Optimal State and Parameter Estimation for Boolean Dynamical Systems

Apr 29th, 2014 | By | Category: Other, Systems Engineering

Optimal State and Parameter Estimation for Boolean Dynamical Systems - BResearchers: Ulisses Braga-Neto

This research concerns a novel signal model for discrete-time Boolean dynamical systems under noisy observational conditions, which extends and unifies previously proposed models for biochemical regulatory networks, such as Boolean Network with perturbation (BNp) model and the Probabilistic Boolean Network (PBN) model.

This novel signal model and its optimal state estimator, called the Boolean Kalman Filter, was introduced in a recent publication (see below). Joint estimation of state and parameters is considered for network inference under incomplete pathway knowledge. Current work involves several extensions and open research problems regarding this methodology, and its application in the inference and control of biochemical regulatory networks.

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