@article{LangVolkamerBehmetal., author = {Lang, Annemarie and Volkamer, Andrea and Behm, Laura and R{\"o}blitz, Susanna and Ehrig, Rainald and Schneider, Marlon and Geris, Lisbet and Wichard, Joerg and Buttgereit, Frank}, title = {In silico Methods - Computational Alternatives to Animal Testing}, series = {ALTEX}, volume = {35}, journal = {ALTEX}, number = {1}, doi = {10.14573/altex.1712031}, pages = {126 -- 128}, abstract = {A seminar and interactive workshop on "In silico Methods - Computational Alternatives to Animal Testing" was held in Berlin, Germany, organized by Annemarie Lang, Frank Butt- gereit and Andrea Volkamer at the Charit{\´e}-Universit{\"a}tsmedizin Berlin, on August 17-18, 2017. During the half-day seminar, the variety and applications of in silico methods as alternatives to animal testing were presented with room for scientific discus- sions with experts from academia, industry and the German fed- eral ministry (Fig. 1). Talks on computational systems biology were followed by detailed information on predictive toxicology in order to display the diversity of in silico methods and the potential to embrace them in current approaches (Hartung and Hoffmann, 2009; Luechtefeld and Hartung, 2017). The follow- ing interactive one-day Design Thinking Workshop was aimed at experts, interested researchers and PhD-students interested in the use of in silico as alternative methods to promote the 3Rs (Fig. 2). Forty participants took part in the seminar while the workshop was restricted to sixteen participants.}, language = {en} } @article{LangeSchwiegerPloentzkeetal., author = {Lange, Alexander and Schwieger, Robert and Pl{\"o}ntzke, Julia and Sch{\"a}fer, Stefan and R{\"o}blitz, Susanna}, title = {Follicular competition in cows: the selection of dominant follicles as a synergistic effect}, series = {Journal of Mathematical Biology}, journal = {Journal of Mathematical Biology}, doi = {https://doi.org/10.1007/s00285-018-1284-0}, abstract = {The reproductive cycle of mono-ovulatory species such as cows or humans is known to show two or more waves of follicular growth and decline between two successive ovulations. Within each wave, there is one dominant follicle escorted by subordinate follicles of varying number. Under the surge of the luteinizing hormone a growing dominant follicle ovulates. Rarely the number of ovulating follicles exceeds one. In the biological literature, the change of hormonal concentrations and individually varying numbers of follicular receptors are made responsible for the selection of exactly one dominant follicle, yet a clear cause has not been identified. In this paper, we suggest a synergistic explanation based on competition, formulated by a parsimoniously defined system of ordinary differential equations (ODEs) that quantifies the time evolution of multiple follicles and their competitive interaction during one wave. Not discriminating between follicles, growth and decline are given by fixed rates. Competition is introduced via a growth-suppressing term, equally supported by all follicles. We prove that the number of dominant follicles is determined exclusively by the ratio of follicular growth and competition. This number turns out to be independent of the number of subordinate follicles. The asymptotic behavior of the corresponding dynamical system is investigated rigorously, where we demonstrate that the ω-limit set only contains fixed points. When also including follicular decline, our ODEs perfectly resemble ultrasound data of bovine follicles. Implications for the involved but not explicitly modeled hormones are discussed.}, language = {en} } @article{SchwiegerSiebertRoeblitz, author = {Schwieger, Robert and Siebert, Heike and R{\"o}blitz, Susanna}, title = {Correspondence of Trap Spaces in Different Models of Bioregulatory Networks}, series = {SIAM Journal on Applied Dynamical Systems}, volume = {17}, journal = {SIAM Journal on Applied Dynamical Systems}, number = {2}, doi = {https://doi.org/10.1137/17M1155302}, pages = {1742 -- 1765}, abstract = {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.}, language = {en} } @inproceedings{ManciniMariMassinietal., author = {Mancini, Toni and Mari, Federico and Massini, Annalisa and Melatti, Igor and Salvo, Ivano and Sinisi, Stefano and Tronci, Enrico and Ehrig, Rainald and R{\"o}blitz, Susanna and Leeners, Brigitte}, title = {Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction}, series = {Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, Workshop of the Federated Logic Conference 2018 (FLoC 2018), Oxford}, booktitle = {Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, Workshop of the Federated Logic Conference 2018 (FLoC 2018), Oxford}, doi = {10.29007/g864}, pages = {1 -- 16}, abstract = {In Silico Clinical Trials (ISCT), i.e., clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present a case study aiming at quantifying, by means of a multi-arm ISCT supervised by intelligent search, the potential impact of precision medicine approaches on a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction.}, language = {en} }