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Biomedical subjects

Edda Klipp

Publications and source records attributed to Edda Klipp.

8 recordsLinked to original sources
PubMed2005

Integrative model of the response of yeast to osmotic shock.

Integration of experimental studies with mathematical modeling allows insight into systems properties, prediction of perturbation effects and generation of hypotheses for further research. We present a comprehensive mathematical description of the cellular response of yeast to hyperosmotic shock. The model integrates a biochemical reaction network comprising receptor stimulation, mitogen-activated protein kinase cascade dynamics, activation of gene expression and adaptation of cellular metabolism with a thermodynamic description of volume regulation and osmotic pressure. Simulations agree well with experimental results obtained under different stress conditions or with specific mutants. The model is predictive since it suggests previously unrecognized features of the system with respect to osmolyte accumulation and feedback control, as confirmed with experiments. The mathematical description presented is a valuable tool for future studies on osmoregulation in yeast and-with appropriate modifications-other organisms. It also serves as a starting point for a comprehensive description of cellular signaling.

Cell Membrane↗
PubMed2004

Modelling the dynamics of the yeast pheromone pathway.

We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.

Cell Cycle Proteins↗
PubMed2004

Finding kinetic parameters using text mining.

The mathematical modeling and description of complex biological processes has become more and more important over the last years. Systems biology aims at the computational simulation of complex systems, up to whole cell simulations. An essential part focuses on solving a large number of parameterized differential equations. However, measuring those parameters is an expensive task, and finding them in the literature is very laborious. We developed a text mining system that supports researchers in their search for experimentally obtained parameters for kinetic models. Our system classifies full text documents regarding the question whether or not they contain appropriate data using a support vector machine. We evaluated our approach on a manually tagged corpus of 800 documents and found that it outperforms keyword searches in abstracts by a factor of five in terms of precision.

Computational Biology↗
PubMed2004

Alternative pathways might mediate toxicity of high concentrations of superoxide dismutase.

One of the most important antioxidant enzymes is superoxide dismutase (SOD), which catalyzes the dismutation of superoxide radicals to peroxide. The gene for CuZnSOD lies in humans on chromosome 21, and its activity is increased in patients with Down syndrome. However, instead of being beneficial, increased lipid peroxidation is associated with this increased expression, and also studies on bacteria and transgenic animals show that high levels of SOD actually lead to increased lipid peroxidation and hypersensitivity to oxidative stress. Using mathematical models, we investigated the question of how overexpression of SOD can lead to increased oxidative stress, although it is an antioxidant enzyme. We considered several possibilities that have been proposed in the literature, such as CuZnSOD-catalyzed hydroxyl radical formation, superoxide-mediated inhibition of membrane peroxidation, and short-circuiting of the Cu(I)ZnSOD/Cu(II)ZnSOD redox cycle. We found that one of the proposed mechanisms under certain circumstances is able to explain the increased oxidative stress caused by SOD. Furthermore, we identified an additional mechanism that agrees well with experimental observations. We call it the "alternative pathway" mechanism, because it depends on superoxide radicals having alternative pathways besides their reaction with SOD. The alternative pathway mechanism is a very general explanation for SOD-associated oxidative stress, because it does not depend on the specific type of SOD, nor on the redox status of the cell. We therefore think that it might be the common mechanism for the detrimental effects seen in cells and organisms with increased levels of the different forms of superoxide dismutase.

Antioxidants↗
PubMed2004

Inferring dynamic properties of biochemical reaction networks from structural knowledge.

Functional properties of biochemical networks depend on both the network structure and the kinetic parameters. Extensive data on metabolic network topologies have been collected in databases, but much less information is available about the kinetic constants or metabolite concentrations. Depending on the values of these parameters, metabolic fluxes and control coefficients may vary within a wide range. Nevertheless, some of the parameters may have little influence on the observables of interest. We address the question whether, despite uncertainty about kinetic parameters, probabilistic statements can be made about dynamic network features. To this end, we perform a variability analysis of the parameters: assuming that the parameters follow statistical distributions, we compute the resulting distributions of the network properties like metabolic fluxes, concentrations, or control coefficients by Monte Carlo simulation. In this manner, we study systematically the possible distributions arising from typical topologies of biochemical networks such as linear chains, branched networks, and signaling and gene expression cascades. This analysis reveals how much information about dynamic behavior can be drawn from structural knowledge.

Biochemistry↗
PubMed2002

Prediction of temporal gene expression. Metabolic opimization by re-distribution of enzyme activities.

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time-dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.

Databases as Topic↗
PubMedDate not supplied

A theory of optimal differential gene expression.

We investigate a model of optimal regulation, intended to describe large-scale differential gene expression. Relations between the optimal expression patterns and the function of genes are deduced from an optimality principle: the regulators have to maximise a fitness function which they influence directly via a cost term, and indirectly via their control on important cell variables, such as metabolic fluxes. According to the model, the optimal linear response to small perturbations reflects the regulators' functions, namely their linear influences on the cell variables. The optimal behaviour can be realised by a linear feedback mechanism. Known or assumed properties of response coefficients lead to predictions about regulation patterns. A symmetry relation predicted for deletion experiments is verified with gene expression data. Where the optimality assumption is valid, our results justify the use of expression data for functional annotation and for pathway reconstruction and suggest the use of linear factor models for the analysis of gene expression data.

Adaptation, Physiological↗