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Chait, R. P., Ruess, J., Bergmiller, T., Tkadik, G., &; Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/s41467-017-01683-1 
LibreCat: https://research-explorer.ista.ac.at/record/613
DOI: 10.1038/s41467-017-01683-1

Der, R., &; Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE.https://doi.org/10.1109/DEVLRN.2016.7846789 
LibreCat: https://research-explorer.ista.ac.at/record/652
DOI: 10.1109/DEVLRN.2016.7846789

Der, R., &; Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation.https://doi.org/10.3389/fnbot.2017.00008 
LibreCat: https://research-explorer.ista.ac.at/record/658
DOI: 10.3389/fnbot.2017.00008

Humplik, J., &; Tkadik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005763 
LibreCat: https://research-explorer.ista.ac.at/record/720
DOI: 10.1371/journal.pcbi.1005763

Harpaz, R., Tkadik, G., &; Schneidman, E. (2017). Discrete modes of social information processing predict individual behavior of fish in a group. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1703817114 
LibreCat: https://research-explorer.ista.ac.at/record/725
DOI: 10.1073/pnas.1703817114
PMID: 28874581

Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkadik, G., &; Berry, M. (2017). Data from: Error-robust modes of the retinal population code. Dryad.https://doi.org/10.5061/dryad.1f1rc 
LibreCat: https://research-explorer.ista.ac.at/record/9709
DOI: 10.5061/dryad.1f1rc

Chalk, M. J., Masset, P., Gutkin, B., &; Denève, S. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005582 
LibreCat: https://research-explorer.ista.ac.at/record/680
DOI: 10.1371/journal.pcbi.1005582

Chalk, M. J., Masset, P., Gutkin, B., &; Denève, S. (2017). Supplementary appendix. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005582.s001 
LibreCat: https://research-explorer.ista.ac.at/record/9855
DOI: 10.1371/journal.pcbi.1005582.s001

Mitosch, K., Rieckh, G., &; Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. Cell Press.https://doi.org/10.1016/j.cels.2017.03.001 
LibreCat: https://research-explorer.ista.ac.at/record/666
DOI: 10.1016/j.cels.2017.03.001

Martin Del Campo Sanchez, A., Cepeda Humerez, S. A., &; Uhler, C. (2017). Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of Statistics. Wiley-Blackwell.https://doi.org/10.1111/sjos.12251 
LibreCat: https://research-explorer.ista.ac.at/record/2016
DOI: 10.1111/sjos.12251
WoS: 000400985000001
arXiv: 1410.1242

Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S.,https://doi.org/10.1038/s41467-017-02159-y__Marre, O. (2017). Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/s41467-017-02159-y 
LibreCat: https://research-explorer.ista.ac.at/record/1104
DOI: 10.1038/s41467-017-02159-y
WoS: 000417241200004

Levina (Martius), A., &; Priesemann, V. (2017). Subsampling scaling. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms15140 
LibreCat: https://research-explorer.ista.ac.at/record/993
DOI: 10.1038/ncomms15140
WoS: 000400560700001

Friedlander, T., Prizak, R., Barton, N. H., &; Tkadik, G. (2017). Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/s41467-017-00238-8 
LibreCat: https://research-explorer.ista.ac.at/record/955
DOI: 10.1038/s41467-017-00238-8
WoS: 000407198800005

De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. https://doi.org/10.1103/PhysRevE.95.062419__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.95.062419 
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. https://doi.org/10.1103/PhysRevE.96.010401__Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics.https://doi.org/10.1103/PhysRevE.96.010401 
LibreCat: https://research-explorer.ista.ac.at/record/947
DOI: 10.1103/PhysRevE.96.010401
WoS: 000405194200002

Zagórski, M. P., Tabata, Y., Brandenberg, N., Lutolf, M., Tkadik, G., Bollenbach, T.,https://doi.org/10.1126/science.aam5887__Kicheva, A. (2017). Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.aam5887 
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. https://doi.org/10.1088/1742-5468/aa85c3__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa85c3 
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. Elsevier.https://doi.org/10.1016/j.conb.2017.08.001 
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. American Physical Society.https://doi.org/10.1103/PhysRevE.96.060401 
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. International Federation of Automatic Control.https://doi.org/10.1016/j.automatica.2017.03.030 
LibreCat: https://research-explorer.ista.ac.at/record/1007
DOI: 10.1016/j.automatica.2017.03.030
WoS: 000403513900006

Bergmiller, T., Andersson, A. M., Tomasek, K., Balleza, E., Kiviet, D., Hauschild, R.,https://doi.org/10.1126/science.aaf4762__Guet, C. C. (2017). Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.aaf4762 
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.,https://doi.org/10.1016/j.devcel.2017.09.014__Heisenberg, C.-P. J. (2017). An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. Cell Press.https://doi.org/10.1016/j.devcel.2017.09.014 
LibreCat: https://research-explorer.ista.ac.at/record/735
DOI: 10.1016/j.devcel.2017.09.014
WoS: 000413443700011

Chalk, M. J., Marre, O., &; Tkadik, G. (2016). Relevant sparse codes with variational information bottleneck (Vol. 29, pp. 1965–1973). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.
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 (Vol. 29, pp. 3610–3618). Presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information Processing Systems.
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. Society for Industrial and Applied Mathematics .https://doi.org/10.1137/15M103306X 
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. Elsevier.https://doi.org/10.1016/j.plrev.2016.06.005 
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. https://doi.org/10.1088/1742-5468/aa4e8f__Journal of Statistical Mechanics: Theory and Experiment. IOPscience.https://doi.org/10.1088/1742-5468/aa4e8f 
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.,https://doi.org/10.1128/JCM.01511-16__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. American Society for Microbiology.https://doi.org/10.1128/JCM.01511-16 
LibreCat: https://research-explorer.ista.ac.at/record/1203
DOI: 10.1128/JCM.01511-16

Martius, G. S., Hostettler, R., Knoll, A., &; Der, R. (2016). Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE.https://doi.org/10.1109/IROS.2016.7759138 
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 (pp. 1–19). Presented at the AIAA: Aviation Technology, Integration, and Operations Conference, Washington, D.C., USA: AIAA.https://doi.org/10.2514/6.2016-3764 
LibreCat: https://research-explorer.ista.ac.at/record/1220
DOI: 10.2514/6.2016-3764

Sokolowski, T. R., 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. American Institute of Physics.https://doi.org/10.1103/PhysRevE.93.022404 
LibreCat: https://research-explorer.ista.ac.at/record/1242
DOI: 10.1103/PhysRevE.93.022404

Recouvreux, P., Sokolowski, T. R., Grammoustianou, A., Tenwolde, P., &; Dogterom, M. (2016). Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1419248113 
LibreCat: https://research-explorer.ista.ac.at/record/1244
DOI: 10.1073/pnas.1419248113

Tkadik, G., &; Bialek, W. (2016). Information processing in living systems. Annual Review of Condensed Matter Physics. Annual Reviews.https://doi.org/10.1146/annurev-conmatphys-031214-014803 
LibreCat: https://research-explorer.ista.ac.at/record/1248
DOI: 10.1146/annurev-conmatphys-031214-014803

De Martino, D. (2016). The dual of the space of interactions in neural network models. International Journal of Modern Physics C. World Scientific Publishing.https://doi.org/10.1142/S0129183116500674 
LibreCat: https://research-explorer.ista.ac.at/record/1260
DOI: 10.1142/S0129183116500674
arXiv: 1505.02963