This Research Training Group (RTG) grant will train undergraduate students, graduate students and postdoctoral researchers, in statistical and inverse problem methodologies applied to mathematical models for biological systems. The research component of the RTG will revolve around a number of projects that represent different disciplinary applications of modeling and development of parameter estimation methodologies in the biological sciences. Each project will involve at least four RTG participants, including faculty, students and/or postdoctoral researchers. Some projects will focus more on development of new methodologies, while others aim more at utilizing these methodologies in a particular biological setting. Important cross-cutting themes will include parameter estimation and parameter identifiability, model selection, model robustness, uncertainty quantification and model-based experimental design. Students will receive preparation for research activities in this area through a number of supporting courses, the majority of which will be offered at the graduate level, but will be accessible to advanced undergraduates. Interdisciplinarity and team-working skills will be emphasized and developed through careful mentoring and using a number of activities, such as regular research presentations, both within and between the project groups, and journal clubs. Professional development sessions will help prepare participants for current and future careers in interdisciplinary environments both inside and outside of academia. These will be offered at various levels, designed to cater for the needs of the different participants, in some cases focusing on issues unique to scientists working in the biological realm. An annual RTG workshop, including external speakers, will showcase participants' accomplishments and give opportunity to reflect on the successful (and less successful) aspects of the year's research and training activities. In alternate years, the workshop will be expanded to include a week-long lecture, tutorial and laboratory course, providing a condensed presentation of chosen aspects of the RTG curriculum to external participants, primarily advanced undergraduates and early-stage graduate students.
Ever increasing amounts of biological data are being collected, at a range of scales from the molecular and cellular through to the population and ecosystem. Mechanistic mathematical models are increasingly being used as a way of making sense of this data, providing important insights into the workings of biological systems. The success of this enterprise not only requires development and analysis of biologically appropriate models but confrontation of these models with biological data. This, in turn requires development of methodologies that allow this confrontation to occur, including practical methods that allow researchers to analyze biological data using mechanistic models. The main objective of this RTG is to develop training and research activities that will train cohorts of mathematical scientists with strengths in both applied mathematics and statistics who will have the interdisciplinary skills to work effectively with biologists, preparing them for the challenges and opportunities of the scientific workplace of the 21st century.
