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1.
Huang, Y.* ; Urban, C.* ; Hubel, P.* ; Stukalov, A.* & Pichlmair, A.: Protein turnover regulation is critical for influenza A virus infection. Cell Syst. 15, 911-929 (2024)
2.
Ntini, E.* ; Budach, S.* ; Vang Ørom, U.A.* & Marsico, A.: Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs. Cell Syst. 14, 906-922.e6 (2023)
3.
Theis, F.J. et al.: What do you most hope spatial molecular profiling will help us understand? Part 1. Cell Syst. 14, 423-427 (2023)
4.
Boe, R.H.* ; Ayyappan, V.* ; Schuh, L. & Raj, A.*: Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks. Cell Syst. 13, 1016-1032.e6 (2022)
5.
Aliee, H. & Theis, F.J.: AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Syst. 12, 706-715.e4 (2021)
6.
Ji, Y. ; Lotfollahi, M. ; Wolf, F.A. & Theis, F.J.: Machine learning for perturbational single-cell omics. Cell Syst. 12, 522-537 (2021)
7.
Schuh, L. et al.: H4K20 methylation is differently regulated by dilution and demethylation in proliferating and cell-cycle-arrested xenopus embryos. Cell Syst. 11, 653-662.e8 (2020)
8.
Schuh, L. et al.: Gene networks with transcriptional bursting recapitulate rare transient coordinated high expression states in cancer. Cell Syst. 10, 363-378.e12 (2020)
9.
Fröhlich, F. et al.: Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model. Cell Syst. 7, 567-579 (2018)
10.
Loos, C. ; Möller, K.* ; Fröhlich, F. ; Hucho, T.* & Hasenauer, J.: A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability. Cell Syst. 6, 593-603.e13 (2018)
11.
Martinez-Corral, R.* ; Raimundez-Alvarez, E. ; Lin, Y.* ; Elowitz, M.B.* & Garcia-Ojalvo, J.*: Self-amplifying pulsatile protein dynamics without positive feedback. Cell Syst. 7, 453-462 (2018)
12.
Jagiella, N. ; Rickert, D. ; Theis, F.J. & Hasenauer, J.: Parallelization and high-performance computing enables automated statistical inference of multi-scale models. Cell Syst. 4, 194–206.e9 (2017)
13.
Blasi, T. et al.: Combinatorial histone acetylation patterns are generated by motif-specific reactions. Cell Syst. 2, 49-58 (2016)
14.
Carpenter, A.* et al.: What is the key best practice for collaborating with a computational biologist? Cell Syst. 3, 7-11 (2016)
15.
Feigelman, J. et al.: Analysis of cell lineage trees by exact bayesian inference identifies negative autoregulation of nanog in mouse embryonic stem cells. Cell Syst. 3, 480-490 (2016)
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