I. Tomanek et al., “Gene amplification as a form of population-level gene expression regulation,” Nature Ecology &; Evolution, vol. 4, no. 4. Springer Nature, pp. 612–625, 2020.

W. Bialek, T. Gregor, and G. Tkadik, “Action at a distance in transcriptional regulation,” arXiv:1912.08579. ArXiv.

M. D. Petkova, G. Tkadik, W. Bialek, E. F. Wieschaus, and T. Gregor, “Optimal decoding of cellular identities in a genetic network,” Cell, vol. 176, no. 4. Cell Press, p. 844–855.e15, 2019.

D. De Martino, “Feedback-induced self-oscillations in large interacting systems subjected to phase transitions,” Journal of Physics A: Mathematical and Theoretical, vol. 52, no. 4. IOP Publishing, 2019.

K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Temporal order and precision of complex stress responses in individual bacteria,” Molecular systems biology, vol. 15, no. 2. Embo Press, 2019.

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.

M. Lang and J. Stelling, “Modular parameter identification of biomolecular networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society for Industrial and Applied Mathematics , pp. 8–08, 2016.

G. Tkadik, “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, vol. 17. Elsevier, pp. 166–167, 2016.

D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization, applied to metabolism &;amp; growth,”  Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.

F. Hu et al., “Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination,” Journal of Clinical Microbiology, vol. 54, no. 12. American Society for Microbiology, pp. 3010–3017, 2016.

G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea, 2016, vol. 2016–November.

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.