TY - GEN A1 - Bockmayr, Alexander A1 - Siebert, Heike A1 - Röblitz, Susanna A1 - Schütte, Christof A1 - Deuflhard, Peter ED - Deuflhard, Peter ED - Grötschel, Martin ED - Hömberg, Dietmar ED - Kramer, Jürg ED - Mehrmann, Volker ED - Polthier, Konrad ED - Schmidt, Frank ED - Schütte, Christof ED - Skutela, Martin ED - Sprekels, Jürgen T1 - Advanced mathematical modeling in systems biology T2 - MATHEON-Mathematics for Key Technologies Y1 - 2014 VL - 1 SP - 29 EP - 44 PB - European Mathematical Society ER - TY - GEN A1 - Schwieger, Robert A1 - Siebert, Heike A1 - Röblitz, Susanna T1 - Correspondence of Trap Spaces in Different Models of Bioregulatory Networks N2 - Mathematical models for bioregulatory networks can be based on different formalisms, depending on the quality of available data and the research question to be answered. Discrete boolean models can be constructed based on qualitative data, which are frequently available. On the other hand, continuous models in terms of ordinary differential equations (ODEs) can incorporate time-series data and give more detailed insight into the dynamics of the underlying system. A few years ago, a method based on multivariate polynomial interpolation and Hill functions has been developed for an automatic conversion of boolean models to systems of ordinary differential equations. This method is frequently used by modellers in systems biology today, but there are only a few results available about the conservation of mathematical structures and properties across the formalisms. Here, we consider subsets of the phase space where some components stay fixed, called trap spaces, and demonstrate how boolean trap spaces can be linked to invariant sets in the continuous state space. This knowledge is of practical relevance since finding trap spaces in the boolean setting, which is relatively easy, allows for the construction of reduced ODE models. T3 - ZIB-Report - 18-10 KW - dynamical systems KW - trap spaces KW - attractors KW - invariant sets KW - ordinary differential equations KW - Boolean networks Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-67121 SN - 1438-0064 ER - TY - GEN A1 - Stötzel, Claudia A1 - Röblitz, Susanna A1 - Siebert, Heike T1 - Complementing ODE-based System Analysis Using Boolean Networks Derived from an Euler-like Transformation N2 - In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations. T3 - ZIB-Report - 15-46 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-56164 SN - 1438-0064 ER - TY - JOUR A1 - Stötzel, Claudia A1 - Röblitz, Susanna A1 - Siebert, Heike T1 - Complementing ODE-based System Analysis Using Boolean Networks Derived from an Euler-like Transformation JF - PLOS ONE Y1 - 2015 U6 - https://doi.org/10.1371/journal.pone.0140954 ER - TY - JOUR A1 - Thiel, Denise A1 - Djurdjevac Conrad, Natasa A1 - Ntini, Evgenia A1 - Peschutter, Ria A1 - Siebert, Heike A1 - Marsico, Annalisa T1 - Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis JF - BMC Bioinformatics N2 - Background: Although several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them have unknown function. Recent evidence has shown the importance of both lncRNAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of ’guilt-by-association’, no strategy exists so far which identifies mRNA-lncRNA functional modules based on the 3D chromatin interaction graph. Results: To better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides in the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs. Conclusions: Interestingly, we find that lncRNA network hubs tend to be significantly enriched in evolutionary conserved lncRNAs and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we mine putative regulatory functions for uncharacterized lncRNAs. Y1 - 2019 U6 - https://doi.org/10.1186/s12859-019-2900-8 VL - 20 IS - 1471-2105 ER - TY - JOUR A1 - Schwieger, Robert A1 - Siebert, Heike A1 - Röblitz, Susanna T1 - Correspondence of Trap Spaces in Different Models of Bioregulatory Networks JF - SIAM Journal on Applied Dynamical Systems N2 - Mathematical models for bioregulatory networks can be based on different formalisms, depending on the quality of available data and the research question to be answered. Discrete boolean models can be constructed based on qualitative data, which are frequently available. On the other hand, continuous models in terms of ordinary differential equations (ODEs) can incorporate time-series data and give more detailed insight into the dynamics of the underlying system. A few years ago, a method based on multivariate polynomial interpolation and Hill functions has been developed for an automatic conversion of boolean models to systems of ordinary differential equations. This method is frequently used by modellers in systems biology today, but there are only a few results available about the conservation of mathematical structures and properties across the formalisms. Here, we consider subsets of the phase space where some components stay fixed, called trap spaces, and demonstrate how boolean trap spaces can be linked to invariant sets in the continuous state space. This knowledge is of practical relevance since finding trap spaces in the boolean setting, which is relatively easy, allows for the construction of reduced ODE models. Y1 - 2018 U6 - https://doi.org/https://doi.org/10.1137/17M1155302 VL - 17 IS - 2 SP - 1742 EP - 1765 ER - TY - JOUR A1 - Berg, Mascha A1 - Plöntzke, Julia A1 - Siebert, Heike A1 - Röblitz, Susanna T1 - Modelling Oscillatory Patterns in the Bovine Estrous Cycle with Boolean Delay Equations JF - Bulletin of Mathematical Biology N2 - Boolean delay equations (BDEs), with their relatively simple and intuitive mode of modelling, have been used in many research areas including, for example, climate dynamics and earthquake propagation. Their application to biological systems has been scarce and limited to the molecular level. Here, we derive and present two BDE models. One is directly derived from a previously published ordinary differential equation (ODE) model for the bovine estrous cycle, whereas the second model includes a modification of a particular biological mechanism. We not only compare the simulation results from the BDE models with the trajectories of the ODE model, but also validate the BDE models with two additional numerical experiments. One experiment induces a switch in the oscillatory pattern upon changes in the model parameters, and the other simulates the administration of a hormone that is known to shift the estrous cycle in time. The models presented here are the first BDE models for hormonal oscillators, and the first BDE models for drug administration. Even though automatic parameter estimation still remains challenging, our results support the role of BDEs as a framework for the systematic modelling of complex biological oscillators. Y1 - 2021 U6 - https://doi.org/10.1007/s11538-021-00942-z VL - 83 IS - 121 SP - 1 EP - 25 ER -