References of "Bullinger, Eric"
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See detailReview of three Recent Books on the Boundary of Bioinformatics and Systems Biology
Bullinger, Eric ULg; Schliemann, Monica ULg

in BioMedical Engineering OnLine (2010), 9(1), 33

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See detailLimiting the parameter search space for dynamic models with rational kinetics using semi-definite programming
Fey, Dirk; Bullinger, Eric ULg

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 ▲]

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See detailA Systems Biology Approach to Apoptosis Signalling
Schliemann, Monica ULg; Fey, Dirk; Bullinger, Eric ULg

Scientific conference (2009, November 17)

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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 Systems Biology Approach to Apoptosis Signalling
Bullinger, Eric ULg

Conference (2009, October 22)

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See detailSystems Biology Approaches to the Study of Apoptosis
Huber, Heinrich; Bullinger, Eric ULg; Rehm, Markus

in Yin, Xiao-Ming; Dong, Zheng (Eds.) Essentials of Apoptosis, 2nd Edition (2009)

Today, we can avail of comprehensive information on the molecular mechanisms of apoptosis signaling that was gathered during decades of intense research. This chapter presents how mathematical approaches ... [more ▼]

Today, we can avail of comprehensive information on the molecular mechanisms of apoptosis signaling that was gathered during decades of intense research. This chapter presents how mathematical approaches in the field of cellular signaling are used to integrate this complex and heterogeneous information into computational models with the aim to elucidate the functional properties of apopto- tic signaling networks. Mathematical modeling allows one to describe properties of signaling systems that emanate from the interplay of the system’s individual compo- nents and has a longstanding and successful history in the fields of physics, chemistry, and their applied engineering sciences. Systems analyses can serve to describe and identify signaling dynamics,molecular switches, thresholds, and feedback regulatory mechanisms and allow systems properties such as stability and robustness toward external perturbations to be identified. Crucially, systems analyses can also serve to generate novel qualitative and quantitative research hypotheses, which in turn allow for more focused experimental research approaches. This chapter provides a concise and critical overview on the current state of systems biology in the field of apoptotic signaling and the methodology employed. [less ▲]

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See detailRobustness in Systems Biology
Bullinger, Eric ULg

Scientific conference (2009, July)

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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 detailA Systems Biology Approach to Apoptosis Signalling
Schliemann, Monica ULg; Scheurich, Peter; Fey, Dirk et al

Scientific conference (2009, June 17)

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See detailA Systems Biology Approach to Apoptosis Signalling
Schliemann, Monica ULg; Scheurich, Peter; Bullinger, Eric ULg

Conference (2009, May 15)

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See detailA Systems Biology Approach to Apoptosis Signalling
Schliemann, Monica ULg; Scheurich, Peter; Bullinger, Eric ULg

Scientific conference (2009, April 22)

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See detailModelling and Simulation of Rat Swimming in a Water Maze Experiment
Fey, D.; Commins, Séan; Bullinger, Eric ULg

Poster (2009)

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See detailThe Percentage Occupancy Hit Or Miss Transform’.
Murray, Paul; Marshall, Stephen; Bullinger, Eric ULg

in Proc. 17th European Signal Processing Conference (EUSIPCO 2009) (2009)

The Hit or Miss Transform (HMT) is a well known morphological technique which can be used for shape and object recognition in digital image processing. The standard HMT is a particularly powerful tool for ... [more ▼]

The Hit or Miss Transform (HMT) is a well known morphological technique which can be used for shape and object recognition in digital image processing. The standard HMT is a particularly powerful tool for locating objects which are noise free in both the background and foreground regions, do not exhibit internal texture and where objects have well defined edges. Often for various reasons, objects of interest do not exhibit such qualities and as a result may not be detected by the standard HMT. This paper proposes a percentage occupancy based Hit or Miss Transform for the detection of objects subject to noisy edges, internal texture, holes and non homogeneous intensity. [less ▲]

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See detailExploiting Biology Specific Properties for the Estimation of Kinetic Parameters
Fey, D.; Bullinger, Eric ULg

Poster (2008, August)

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See detailSystems Theory in Systems Biology
Bullinger, Eric ULg

Conference (2008, June 20)

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See detailSystems Biology at the Example of Apoptosis Signalling
Bullinger, Eric ULg

Scientific conference (2008, June 12)

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See detailIdentifikation biochemischer Reaktionsnetzwerke: Ein beobachterbasierter Ansatz
Bullinger, Eric ULg; Fey, Dirk; Farina, Marcello et al

in 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 ▲]

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See detailIdentification of Biochemical reaction networks: challenges and possible solutions
Bullinger, Eric ULg

Scientific conference (2008, April 25)

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