Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes
D. De Martino, Physical Review E 96 (2017).
LibreCat: https://research-explorer.ista.ac.at/record/548
DOI: 10.1103/PhysRevE.96.060401

Zeros of nonlinear systems with input invariances
M. Lang, E. Sontag, Automatica 81C (2017) 46–55.
LibreCat: https://research-explorer.ista.ac.at/record/1007
DOI: 10.1016/j.automatica.2017.03.030
WoS: 000403513900006

Multi-electrode array recording from salamander retinal ganglion cells
O. Marre, G. Tkadik, D. Amodei, E. Schneidman, W. Bialek, M. Berry, (2017).
LibreCat: https://research-explorer.ista.ac.at/record/5562
DOI: 10.15479/AT:ISTA:61

Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity
T. Bergmiller, A.M. Andersson, K. Tomasek, E. Balleza, D. Kiviet, R. Hauschild, G. Tkadik, C.C. Guet, (2017).
LibreCat: https://research-explorer.ista.ac.at/record/5560
DOI: 10.15479/AT:ISTA:53

Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity
T. Bergmiller, A.M. Andersson, K. Tomasek, E. Balleza, D. Kiviet, R. Hauschild, G. Tkadik, C.C. Guet, Science 356 (2017) 311–315.
LibreCat: https://research-explorer.ista.ac.at/record/665
DOI: 10.1126/science.aaf4762

An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate
V. Barone, M. Lang, G. Krens, S. Pradhan, S. Shamipour, K. Sako, M.K. Sikora, C.C. Guet, C.-P.J. Heisenberg, Developmental Cell 43 (2017) 198–211.
LibreCat: https://research-explorer.ista.ac.at/record/735
DOI: 10.1016/j.devcel.2017.09.014
WoS: 000413443700011

Relevant sparse codes with variational information bottleneck
M.J. Chalk, O. Marre, G. Tkadik, in:, Neural Information Processing Systems, 2016, pp. 1965–1973.
LibreCat: https://research-explorer.ista.ac.at/record/1082

Estimating nonlinear neural response functions using GP priors and Kronecker methods
C. Savin, G. Tkadik, in:, Neural Information Processing Systems, 2016, pp. 3610–3618.
LibreCat: https://research-explorer.ista.ac.at/record/1105

Modular parameter identification of biomolecular networks
M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) 8–08.
LibreCat: https://research-explorer.ista.ac.at/record/1170
DOI: 10.1137/15M103306X

Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on &quot;Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function&quot; by O. C. Martin et al.
G. Tkadik, Physics of Life Reviews 17 (2016) 166–167.
LibreCat: https://research-explorer.ista.ac.at/record/1171
DOI: 10.1016/j.plrev.2016.06.005