J. Ruess, M. Pleska, C. C. Guet, and G. Tkadik, “Molecular noise of innate immunity shapes bacteria-phage ecologies,” PLoS Computational Biology, vol. 15, no. 7. Public Library of Science, 2019.

J. Ruess, M. Pleska, C. C. Guet, and G. Tkadik, “Supporting text and results.” Public Library of Science, 2019.

T. R. Sokolowski et al., GFRD in all dimensions,” The Journal of Chemical Physics, vol. 150, no. 5. AIP Publishing, 2019.

S. A. Cepeda Humerez, J. Ruess, and G. Tkadik, “Estimating information in time-varying signals,” PLoS computational biology, vol. 15, no. 9. Public Library of Science, p. e1007290, 2019.

M. Lang and M. Shkolnikov, “Harmonic dynamics of the Abelian sandpile,” Proceedings of the National Academy of Sciences, vol. 116, no. 8. National Academy of Sciences, pp. 2821–2830, 2019.

B. Kavcic, A. Sakashita, H. Noguchi, and P. Ziherl, “Limiting shapes of confined lipid vesicles,” Soft Matter, vol. 15, no. 4. Royal Society of Chemistry, pp. 602–614, 2019.

S. A. Cepeda Humerez, “Estimating information flow in single cells,” Institute of Science and Technology Austria, 2019.

R. Prizak, “Coevolution of transcription factors and their binding sites in sequence space,” Institute of Science and Technology Austria, 2019.

J. W. J. L. Wang, F. Lombardi, X. Zhang, C. Anaclet, and P. C. Ivanov, “Non-equilibrium critical dynamics of bursts in ;8 and ;4 rhythms as fundamental characteristic of sleep and wake micro-architecture,” PLoS Computational Biology, vol. 15, no. 11. Public Library of Science, 2019.

M. Carballo-Pacheco et al., “Receptor crosstalk improves concentration sensing of multiple ligands,” Physical Review E, vol. 99, no. 2. American Physical Society, 2019.

M. Hledik, T. R. Sokolowski, and G. Tkadik, “A tight upper bound on mutual information,” in IEEE Information Theory Workshop, ITW 2019, Visby, Sweden, 2019.

A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” Heliyon, vol. 4, no. 4. Elsevier, 2018.

P. Misun, A. Birchler, M. Lang, A. Hierlemann, and O. Frey, “Fabrication and operation of microfluidic hanging drop networks,” Methods in Molecular Biology, vol. 1771. Springer, pp. 183–202, 2018.

A. Granados, J. Pietsch, S. A. Cepeda Humerez, I. Farquhar, G. Tkadik, and P. Swain, “Distributed and dynamic intracellular organization of extracellular information,” PNAS, vol. 115, no. 23. National Academy of Sciences, pp. 6088–6093, 2018.

K. Bodova, T. Priklopil, D. Field, N. H. Barton, and M. Pickup, “Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system,” Genetics, vol. 209, no. 3. Genetics Society of America, pp. 861–883, 2018.

K. Bodova, T. Priklopil, D. Field, N. H. Barton, and M. Pickup, “Supplemental material for Bodova et al., 2018.” Genetics Society of America, 2018.

K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkadik, “Probabilistic models of individual and collective animal behavior,” PLoS One, vol. 13, no. 3. Public Library of Science, 2018.

M. Pleska, M. Lang, D. Refardt, B. Levin, and C. C. Guet, “Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity,” Nature Ecology and Evolution, vol. 2, no. 2. Springer Nature, pp. 359–366, 2018.

K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkadik, “Implementation of the inference method in Matlab.” Public Library of Science, 2018.

U. Ferrari, S. Deny, M. J. Chalk, G. Tkadik, O. Marre, and T. Mora, “Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons,” Physical Review E, vol. 98, no. 4. American Physical Society, 2018.

M. J. Chalk, O. Marre, and G. Tkadik, “Toward a unified theory of efficient, predictive, and sparse coding,” PNAS, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.

K. Bodova, J. Haskovec, and P. Markowich, “Well posedness and maximum entropy approximation for the dynamics of quantitative traits,” Physica D: Nonlinear Phenomena, vol. 376–377. Elsevier, pp. 108–120, 2018.

A. Palmer, R. P. Chait, and R. Kishony, “Nonoptimal gene expression creates latent potential for antibiotic resistance,” Molecular Biology and Evolution, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018.

V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkadik, “Nonlinear decoding of a complex movie from the mammalian retina,” PLoS Computational Biology, vol. 14, no. 5. Public Library of Science, 2018.

D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkadik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1. Springer Nature, 2018.

C. Igler, M. Lagator, G. Tkadik, J. P. Bollback, and C. C. Guet, “Evolutionary potential of transcription factors for gene regulatory rewiring,” Nature Ecology and Evolution, vol. 2, no. 10. Nature Publishing Group, pp. 1633–1643, 2018.

R. P. Chait, J. Ruess, T. Bergmiller, G. Tkadik, and C. C. Guet, “Shaping bacterial population behavior through computer interfaced control of individual cells,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.

R. Der and G. S. Martius, “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, 2017.

R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.

J. Humplik and G. Tkadik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017.

R. Harpaz, G. Tkadik, and E. Schneidman, “Discrete modes of social information processing predict individual behavior of fish in a group,” PNAS, vol. 114, no. 38. National Academy of Sciences, pp. 10149–10154, 2017.

J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkadik, and M. Berry, “Data from: Error-robust modes of the retinal population code.” Dryad, 2017.

M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Sensory noise predicts divisive reshaping of receptive fields,” PLoS Computational Biology, vol. 13, no. 6. Public Library of Science, 2017.

M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Supplementary appendix.” Public Library of Science, 2017.

K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” Cell Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017.

A. Martin Del Campo Sanchez, S. A. Cepeda Humerez, and C. Uhler, “Exact goodness-of-fit testing for the Ising model,” Scandinavian Journal of Statistics, vol. 44, no. 2. Wiley-Blackwell, pp. 285–306, 2017.

S. Deny et al., “Multiplexed computations in retinal ganglion cells of a single type,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.

A. Levina (Martius) and V. Priesemann, “Subsampling scaling,” Nature Communications, vol. 8. Nature Publishing Group, 2017.

T. Friedlander, R. Prizak, N. H. Barton, and G. Tkadik, “Evolution of new regulatory functions on biophysically realistic fitness landscapes,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.

D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,”  Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017.

D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost of cellular growth control,”  Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.

M. P. Zagórski et al., “Decoding of position in the developing neural tube from antiparallel morphogen gradients,” Science, vol. 356, no. 6345. American Association for the Advancement of Science, pp. 1379–1383, 2017.

S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions in integer linear problems,”  Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no. 9. IOPscience, 2017.

C. Savin and G. Tkadik, “Maximum entropy models as a tool for building precise neural controls,” Current Opinion in Neurobiology, vol. 46. Elsevier, pp. 120–126, 2017.

D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” Physical Review E, vol. 96, no. 6. American Physical Society, 2017.

M. Lang and E. Sontag, “Zeros of nonlinear systems with input invariances,” Automatica, vol. 81C. International Federation of Automatic Control, pp. 46–55, 2017.

T. Bergmiller et al., “Biased partitioning of the multidrug efflux pump AcrAB TolC underlies long lived phenotypic heterogeneity,” Science, vol. 356, no. 6335. American Association for the Advancement of Science, pp. 311–315, 2017.

V. Barone et al., “An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate,” Developmental Cell, vol. 43, no. 2. Cell Press, pp. 198–211, 2017.

M. J. Chalk, O. Marre, and G. Tkadik, “Relevant sparse codes with variational information bottleneck,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain, 2016, vol. 29, pp. 1965–1973.

C. Savin and G. Tkadik, “Estimating nonlinear neural response functions using GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.