RTG: Parameter Estimation Methodologies for Mechanistic Biological Models

该研究训练组（RTG）资助将培训本科生、研究生和博士后研究人员，研究应用于生物系统数学模型的统计和反问题方法。RTG的研究部分将围绕一些项目展开，这些项目代表了生物科学中建模和参数估计方法发展的不同学科应用。每个项目将涉及至少四名RTG参与者，包括教师，学生和/或博士后研究人员。一些项目将更加注重发展新的方法，而另一些项目则更多地着眼于在特定的生物环境中利用这些方法。重要的交叉主题将包括参数估计和参数可辨识性、模型选择、模型鲁棒性、不确定性量化和基于模型的实验设计。学生将通过一些辅助性课程为该领域的研究活动做准备，其中大部分课程将在研究生阶段提供，但高年级本科生也可以使用。将通过仔细的指导和使用一些活动，例如项目小组内部和小组之间的定期研究报告，以及期刊俱乐部，来强调和发展跨学科性和团队合作技能。专业发展课程将帮助参与者在学术界内外的跨学科环境中为当前和未来的职业生涯做好准备。这些活动将在不同的层次上提供，旨在满足不同参与者的需求，在某些情况下将侧重于在生物学领域工作的科学家所特有的问题。包括外部发言人在内的年度RTG讲习班将展示与会者的成就，并提供机会反思年度研究和培训活动的成功（和不太成功）方面。每隔一年，研讨会将扩大到包括为期一周的讲座、辅导和实验课程，向外部参与者（主要是高级本科生和早期研究生）提供RTG课程选择方面的浓缩介绍。从分子和细胞到人口和生态系统，正在收集越来越多的生物数据。机械数学模型越来越多地被用作理解这些数据的一种方式，为生物系统的运作提供了重要的见解。这项事业的成功不仅需要开发和分析生物学上合适的模型，而且需要将这些模型与生物学数据进行对抗。这反过来又需要开发允许这种对抗发生的方法，包括允许研究人员使用机制模型分析生物数据的实用方法。该RTG的主要目标是开展培训和研究活动，培养具有应用数学和统计学优势的数学科学家群体，他们将拥有与生物学家有效合作的跨学科技能，为21世纪科学工作场所的挑战和机遇做好准备。

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.