Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli
D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).
LibreCat: https://research-explorer.ista.ac.at/record/1394
DOI: 10.1088/1478-3975/13/3/036005

A general approximation for the dynamics of quantitative traits
K. Bodova, G. Tkadik, N.H. Barton, Genetics 202 (2016) 1523–1548.
LibreCat: https://research-explorer.ista.ac.at/record/1420
DOI: 10.1534/genetics.115.184127
arXiv: 1510.08344

Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis
D. De Martino, Physical Biology 13 (2016).
LibreCat: https://research-explorer.ista.ac.at/record/1485
DOI: 10.1088/1478-3975/13/1/016003

Adaptive moment closure for parameter inference of biochemical reaction networks
C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.
LibreCat: https://research-explorer.ista.ac.at/record/1148
DOI: 10.1016/j.biosystems.2016.07.005

Self-organized control of an tendon driven arm by differential extrinsic plasticity
G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Error-robust modes of the retinal population code
J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkadik, M. Berry, PLoS Computational Biology 12 (2016).
LibreCat: https://research-explorer.ista.ac.at/record/1197
DOI: 10.1371/journal.pcbi.1005148

Neurons equipped with intrinsic plasticity learn stimulus intensity statistics
T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293.
LibreCat: https://research-explorer.ista.ac.at/record/948

Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information
P. Hillenbrand, U. Gerland, G. Tkadik, PLoS One 11 (2016).
LibreCat: https://research-explorer.ista.ac.at/record/1270
DOI: 10.1371/journal.pone.0163628

Computation of positional information in an Ising model
P. Hillenbrand, U. Gerland, G. Tkadik, (2016).
LibreCat: https://research-explorer.ista.ac.at/record/9870
DOI: 10.1371/journal.pone.0163628.s002

Error bound on an estimator of position
P. Hillenbrand, U. Gerland, G. Tkadik, (2016).
LibreCat: https://research-explorer.ista.ac.at/record/9869
DOI: 10.1371/journal.pone.0163628.s001

Computation of positional information in a discrete morphogen field
P. Hillenbrand, U. Gerland, G. Tkadik, (2016).
LibreCat: https://research-explorer.ista.ac.at/record/9871
DOI: 10.1371/journal.pone.0163628.s003

Studying the complexities of transcriptional regulation
G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.
LibreCat: https://research-explorer.ista.ac.at/record/1128

Intrinsic limits to gene regulation by global crosstalk
T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkadik, Nature Communications 7 (2016).
LibreCat: https://research-explorer.ista.ac.at/record/1358
DOI: 10.1038/ncomms12307

Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study
F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).
LibreCat: https://research-explorer.ista.ac.at/record/10794
DOI: 10.3389/fenvs.2015.00042

Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space
J. Ruess, Journal of Chemical Physics 143 (2015).
LibreCat: https://research-explorer.ista.ac.at/record/1539
DOI: 10.1063/1.4937937

Iterative experiment design guides the characterization of a light-inducible gene expression circuit
J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.
LibreCat: https://research-explorer.ista.ac.at/record/1538
DOI: 10.1073/pnas.1423947112
PMID: 26085136

Editorial: Emergent neural computation from the interaction of different forms of plasticity
M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).
LibreCat: https://research-explorer.ista.ac.at/record/1564
DOI: 10.3389/fncom.2015.00145

Novel plasticity rule can explain the development of sensorimotor intelligence
R. Der, G.S. Martius, PNAS 112 (2015) 24–32.
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Adaptive moment closure for parameter inference of biochemical reaction networks
S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.
LibreCat: https://research-explorer.ista.ac.at/record/1658
DOI: 10.1007/978-3-319-23401-4_8

High accuracy decoding of dynamical motion from a large retinal population
O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkadik, M. Berry, PLoS Computational Biology 11 (2015).
LibreCat: https://research-explorer.ista.ac.at/record/1697
DOI: 10.1371/journal.pcbi.1004304