Christoph Zechner

Learn More
Mathematical methods combined with measurements of single-cell dynamics provide a means to reconstruct intracellular processes that are only partly or indirectly accessible experimentally. To obtain reliable reconstructions, the pooling of measurements from several cells of a clonal population is mandatory. However, cell-to-cell variability originating from(More)
MOTIVATION After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was(More)
BACKGROUND Hyperglycemia is commonly present in the perioperative period in patients undergoing cardiac surgery, even during administration of insulin. A direct relationship between postoperative hyperglycemia and mortality has been established in diabetic patients undergoing cardiac surgery. However, this relationship might be confounded because patients(More)
— Robust estimation of kinetic parameters of intra-cellular processes requires large amounts of quantitative data. Due to the high uncertainty of such processes and the fact that recent single-cell measurement techniques have limited resolution and dimensionality, estimation should pool recordings of multiple cells of an isogenic cell population. However,(More)
Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special(More)
The dynamics of stochastic reaction networks within cells are inevitably modulated by factors considered extrinsic to the network such as, for instance, the fluctuations in ribosome copy numbers for a gene regulatory network. While several recent studies demonstrate the importance of accounting for such extrinsic components, the resulting models are(More)
In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational(More)
In this paper we investigate the problem of learning Echo State Networks (ESN) with adaptable filter neurons and delay&sum readouts. A brute-force solution to this learning problem is often impractical due to nonlinearity and high dimensionality of the resulting optimization problem. In this work we propose an approximate solution to the ESN learning by(More)
With the advance of experimental techniques such as time-lapse fluorescence microscopy, the availability of single-cell trajectory data has vastly increased, and so has the demand for computational methods suitable for parameter inference with this type of data. However, most of currently available methods treat single-cell trajectories independently,(More)