Roozbeh Dehghannasiri
ABSTRACT
Of major interest to systems biology is to characterize the way that genes interact with each other in the context of gene regulatory networks (GRNs). In particular, Kalman filtering has been shown to be effective in this regard for GRNs modeled via ordinary differential equations. Performance of the Kalman-filter-based approach is highly dependent on the accuracy level of the noise statistics considered in the inference problem. However, in many cases the exact knowledge of noise statistics is missing. Therefore, robust inference is of great practical importance. In this paper, we propose an inference method based on intrinsically Bayesian robust (IBR) Kalman filtering. The IBR Kalman filter provides optimal performance on average relative to an uncertainty class of possible noise statistics. The IBR Kalman filter can be found in a similar way that an ordinary Kalman filter is found except that the noise covariance matrices are replaced by their "effective" counterparts and the Kalman gain matrix is replaced by the effective Kalman gain matrix. In this paper, we apply IBR Kalman filtering to infer a yeast cell cycle network whose dynamics are modeled via a set of continuous nonlinear ordinary differential equations.
- R. Dehghannasiri, M. S. Esfahani, and E. R. Dougherty. A Bayesian framework for robust Kalman filtering under uncertain noise statistics. In Proceedings of the Asilomar Conf. Signals, Systems and Computers, 2016.Google Scholar
Cross Ref
- L. Qian, H. Wang, and E. R. Dougherty. Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and Kalman filtering. IEEE Trans. Signal Process., 56(7):3327--3339, 2008. Google ScholarDigital Library
Index Terms
- Inference of Nonlinear ODE-Based Gene Regulatory Networks via Intrinsically Bayesian Robust Kalman Filtering
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