NDLI: Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach

Content Provider	ACM Digital Library
Author	Meskin, N. Nounou, M. Nounou, H. Datta, A.
Abstract	Recent advances in high-throughput technologies for biological data acquisition have spurred a broad interest in the construction of mathematical models for biological phenomena. The development of such mathematical models relies on the estimation of unknown parameters of the system using the time-course profiles of different metabolites in the system. One of the main challenges in the parameter estimation of biological phenomena is the fact that the number of unknown parameters is much more than the number of metabolites in the system. Moreover, the available metabolite measurements are corrupted by noise. In this paper, a new parameter estimation algorithm is developed based on the stochastic estimation framework for nonlinear systems, namely the unscented Kalman filter (UKF). A new iterative UKF algorithm with covariance resetting is developed in which the UKF algorithm is applied iteratively to the available noisy time profiles of the metabolites. The proposed estimation algorithm is applied to noisy time-course data synthetically produced from a generic branched pathway as well as real time-course profile for the Cad system of E. coli. The simulation results demonstrate the effectiveness of the proposed scheme.
Starting Page	537
Ending Page	543
Page Count	7
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2013.19
Volume Number	10
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Noise measurement,Parameter estimation,Mathematical model,Convergence,Estimation,Noise,Biology,unscented Kalman filter,Noise measurement,Parameter estimation,Mathematical model,Convergence,Estimation,Noise,Biology,S-systems,Biological phenomena,parameter estimation
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach

Parameter estimation of biological phenomena modeled by S-systems: An Extended Kalman filter approach

Estimation of Frequency and Fundamental Power Components Using an Unscented Kalman Filter

Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity

Adaptive Unscented Kalman Filter at the presence of non-additive measurement noise

Parameter estimation of Hammerstein-Wiener ARMAX systems using unscented Kalman filter

Joint unscented Kalman filter for dual estimation in a bifilar pendulum for a small UAV

Chaotic synchronization of Lorenz system using Unscented Kalman Filter

Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review

Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach

Similar Documents

Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach

Parameter estimation of biological phenomena modeled by S-systems: An Extended Kalman filter approach

Estimation of Frequency and Fundamental Power Components Using an Unscented Kalman Filter

Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity

Adaptive Unscented Kalman Filter at the presence of non-additive measurement noise

Parameter estimation of Hammerstein-Wiener ARMAX systems using unscented Kalman filter

Joint unscented Kalman filter for dual estimation in a bifilar pendulum for a small UAV

Chaotic synchronization of Lorenz system using Unscented Kalman Filter

Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review

Parameter Estimation of Biological Phenomena: An Unscented Kalman Filter Approach