TY - JOUR A1 - Kubis, Lea A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Lausser, Ludwig A1 - Tümpel, Stefan A1 - Pfister, Astrid S. A1 - Kühl, Michael A1 - Kestler, Hans A. T1 - A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells JF - PLOS ONE N2 - Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases. UR - https://doi.org/10.1371/journal.pone.0195126 Y1 - 2018 UR - https://doi.org/10.1371/journal.pone.0195126 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27280 SN - 1932-6203 VL - 13 (2018) IS - 3 PB - Public Library of Science CY - San Francisco ER - TY - JOUR A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - Response to the letter to the editor: On the feasibility of dynamical analysis of network models of biochemical regulation JF - Bioinformatics UR - https://doi.org/10.1093/bioinformatics/btac318 Y1 - 2022 UR - https://doi.org/10.1093/bioinformatics/btac318 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40238 SN - 1367-4811 VL - 38 IS - 14 SP - 3676 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - Capturing dynamic relevance in Boolean networks using graph theoretical measures JF - Bioinformatics N2 - Motivation Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. Results Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. UR - https://doi.org/10.1093/bioinformatics/btab277 Y1 - 2021 UR - https://doi.org/10.1093/bioinformatics/btab277 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27194 SN - 1460-2059 SN - 1367-4803 N1 - Supplementary data are available at Bioinformatics online. VL - 37 IS - 20 SP - 3530 EP - 3537 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Annika M. T. U. A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Maier, Julia A1 - Buchholz, Malte A1 - Gress, Thomas M. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective JF - npj Systems Biology and Applications N2 - AbstractPancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes. UR - https://doi.org/10.1038/s41540-023-00283-8 Y1 - 2023 UR - https://doi.org/10.1038/s41540-023-00283-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38071 SN - 2056-7189 VL - 9 IS - 1 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Hühne, Rolf A1 - Kessler, Viktor A1 - Fürstberger, Axel A1 - Werle, Silke D. A1 - Platzer, Matthias A1 - Sühnel, Jürgen A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - 3D Network exploration and visualisation for lifespan data JF - BMC Bioinformatics N2 - Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. UR - https://doi.org/10.1186/s12859-018-2393-x KW - lifespan KW - ageing KW - gene network KW - 3D visualization KW - ageing factor database KW - AgeFactDB KW - differentially expressed genes Y1 - 2018 UR - https://doi.org/10.1186/s12859-018-2393-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27327 SN - 1471-2105 VL - 19 (2018) PB - BioMed Central CY - London ER - TY - JOUR A1 - Lausser, Ludwig A1 - Schäfer, Lisa M. A1 - Werle, Silke D. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - Detecting Ordinal Subcascades JF - Neural Processing Letters N2 - Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations. UR - https://doi.org/10.1007/s11063-020-10362-0 KW - ordinal classification KW - classifier cascades KW - error bounds KW - subsets KW - supersets Y1 - 2020 UR - https://doi.org/10.1007/s11063-020-10362-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27555 SN - 1573-773X SN - 1370-4621 VL - 52 IS - 3 SP - 2583 EP - 2605 PB - Springer Science+Business Media CY - Dordrecht ER - TY - JOUR A1 - Seufferlein, Thomas A1 - Lausser, Ludwig A1 - Stein, Alexander A1 - Arnold, Dirk A1 - Prager, Gerald A1 - Kasper-Virchow, Stefan A1 - Niedermeier, Michael A1 - Müller, Lothar A1 - Kubicka, Stefan A1 - König, Alexander A1 - Büchner-Steudel, Petra A1 - Wille, Kai A1 - Berger, Andreas W. A1 - Kestler, Angelika M. R. A1 - Kraus, Johann M. A1 - Werle, Silke D. A1 - Perkhofer, Lukas A1 - Ettrich, Thomas J. A1 - Kestler, Hans A. T1 - Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial JF - PLOS ONE N2 - Background Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Patients and methods 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity. Conclusions We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress. UR - https://doi.org/10.1371/journal.pone.0304324 Y1 - 2024 UR - https://doi.org/10.1371/journal.pone.0304324 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49003 SN - 1932-6203 VL - 19 IS - 6 PB - PLOS CY - San Francisco ER -