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Biomedical subjects

Olaf Wolkenhauer

Publications and source records attributed to Olaf Wolkenhauer.

At least 19 recordsLinked to original sources
PubMed2007

2-DE profiling of GDNF overexpression-related proteome changes in differentiating ST14A rat progenitor cells.

Targeted differentiation of neural progenitor cells (NPCs) is a challenge for treatment of neurodegenerative diseases by cell replacement therapy and cell signalling manipulation. Here, we applied a proteome profiling approach to the rat striatal progenitor model cell line ST14A in order to elucidate cellular differentiation processes. Native cells and cells transfected with the glial cell line-derived neurotrophic factor (GDNF) gene were investigated at the proliferative state and at seven time points up to 72 h after induction of differentiation. 2-DE combined with MALDI-MS was used to create a reference 2-DE-map of 652 spots of which 164 were identified and assigned to 155 unique proteins. For identification of protein expression changes during cell differentiation, spot patterns of triplicate gels were matched to the 2-DE-map. Besides proteins that display expression changes in native cells, we also noted 43 protein-spots that were differentially regulated by GDNF overexpression in more than four time points of the experiment. The expression patterns of putative differentiation markers such as annexin 5 (ANXA5), glucosidase II beta subunit (GLU2B), phosphatidylethanolamine-binding protein 1 (PEBP1), myosin regulatory light chain 2-A (MLRA), NASCENT polypeptide-associated complex alpha (NACA), elongation factor 2 (EF2), peroxiredoxin-1 (PRDX1) and proliferating cell nuclear antigen (PCNA) were verified by Western blotting. The results reflect the large rearrangements of the proteome during the differentiation process of NPCs and their strong modification by neurotrophic factors like GDNF.

Animals↗
PubMed2006

Switching feedback mechanisms realize the dual role of MCIP in the regulation of calcineurin activity.

Calcineurin (CaN) assists T-cell activation, growth and differentiation of skeletal and cardiac myocytes, memory, and apoptosis. It also activates transcription of the nuclear factor of activated T-cells (NFAT) family including hypertrophic target genes. It has been reported that the modulatory calcineurin-interacting protein (MCIP) inhibits the CaN activity and thereby reduces the hypertrophic response. However, it has been shown that MCIP facilitates or permits the hypertrophic response under some stress conditions such as isoproterenol infusion or pressure overload by transverse aortic constriction. As there is no direct experimental evidence that can explain these paradoxical phenomena, there has been a controversy concerning the functional role of MCIP in developing the hypertrophic response. It is therefore crucial to establish a hypothesis that can clearly explain these phenomena. Towards this end, we propose in this paper a hypothesis that is based on available experimental evidence as well as mathematical modeling and computer simulations. We hypothesize that there is a threshold in the nuclear NFAT concentration above which MCIP is switched on. Below this threshold, the inhibition of active CaN by MCIP is negligible, while the activated protein kinase increases the dissociation rate of the CaN/MCIP complex. This leads to an augmentation of active CaN. This mechanism realizes the positive effect (i.e., removing any negative feedback) of MCIP in the hypertrophic response. On the other hand, the over-expression of active CaN increases nuclear NFAT to values above the threshold, while CaN is inhibited through binding of MCIP (expressed by the nuclear NFAT). This mechanism realizes the introduction of a negative feedback mechanism. To unravel this switching feedback mechanism, we have developed a mathematical model for which computer simulations are in agreement with the existing experimental data. The simulations demonstrate how the apparently paradoxical behavior can emerge as a result of cellular conditions.

Animals↗
PubMed2006

Intracellular delay limits cyclic changes in gene expression.

Based on previously published experimental observations and mathematical models for Hes1, p53 and NF-kappaB gene expression, we improve these models through a distributed delay formulation of the time lag between transcription factor binding and mRNA production. This description of natural variability for delays introduces a transition from a stable steady state to limit cycle oscillations and then a second transition back to a stable steady state which has not been observed in previously published models. We demonstrate our approach for two models. The first model describes Hes1 autorepression with equations for Hes1 mRNA production and Hes1 protein translation. The second model describes Hes1 repression by the protein complex Gro/TLE1/Hes1, where Gro/TLE1 is activated by Hes1 phosphorylation. Finally, we discuss our analytical and numerical results in relation to experimental data.

Algorithms↗
PubMed2006

Approximations and their consequences for dynamic modelling of signal transduction pathways.

Signal transduction is the process by which the cell converts one kind of signal or stimulus into another. This involves a sequence of biochemical reactions, carried out by proteins. The dynamic response of complex cell signalling networks can be modelled and simulated in the framework of chemical kinetics. The mathematical formulation of chemical kinetics results in a system of coupled differential equations. Simplifications can arise through assumptions and approximations. The paper provides a critical discussion of frequently employed approximations in dynamic modelling of signal transduction pathways. We discuss the requirements for conservation laws, steady state approximations, and the neglect of components. We show how these approximations simplify the mathematical treatment of biochemical networks but we also demonstrate differences between the complete system and its approximations with respect to the transient and steady state behavior.

Feedback, Physiological↗
PubMed2006

Dynamics of biological systems: role of systems biology in medical research.

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

Animals↗
PubMed2005

A unified framework for unraveling the functional interaction structure of a biomolecular network based on stimulus-response experimental data.

We propose a unified framework for the identification of functional interaction structures of biomolecular networks in a way that leads to a new experimental design procedure. In developing our approach, we have built upon previous work. Thus we begin by pointing out some of the restrictions associated with existing structure identification methods and point out how these restrictions may be eased. In particular, existing methods use specific forms of experimental algebraic equations with which to identify the functional interaction structure of a biomolecular network. In our work, we employ an extended form of these experimental algebraic equations which, while retaining their merits, also overcome some of their disadvantages. Experimental data are required in order to estimate the coefficients of the experimental algebraic equation set associated with the structure identification task. However, experimentalists are rarely provided with guidance on which parameters to perturb, and to what extent, to perturb them. When a model of network dynamics is required then there is also the vexed question of sample rate and sample time selection to be resolved. Supplying some answers to these questions is the main motivation of this paper. The approach is based on stationary and/or temporal data obtained from parameter perturbations, and unifies the previous approaches of Kholodenko et al. (PNAS 99 (2002) 12841-12846) and Sontag et al. (Bioinformatics 20 (2004) 1877-1886). By way of demonstration, we apply our unified approach to a network model which cannot be properly identified by existing methods. Finally, we propose an experiment design methodology, which is not limited by the amount of parameter perturbations, and illustrate its use with an in numero example.

Computational Biology↗
PubMed2005

Feedback dynamics and cell function: Why systems biology is called Systems Biology.

A new paradigm, like Systems Biology, should challenge the way research has been conducted previously. This Opinion article aims to present Systems Biology, not as the application of engineering principles to biology but as a merger of systems- and control theory with molecular- and cell biology. In our view, the central dogma of Systems Biology is that it is system dynamics that gives rise to the functioning and function of cells. The concepts of feedback regulation and control of pathways and the coordination of cell function are emphasized as an important area of Systems Biology research. The hurdles and risks for this area are discussed from the perspective of dynamic pathway modelling. Most of all, the aim of this article is to promote mathematical modelling and simulation as a part of molecular- and cell biology. Systems Biology is a success if it is widely accepted that there is nothing more practical than a good theory.

Computational Biology↗
PubMed2005

The dynamic systems approach to control and regulation of intracellular networks.

Systems theory and cell biology have enjoyed a long relationship that has received renewed interest in recent years in the context of systems biology. The term 'systems' in systems biology comes from systems theory or dynamic systems theory: systems biology is defined through the application of systems- and signal-oriented approaches for an understanding of inter- and intra-cellular dynamic processes. The aim of the present text is to review the systems and control perspective of dynamic systems. The biologist's conceptual framework for representing the variables of a biochemical reaction network, and for describing their relationships, are pathway maps. A principal goal of systems biology is to turn these static maps into dynamic models, which can provide insight into the temporal evolution of biochemical reaction networks. Towards this end, we review the case for differential equation models as a 'natural' representation of causal entailment in pathways. Block-diagrams, commonly used in the engineering sciences, are introduced and compared to pathway maps. The stimulus-response representation of a molecular system is a necessary condition for an understanding of dynamic interactions among the components that make up a pathway. Using simple examples, we show how biochemical reactions are modelled in the dynamic systems framework and visualized using block-diagrams.

Animals↗
PubMed2005

A hybrid systems framework for cellular processes.

With the availability of technologies that allow us to obtain stimulus-response time series data for modeling and system identification, there is going to be an increasing need for conceptual frameworks in which to formulate and test hypotheses about intra- and inter-cellular dynamics, in general and not just dependent on a particular cell line, cell type, organism, or technology. While the semantics can be quite different, biologists and systems scientists use in many cases a similar language (notion of feedback, regulation, etc.). A more abstract system-theoretic framework for signals, systems, and control could provide the biologist with an interface between the domains. Apart from recent examples to identify functional elements and describing them in engineering terms, there have been various more abstract developments to describe dynamics at the cell level in the past. This includes Rosen's (M,R)-systems. This paper presents an abstract and general compact mathematical framework of intracellular dynamics, regulation and regime switching inspired by (M,R)-theory and based on hybrid automata.

Algorithms↗
PubMed2004

Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis.

MOTIVATION: When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk's lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis. RESULTS: We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others.

Algorithms↗
PubMed2004

Optimal sampling time selection for parameter estimation in dynamic pathway modeling.

Systems Biology is an emerging research area, which considers mathematical representations of inter- and intra-cellular dynamics. Among the many research problems that have been addressed, dynamic modeling of signal transduction pathways has received increasing attention. The usual approach to represent intra-cellular dynamics are nonlinear, usually ordinary, differential equations. The purpose of the models is to test and generate hypothesis of specific pathways and it is therefore required to estimate model parameters from experimental data. The experiments to generate data are complex and expensive, as a consequence of which the time series available are usually rather short, with few if any replicates. Almost certainly, not all variables one would like to include in a model can be measured. Parameter estimation is therefore an important research problem in Systems Biology and the focus of this paper. In particular, we are interested in optimizing the sampling time selection in order to minimize the variance of the parameter estimation error. With few sampling time points feasible, their selection is of practical importance in experimental design. Finally, the theoretical results are supported with an application.

Computer Simulation↗
PubMed2004

Modeling and simulation of intracellular dynamics: choosing an appropriate framework.

Systems biology is a reemerging paradigm which, among other things, focuses on mathematical modeling and simulation of biochemical reaction networks in intracellular processes. For most simulation tools and publications, they are usually characterized by either preferring stochastic simulation or rate equation models. The use of stochastic simulation is occasionally accompanied with arguments against rate equations. Motivated by these arguments, we discuss in this paper the relationship between these two forms of representation. Toward this end, we provide a novel compact derivation for the stochastic rate constant that forms the basis of the popular Gillespie algorithm. Comparing the mathematical basis of the two popular conceptual frameworks of generalized mass action models and the chemical master equation, we argue that some of the arguments that have been put forward are ignoring subtle differences and similarities that are important for answering the question in which conceptual framework one should investigate intracellular dynamics.

Algorithms↗
PubMed2004

Analysis of DNA microarray data.

Recent advances in DNA microarray technology have great impact on many areas of biomedical research and pharmacogenomics: discovering novel targets and genes, elucidating signatures of complex diseases, transcriptional profiling of models for diseases, and the development of individually optimized drugs based on differential gene expression patterns. Consequently, there is demand for robust methods for data analysis and the choice of adequate statistical tests. This review guides through all steps in the cDNA microarray data analysis pipeline and gives a basic understanding of the challenges in interpreting large microarray datasets.

Animals↗
PubMed2003

Investigations into the analysis and modeling of the TNF alpha-mediated NF-kappa B-signaling pathway.

In this study, we propose a system-theoretic approach to the analysis and quantitative modeling of the TNFalpha-mediated NF-kappaB-signaling pathway. Tumor necrosis factor alpha (TNFalpha) is a potent proinflammatory cytokine that plays an important role in immunity and inflammation, in the control of cell proliferation, differentiation, and apoptosis. To date, there have been numerous approaches to model cellular dynamics. The most prominent uses ordinary differential equations (ODEs) to describe biochemical reactions. This approach can provide us with mathematically well-founded and tractable interpretations regarding pathways, especially those best described by enzyme reactions. This work first introduces a graphical method to intuitively represent the TNFalpha-mediated NF-kappaB-signaling pathway and then utilizes ODEs to quantitatively model the pathway. The simulation study shows qualitative validation of the proposed model compared with experimental results for this pathway. The proposed system-theoretic approach is expected to be further applicable to predict the signaling behavior of NF-kappaB in a quantitative manner for any variation of the ligand, TNFalpha.

Computer Simulation↗
PubMed2003

The statistical distribution of the intensity of pixels within spots of DNA microarrays: what is the appropriate single-value representative?

This paper opens a discussion about an important issue in the analysis of data from spotted DNA microarrays: how to summarise into a single value the distribution for the intensity values of the pixels within a spot. Although the most popular statistic used is the median, there is no clear study demonstrating why it is more appropriate than other measures of central tendency such as the mean or the mode. Here, we argue that the median intensity is not the most appropriate measure for many common cases and discuss a frequently encountered case of a 'doughnut'-shaped spot for which the mode is closest to the 'expected' spot intensity. For an 'ideal' spot with a clear boundary and uniformly hybridised, the intensity of its pixels should approximately be normally distributed. In practical situations, these two requirements are often not met due to the physical properties of pins and the particularities of the printing and hybridisation processes. As a consequence, the distribution of the intensity of the pixels is usually negatively skewed. This asymmetry results in a larger displacement for the mean and median than for the mode from the ideal situation mentioned above.

Algorithms↗
PubMed2003

Microarray data clustering based on temporal variation: FCV with TSD preclustering.

The aim of this paper is to present a new clustering algorithm for short time-series gene expression data that is able to characterise temporal relations in the clustering environment (ie data-space), which is not achieved by other conventional clustering algorithms such as k -means or hierarchical clustering. The algorithm called fuzzy c -varieties clustering with transitional state discrimination preclustering (FCV-TSD) is a two-step approach which identifies groups of points ordered in a line configuration in particular locations and orientations of the data-space that correspond to similar expressions in the time domain. We present the validation of the algorithm with both artificial and real experimental datasets, where k -means and random clustering are used for comparison. The performance was evaluated with a measure for internal cluster correlation and the geometrical properties of the clusters, showing that the FCV-TSD algorithm had better performance than the k -means algorithm on both datasets.

Algorithms↗
PubMed2002

Information-theoretic analysis of protein sequences shows that amino acids self-cluster.

We analyse for each of 20 amino acids X the statistics of spacings between consecutive occurrences of X within the well-characterized Saccharomyces cerevisiae genome. The occurrences of amino acids may exhibit near random, clustered or smoothed out behaviour, like one-dimensional stochastic processes along the protein chain. If amino acids are distributed randomly within a sequence, then they follow a Poisson process, and a histogram of the number of observations of each gap size would asymptotically follow a negative exponential distribution. The novelty of the present approach lies in the use of differential geometric methods to quantify information on sequencing of amino acids and groups of amino acids, via the sequences of intervals between their occurrences. The differential geometry arises from an information-theoretic distance function on the two-dimensional space of stochastic processes subordinate to gamma distributions-which latter include the random process as a special case. We find that maximum-likelihood estimates of parametric statistics show that all 20 amino acids tend to cluster, some substantially. In other words, the frequencies of short gap lengths tend to be higher and the variance of the gap lengths is greater than expected by chance. This may be because localizing amino acids with the same properties may favour secondary structure formation or transmembrane domains. Gap sizes of 1 or 2 are generally disfavoured, 1 strongly so. The only exceptions to this are Gln and Ser, as a result of poly(Gln) or poly(Ser) sequences. There are preferences for gaps of 4 and 7 that can be attributed to alpha -helices. In particular, a favoured gap of 7 for Leu is found in coiled coils. Our method contributes to the characterization of whole sequences by extracting and quantifying stable stochastic features.

Amino Acids↗