Sven Sahle

466 total citations
23 papers, 270 citations indexed

About

Sven Sahle is a scholar working on Molecular Biology, Computer Networks and Communications and Immunology. According to data from OpenAlex, Sven Sahle has authored 23 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 7 papers in Computer Networks and Communications and 5 papers in Immunology. Recurrent topics in Sven Sahle's work include Nonlinear Dynamics and Pattern Formation (7 papers), Gene Regulatory Network Analysis (4 papers) and Protein Structure and Dynamics (3 papers). Sven Sahle is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (7 papers), Gene Regulatory Network Analysis (4 papers) and Protein Structure and Dynamics (3 papers). Sven Sahle collaborates with scholars based in Germany, United States and United Kingdom. Sven Sahle's co-authors include Gerold Baier, Ursula Kummer, Ursula Klingmüller, Hauke Busch, Norbert Gretz, Thomas S. Weiß, Tim Maiwald, Annette Schneider, Anastasia Bachmann and Jennifer Levering and has published in prestigious journals such as The Journal of Chemical Physics, PLoS ONE and Cancer Research.

In The Last Decade

Sven Sahle

22 papers receiving 268 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sven Sahle Germany 10 109 92 83 36 29 23 270
Xuechen Li China 9 52 0.5× 93 1.0× 180 2.2× 52 1.4× 38 1.3× 12 368
Anael Verdugo United States 8 256 2.3× 67 0.7× 65 0.8× 10 0.3× 46 1.6× 14 396
David D. van Niekerk South Africa 10 213 2.0× 10 0.1× 53 0.6× 10 0.3× 15 0.5× 22 348
Konstantin Tretyakov Estonia 6 103 0.9× 21 0.2× 56 0.7× 59 1.6× 13 0.4× 13 299
Djomangan Adama Ouattara France 8 160 1.5× 11 0.1× 8 0.1× 24 0.7× 36 1.2× 14 267
Zhaojuan Wang China 14 71 0.7× 79 0.9× 86 1.0× 11 0.3× 13 0.4× 42 490
Bingbo Wang China 12 378 3.5× 86 0.9× 55 0.7× 9 0.3× 8 0.3× 29 544
Shimin Li China 13 29 0.3× 120 1.3× 37 0.4× 29 0.8× 17 0.6× 52 438

Countries citing papers authored by Sven Sahle

Since Specialization
Citations

This map shows the geographic impact of Sven Sahle's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sven Sahle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sven Sahle more than expected).

Fields of papers citing papers by Sven Sahle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sven Sahle. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sven Sahle. The network helps show where Sven Sahle may publish in the future.

Co-authorship network of co-authors of Sven Sahle

This figure shows the co-authorship network connecting the top 25 collaborators of Sven Sahle. A scholar is included among the top collaborators of Sven Sahle based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sven Sahle. Sven Sahle is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Köster, Mario, et al.. (2025). A comparative computational analysis of IFN-alpha pharmacokinetics and its induced cellular response in mice and humans. PLoS Computational Biology. 21(9). e1013509–e1013509.
2.
Müller, Susanne, Sven Sahle, Frank Bergmann, et al.. (2023). The pH-dependent lactose metabolism of Lactobacillus delbrueckii subsp. bulgaricus: An integrative view through a mechanistic computational model. Journal of Biotechnology. 374. 90–100. 5 indexed citations
3.
Keegan, Liam, Paula Fernández‐Palanca, Reham Hassan, et al.. (2022). Spatial modeling reveals nuclear phosphorylation and subcellular shuttling of YAP upon drug-induced liver injury. eLife. 11. 10 indexed citations
4.
Keegan, Liam P., Julian Schmitt, Sven Sahle, et al.. (2021). Mathematical modeling of YAP and TAZ nuclear/cytoplasmic shuttling in liver cancer cells. Zeitschrift für Gastroenterologie. 1 indexed citations
5.
Brandl, Julian, et al.. (2019). Quantitative systems pharmacology of interferon alpha administration: A multi-scale approach. PLoS ONE. 14(2). e0209587–e0209587. 7 indexed citations
6.
Stalidzāns, Egils, et al.. (2018). Misinterpretation risks of global stochastic optimisation of kinetic models revealed by multiple optimisation runs. Mathematical Biosciences. 307. 25–32. 4 indexed citations
7.
Pinna, Federico, Michaela Bissinger, Nicolas Huber, et al.. (2017). A20/TNFAIP3 Discriminates Tumor Necrosis Factor (TNF)-Induced NF-κB from JNK Pathway Activation in Hepatocytes. Frontiers in Physiology. 8. 610–610. 16 indexed citations
8.
Schildberg, Frank A., Federico Pinna, Ute Albrecht, et al.. (2017). Quantitative and integrative analysis of paracrine hepatocyte activation by nonparenchymal cells upon lipopolysaccharide induction. FEBS Journal. 284(5). 796–813. 1 indexed citations
9.
Kummer, Ursula, et al.. (2015). A new model for the aerobic metabolism of yeast allows the detailed analysis of the metabolic regulation during glucose pulse. Biophysical Chemistry. 206. 40–57. 7 indexed citations
10.
Pinna, Federico, Sven Sahle, Michaela Bissinger, et al.. (2013). Abstract 5235: A model for TNFα-mediated NFκB signalling: A systems biology study on hepatocytes and liver cancer cells.. Cancer Research. 73(8_Supplement). 5235–5235. 2 indexed citations
11.
Sahle, Sven & Ursula Kummer. (2012). Special Issue: International Conference on Systems Biology 2011: Introduction. FEBS Journal. 279(18). 3289–3289. 1 indexed citations
12.
Wegner, Katja, Anastasia Bachmann, Philippe Lucarelli, et al.. (2012). Dynamics and feedback loops in the transforming growth factor β signaling pathway. Biophysical Chemistry. 162. 22–34. 26 indexed citations
13.
Pinna, Federico, Sven Sahle, Michaela Bissinger, et al.. (2012). A Systems Biology Study on NFκB Signaling in Primary Mouse Hepatocytes. Frontiers in Physiology. 3. 466–466. 5 indexed citations
14.
Levering, Jennifer, Ursula Kummer, Konrad B. Becker, & Sven Sahle. (2012). Glycolytic oscillations in a model of a lactic acid bacterium metabolism. Biophysical Chemistry. 172. 53–60. 12 indexed citations
15.
Maiwald, Tim, Annette Schneider, Hauke Busch, et al.. (2010). Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon α‐induced early antiviral signalling. FEBS Journal. 277(22). 4741–4754. 40 indexed citations
16.
Baier, Gerold & Sven Sahle. (1998). Homogeneous and Spatio-temporal Chaos in Biochemical Reactions With Feedback Inhibition. Journal of Theoretical Biology. 193(2). 233–242. 10 indexed citations
17.
Baier, Gerold, Ursula Kummer, & Sven Sahle. (1998). An Electrochemically Induced Oscillatory Instability. The Journal of Physical Chemistry A. 103(1). 33–37. 5 indexed citations
18.
Baier, Gerold & Sven Sahle. (1997). Spatio‐temporal patterns with hyperchaotic dynamics in diffusively coupled biochemical oscillators. Discrete Dynamics in Nature and Society. 1(2). 161–167. 2 indexed citations
19.
Baier, Gerold & Sven Sahle. (1995). Design of hyperchaotic flows. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 51(4). R2712–R2714. 60 indexed citations
20.
Baier, Gerold & Sven Sahle. (1994). Hyperchaos and chaotic hierarchy in low-dimensional chemical systems. The Journal of Chemical Physics. 100(12). 8907–8911. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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