A new dynamical layout algorithm for complex biochemical reaction networks

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]

Information transfer in signaling pathways : a study using coupled simulated and experimental data

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 Ca2+-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

Fold-Hopf Bursting in a Model for Calcium Signal Transduction

We study a recent model for calcium signal transduction. This model displays spiking, bursting and chaotic oscillations in accordance with experimental results. We calculate bifurcation diagrams and study the bursting behaviour in detail. This behaviour is classified according to the dynamics of separated slow and fast subsystems. It is shown to be of the Fold-Hopf type, a type which was previously only described in the context of neuronal systems, but not in the context of signal transduction in the cell.Comment: 13 pages, 5 figure

Information transfer in signaling pathways : a study using coupled simulated and experimental data

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 Ca2+-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

Transition from stochastic to deterministic behavior in calcium oscillations

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

Simplification of biochemical models: a general approach based on the analysis of the impact of individual species and reactions on the systems dynamics

Background: Given the complex mechanisms underlying biochemical processes systems biology researchers tend to build ever increasing computational models. However, dealing with complex systems entails a variety of problems, e.g. difficult intuitive understanding, variety of time scales or non-identifiable parameters. Therefore, methods are needed that, at least semi-automatically, help to elucidate how the complexity of a model can be reduced such that important behavior is maintained and the predictive capacity of the model is increased. The results should be easily accessible and interpretable. In the best case such methods may also provide insight into fundamental biochemical mechanisms. Results: We have developed a strategy based on the Computational Singular Perturbation (CSP) method which can be used to perform a "biochemically-driven" model reduction of even large and complex kinetic ODE systems. We provide an implementation of the original CSP algorithm in COPASI (a COmplex PAthway SImulator) and applied the strategy to two example models of different degree of complexity - a simple one-enzyme system and a full-scale model of yeast glycolysis. Conclusion: The results show the usefulness of the method for model simplification purposes as well as for analyzing fundamental biochemical mechanisms. COPASI is freely available at http://www.copasi.org

A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study

Background: Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts are low, stochastic formalisms like Monte Carlo simulations are more appropriate and well established. However, compared to the wealth of computational methods used to fit and analyze deterministic models, there is only little available to quantify the exactness of the fit of stochastic models compared to experimental data or to analyze different aspects of the modeling results. Results: Here, we developed a method to fit stochastic simulations to experimental high-throughput data, meaning data that exhibits distributions. The method uses a comparison of the probability density functions that are computed based on Monte Carlo simulations and the experimental data. Multiple parameter values are iteratively evaluated using optimization routines. The method improves its performance by selecting parameters values after comparing the similitude between the deterministic stability of the system and the modes in the experimental data distribution. As a case study we fitted a model of the IRF7 gene expression circuit to time-course experimental data obtained by flow cytometry. IRF7 shows bimodal dynamics upon IFN stimulation. This dynamics occurs due to the switching between active and basal states of the IRF7 promoter. However, the exact molecular mechanisms responsible for the bimodality of IRF7 is not fully understood. Conclusions: Our results allow us to conclude that the activation of the IRF7 promoter by the combination of IRF7 and ISGF3 is sufficient to explain the observed bimodal dynamics

(Un-)Bestimmte Zeichen

Clear conceptual sharpness—distinctness: that is the strength of language. Where the clearly defining mind reaches its limits, conceptual confusion and poetic potential arise. To make such ideas nevertheless semiotically graspable and aesthetically usable, an indistinct medium is needed. Music seems particularly suitable for this. This is especially of interest in relation to the 18th century, the age of aesthetics and sentimentalism. Gerstenberg's examination of overlapping literary and musical media concepts and genres is an attempt to make the impossible poetically possible: To combine the distinctness of language with the indistinctness of music and thus to make the aporia of ‘certain sentiments’ tangible.PublishedKlare begriffliche Schärfe – Bestimmtheit –, das ist die Stärke von Sprache. Dort, wo der klar definierende Verstand an seine Grenzen kommt, entstehen begriffliche Unschärfen und poetisches Potenzial. Um solche Inhalte trotzdem semiotisch fass- und ästhetisch nutzbar zu machen, braucht es ein un-bestimmtes Medium: Musik scheint dazu besonders geeignet. Das ist im 18. Jahrhundert, dem Zeitalter von Ästhetik und Empfindsamkeit, von besonderem Interesse. Gerstenbergs Auseinandersetzung mit intermedialen, literarisch-musikalischen Medienkonzepten ist der Versuch, das Unmögliche poetisch möglich zu machen: die Bestimmtheit der Sprache mit der Unbestimmtheit der Musik zu kombinieren und so die Aporie „bestimmter Empfindungen“ erfahrbar zu machen

Neuroscience research - a new hope for teachers?

Ausgangspunkt für das Forschungsvorhaben war die Beobachtung des Phänomens, dass Fortbildungsveranstaltungen mit Themen der Hirnforschung von Lehrkräften (und anderen pädagogisch Interessierten) überproportional angefragt werden und zunehmend einen regelrechten Hype auslösen. Für die Studie wurden die neurowissenschaftlichen Erkenntnisse, die für die Pädagogik besonders relevant sind, der einschlägigen Literatur entnommen und mit eigens für diesen Zweck geführten Interviews mit den Hirnforschern Manfred Spitzer, Wolf Singer und Gerhard Roth untermauert und ergänzt. Exemplarisch für die Rezeption der Hirnforschung durch die Pädagogik stehen Interviews mit der Lernforscherin Elsbeth Stern und der Pädagogin Anette Scheunpflug. In einer ersten Erhebungsphase wurden Einstellungen von Lehrkräften zur Hirnforschung mit einer explorativ angelegten Fragebogenstudie erfasst. Die quantitativen Daten bestätigen die Vermutung, dass überhöhte Hoffnungen und Erwartungen in die Neurowissenschaften gesetzt werden, denen gleichzeitig eine geringe Wissensbasis gegenübersteht, wie die Antworten in einem offenen Fragebogenteil zeigen. In einer zweiten Phase wurde eine qualitativ ausgewertete Interviewstudie mit einem Sample von zehn Lehrkräften aus unterschiedlichen Schularten (Förderschule, Grundschule, Hauptschule, Realschule, Gymnasium) durchgeführt. Querschnittlich über alle Lehrkräfte hinweg wurde der zentralen Frage nachgegangen, welche Botschaften der Hirnforschung Einzug in die Köpfe der Lehrer gehalten haben und wie diese beurteilt werden. Die Analyse zeigt, dass unspezifische Wahrnehmungen und überhöhte Erwartungen einerseits und geringes Wissen über Hirnforschung andererseits in engem Zusammenhang mit dem drängendsten Problem, dem „schwierigen Schüler“, stehen, der von den Lehrkräften als zunehmend auffällig und belastend empfunden wird. Typisierende Profile auf der Basis des Gesamtinterviews der einzelnen Lehrkräfte geben Einblick in gemeinsame, aber auch divergierende Motive und Bedürfnisse und machen auf eine große Heterogenität unter den Lehrkräften aufmerksam. Fazit der Studie ist, dass im Bereich der Hirnforschung ein hoher Lehreraus- und -fortbildungsbedarf besteht, um unrealistischen Erwartungen und falschen Hoffnungen entgegen zu wirken. Die Arbeit gibt inhaltliche und konzeptionelle Hinweise für die Weiterentwicklung eines professionalisierenden Fortbildungsangebots. Forschung und Fortbildung sind darüber hinaus aufgefordert, nach pädagogischen Lösungen für wichtige Problemlagen der Lehrkräfte zu suchen.Starting point for the research project was the phenomenon of overproportional demand for further training on neuroscience research. Courses on this subject are well-attended by teaching staff (and others interested in teaching) and therefore causing a downright hype. For this study neuroscientific findings and resulting messages for teaching methods were extracted from the relevant literature and underpinned by special interviews carried out with the neuroscientists Manfred Spitzer, Wolf Singer and Gerhard Roth. Exemplary for the reception of neuroscience research by experts in pedagogy interviews were carried out with educational researcher Elsbeth Stern and educationalist Anette Scheunpflug. In the first data collection stage the attitudes of teachers towards neuroscience research were recorded using a questionnaire-based exploratory survey. The quantitative data confirm the impression that there are excessive hopes and expectations placed on neuroscience. Nevertheless these hopes are contrasted with a low level of knowledge revealed by answers to open-ended questions of the questionnaires. In the second stage a qualitative analysis interview study was carried out based on a sample of ten teachers from different types of school (primary school, special-needs school, secondary school). With the help of structured guideline interviews the study examined the question which messages from neuroscience are well accepted by the teachers. The analysis shows non-specific perception and excessive expectations on the one hand and insufficient body of knowledge about neuroscience research on the other. This is closely connected with the most pressing problem of the “difficult pupil” and it is being felt as an increasingly prominent and burning problem. Typical individual profiles give an insight into motives and requirements as well as the large heterogeneity among teachers. The conclusion of the study is that within the area of neuroscience research there is a high demand for (further) teacher training in order to counteract these unrealistic expectations and false hopes. The thesis gives contentual as well as conceptual hints for the further development of teacher training courses. There are also gaps in educational research in finding solutions for important problem areas in teaching