The NC State Research Training Group on Parameter Estimation for Mechanistic Biological Models (http://rtg.math.ncsu.edu), funded by the National Science Foundation (NSF), will run a tutorial workshop on parameter estimation for biological models between July 28th and July 31st, 2016, at North Carolina State University, Raleigh, NC.
Mathematical modeling of biological systems is a rapidly growing area of research. Typically, some (and often many) of a model's parameters and/or states are unknown and have to be inferred from the available data. However, for many systems only partial observations are available. Much effort has been devoted to solving this problem. Some key questions considered in this context are: How sensitive is a model's output to changes in its parameters (sensitivity analysis)? Which parameters can be estimated uniquely from a model's input and output (identifiability analysis)? What are the uncertainties of parameters estimated by fitting a model to data? How are predictions of a model impacted by uncertainties in its parameters (and structure) (uncertainty quantification)?
This workshop will cover these concepts at an introductory level with a special emphasis on illustrating their practical application. The primary target audience for the workshop is graduate students interested in learning basic techniques associated with modern methods of identifiability theory, parameter estimation, and uncertainty quantification in models arising in biology. We also welcome applications from advanced undergraduates, postdocs and others who may be interested in these topics.
Individuals wishing to participate should contact Mette Olufsen (email@example.com). Full consideration will be given to applications received by May 1st, although applications received after that date will be considered if space permits. Financial support is available for a limited number of US students and postdocs. If you wish to request financial support, please attach a copy of your CV and ask your advisor to email a letter of recommendation to the same email.