  Limiting the parameter search space for dynamic models with rational kinetics using semi-definite programming; Bullinger, Eric  in Proceeding of the 11th International Symposium on Computer Applications in Biotechnology (2010) Estimation of kinetic parameters is a key step in modelling, as direct measurements are often expensive, time-consuming or even infeasible. The class of dynamic models in polynomial form is particularly ... [more ▼] Estimation of kinetic parameters is a key step in modelling, as direct measurements are often expensive, time-consuming or even infeasible. The class of dynamic models in polynomial form is particularly relevant in systems biology and biochemical engineering, as those models naturally arise from modelling biochemical reactions using for instance mass action, Michaelis-Menten or Hill kinetics. Often the parameters are not uniquely identifiable for a given model structure and measurement set. Thus the question of which parameters are consistent or inconsistent with the data arises naturally. Here we present a method capable of proving inconsistency of entire parameter regions with the data. Based on the polynomial representation of the system, we formulate a feasibility problem that can be solved efficiently by semi-definite programming. The feasibility problem allows us to check consistency of entire parameter regions by using upper and lower bounds on the parameters. This drastically limits the search space for subsequent parameter estimation methods. In contrast to similar approaches in the literature, the here presented approach does not require a steady state assumption. Measurements at discrete time points are used, but neither regular sampling intervals, nor a time discretisation of the system is required. Measurement uncertainties are dealt with using upper and lower bounds on the measured states. [less ▲] Detailed reference viewed: 10 (0 ULg)A Systems Biology Approach to Apoptosis SignallingSchliemann, Monica ; ; Bullinger, Eric Scientific conference (2009, November 17) Detailed reference viewed: 5 (0 ULg)A Systems Biology Approach to Apoptosis SignallingSchliemann, Monica ; ; et alScientific conference (2009, June 17) Detailed reference viewed: 1 (0 ULg)Identifikation biochemischer Reaktionsnetzwerke: Ein beobachterbasierter AnsatzBullinger, Eric ; ; et alin At-Automatisierungstechnik (2008), 56(5), 269-279 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 24 (7 ULg)Parameter estimation in biochemical reaction networks: An observer-based approach; Bullinger, Eric Conference (2008, March 27) Detailed reference viewed: 1 (0 ULg)Model Extension Facilitates Parameter Estimation for Kinetic Reaction Networks; Bullinger, Eric Poster (2007, September) Detailed reference viewed: 1 (0 ULg)Model Extension Facilitates Parameter Estimation for Kinetic Reaction Networks; Bullinger, Eric Poster (2007, September) Detailed reference viewed: 2 (0 ULg) |
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