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182 Publications
2017 | Journal Article | IST-REx-ID: 959 | 
De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.95.062419
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2017 | Journal Article | IST-REx-ID: 947 |
De Martino, D., Capuani, F., & De Martino, A. (2017). Quantifying the entropic cost of cellular growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.96.010401
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2017 | Journal Article | IST-REx-ID: 943 |
Zagórski, M. P., Tabata, Y., Brandenberg, N., Lutolf, M., Tkačik, G., Bollenbach, T., … 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
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2017 | Journal Article | IST-REx-ID: 823 |
Colabrese, S., De Martino, D., Leuzzi, L., & Marinari, E. (2017). Phase transitions in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa85c3
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2017 | Journal Article | IST-REx-ID: 730
Savin, C., & Tkačik, 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
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2017 | Journal Article | IST-REx-ID: 548 |
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
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2017 | Journal Article | IST-REx-ID: 1007 |
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
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2017 | Research Data | IST-REx-ID: 5562 |
Marre, O., Tkačik, G., Amodei, D., Schneidman, E., Bialek, W., & Berry, M. (2017). Multi-electrode array recording from salamander retinal ganglion cells. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:61
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2017 | Research Data | IST-REx-ID: 5560 |
Bergmiller, T., Andersson, A. M., Tomasek, K., Balleza, E., Kiviet, D., Hauschild, R., … Guet, C. C. (2017). Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:53
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2017 | Journal Article | IST-REx-ID: 665
Bergmiller, T., Andersson, A. M., Tomasek, K., Balleza, E., Kiviet, D., Hauschild, R., … 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
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2017 | Journal Article | IST-REx-ID: 735
Barone, V., Lang, M., Krens, G., Pradhan, S., Shamipour, S., Sako, K., … 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
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2016 | Conference Paper | IST-REx-ID: 1082 |
Chalk, M. J., Marre, O., & Tkačik, 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.
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2016 | Conference Paper | IST-REx-ID: 1105
Savin, C., & Tkačik, 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.
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2016 | Journal Article | IST-REx-ID: 1170
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
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2016 | Journal Article | IST-REx-ID: 1171
Tkačik, G. (2016). Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. Elsevier. https://doi.org/10.1016/j.plrev.2016.06.005
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2016 | Journal Article | IST-REx-ID: 1188 |
De Martino, D., & Masoero, D. (2016). Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa4e8f
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2016 | Journal Article | IST-REx-ID: 1203 |
Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T., … 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
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2016 | Conference Paper | IST-REx-ID: 1214
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
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2016 | Conference Paper | IST-REx-ID: 1220 |
Mikić, 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
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2016 | Journal Article | IST-REx-ID: 1242 |
Sokolowski, T. R., Walczak, A., Bialek, W., & Tkačik, 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
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