Ruess, J., Pleska, M., Guet, C. C., &; Tkadik, G. (2019). Molecular noise of innate immunity shapes bacteria-phage ecologies. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1007168 

Ruess, J., Pleska, M., Guet, C. C., &; Tkadik, G. (2019). Supporting text and results. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1007168.s001 

Sokolowski, T. R., Paijmans, J., Bossen, L., Miedema, T., Wehrens, M., Becker, N. B.,https://doi.org/10.1063/1.5064867__ten Wolde, P. R. (2019). eGFRD in all dimensions. The Journal of Chemical Physics. AIP Publishing.https://doi.org/10.1063/1.5064867 

Cepeda Humerez, S. A., Ruess, J., &; Tkadik, G. (2019). Estimating information in time-varying signals. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1007290 

Lang, M., &; Shkolnikov, M. (2019). Harmonic dynamics of the Abelian sandpile. Proceedings of the National Academy of Sciences. National Academy of Sciences.https://doi.org/10.1073/pnas.1812015116 

Kavcic, B., Sakashita, A., Noguchi, H., &; Ziherl, P. (2019). Limiting shapes of confined lipid vesicles. Soft Matter. Royal Society of Chemistry.https://doi.org/10.1039/c8sm01956h 

Cepeda Humerez, S. A. (2019). Estimating information flow in single cells. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:6473 

Prizak, R. (2019). Coevolution of transcription factors and their binding sites in sequence space. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:th6071 

Wang, J. W. J. L., Lombardi, F., Zhang, X., Anaclet, C., &; Ivanov, P. C. (2019). Non-equilibrium critical dynamics of bursts in ;8 and ;4 rhythms as fundamental characteristic of sleep and wake micro-architecture. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1007268 

Carballo-Pacheco, M., Desponds, J., Gavrilchenko, T., Mayer, A., Prizak, R., Reddy, G.,https://doi.org/10.1103/PhysRevE.99.022423__Mora, T. (2019). Receptor crosstalk improves concentration sensing of multiple ligands. Physical Review E. American Physical Society.https://doi.org/10.1103/PhysRevE.99.022423 

Hledik, M., Sokolowski, T. R., &; Tkadik, G. (2019). A tight upper bound on mutual information. In IEEE Information Theory Workshop, ITW 2019. Visby, Sweden: IEEE.https://doi.org/10.1109/ITW44776.2019.8989292 

De Martino, A., &; De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. Elsevier.https://doi.org/10.1016/j.heliyon.2018.e00596 

Misun, P., Birchler, A., Lang, M., Hierlemann, A., &; Frey, O. (2018). Fabrication and operation of microfluidic hanging drop networks. Methods in Molecular Biology. Springer.https://doi.org/10.1007/978-1-4939-7792-5_15 

Granados, A., Pietsch, J., Cepeda Humerez, S. A., Farquhar, I., Tkadik, G., &; Swain, P. (2018). Distributed and dynamic intracellular organization of extracellular information. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1716659115 

Bodova, K., Priklopil, T., Field, D., Barton, N. H., &; Pickup, M. (2018). Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. Genetics. Genetics Society of America.https://doi.org/10.1534/genetics.118.300748 

Bodova, K., Priklopil, T., Field, D., Barton, N. H., &; Pickup, M. (2018). Supplemental material for Bodova et al., 2018. Genetics Society of America.https://doi.org/10.25386/genetics.6148304.v1 

Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &; Tkadik, G. (2018). Probabilistic models of individual and collective animal behavior. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0193049 

Pleska, M., Lang, M., Refardt, D., Levin, B., &; Guet, C. C. (2018). Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. Springer Nature.https://doi.org/10.1038/s41559-017-0424-z 

Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &; Tkadik, G. (2018). Implementation of the inference method in Matlab. Public Library of Science.https://doi.org/10.1371/journal.pone.0193049.s001 

Ferrari, U., Deny, S., Chalk, M. J., Tkadik, G., Marre, O., &; Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. American Physical Society.https://doi.org/10.1103/PhysRevE.98.042410 

Chalk, M. J., Marre, O., &; Tkadik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1711114115 

Bodova, K., Haskovec, J., &; Markowich, P. (2018). Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. Elsevier.https://doi.org/10.1016/j.physd.2017.10.015 

Palmer, A., Chait, R. P., &; Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. Oxford University Press.https://doi.org/10.1093/molbev/msy163 

Botella Soler, V., Deny, S., Martius, G. S., Marre, O., &; Tkadik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1006057 

De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., &; Tkadik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications. Springer Nature.https://doi.org/10.1038/s41467-018-05417-9 

Igler, C., Lagator, M., Tkadik, G., Bollback, J. P., &; Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. Nature Publishing Group.https://doi.org/10.1038/s41559-018-0651-y 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

Levina (Martius), A., &; Priesemann, V. (2017). Subsampling scaling. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms15140 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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.

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.