TY - GEN A1 - Plöntzke, Julia A1 - Berg, Mascha A1 - Stötzel, Claudia A1 - Röblitz, Susanna T1 - A systems biology approach to bovine fertility and metabolism: Development of a glucose insulin model. N2 - To counteract the antagonistic relationship between milk yield and fertility in dairy cow, a deeper understanding of the underlying biological mechanisms is required. For this purpose, we study physiological networks related to reproduction and metabolism in dairy cows. We interactively develop dynamic, mechanistic models by fitting the models to experimental data and mechanistic knowledge. We have already developed models for potassium balance and hormonal regulation of fertility in the dairy cow, which will briefly be reviewed here. The main focus of this article is a glucose-insulin model currently developed by us. This model links the bovine hormonal cycle and the potassium balance to glucose and thus to energy metabolism. The models can be applied in scientific research, education, experimental planning, drug development and production on farms. T3 - ZIB-Report - 15-51 Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-56453 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 - Fackeldey, Konstantin A1 - Röblitz, Susanna A1 - Scharkoi, O. A1 - Weber, Marcus T1 - Soft Versus Hard Metastable Conformations in Molecular Simulations JF - Particle Methods II, Fundamentals and Applications, Barcelona, Spain 26-28 Oct. 2011, E. Onate and D.R.J. Owen (eds.) Y1 - 2011 SP - 899 EP - 909 ER - TY - GEN A1 - Gupta, Pooja A1 - Röblitz, Susanna A1 - Krause, Carola A1 - Knaus, Petra A1 - Schütte, Christof T1 - Mathematical modeling of the Smad and Non-Smad BMP signaling pathways in context of cell density T2 - Computational Models in biology and medicine, 2013, Dresden, Germany Y1 - 2013 ER - TY - CHAP A1 - Gupta, Pooja A1 - Krause, Carola A1 - Rikeit, Paul A1 - Röblitz, Susanna A1 - Knaus, Petra A1 - Schütte, Christof T1 - Modeling of the BMP mediated co-regulation of the Smad and Non-Smad pathways in the context of cell density T2 - 10th International BMP conference, 2014, Berlin, Germany Y1 - 2014 ER - TY - JOUR A1 - Berg, Mascha A1 - Plöntzke, Julia A1 - Leonhard-Marek, Sabine A1 - Müller, Kerstin-Elisabeth A1 - Röblitz, Susanna T1 - A dynamic model to simulate potassium balance in dairy cows JF - Journal of Dairy Science N2 - High-performing dairy cows require a particular composition of nutritional ingredients, adapted to their individual requirements and depending on their production status. The optimal dimensioning of minerals in the diet, one being potassium, is indispensable for the prevention of imbalances. Potassium balance in cows is the result of potassium intake, distribution in the organism, and excretion, and it is closely related to glucose and electrolyte metabolism. In this paper, we present a dynamical model for potassium balance in lactating and nonlactating dairy cows based on ordinary differential equations. Parameter values were obtained from clinical trial data and from the literature. To verify the consistency of the model, we present simulation outcomes for 3 different scenarios: potassium balance in (1) nonlactating cows with varying feed intake, (2) nonlactating cows with varying potassium fraction in the diet, and (3) lactating cows with varying milk production levels. The results give insights into the short- and long-term potassium metabolism, providing an important step toward the understanding of the potassium network, the design of prophylactic feed additives, and possible treatment strategies. Y1 - 2017 U6 - https://doi.org/10.3168/jds.2016-12443 VL - 100 IS - 12 SP - 9799 EP - 9814 ER - TY - JOUR A1 - Igde, Sinaida A1 - Röblitz, Susanna A1 - Müller, Anne A1 - Kolbe, Katharina A1 - Boden, Sophia A1 - Fessele, Claudia A1 - Lindhorst, Thisbe A1 - Weber, Marcus A1 - Hartmann, Laura T1 - Linear Precision Glycomacromolecules with Varying Interligand Spacing and Linker Functionalities Binding to Concanavalin A and the Bacterial Lectin FimH JF - Marcomolecular Bioscience Y1 - 2017 U6 - https://doi.org/10.1002/mabi.201700198 VL - 17 IS - 12 SP - 1700198 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 - TY - JOUR A1 - Yousefian, Maryam A1 - Frank, Anne-Simone A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition JF - BMC Bioinformatics N2 - The dynamics of many gene regulatory networks (GRNs) is characterized by the occurrence of metastable phenotypes and stochastic phenotype switches. The chemical master equation (CME) is the most accurate description to model such stochastic dynamics, whereby the long-time dynamics of the system is encoded in the spectral properties of the CME operator. Markov State Models (MSMs) provide a general framework for analyzing and visualizing stochastic multistability and state transitions based on these spectral properties. Until now, however, this approach is either limited to low-dimensional systems or requires the use of high-performance computing facilities, thus limiting its usability. Y1 - 2025 U6 - https://doi.org/10.1186/s12859-025-06174-5 VL - 26 IS - 147 ER - TY - CHAP A1 - Yousefian, Maryam A1 - Donati, Luca A1 - Sikorski, Alexander A1 - Weber, Marcus A1 - Röblitz, Susanna T1 - Exploring Metastable Dynamics of Gene Regulatory Networks with ISOKANN T2 - Computational Methods in Systems Biology. CMSB 2025 N2 - Stochastic dynamical systems like gene regulatory networks (GRNs) often exhibit behavior characterized by metastable sets (representing cellular phenotypes), in which trajectories remain for long times, whereas switches between these sets in the phase space are rare events. One way to capture these rare events is to infer the system’s long-term behavior from the spectral characteristics (eigenvalues and eigenvectors) of its Koopman operator. For GRNs, the Koopman operator is based on the chemical master equation (CME), which provides a precise mathematical modeling framework for stochastic GRNs. Since the CME is typically analytically intractable, methods based on discretizing the CME operator have been developed. However, determining the number and location of metastable sets in the phase space as well as the transition rates between them remains computationally challenging, especially for large GRNs with many genes and interactions. A promising alternative method, called ISOKANN (invariant subspaces of Koopman operators with artificial neural networks) has been developed in the context of molecular dynamics. ISOKANN uses a combination of the power iteration and neural networks to learn the basis functions of an invariant subspace of the Koopman operator. In this paper, we extend the application of ISOKANN to the CME operator and apply it to two small GRNs: a genetic toggle switch model and a model for macrophage polarization. Our work opens a new field of application for the ISOKANN algorithm and demonstrates the potential of this algorithm for studying large GRNs. Y1 - 2026 U6 - https://doi.org/10.1007/978-3-032-01436-8_8 VL - 15959 SP - 126 EP - 149 ER -