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See detailParameter identification for biological models
Fey, Dirk ULg

Doctoral thesis (2011)

This thesis concerns the identification of dynamic models in systems biology. and is structured into two parts. Both parts concern building dynamic models from observed data, but are quite different in ... [more ▼]

This thesis concerns the identification of dynamic models in systems biology. and is structured into two parts. Both parts concern building dynamic models from observed data, but are quite different in perspective, rationale and mathematics. The first part considers the development of novel identification techniques that are particularly tailored to (molecular) biology and considers two approaches. The first approach reformulates the parameter estimation problem as a feasibility problem. This reformulation allows the invalidation of models by analysing entire parameter regions. The second approach utilises nonlinear observers and a transformation of the model equations into parameter free coordinates. The parameter free coordinates allow the design of a globally convergent observer, which in turn estimates the parameter values, and further, allows to identify modelling errors or unknown inputs/influences. Both approaches are bottom up approaches that require a mechanistic understanding of the underlying processes (in terms of a biochemical reaction network) leading to complex nonlinear models. The second part is an example of what can be done with classical, well developed tools from systems identification when applied to hitherto unattended problems.In particular, part two of my thesis develops a modelling framework for rat movements in an experimental setup that it widely used to study learning and memory.The approach is a top down approach that is data driven resulting in simple linear models. [less ▲]

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See detailFeedback control strategies for spatial navigation revealed by dynamic modelling of learning in the Morris water maze
Fey, Dirk ULg; Commins, Sean; Bullinger, Eric ULg

in Journal of Computational Neuroscience (2011), 30(2), 447-454

The Morris water maze is an experimental procedure in which animals learn to escape swimming in a pool using environmental cues. Despite its success in neuroscience and psychology for studying spatial ... [more ▼]

The Morris water maze is an experimental procedure in which animals learn to escape swimming in a pool using environmental cues. Despite its success in neuroscience and psychology for studying spatial learning and memory, the exact mnemonic and navigational demands of the task are not well understood. Here, we provide a mathematical model of rat swimming dynamics on a behavioural level. The model consists of a random walk, a heading change and a feedback control component in which learning is reflected in parameter changes of the feedback mechanism. The simplicity of the model renders it accessible and useful for analysis of experiments in which swimming paths are recorded. Here, we used the model to analyse an experiment in which rats were trained to find the platform with either three or one extramaze cue. Results indicate that the 3-cues group employs stronger feedback relying only on the actual visual input, whereas the 1-cue group employs weaker feedback relying to some extent on memory. Because the model parameters are linked to neurological processes, identifying different parameter values suggests the activation of different neuronal pathways. [less ▲]

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See detailIdentification of biochemical reaction networks using a parameter-free coordinate system
Fey, Dirk ULg; Findeisen, Rolf; Bullinger, Eric ULg

in Iglesias, P. A.; Ingalls, B. (Eds.) Control-Theoretic Approaches in Systems Biology (2009)

A fundamental step in systems biology is the estimation of kinetic parameters, such as association and dissociation constants. Often, their direct estimation from in-vivo studies on isolated reactions is ... [more ▼]

A fundamental step in systems biology is the estimation of kinetic parameters, such as association and dissociation constants. Often, their direct estimation from in-vivo studies on isolated reactions is expensive, time-consuming or even infeasible. Therefore, it is necessary to estimate them from indirect measurements, such as time-series data. This chapter proposes an observer-based parameter estimation methodology particularly suited for biochemical reaction networks in which the reaction kinetics are described by polynomial or rational functions. The parameter estimation is performed in three steps. First, the system is transformed into a new set of coordinates in which the system is parameter-free. This facilitates the design of a standard observer in the second step. Finally, the parameter estimates are obtained in a straight-forward way from the observer states, transforming them back to the original coordinates. The approach is illustrated by an example of a MAP kinase signaling pathway. [less ▲]

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See detailA dissipative approach to the identification of biochemical reaction networks
Fey, Dirk ULg; Bullinger, Eric ULg

in 15th IFAC Symposium on System Identification, Saint Malo, France (2009, July)

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See detailExact Model Reduction of Combinatorial Reaction Networks
Conzelmann, Holger; Fey, Dirk ULg; Gilles, E. D.

in BMC Systems Biology (2008), 2

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See detailParameter estimation in kinetic reaction models using nonlinear observers is facilitated by model extensions
Fey, Dirk ULg; Findeisen, Rolf; Bullinger, Eric ULg

in 17th IFAC World Congress, Seoul, Korea (2008)

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See detailModeling Neuronal Adaptation in the Brain: Integrating Receptor Signaling and Electrophysiology
Vadigepalli, R.; Fey, Dirk ULg; Schwaber, James S

in 2nd Conference on Foundations of Systems Biology in Engineering, Stuttgart, Germany (2007)

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