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Lombardi, F., Gómez-Extremera, M., Bernaola-Galván, P., Vetrivelan, R., Saper, C. B., Scammell, T. E., &; Ivanov, P. C. (2020). Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake. Journal of Neuroscience. Society for Neuroscience.https://doi.org/10.1523/jneurosci.1278-19.2019 

Grah, R. (2020). Gene regulation across scales – how biophysical constraints shape evolution. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:8155 

Grah, R., Zoller, B., &; Tkadik, G. (2020). Normative models of enhancer function. bioRxiv. Cold Spring Harbor Laboratory.https://doi.org/10.1101/2020.04.08.029405 

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Kavcic, B. (2020). Perturbations of protein synthesis: from antibiotics to genetics and physiology. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:8657 

Kavcic, B., Tkadik, G., &; Bollenbach, M. T. (2020). Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. Springer Nature.https://doi.org/10.1038/s41467-020-17734-z 

Kavcic, B., Tkadik, G., &; Bollenbach, M. T. (2020). A minimal biophysical model of combined antibiotic action. bioRxiv. Cold Spring Harbor Laboratory.https://doi.org/10.1101/2020.04.18.047886 

Tomanek, I., Grah, R., Lagator, M., Andersson, A. M. C., Bollback, J. P., Tkadik, G., &; Guet, C. C. (2020). Gene amplification as a form of population-level gene expression regulation. Nature Ecology &; Evolution. Springer Nature.https://doi.org/10.1038/s41559-020-1132-7 

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De Martino, D. (2019). Feedback-induced self-oscillations in large interacting systems subjected to phase transitions. Journal of Physics A: Mathematical and Theoretical. IOP Publishing.https://doi.org/10.1088/1751-8121/aaf2dd 

Mitosch, K., Rieckh, G., &; Bollenbach, M. T. (2019). Temporal order and precision of complex stress responses in individual bacteria. Molecular Systems Biology. Embo Press.https://doi.org/10.15252/msb.20188470 

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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 

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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 

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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 

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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 

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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 

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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.

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

Chalk, M. J., Gutkin, B., &; Denève, S. (2016). Neural oscillations as a signature of efficient coding in the presence of synaptic delays. ELife. eLife Sciences Publications.https://doi.org/10.7554/eLife.13824 

Stone, L., Baym, M., Lieberman, T., Chait, R. P., Clardy, J., &; Kishony, R. (2016). Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. Nature Publishing Group.https://doi.org/10.1038/nchembio.2176 

Lang, M., &; Sontag, E. (2016). Scale-invariant systems realize nonlinear differential operators (Vol. 2016–July). Presented at the ACC: American Control Conference, Boston, MA, USA: IEEE.https://doi.org/10.1109/ACC.2016.7526722 

Chait, R. P., Palmer, A., Yelin, I., &; Kishony, R. (2016). Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms10333 

Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., &; Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. Science. American Association for the Advancement of Science.https://doi.org/10.1126/science.aag0822 

De Martino, D., Capuani, F., &; De Martino, A. (2016). Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/3/036005 

Bodova, K., Tkadik, G., &; Barton, N. H. (2016). A general approximation for the dynamics of quantitative traits. Genetics. Genetics Society of America.https://doi.org/10.1534/genetics.115.184127 

De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd.https://doi.org/10.1088/1478-3975/13/1/016003 

Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., &; Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. Elsevier.https://doi.org/10.1016/j.biosystems.2016.07.005 

Martius, G. S., Hostettler, R., Knoll, A., &; Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press.https://doi.org/10.7551/978-0-262-33936-0-ch029 

Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkadik, G., &; Berry, M. (2016). Error-robust modes of the retinal population code. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1005148 

Monk, T., Savin, C., &; Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628 

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Computation of positional information in an Ising model. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s002 

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Error bound on an estimator of position. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s001 

Hillenbrand, P., Gerland, U., &; Tkadik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science.https://doi.org/10.1371/journal.pone.0163628.s003 

Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.

Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., &; Tkadik, G. (2016). Intrinsic limits to gene regulation by global crosstalk. Nature Communications. Nature Publishing Group.https://doi.org/10.1038/ncomms12307 

Parise, F., Lygeros, J., &; Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. Frontiers.https://doi.org/10.3389/fenvs.2015.00042 

Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. American Institute of Physics.https://doi.org/10.1063/1.4937937