De Martino D. 2017. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics.  Physical Review E Statistical Nonlinear and Soft Matter Physics . 95(6), 062419.
LibreCat: https://research-explorer.ista.ac.at/record/959
DOI: 10.1103/PhysRevE.95.062419
WoS: 000404546400004

De Martino D, Capuani F, De Martino A. 2017. Quantifying the entropic cost of cellular growth control.  Physical Review E Statistical Nonlinear and Soft Matter Physics . 96(1), 010401.
LibreCat: https://research-explorer.ista.ac.at/record/947
DOI: 10.1103/PhysRevE.96.010401
WoS: 000405194200002

Zagórski MP, Tabata Y, Brandenberg N, Lutolf M, Tkadik G, Bollenbach T, Briscoe J, Kicheva A. 2017. Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. 356(6345), 1379–1383.
LibreCat: https://research-explorer.ista.ac.at/record/943
DOI: 10.1126/science.aam5887
WoS: 000404351500036
PMID: 28663499

Colabrese S, De Martino D, Leuzzi L, Marinari E. 2017. Phase transitions in integer linear problems.  Journal of Statistical Mechanics: Theory and Experiment. 2017(9), 093404.
LibreCat: https://research-explorer.ista.ac.at/record/823
DOI: 10.1088/1742-5468/aa85c3
WoS: 000411842900001

Savin C, Tkadik G. 2017. Maximum entropy models as a tool for building precise neural controls. Current Opinion in Neurobiology. 46, 120–126.
LibreCat: https://research-explorer.ista.ac.at/record/730
DOI: 10.1016/j.conb.2017.08.001
WoS: 000416196400016

De Martino D. 2017. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 96(6), 060401.
LibreCat: https://research-explorer.ista.ac.at/record/548
DOI: 10.1103/PhysRevE.96.060401

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

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

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

Bergmiller T, Andersson AM, Tomasek K, Balleza E, Kiviet D, Hauschild R, Tkadik G, Guet CC. 2017. Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity. Science. 356(6335), 311–315.
LibreCat: https://research-explorer.ista.ac.at/record/665
DOI: 10.1126/science.aaf4762

Barone V, Lang M, Krens G, Pradhan S, Shamipour S, Sako K, Sikora MK, Guet CC, Heisenberg C-PJ. 2017. An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. 43(2), 198–211.
LibreCat: https://research-explorer.ista.ac.at/record/735
DOI: 10.1016/j.devcel.2017.09.014
WoS: 000413443700011

Chalk MJ, Marre O, Tkadik G. 2016. Relevant sparse codes with variational information bottleneck. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 1965–1973.
LibreCat: https://research-explorer.ista.ac.at/record/1082

Savin C, Tkadik G. 2016. Estimating nonlinear neural response functions using GP priors and Kronecker methods. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3610–3618.
LibreCat: https://research-explorer.ista.ac.at/record/1105

Lang M, Stelling J. 2016. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 38(6), 8–08.
LibreCat: https://research-explorer.ista.ac.at/record/1170
DOI: 10.1137/15M103306X

Tkadik G. 2016. 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. Physics of Life Reviews. 17, 166–167.
LibreCat: https://research-explorer.ista.ac.at/record/1171
DOI: 10.1016/j.plrev.2016.06.005

De Martino D, Masoero D. 2016. Asymptotic analysis of noisy fitness maximization, applied to metabolism &;amp; growth.  Journal of Statistical Mechanics: Theory and Experiment. 2016(12), 123502.
LibreCat: https://research-explorer.ista.ac.at/record/1188
DOI: 10.1088/1742-5468/aa4e8f

Hu F, Rishishwar L, Sivadas A, Mitchell G, King J, Murphy T, Gilsdorf J, Mayer L, Wang X. 2016. Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. 54(12), 3010–3017.
LibreCat: https://research-explorer.ista.ac.at/record/1203
DOI: 10.1128/JCM.01511-16

Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International Conference on Intelligent Robots and Systems IROS  vol. 2016–November, 7759138.
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

Miki G, Stoll A, Bevirt J, Grah R, Moore M. 2016. Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency. AIAA: Aviation Technology, Integration, and Operations Conference, 1–19.
LibreCat: https://research-explorer.ista.ac.at/record/1220
DOI: 10.2514/6.2016-3764

Sokolowski TR, Walczak A, Bialek W, Tkadik G. 2016. Extending the dynamic range of transcription factor action by translational regulation. Physical Review E Statistical Nonlinear and Soft Matter Physics. 93(2), 022404.
LibreCat: https://research-explorer.ista.ac.at/record/1242
DOI: 10.1103/PhysRevE.93.022404