Multiple model-informed open-loop control of uncertain intracellular signaling dynamics.

ABSTRACT: Computational approaches to tune the activation of intracellular signal transduction pathways both predictably and selectively will enable researchers to explore and interrogate cell biology with unprecedented precision. Techniques to control complex nonlinear systems typically involve the application of control theory to a descriptive mathematical model. For cellular processes, however, measurement assays tend to be too time consuming for real-time feedback control and models offer rough approximations of the biological reality, thus limiting their utility when considered in isolation. We overcome these problems by combining nonlinear model predictive control with a novel adaptive weighting algorithm that blends predictions from multiple models to derive a compromise open-loop control seq

SUBMITTER: Perley JP 

PROVIDER: S-EPMC3983080 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature