The objective of this project is to establish nonlinear dynamics and control theory techniques that facilitate the design of experimental strategies to predictably manipulate the intracellular signaling pathway dynamics. The development of this type of dynamical analysis and control theory-inspired experiment design has been hindered by the lack of adequately accurate mathematical models and incomplete knowledge of the intracellular signaling pathways. Recent developments and preliminary experiments using nonlinear model predictive controller design with sparse grid-based optimization have set the stage for the development of effective control strategies despite these uncertainties. The refinement of computationally efficient sparse grid-based techniques to rapidly screen model structures and parameters for compatibility with the available experimental data will elucidate data-consistent signaling dynamics, facilitate experiment design to discern between data-compatible mechanisms, and support robust controller design. The extension of current robust controller design techniques to select controller parameters that minimize the effects from uncertainties in the model parameters and structure and small perturbations in the controller parameter values will maximize the likelihood that the resulting controller actions, when realized in the laboratory, will achieve their desired objectives. The robust controller design techniques developed to minimize the effects of large degrees of uncertainty in the model parameters and structure will be immediately applicable to control a number of highly uncertain systems.
Intracellular signaling pathways transmit and coordinate signals within the cell to direct fundamental cellular processes such as proliferation, differentiation, migration and apoptosis. The ability to predict signaling network dynamics will ultimately provide us with ways to alter cellular responses in a controlled manner. This ability will find application in medicine and pharmacy, agriculture, and biotechnology. The intracellular signaling pathways involve numerous chemical species that participate in a complex sequence of events orchestrated by interactions, crosstalk and feedback. The complexity of these networks hinders the ability of unaided intuition to efficiently design experiments to manipulate the signaling events in a desired manner. This interdisciplinary research combines and extends expertise and techniques from control theory, computational mathematics, and cell biology to derive approaches to quantitatively design experiments. Undergraduates and graduate students will participate in the research and primary dissemination of the research results will be through peer-reviewed journal publications, conference presentations, a technical workshop, course material, and the Web.
