TY  - JOUR
A1  - Steuer, Ralf
T1  - Effects of stochasticity in models of the cell cycle : from quantized cycle times to noise-induced oscillations
N2  - Noise and fluctuations are ubiquitous in living systems. Still, the interaction between complex biochemical regulatory systems and the inherent fluctuations ('noise') is only poorly understood. As a paradigmatic example, we study the implications of noise on a recently proposed model of the eukaryotic cell cycle, representing a complex network of interactions between several genes and proteins. The purpose of this work is twofold: First, we show that the inclusion of noise into the description of the system accounts for several recent experimental findings, as e.g. the existence of quantized cycle times in wee1(-) cdc25Delta double-mutant cells of fission yeast. In the main part, we then focus on more general aspects of the interplay between noise and the dynamics of the system. In particular, we demonstrate that a stochastic description leads to qualitative changes in the dynamics, such as the emergence of noise-induced oscillations. These findings will be discussed in the light of an ongoing debate on models of cell division as limit-cycle oscillators versus checkpoint mechanisms. (C) 2004 Elsevier Ltd. All rights reserved
Y1  - 2004
SN  - 0022-5193
ER  - 
TY  - JOUR
A1  - Steuer, Ralf
A1  - Ebeling, Werner
A1  - Bengner, Thomas
A1  - Dehnicke, C.
A1  - Hattig, H.
A1  - Meencke, H. J.
T1  - Entropy and complexity analysis of intracranially recorded EEG
N2  - We present an entropy and complexity analysis of intracranially recorded EEG from patients suffering from a left frontal lobe epilepsy. Our approach is based on symbolic dynamics and Shannon entropy. In particular, we will discuss the possibility to monitor long-term dynamical changes in brain electrical activity. This might offer an alternative approach for the analysis and more fundamental understanding of human epilepsies
Y1  - 2004
SN  - 0218-1274
ER  - 
TY  - JOUR
A1  - Daub, Carsten O.
A1  - Steuer, Ralf
A1  - Selbig, Joachim
A1  - Kloska, Sebastian
T1  - Estimating mutual information using B-spline functions : an improved similarity measure for analysing gene expression data
N2  - Background: The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. In the context of the clustering of genes with similar patterns of expression it has been suggested as a general quantity of similarity to extend commonly used linear measures. Since mutual information is defined in terms of discrete variables, its application to continuous data requires the use of binning procedures, which can lead to significant numerical errors for datasets of small or moderate size. Results: In this work, we propose a method for the numerical estimation of mutual information from continuous data. We investigate the characteristic properties arising from the application of our algorithm and show that our approach outperforms commonly used algorithms: The significance, as a measure of the power of distinction from random correlation, is significantly increased. This concept is subsequently illustrated on two large-scale gene expression datasets and the results are compared to those obtained using other similarity measures. A C++ source code of our algorithm is available for non- commercial use from kloska@scienion.de upon request. Conclusion: The utilisation of mutual information as similarity measure enables the detection of non-linear correlations in gene expression datasets. Frequently applied linear correlation measures, which are often used on an ad-hoc basis without further justification, are thereby extended
Y1  - 2004
SN  - 1471-2105
ER  -