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Wed, 18 Jun 2008 15:27:35 +0200Wed, 18 Jun 2008 15:27:35 +0200Determination of interaction between MCT1 and CAII via a mathematical and physiological approach
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/1991
The enzyme carbonic anhydrase isoform II (CAII), catalysing the hydration and dehydration of CO2, enhances transport activity of the monocarboxylate transporter isoform I (MCT1, SLC16A1) expressed in Xenopus oocytes by a mechanism that does not require CAII catalytic activity (Becker et al. (2005) J. Biol. Chem., 280). In the present study, we have investigated the mechanism of the CAII induced increase in transport activity by using electrophysiological techniques and a mathematical model of the MCT1 transport cycle. The model consists of six states arranged in cyclic fashion and features an ordered, mirror-symmetric, binding mechanism were binding and unbinding of the proton to the transport protein is considered to be the rate limiting step under physiological conditions. An explicit rate expression for the substrate °ux is derived using model reduction techniques. By treating the pools of intra- and extracellular MCT1 substrates as dynamic states, the time dependent kinetics are obtained by integration using the derived expression for the substrate °ux. The simulations were compared with experimental data obtained from MCT1-expressing oocytes injected with di®erent amounts of CAII. The model suggests that CAII increases the e®ective rate constants of the proton reactions, possibly by working as a proton antenna.J. Almquist; H. Schmidt; P. Lang; J. Deitmer; M. Jirstrand; D. Prätzel-Wolters; H. Beckerreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/1991Wed, 18 Jun 2008 15:27:35 +0200Parameter Influence On The Zeros Of NetworkDeterminants
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/1489
To a network N(q) with determinant D(s;q) depending on a parameter vector q Î Rr via identification of some of its vertices, a network N^ (q) is assigned. The paper deals with procedures to find N^ (q), such that its determinant D^ (s;q) admits a factorization in the determinants of appropriate subnetworks, and with the estimation of the deviation of the zeros of D^ from the zeros of D. To solve the estimation problem state space methods are applied.S. Feldmann; P. Lang; D. Prätzel-Woltersreporthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/1489Mon, 02 Feb 2004 15:32:43 +0100Multiparameter, Polynomial Adaptive Tracking for Minimum Phase Systems
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/714
A multiparameter, polynomial feedback strategy is introduced to solve the universal adapative tracking problem for a class of multivariable minimum phase system and reference signals generated by a known linear time-invariant differential equation. For 2-input, 2-output, minimum phase systems (A,B,C) with det(CB)0, a different polynomial tracking controller is given which does not invoke a spectrum unmixing set.Sergey Nikitin; A. Ilchmann; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/714Tue, 17 Oct 2000 00:00:00 +0200Adaptive Tracking for Scalar Minimum Phase Systems
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/672
We present the concept of a universal adaptive tracking controller for classes of linear systems. For the class of scalar minimum phase systems of relative degree one, adaptive tracking is shown for arbitrary finite dimensional reference signals. The controller requires no identificaiton of the system parameters. Robustness properties are explored.Uwe Helmke; D. Prätzel-Wolters; Stephan Schmidpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/672Sun, 09 Jul 2000 00:00:00 +0200Multiparameter, Polynomial Adaptive Stabilizers
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/688
Sergey Nikitin; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/688Sun, 09 Jul 2000 00:00:00 +0200Simple Adaptive Control of a Discrete Almost Strict Positive Real Heat Treatment System
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/690
Dieter Prätzel-Wolters; R.D. Reinkepreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/690Sun, 09 Jul 2000 00:00:00 +0200Modelling and Controller Design for Heat Treatment Processing of Enamelled Wires
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/691
S. Chen; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/691Sun, 09 Jul 2000 00:00:00 +0200Adaptive Synchronization of Interconnected Linear Systems
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/670
In this paper we introduce the concept of an adaptive synchronization controller. Synchronization is modelled as an adaptive tracking problem for families of interconnected linear systems. Stabilization and tracking results are obtained for minimum phase systems.Uwe Helmke; D. Prätzel-Wolters; Stephan Schmidpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/670Wed, 28 Jun 2000 00:00:00 +0200Sufficient Conditions for Adaptive Stabilization and Tracking
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/671
We consider universal adaptive stabilization and tracking controllers for classes of linear systems. Under the technical assumption of linear scaling invariance necessary and sufficient conditions for adaptive stabilization are derived. For scalar systems sufficient conditions for adaptive tracking of finite dimensional reference signals are explored.Uwe Helmke; D. Prätzel-Wolters; Stephan Schmidpreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/671Wed, 28 Jun 2000 00:00:00 +0200Stability and Robustness Properties of Universal Adaptive Controllers for First Order Linear Systems
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/658
The question: What is an adaptive controller? is as old as the word adaptive control itself. In this paper we will adopt a pragmatic viewpoint which identifies adaptive controllers with nonlinear feedback controllers, designed for classes (families) of linear systems. In contrast to classical linear feedback controllers which are designed for individual systems, these non-linear controllers are required to achieve a specific design objective (such as e.g. stability, tracking or decoupling) for a whole prescribed family of linear systems.Uwe Helmke; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/658Mon, 26 Jun 2000 00:00:00 +0200Learning and Replication of Periodic Signals in Neural-Like Networks
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/575
The paper describes the concepts and background theory for the analysis of a neural-like network for learning and replication of periodic signals containing a finite number of distinct frequency components. The approach is based on the combination of ideas from dynamic neural networks and systems and control theory where concepts of dynamics, adaptive control and tracking of specified time signals are fundamental. The proposed procedure is a two stage process consisting of a learning phase when the network is driven by the required signal followed by a replication phase where the network operates in an autonomous feedback mode whilst continuing to generate the required signal to a desired acccuracy for a specified time. The analysis draws on currently available control theory and, in particular, on concepts from model reference adaptive control.R. Reinke; D.H. Owens; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/575Wed, 07 Jun 2000 00:00:00 +0200Periodic Signals in Neural-Like Networks - an Averaging Analysis
https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/587
The paper describes the concepts and background theory of the analysis of a neural-like network for the learning and replication of periodic signals containing a finite number of distinct frequency components. The approach is based on a two stage process consisting of a learning phase when the network is driven by the required signal followed by a replication phase where the network operates in an autonomous feedback mode whilst continuing to generate the required signal to a desired accuracy for a specified time. The analysis focusses on stability properties of a model reference adaptive control based learning scheme via the averaging method. The averaging analysis provides fast adaptive algorithms with proven convergence properties.R. Reinke; D. Prätzel-Wolterspreprinthttps://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/587Wed, 07 Jun 2000 00:00:00 +0200