Parameter identification; identifiability; systems biology
Abstract :
[en] Dynamic models present a fundamental tool in systems biology, but rely on kinetic parameters, such as association and dissociation constants. Their direct estimation from studies on isolated reactions is usually expensive, time-consuming or even infeasible for large models. As a consequence, they must be estimated from indirect measurements, usually in the form of time-series data. We describe an observer-based parameter estimation approach taking the specific structure of biochemical reaction networks into account. Considering reaction kinetics described by polynomial or rational functions, we propose a three step approach. In a first step, the estimation of not directly measured states is decoupled from the estimation of the parameters using a suitable model extension. In a second step, a specially suited nonlinear observer estimates the extended state. Based on the obtained state estimates, the parameter estimates are calculated in a straightforward way in the final step. The applicability of the approach is exemplified considering a simplified model of the circadian rhythm.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Bullinger, Eric ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes computationnelles pour la biologie systémique
Fey, Dirk; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Méthodes computationnelles pour la biologie systémique
Farina, Marcello
Findeisen, Rolf
Language :
German
Title :
Identifikation biochemischer Reaktionsnetzwerke: Ein beobachterbasierter Ansatz
Alternative titles :
[fr] Identification of biochemical reaction networks: An observer based approach
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Bibliography
S. Audoly, G. Bellu, L. D'Angiò, M. P. Saccomani und C. Cobelli. "Global identifiability of nonlinear models of biological systems". IEEE T Bio-med Eng, 48(1):55-65, 2001.
A. Cornish-Bowden. Fundamentals of Enzyme Kinetics. Portland Press, 3. Auflage, 2004.
R. Costenoble, D. Müller, T. Barl, W.M. van Gulik, W.A. van Winden, M. Reuss und J. J. Heijnen. "13C-labeled metabolic flux analysis of a fed-batch culture of elutriated Saccharomyces cerevisiae" FEMS Yeast Res, 7(4):511-526, 2007.
M. Farina, R. Findeisen, E. Bullinger, S. Bittanti, F. Allgöwer und P. Wellstead. "Results towards identifiability properties of biochemical reaction networks". In Proc. of the 45th IEEE Conf. on Decision and Control, San Diego, USA, pages 2104-2109, 2006.
M. Farina, E. Bullinger, R. Findeisen und S. Bittanti. "An observer based strategy for parameter identification in systems biology". In 2nd Conf. Foundations of Systems Biology in Engineering, Stuttgart, Germany, pages 521-526, 2007.
X. J. Feng, S. Hooshangi, D. Chen, G. Li, R. Weiss und H. Rabitz. "Optimizing genetic circuits by global sensitivity analysis". Biophys J, 87(4):2195-2202, 2004.
D. Fey, R. Findeisen und E. Bullinger. "Parameter estimation in kinetic reaction models using nonlinear observers facilitated by model extensions". In 17th IFAC World Congress, Seoul, Korea, 2008. Im Druck.
K.G. Gadkar, R. Gunawan und F. J. Doyle III. "Iterative approach to model identification of biological networks". BMC Bioinf, 6:155, 2005.
H. Garnier, M. Mensler und A. Richard. "Continuous-time model identification from sampled data implementation issues and performance evaluation". Int J Control, 76(13): 1337-1357, 2003.
E. Gilles. "Control-key to better understanding biological systems". at - Automatisierungstechnik, 50:7-17, 2002.
A. L. Hodgkin und A. F. Huxley. "A quantitative description of membrane current and its application to conduction and excitation in nerve". J Physiol, 117(4):500-544, 1952.
J. Keener und J. Sneyd. Mathematical Physiology, Band 8 aus Interdisciplinary Applied Mathematics. Springer-Verlag, New York, 2. Auflage, 2001.
E. Klipp, R. Herwig, A. Kowald, C. Wierling und H. Lehrach. Systems Biology in Practice: Concepts, Implementation and Application. Wiley-VCH, Weinheim, 2005.
J.C. Leloup, D. Gonze und A. Goldbeter. "Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora". J Biol Rhythms, 14(6): 433-448, Dec 1999.
L. Ljung. "Challenges of non-linear identification". Bode Lecture, 42th IEEE Conf. on Decision and Control, Maui, Hawaii, USA, 2003.
L. Ljung. System Identification - Theory for the User. Prentice Hall, Upper Saddle River, NJ, 2. Auflage, 1999.
L. Ljung und T. Glad. "On global identifiability for arbitrary model parametrization". Automatica, 30(2):265-276, 1994.
C. G. Moles, P. Mendes und J.R. Banga. "Parameter estimation in biochemical pathways: A comparison of global optimization methods". Genome Res, 13(11):2467-2474, 2003.
D. Noble. "Cardiac action and pacemaker potentials based on the Hodgkin-Huxley equations". Nature, 188:495-497, 1960.
M. Peifer und J. Timmer. "Parameter estimation in ordinary differential equations for biochemical processes using the method of multiple shooting". IET Syst Biol, 1(2):78-88, 2007.
P. K. Polisetty, E.O. Voit und E. P. Gatzke. "Identification of metabolic system parameters using global optimization methods". Theor Biol Med Model, 3, 2006.
E. Schrödinger. "What is life". Dublin Institute for Advanced Studies, February 1943, 1943.
E. Schrödinger. What is Life? The physical aspect of the living cell. Cambridge University Press, Cambridge, 1944.
A.B. Singer, J.W. Taylor, P. I. Barton und W. H. Green. "Global dynamic optimization for parameter estimation in chemical kinetics". J Phys Chem A, 110(3):971-976, 2006.
E. Sontag. "Molecular systems biology and control". European J Control, 11(4-5):396-435, 2005.
E. Sontag, A. Kiyatkin und B. N. Kholodenko. "Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data". Bioinformatics, 20(12):1877-1886, 2004.
A. Vargas und J. A. Moreno. "Approximate high-gain observers for uniformly observable nonlinear systems". In Proc. of the 39th IEEE Conf. on Decision and Control, Sydney, Australia, pages 784-789, 2000.
A. Vargas und J. A. Moreno. "Approximate high-gain observers for non-Lipschitz observability forms". Int J Control, 78(4):247-253, 2005.
J. D. Watson und F. H. Crick. "Molecular structure of nucleic acids; a structure for Deoxyribose Nucleic Acid". Nature, 171(4356):737-738, 1953.
X. Xia und C. H. Moog. "Identifiability of nonlinear systems with application to HIV/AIDS models". IEEE Trans Autom Control, 48(2):330-336, 2003.
X. Xia und M. Zeitz. "On nonlinear continuous observers". Int J Control, 66:943-954, 1997.
D. E. Zak, G. E. Gonye, J. S. Schwaber und F. J. Doyle III. "Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: Insights from an identifiability analysis of an in silico network". Genome Res, 13(11):2396-2405, 2003.
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