@article{SteuerZhouKurths2003,
  author    = {Steuer, Ralf and Zhou, Changsong and Kurths, J{\"u}rgen},
  title     = {Constructive effects of fluctuations in genetic and biochemical regulatory systems},
  issn      = {0303-2647},
  year      = {2003},
  abstract  = {Biochemical and genetic regulatory systems that involve low concentrations of molecules are inherently noisy. This intrinsic stochasticity, has received considerable interest recently, leading to new insights about the sources and consequences of noise in complex systems of genetic regulation. However, most prior work was devoted to the reduction of fluctuation and the robustness of cellular function with respect to intrinsic noise. Here, we focus on several scenarios in which the inherent molecular fluctuations are not merely a nuisance, but act constructively and bring about qualitative changes in the dynamics of the system. It will be demonstrated that in many typical situations biochemical and genetic regulatory systems may utilize intrinsic noise to their advantage. (C) 2002 Elsevier Ireland Ltd. All rights reserved},
  language  = {en}
}
@article{Steuer2004,
  author    = {Steuer, Ralf},
  title     = {Effects of stochasticity in models of the cell cycle : from quantized cycle times to noise-induced oscillations},
  issn      = {0022-5193},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{SteuerEbelingBengneretal.2004,
  author    = {Steuer, Ralf and Ebeling, Werner and Bengner, Thomas and Dehnicke, C. and Hattig, H. and Meencke, H. J.},
  title     = {Entropy and complexity analysis of intracranially recorded EEG},
  issn      = {0218-1274},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{DaubSteuerSelbigetal.2004,
  author    = {Daub, Carsten O. and Steuer, Ralf and Selbig, Joachim and Kloska, Sebastian},
  title     = {Estimating mutual information using B-spline functions : an improved similarity measure for analysing gene expression data},
  issn      = {1471-2105},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{SteuerKurthsFiehnetal.2003,
  author    = {Steuer, Ralf and Kurths, J{\"u}rgen and Fiehn, Oliver and Weckwerth, Wolfram},
  title     = {Interpreting correlations in metabolomic networks},
  issn      = {0300-5127},
  year      = {2003},
  abstract  = {Correlations, as observed between the concentrations of metabolites in a biological sample, may be used to gain additional information about the physiological state of a given tissue. in this mini-review, we discuss the integration of these observed correlations into metabolomic networks and their relationships with the underlying biochemical pathways},
  language  = {en}
}
@phdthesis{Steuer2005,
  author    = {Steuer, Ralf},
  title     = {Nonlinear dynamics an molecular biology : from gene expression to metabolic networks},
  address   = {Potsdam},
  pages     = {iv, 130 S.},
  year      = {2005},
  language  = {en}
}
@article{SteuerGrossSelbigetal.2006,
  author    = {Steuer, Ralf and Gross, Thilo and Selbig, Joachim and Blasius, Bernd},
  title     = {Structural kinetic modeling of metabolic networks},
  series = {Proceedings of the National Academy of Sciences of the United States of America},
  volume    = {103},
  journal   = {Proceedings of the National Academy of Sciences of the United States of America},
  number    = {32},
  publisher = {National Academy of Sciences},
  address   = {Washington},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.0600013103},
  pages     = {11868 -- 11873},
  year      = {2006},
  abstract  = {To develop and investigate detailed mathematical models of metabolic processes is one of the primary challenges in systems biology. However, despite considerable advance in the topological analysis of metabolic networks, kinetic modeling is still often severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and their associated parameter values. Here we propose a method that aims to give a quantitative account of the dynamical capabilities of a metabolic system, without requiring any explicit information about the functional form of the rate equations. Our approach is based on constructing a local linear model at each point in parameter space, such that each element of the model is either directly experimentally accessible or amenable to a straightforward biochemical interpretation. This ensemble of local linear models, encompassing all possible explicit kinetic models, then allows for a statistical exploration of the comprehensive parameter space. The method is exemplified on two paradigmatic metabolic systems: the glycolytic pathway of yeast and a realistic-scale representation of the photosynthetic Calvin cycle.},
  language  = {en}
}