has brought about a number of drawbacks, including 1) inconsistent assumptions that make model comparison difficult, 2) a lack of transparency due to the sheer difficulty of reproducing these models, and 3) the limitation of IRM modeling to a relatively small number of highly skilled researchers.With this USDA-NIFA SBIR Phase II award, Applied Biomathematics intends to research and develop the computational algorithms necessary to support a program that allows users to model the evolution of resistance to Bt or similar transgenic technologies in complex, multi-crop landscapes for a wide variety of target pests. The main goal of the project is to build a simulation engine capable of tracking responses to selection for resistance at up to 12 different genes, each of which may be associated with resistance to one or more Bt toxins. This task is difficult because such genetic complexity can consume a large amount of computing power and memory. Making the performance of the program good enough to use on typical computing platforms requires careful and creative optimization. Our approach will mix algorithms that simulate individual pests in great detail when necessary with faster approaches that lump individuals into populations with similar characteristics. This approach will be faster than models that are purely individual based but should preserve biological detail better than population-level approximations typically used in models with more than one gene. As part of a program that allows users to build IRM models without special training in mathematics, population genetics, or programming and that defines IRM models through a standard set of user inputs, the improved genetics algorithm will lead to a simulation tool that is powerful, transparent, and widely accessible.