Georgia Institute of Technology College of Sciences

Progress in systems biology relies on the use of mathematical and statistical models for system level studies of biological processes. This talk will focus on discrete models of gene regulatory networks and the challenges they present, in particular data selection and model stability. Careful data selection is important for model identification since the process is sensitive to the amount and type of data used as input. We will discuss a criterion for deciding when a set of data points identifies an algebraic model with special minimality properties. Stability is another important requirement for models of gene regulatory networks. Canalizing functions, a particular class of Boolean functions, show stable dynamic behavior and are thus suitable for expressing gene regulatory relationships. However, in practice, relaxing the canalizing requirement on some variables is appropriate. We will present the class of partially nested canalizing functions and some of their properties and applications.