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Modellierung Biologischer Prozesse

Prof. Dr. Ursula Kummer

Hübner K, Sahle S, Kummer U. (2011). Applications and trends in systems biology in biochemistry. FEBS J. 278(16):2767-857.
Abstract
Systems biology has received an ever increasing interest during the last decade. A large amount of third-party funding is spent on this topic, which involves quantitative experimentation integrated with computational modeling. Industrial companies are also starting to use this approach more and more often, especially in pharmaceutical research and biotechnology. This leads to the question of whether such interest is wisely invested and whether there are success stories to be told for basic science and/or technology/biomedicine. In this review, we focus on the application of systems biology approaches that have been employed to shed light on both biochemical functions and previously unknown mechanisms. We point out which computational and experimental methods are employed most frequently and which trends in systems biology research can be observed. Finally, we discuss some problems that we have encountered in publications in the field.
Pubmed 

doi: 10.1111/j.1742-4658.2011.08217.x.; Epub 2011 Jul 26.
Maiwald T, Schneider A, Busch H, Sahle S, Gretz N, Weiss TS, Kummer U, Klingmüller U. (2010). Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon α-induced early antiviral signalling. FEBS J. 277(22):4741-54.
Abstract
Type I interferons (IFN) are important components of the innate antiviral response. A key signalling pathway activated by IFNα is the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. Major components of the pathway have been identified. However, critical kinetic properties that facilitate accelerated initiation of intracellular antiviral signalling and thereby promote virus elimination remain to be determined. By combining mathematical modelling with experimental analysis, we show that control of dynamic behaviour is not distributed among several pathway components but can be primarily attributed to interferon regulatory factor 9 (IRF9), constituting a positive feedback loop. Model simulations revealed that increasing the initial IRF9 concentration reduced the time to peak, increased the amplitude and enhanced termination of pathway activation. These model predictions were experimentally verified by IRF9 over-expression studies. Furthermore, acceleration of signal processing was linked to more rapid and enhanced expression of IFNα target genes. Thus, the amount of cellular IRF9 is a crucial determinant for amplification of early dynamics of IFNα-mediated signal transduction.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2010 Oct 21.
Surovtsova I, Simus N, Lorenz T, König A, Sahle S, Kummer U. (2009). Accessible methods for the dynamic time-scale decomposition of biochemical systems. Bioinformatics. 25(21):2816-23.
Abstract
MOTIVATION: The growing complexity of biochemical models asks for means to rationally dissect the networks into meaningful and rather independent subnetworks. Such foregoing should ensure an understanding of the system without any heuristics employed. Important for the success of such an approach is its accessibility and the clarity of the presentation of the results. RESULTS: In order to achieve this goal, we developed a method which is a modification of the classical approach of time-scale separation. This modified method as well as the more classical approach have been implemented for time-dependent application within the widely used software COPASI. The implementation includes different possibilities for the representation of the results including 3D-visualization. AVAILABILITY: The methods are included in COPASI which is free for academic use and available at www.copasi.org. CONTACT: irina.surovtsova@bioquant.uni-heidelberg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2009 Jul 24.
Sahle S, Mendes P, Hoops S, Kummer U. (2008). A new strategy for assessing sensitivities in biochemical models. Philos Transact A Math Phys Eng Sci. 366(1880):3619-31.
Abstract
An integral part of any systems biology approach is the modelling and simulation of the respective system under investigation. However, the values of many parameters of the system have often not been determined or are not identifiable due to technical experimental difficulties or other constraints. Sensitivity analysis is often employed to quantify the importance of each of the model's parameters in the behaviour of the system. This approach can also be useful in identifying those parts of the system that are most sensitive with the potential of becoming drug targets. A problem of the commonly used methods of sensitivity analysis is that they constitute local methods meaning that they depend directly on the exact parameter space, which in turn is not known exactly. One way to circumvent this problem is to carry out sensitivity analysis over a wide range of values for all parameters, but this is handicapped by expensive computations when the systems are high dimensional. Another approach is to employ global sensitivity analysis, which in this context is mostly based on random sampling methods. In this paper we present an efficient approach that involves using numerical optimizing methods that search a wide region of parameter space for a given model to determine the maximum and minimum values of its metabolic control coefficients. A relevant example for drug development is presented to demonstrate the strategy using the software COPASI.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2009 Jul 24.
Weidemann A, Richter S, Stein M, Sahle S, Gauges R, Gabdoulline R, Surovtsova I, Semmelrock N, Besson B, Rojas I, Wade R, Kummer U. (2008). SYCAMORE--a systems biology computational analysis and modeling research environment. Bioinformatics. 24(12):1463-4.
Abstract
SYCAMORE is a browser-based application that facilitates construction, simulation and analysis of kinetic models in systems biology. Thus, it allows e.g. database supported modelling, basic model checking and the estimation of unknown kinetic parameters based on protein structures. In addition, it offers some guidance in order to allow non-expert users to perform basic computational modelling tasks. AVAILABILITY: SYCAMORE is freely available for academic use at http://sycamore.eml.org. Commercial users may acquire a license. CONTACT: ursula.kummer@bioquant.uni-heidelberg.de.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2008 May 7.
Pahle J, Green AK, Dixon CJ, Kummer U. (2008). Information transfer in signaling pathways: a study using coupled simulated and experimental data. BMC Bioinformatics. 9:139.
Abstract
BACKGROUND: The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the amplitude, frequency and waveform of the oscillatory Ca(2+)-signal. RESULTS: We set up a computational framework to study information transfer, e.g. for calcium signaling at different levels of activation and different particle numbers in the system. We stochastically coupled simulated and experimentally measured calcium signals to simulated target proteins and used kernel density methods to estimate the transfer entropy from these bivariate time series. We found that, most of the time, the transfer entropy increases with increasing particle numbers. In systems with only few particles, faithful information transfer is hampered by random fluctuations. The transfer entropy also seems to be slightly correlated to the complexity (spiking, bursting or irregular oscillations) of the signal. Finally, we discuss a number of peculiarities of our approach in detail. CONCLUSION: This study presents the first application of transfer entropy to biochemical signaling pathways. We could quantify the information transferred from simulated/experimentally measured calcium signals to a target enzyme under different cellular conditions. Our approach, comprising stochastic coupling and using the information-theoretic measure transfer entropy, could also be a valuable tool for the analysis of other signaling pathways.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2008 May 7.
Kummer U. (2007). Usage of reaction kinetics data stored in databases - a modeler's point of view. In Silico Biol. 7(2 Suppl):S65-71.
Abstract
Computational approaches to biochemistry like modeling and simulation are dependent on the availability of kinetic information. This information can either be directly derived from experimental data generated by collaborators or has to be digged up from literature, often both. More recently, data stored in databases has started to be a valuable addition as a source of enzyme kinetic data. In order to faciliate modeling and simulation, various tools have been developed in recent years. However, automatizing steps in setting up, analyzing or simulating models requires the data to be in defined formats. Crucial points are addressed below.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2008 May 7.
Kummer U, Zobeley J, Brasen JC, Fahmy R, Kindzelskii AL, Petty AR, Clark AJ, Petty HR. (2007). Elevated glucose concentrations promote receptor-independent activation of adherent human neutrophils: an experimental and computational approach. Biophys J. 92(7):2597-607.
Abstract
Neutrophil activation plays integral roles in host tissue damage and resistance to infectious diseases. As glucose uptake and NADPH availability are required for reactive oxygen metabolite production by neutrophils, we tested the hypothesis that pathological glucose levels (>or=12 mM) are sufficient to activate metabolism and reactive oxygen metabolite production in normal adherent neutrophils. We demonstrate that elevated glucose concentrations increase the neutrophil's metabolic oscillation frequency and hexose monophosphate shunt activity. In parallel, substantially increased rates of NO and superoxide formation were observed. However, these changes were not observed for sorbitol, a nonmetabolizable carbohydrate. Glucose transport appears to be important in this process as phloretin interferes with the glucose-specific receptor-independent activation of neutrophils. However, LY83583, an activator of glucose flux, promoted these changes at 1 mM glucose. The data suggest that at pathophysiologic concentrations, glucose uptake by mass action is sufficient to activate neutrophils, thus circumventing the normal receptor transduction mechanism. To enable us to mechanistically understand these dynamic metabolic changes, mathematical simulations were performed. A model for glycolysis in neutrophils was created. The results indicated that the frequency change in NAD(P)H oscillations can result from the activation of the hexose monophosphate shunt, which competes with glycolysis for glucose-6-phosphate. Experimental confirmation of these simulations was performed by measuring the effect of glucose concentrations on flavoprotein autofluorescence, an indicator of the rate of mitochondrial electron transport. Moreover, after prolonged exposure to elevated glucose levels, neutrophils return to a "nonactivated" phenotype and are refractile to immunologic stimulation. Our findings suggest that pathologic glucose levels promote the transient activation of neutrophils followed by the suppression of cell activity, which may contribute to nonspecific tissue damage and increased susceptibility to infections, respectively.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2007 Jan 19.
Wegner K, Kummer U. (2005). A new dynamical layout algorithm for complex biochemical reaction networks. BMC Bioinformatics. 6:212.
Abstract
BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at 1). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on 2.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2007 Jan 19.
Kummer U, Krajnc B, Pahle J, Green AK, Dixon CJ, Marhl M. (2005). Transition from stochastic to deterministic behavior in calcium oscillations. Biophys J. 89(3):1603-11.
Abstract
Simulation and modeling is becoming more and more important when studying complex biochemical systems. Most often, ordinary differential equations are employed for this purpose. However, these are only applicable when the numbers of participating molecules in the biochemical systems are large enough to be treated as concentrations. For smaller systems, stochastic simulations on discrete particle basis are more accurate. Unfortunately, there are no general rules for determining which method should be employed for exactly which problem to get the most realistic result. Therefore, we study the transition from stochastic to deterministic behavior in a widely studied system, namely the signal transduction via calcium, especially calcium oscillations. We observe that the transition occurs within a range of particle numbers, which roughly corresponds to the number of receptors and channels in the cell, and depends heavily on the attractive properties of the phase space of the respective systems dynamics. We conclude that the attractive properties of a system, expressed, e.g., by the divergence of the system, are a good measure for determining which simulation algorithm is appropriate in terms of speed and realism.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2005 Jul 1.
Wegner K, Jansen S, Wuchty S, Gauges R, Kummer U. (2004) CombAlign: a protein sequence comparison algorithm considering recombinations. In Silico Biol. 4(3):243-54.
Abstract
The basic linear treatment of sequence comparisons limits the ability of contemporary sequence alignment algorithms to detect non-order-conserving recombinations. Here, we introduce the algorithm combAlign which addresses the assessment of pairwise sequence similarity on non-order-conserving recombinations on a large scale. Emphasizing a two-level approach, combAlign first detects locally well conserved subsequences in a target and a source sequence. Subsequently, the relative placement of alignments is mapped to a graph. Concatenating local alignments to reassemble the target sequence to the fullest extent, the maximum scoring path through the graph denotes the best attainable combAlignment. Parameters influencing this process can be set to meet the user's specific demands. combAlign is applied to examples demonstrating the possibility to reflect evolutionary kinship of proteins even if their domains and motifs are strongly rearranged.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2005 Jul 1.
Gabdoulline RR, Kummer U, Olsen LF, Wade RC. (2003). Concerted simulations reveal how peroxidase compound III formation results in cellular oscillations. Biophys J. 85(3):1421-8.
Abstract
A major problem in mathematical modeling of the dynamics of complex biological systems is the frequent lack of knowledge of kinetic parameters. Here, we apply Brownian dynamics simulations, based on protein three-dimensional structures, to estimate a previously undetermined kinetic parameter, which is then used in biochemical network simulations. The peroxidase-oxidase reaction involves many elementary steps and displays oscillatory dynamics important for immune response. Brownian dynamics simulations were performed for three different peroxidases to estimate the rate constant for one of the elementary steps crucial for oscillations in the peroxidase-oxidase reaction, the association of superoxide with peroxidase. Computed second-order rate constants agree well with available experimental data and permit prediction of rate constants at physiological conditions. The simulations show that electrostatic interactions depress the rate of superoxide association with myeloperoxidase, bringing it into the range necessary for oscillatory behavior in activated neutrophils. Such negative electrostatic steering of enzyme-substrate association presents a novel control mechanism and lies in sharp contrast to the electrostatically-steered fast association of superoxide and Cu/Zn superoxide dismutase, which is also simulated here. The results demonstrate the potential of an integrated and concerted application of structure-based simulations and biochemical network simulations in cellular systems biology.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2005 Jul 1.
Olsen LF, Hauser MJ, Kummer U. (2003). Mechanism of protection of peroxidase activity by oscillatory dynamics. Eur J Biochem. 270(13):2796-804.
Abstract
The peroxidase-oxidase reaction is known to involve reactive oxygen species as intermediates. These intermediates inactivate many types of biomolecules, including peroxidase itself. Previously, we have shown that oscillatory dynamics in the peroxidase-oxidase reaction seem to protect the enzyme from inactivation. It was suggested that this is due to a lower average concentration of reactive oxygen species in the oscillatory state compared to the steady state. Here, we studied the peroxidase-oxidase reaction with either 4-hydroxybenzoic acid or melatonin as cofactors. We show that the protective effect of oscillatory dynamics is present in both cases. We also found that the enzyme degradation depends on the concentration of the cofactor and on the pH of the reaction mixture. We simulated the oscillatory behaviour, including the oscillation/steady state bistability observed experimentally, using a detailed reaction scheme. The computational results confirm the hypothesis that protection is due to lower average concentrations of superoxide radical during oscillations. They also show that the shape of the oscillations changes with increasing cofactor concentration resulting in a further decrease in the average concentration of radicals. We therefore hypothesize that the protective effect of oscillatory dynamics is a general effect in this system.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2005 Jul 1.
Olsen LF, Kummer U, Kindzelskii AL, Petty HR. (2003). A model of the oscillatory metabolism of activated neutrophils. Biophys J. 84(1):69-81.
Abstract
We present a two-compartment model to explain the oscillatory behavior observed experimentally in activated neutrophils. Our model is based mainly on the peroxidase-oxidase reaction catalyzed by myeloperoxidase with melatonin as a cofactor and NADPH oxidase, a major protein in the phagosome membrane of the leukocyte. The model predicts that after activation of a neutrophil, an increase in the activity of the hexose monophosphate shunt and the delivery of myeloperoxidase into the phagosome results in oscillations in oxygen and NAD(P)H concentration. The period of oscillation changes from >200 s to 10-30 s. The model is consistent with previously reported oscillations in cell metabolism and oxidant production. Key features and predictions of the model were confirmed experimentally. The requirement of the hexose monophosphate pathway for 10 s oscillations was verified using 6-aminonicotinamide and dexamethasone, which are inhibitors of glucose-6-phosphate dehydrogenase. The role of the NADPH oxidase in promoting oscillations was confirmed by dose-response studies of the effect of diphenylene iodonium, an inhibitor of the NADPH oxidase. Moreover, the model predicted an increase in the amplitude of NADPH oscillations in the presence of melatonin, which was confirmed experimentally. Successful computer modeling of complex chemical dynamics within cells and their chemical perturbation will enhance our ability to identify new antiinflammatory compounds.
Pubmed 

doi: 10.1111/j.1742-4658.2010.07880.x.; Epub 2005 Jul 1.

/var/www/cos/ / http://www.cos.uni-heidelberg.de/ Prof. Dr. Ursula Kummer