G. Miki, A. Stoll, J. Bevirt, R. Grah, and M. Moore, “Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency,” presented at the AIAA: Aviation Technology, Integration, and Operations Conference, Washington, D.C., USA, 2016, pp. 1–19.

T. R. Sokolowski, A. Walczak, W. Bialek, and G. Tkadik, “Extending the dynamic range of transcription factor action by translational regulation,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 93, no. 2. American Institute of Physics, 2016.

P. Recouvreux, T. R. Sokolowski, A. Grammoustianou, P. Tenwolde, and M. Dogterom, “Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells,” PNAS, vol. 113, no. 7. National Academy of Sciences, pp. 1811–1816, 2016.

G. Tkadik and W. Bialek, “Information processing in living systems,” Annual Review of Condensed Matter Physics, vol. 7. Annual Reviews, pp. 89–117, 2016.

D. De Martino, “The dual of the space of interactions in neural network models,” International Journal of Modern Physics C, vol. 27, no. 6. World Scientific Publishing, 2016.

M. J. Chalk, B. Gutkin, and S. Denève, “Neural oscillations as a signature of efficient coding in the presence of synaptic delays,” eLife, vol. 5, no. 2016JULY. eLife Sciences Publications, 2016.

L. Stone, M. Baym, T. Lieberman, R. P. Chait, J. Clardy, and R. Kishony, “Compounds that select against the tetracycline-resistance efflux pump,” Nature Chemical Biology, vol. 12, no. 11. Nature Publishing Group, pp. 902–904, 2016.

M. Lang and E. Sontag, “Scale-invariant systems realize nonlinear differential operators,” presented at the ACC: American Control Conference, Boston, MA, USA, 2016, vol. 2016–July.

R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments,” Nature Communications, vol. 7. Nature Publishing Group, 2016.

M. Baym et al., “Spatiotemporal microbial evolution on antibiotic landscapes,” Science, vol. 353, no. 6304. American Association for the Advancement of Science, pp. 1147–1151, 2016.

D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.

K. Bodova, G. Tkadik, and N. H. Barton, “A general approximation for the dynamics of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of America, pp. 1523–1548, 2016.

D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP Publishing Ltd., 2016.

C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” Biosystems, vol. 149. Elsevier, pp. 15–25, 2016.

G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in Proceedings of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp. 142–143.

J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkadik, and M. Berry, “Error-robust modes of the retinal population code,” PLoS Computational Biology, vol. 12, no. 11. Public Library of Science, 2016.

T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.

P. Hillenbrand, U. Gerland, and G. Tkadik, “Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information,” PLoS One, vol. 11, no. 9. Public Library of Science, 2016.

P. Hillenbrand, U. Gerland, and G. Tkadik, “Computation of positional information in an Ising model.” Public Library of Science, 2016.

P. Hillenbrand, U. Gerland, and G. Tkadik, “Error bound on an estimator of position.” Public Library of Science, 2016.

P. Hillenbrand, U. Gerland, and G. Tkadik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016.

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

T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkadik, “Intrinsic limits to gene regulation by global crosstalk,” Nature Communications, vol. 7. Nature Publishing Group, 2016.

F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” Frontiers in Environmental Science, vol. 3. Frontiers, 2015.

J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” Journal of Chemical Physics, vol. 143, no. 24. American Institute of Physics, 2015.

J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.

M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.

R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. 24–32, 2015.

S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.

O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkadik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015.

G. Tkadik et al., “Thermodynamics and signatures of criticality in a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.

J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015.

G. Tkadik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” Genetics, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.

T. R. Sokolowski and G. Tkadik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6. American Institute of Physics, 2015.

T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015.

T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” PLoS Computational Biology, vol. 11, no. 3. Public Library of Science, 2015.

T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation code.” Public Library of Science, 2015.

M. Tugrul, T. Paixao, N. H. Barton, and G. Tkadik, “Other fitness models for comparison &; for interacting TFBSs.” Public Library of Science, 2015.

M. Tugrul, T. Paixao, N. H. Barton, and G. Tkadik, “Dynamics of transcription factor binding site evolution,” PLoS Genetics, vol. 11, no. 11. Public Library of Science, 2015.

S. A. Cepeda Humerez, G. Rieckh, and G. Tkadik, “Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation,” Physical Review Letters, vol. 115, no. 24. American Physical Society, 2015.

G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.

C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.

A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and G. Tkadik, “Variance predicts salience in central sensory processing,” eLife, no. November. eLife Sciences Publications, 2014.

R. Kollár, K. Bodova, J. Nosek, and d. Tomáska, “Mathematical model of alternative mechanism of telomere length maintenance,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 3. American Institute of Physics, 2014.

T. Ezard, R. Prizak, and R. Hoyle, “The fitness costs of adaptation via phenotypic plasticity and maternal effects,” Functional Ecology, vol. 28, no. 3. Wiley-Blackwell, pp. 693–701, 2014.

J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases in finite populations,” Journal of Theoretical Biology, vol. 360. Elsevier, pp. 149–162, 2014.

C. Savin and J. Triesch, “Emergence of task-dependent representations in working memory circuits,” Frontiers in Computational Neuroscience, vol. 8, no. MAY. Frontiers Research Foundation, 2014.

K. Bodova, D. Paydarfar, and D. Forger, “Characterizing spiking in noisy type II neurons,”  Journal of Theoretical Biology, vol. 365. Academic Press, pp. 40–54, 2014.

V. Botella Soler and P. Glendinning, “Hierarchy and polysynchrony in an adaptive network ,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 6. American Institute of Physics, 2014.

G. Rieckh and G. Tkadik, “Noise and information transmission in promoters with multiple internal states,” Biophysical Journal, vol. 106, no. 5. Biophysical Society, pp. 1194–1204, 2014.

G. Tkadik, A. Ghosh, E. Schneidman, and R. Segev, “Adaptation to changes in higher-order stimulus statistics in the salamander retina,” PLoS One, vol. 9, no. 1. Public Library of Science, 2014.

R. Prizak, T. Ezard, and R. Hoyle, “Fitness consequences of maternal and grandmaternal effects,” Ecology and Evolution, vol. 4, no. 15. Wiley-Blackwell, pp. 3139–3145, 2014.

K. Simmons et al., “Data from: Transformation of stimulus correlations by the retina.” Dryad, 2014.

G. Tkadik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” PLoS Computational Biology, vol. 10, no. 1. Public Library of Science, 2014.

M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations scale up and scale down the brain dynamics ,” in Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, M. Meyer and Z. Pesenson, Eds. Wiley-VCH, 2013.

K. Rajan, O. Marre, and G. Tkadik, “Learning quadratic receptive fields from neural responses to natural stimuli,” Neural Computation, vol. 25, no. 7. MIT Press , pp. 1661–1692, 2013.

G. Tkadik, O. Marre, T. Mora, D. Amodei, M. Berry, and W. Bialek, “The simplest maximum entropy model for collective behavior in a neural network,” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing Ltd., 2013.

M. Berry, G. Tkadik, J. Dubuis, O. Marre, and R. Da Silveira, “A simple method for estimating the entropy of neural activity,” Journal of Statistical Mechanics Theory and Experiment, vol. 2013, no. 3. IOP Publishing Ltd., 2013.

E. Granot Atedgi, G. Tkadik, R. Segev, and E. Schneidman, “Stimulus-dependent maximum entropy models of neural population codes,” PLoS Computational Biology, vol. 9, no. 3. Public Library of Science, 2013.

V. Botella Soler, J. Oteo, J. Ros, and P. Glendinning, “Lyapunov exponent and topological entropy plateaus in piecewise linear maps,” Journal of Physics A: Mathematical and Theoretical, vol. 46, no. 12. IOP Publishing Ltd., 2013.

G. Tkadik, E. Granot Atedgi, R. Segev, and E. Schneidman, “Retinal metric: a stimulus distance measure derived from population neural responses,” Physical Review Letters, vol. 110, no. 5. American Physical Society, 2013.

J. Dubuis, G. Tkadik, E. Wieschaus, T. Gregor, and W. Bialek, “Positional information, in bits,” PNAS, vol. 110, no. 41. National Academy of Sciences, pp. 16301–16308, 2013.

Y. Wakamoto et al., “Dynamic persistence of antibiotic-stressed mycobacteria,” Science, vol. 339, no. 6115. American Association for the Advancement of Science, pp. 91–95, 2013.

K. Simmons et al., “Transformation of stimulus correlations by the retina,” PLoS Computational Biology, vol. 9, no. 12. Public Library of Science, 2013.

G. Stephens, T. Mora, G. Tkadik, and W. Bialek, “Statistical thermodynamics of natural images,” Physical Review Letters, vol. 110, no. 1. American Physical Society, 2013.

G. Tkadik, A. Walczak, and W. Bialek, “Optimizing information flow in small genetic networks. III. A self-interacting gene,”  Physical Review E statistical nonlinear and soft matter physics , vol. 85, no. 4. American Institute of Physics, 2012.

G. Rieckh, W. Kreuzer, H. Waubke, and P. Balazs, “A 2.5D-Fourier-BEM model for vibrations in a tunnel running through layered anisotropic soil,”  Engineering Analysis with Boundary Elements, vol. 36, no. 6. Elsevier, pp. 960–967, 2012.

G. Tkadik and A. Walczak, “Information transmission in genetic regulatory networks a review,” Journal of Physics: Condensed Matter, vol. 23, no. 15. IOP Publishing Ltd., 2011.

G. Tkadik et al., “Natural images from the birthplace of the human eye,” PLoS One, vol. 6, no. 6. Public Library of Science, 2011.