68. Action at a distance in transcriptional regulation
Bialek W, Gregor T, Tkačik G
arXiv.org (2019): 1912.08579
[ arXiv ]

67. Estimating information in time-varying signals
Cepeda-Humerez SA, Ruess J, Tkačik G
PLOS Comput Biol 15 (2019): e1007290
[ site ] [ arXiv ]

66. A tight upper bound on mutual information
Hledik M, Sokolowski TR, Tkačik G
IEEE Info Th Workshop (ITW) (2019).
[ pdf ] [ arXiv ] 

65. Molecular noise shapes bacteria-phage ecologies
Ruess J, Pleska M, Guet CC, Tkačik G
PLOS Comput Biol 15 (2019): e1007168.
[ site ] [ bioRxiv ]

64. Evolutionary potential of transcription factors for gene regulatory rewiring
Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC
Nature Ecol Evol 2 (2018): 1633-1643
[ site ] [ pdf ]

63. Statistical mechanics for metabolic networks during steady-state growth
De Martino D, Andersson AMC, Bergmiller T, Guet CC, Tkačik G
Nature Communications 9 (2018): 2988
[ site ] [ pdf ] [ arXiv ]

62. Population model learned on different stimulus ensembles predicts network responses in the retina
Ferrari U, Deny S, Chalk M, Tkačik G, Marre O, Mora T
Phys Rev E 98 (2018): 042410
[ site ] [ pdf ] [ arXiv ]

61. Distributed and dynamic intracellular organization of extracellular information
Granados AA, Pietsch JM, Cepeda-Humerez SA, Farquhar IL, Tkačik G, Swain PS
Proc Nat’l Acad Sci USA 115 (2018): 6088-6093
[ site ] [ pdf ] [ bioRxiv ]

60. Probabilistic models of individual and collective animal behavior
Bod’ova K, Mitchell GJ, Harpaz R, Schneidman E, Tkačik G
PLOS One 13 (2018): e0193049
[ site ] [ pdf ] [ arXiv ]

59. Towards a unified theory of efficient, predictive, and sparse coding
Chalk M, Marre O, Tkačik G
Proc Nat’l Acad Sci USA 115 (2018): 181-191
[ site ] [ pdf ] [ bioRxiv ]

58. Nonlinear decoding of a complex movie from the mammalian retina
Botella-Soler V, Deny S, Martius G, Marre O, Tkačik G
PLOS Comput Biol 14 (2018): e1006057
[ site ] [ pdf ] [ arXiv ] 

57. Shaping bacterial population behavior through computer-interfaced control of individual cells
Chait R*, Ruess J*, Bergmiller T, Tkačik G, Guet CC
Nature Communications 8 (2017): 1535
[ site ] [ pdf ]

56. Maximum entropy models as a tool for building precise neural controls
Savin C, Tkačik G
Curr Opin Neurosci 46 (2017): 120-126
[ site ] [ pdf ]

55. Decoding of position in the developing neural tube from antiparallel morphogen gradients
Zagorski M, Tabata Y, Brandenberg N, Lutolf M, Tkačik G, Bollenbach T, Briscoe J, Kicheva A
Science 356 (2017): 1379-1383
[ site ] [ pdf ]

54. Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity
Bergmiller T*, Andersson AMC*, Tomasek K, Balleza E, Kiviet DJ, Hauschild R, Tkačik G, Guet CC
Science 356 (2017): 311-315
[ site ] [ pdf ]

53. Discrete modes of social information processing predict individual behavior of fish in a group
Harpaz R, Tkačik G, Schneidman E
Proc Nat’l Acad Sci USA 114 (2017): 10149-10154
[ site ] [ pdf ] [ arXiv ]

52. Evolution of new regulatory functions on biophysically realistic fitness landscapes
Friedlander T*, Prizak R*, Barton NH, Tkačik G
Nature Communications 8 (2017): 216.
[ site ] [ pdf ] [ arXiv ]

51. Multiplexed computations in retinal ganglion cells of a single type
Deny S, Ferrari U, Mace E, Yger P, Caplette R, Picaud S, Tkačik G, Marre O
Nature Communications 8 (2017): 1964.
[ site ] [ pdf ] [ bioRxiv ]

50. Probabilistic models for neural populations that naturally capture global coupling and criticality
Humplik J, Tkačik G
PLOS Comput Biol 13 (2017): e1005763
[ site ] [ pdf ] [ arXiv ]

49. Error-robust modes of the retinal population code
Prentice JS, Marre O, Ioffe M, Loback AR, Tkačik G, Berry MJ 2nd
PLOS Comput Biol 12 (2016): e1005148
[ site ] [ pdf ]

48.  Estimating nonlinear neural response functions using GP priors and Kronecker methods
Savin C, Tkačik G
Adv Neural Info Proc Syst 29 (2016), Lee et al eds.
[ site ]

47. Relevant sparse codes with variational information bottleneck
Chalk M, Marre O, Tkačik G
Adv Neural Info Proc Syst 29 (2016), Lee et al eds.
[ site ] [ arXiv ]

46. Beyond the French Flag model: Exploiting spatial and gene regulatory interactions for positional information
Hillenbrand P, Gerland U, Tkačik G
PLOS One 11 (2016): e0163628
[ site ] [ pdf ] [ arXiv ]

45. A general approximation for the dynamics of quantitative traits
Bod’ova K, Tkačik G, Barton NH
Genetics 202 (2016): 1523-1548
[ site ] [ pdf ] [ arXiv ]

44. Extending the dynamic range of transcription factor action by translational regulation
Sokolowski TR, Walczak AM, Bialek W, Tkačik G
Phys Rev E 93 (2016): 022404
[ site ] [ pdf ] [ arXiv ]

43. Intrinsic limits to gene regulation by global crosstalk
Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G
Nature Communications 7 (2016): 12307
[ site ] [ pdf ] [ arXiv ]

42. Information processing in biological systems
Tkačik G, Bialek W
Annu Rev Cond Matt Phys 7 (2016): 89-117
[ site ] [ pdf ] [ arXiv ]

41. Dynamics of transcription binding site evolution
Tugrul M, Paixao T, Barton NH, Tkačik G
PLOS Genetics 11 (2015): e1005639
[ site ] [ pdf ] [ arXiv ]

40. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation
Cepeda-Humerez SA, Rieckh G, Tkačik G
Phys Rev Lett 115 (2015): 248101
[ site ] [ pdf ] [ arXiv ]

39. Optimizing information flow in small genetic networks. IV. Spatial coupling
Sokolowski TR, Tkačik G
Phys Rev E 91 (2015): 062710
[ site ] [ pdf ] [ arXiv ]

38. High accuracy decoding of dynamical motion from a large retinal population
Marre O, Botella-Soler V, Simmons KD, Mora T, Tkačik G, Berry MJ 2nd
PLOS Comp Biol 11 (2015): e1004304
[ site ] [ pdf ] [ arXiv ]

37. Positional information, positional error, and read-out precision in morphogenesis: a mathematical framework
Tkačik G, Dubuis JO, Petkova MD, Gregor T
Genetics 199 (2015): 39-59
[ site ] [ pdf ] [ arXiv ]

36. Thermodynamics for a network of neurons: Signatures of criticality
Tkačik G, Mora T, Marre O, Amodei D, Palmer SE, Berry MJ 2nd, Bialek W
Proc Nat’l Acad Sci USA 112 (2015): 11508-11513
[ site ] [ pdf ] [ arXiv ]

35. Variance predicts salience in central sensory processing
Hermundstad AM, Briguglio JJ, Conte MM, Victor JD, Balasubramanian V, Tkačik G
eLife (2014): 10.7554
[ site ] [ pdf ]

34. Searching for collective behavior in a large network of sensory neurons
Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry MJ 2nd
PLOS Comp Biol 10 (2014): e1003408
[ site ] [ pdf ] [ arXiv ]

33. Adaptation to changes in higher-order stimulus statistics in the salamander retina
Tkačik G, Ghosh A, Schneidman E, Segev R
PLOS One 9 (2014): e85841
[ site ] [ pdf ] [ arXiv ]

32. Noise and information transmission in promoters with multiple internal states
Rieckh G, Tkačik G
Biophys J 106 (2014): 1194-1204
[ site ] [ pdf ] [ arXiv ]

31. Positional information, in bits
Dubuis JO*, Tkačik G*, Wieschaus EF, Gregor T, Bialek W
Proc Nat’l Acad Sci USA 110 (2013): 16301-16308
[ site ] [ pdf ] [ arXiv ]

30. Transformation of stimulus correlations by the retina
Simmons KD, Prentice JS, Tkačik G, Homann J, Yee HK, Palmer SE, Nelson PC, Balasubramanian V
PLOS Comp Biol 9 (2013): e1003344
[ site ] [ pdf ] [ arXiv ]

29. Stimulus-dependent maximum entropy models of neural population codes
Granot-Atedgi E*, Tkačik G*, Segev R, Schneidman E
PLOS Comp Bio 9 (2013): e1002922
[ site ] [ pdf ] [ arXiv ]

28. The simplest maximum entropy model for collective behavior in a neural network
Tkačik G, Marre O, Mora T, Amodei D, Berry MJ 2nd, Bialek W
J Stat Mech (2013): P03011
[ site ] [ pdf ] [ arXiv ]

27. A simple method for estimating the entropy of neural activity
Berry MJ 2nd, Tkačik G, Dubuis J, Marre O, Azerado da Silveira R
J Stat Mech (2013): P03015
[ site ] [ pdf ]

26. Retinal metric: a stimulus distance measure derived from population neural responses
Tkačik G, Granot-Atedgi E, Segev R, Schneidman E
Phys Rev Lett 110 (2013): 058104
[ site ] [ pdf ] [ Viewpoint (O Marre, A Destexhe) ] [ arXiv ]

25. Statistical thermodynamics of natural images
Stephens GJ, Mora T, Tkačik G, Bialek W
Phys Rev Lett 110 (2013): 018701
[ arxiv ] [ site ] [ pdf ]

24. Learning quadratic receptive fields from neural responses to natural stimuli
Rajan K, Marre O, Tkačik G
Neural Comput 25 (2013): 1661-1692
[ site ] [ arxiv ] [ pdf ]

23. Optimizing information flow in small genetic networks. III. A self-interacting gene
Tkačik G, Walczak AM, Bialek W
Phys Rev E 85 (2012): 041903
[ site ] [ pdf ]

22. The formation of the Bicoid morphogen gradient requires protein movement from anteriorly localized source
Little SC*, Tkačik G*, Kneeland TB, Wieschaus EF, Gregor T
PLOS Biology 9 (2011): e1000596
[ site ] [ pdf ]

21. Natural images from the birthplace of the human eye
Tkačik G, Garrigan P, Ratliff C, Milčinski G, Klein JM, Seyfarth LH, Sterling P, Brainard D, Balasubramanian V
PLOS One 6 (2011): e20409
[ site ] [ pdf ] [ ScienceNow ] [ PhysicsWorld ]

20. Information transmission in genetic regulatory networks: a review
Walczak AM, Tkačik G
J Phys Condens Matt 23 (2011): 153102
[ site ] [ pdf ]

19. Fast, scalable, Bayesian spike identification for multi-electrode arrays
Prentice JS, Homann J, Simmons KD, Tkačik G, Balasubramanian V, Nelson PC
PLOS One 6 (2011): e19884
[ site ] [ pdf ]

18. Local statistics in natural scenes predict the saliency of synthetic textures
Tkačik G, Prentice JS, Victor JD, Balasubramanian V
Proc Nat’l Acad Sci USA 107 (2010): 18149-54
[ site ] [ pdf ]

17. Optimal population coding by noisy spiking neurons
Tkačik G, Prentice JS, Balasubramanian V, Schneidman E
Proc Nat’l Acad Sci USA 107 (2010): 14419-24
[ site ] [ pdf ]

16. From statistical mechanics to information theory: Understanding biological information processing systems
Lecture notes for QECG 2010 Okinawa summer school
Tkačik G
arXiv.org (2010): 1006.4291
[ site ] [ pdf ]

15. Optimizing information flow in small genetic networks. II. Feed-forward interactions
Walczak AM, Tkačik G, Bialek W
Phys Rev E 81 (2010): 041905
[ site ] [ pdf ]

14. Spin glass models for a network of real neurons
Tkačik G, Schneidman E, Berry MJ II, Bialek W
arXiv.org (2009): 0912.5409
[ site ] [ pdf ]
arXiv.org (2006): q-bio/0611072
[ site ] [ pdf ]

13. The dynamics of adaptation on correlated fitness landscapes
Kryazhimskiy S*, Tkačik G*, Plotkin JB
Proc Nat’l Acad Sci USA 106 (2009): 18638
[ site ] [ pdf ]

12. Optimizing information flow in small genetic networks
Tkačik G, Walczak AM, Bialek W
Phys Rev E 80 (2009): 031920
[ site ] [ pdf ]

11. Cell Biology: Networks, Regulation, Pathways
Tkačik G, Bialek W
Encyclopedia of Complexity and Systems Science ed R Meyers, pp 719-741, Springer (2009), Berlin
[ site ] [ pdf ]

10. Information flow and optimization in transcriptional regulation
Tkačik G, Callan CG, Bialek W
Proc Nat’l Acad Sci USA 105 (2008): 12265-12270
[ site ] [ pdf ] [ News and views (E Siggia) ]

9. Information capacity of genetic regulatory elements
Tkačik G, Callan CG, Bialek W
Phys Rev E 78 (2008): 011910
[ site ] [ pdf ]

8. Diffusion, dimensionality and noise in transcriptional regulation
Tkačik G, Bialek W
Phys Rev E 79 (2008): 051901
[ site ] [ pdf ] [ Viewpoint (R Metzler) ]

7. Decoding spike timing: the differential reverse-correlation method
Tkačik G, Magnasco MO
Biosystems 93 (2008): 90-100
[ site ] [ pdf ]

6. Information flow in biological networks
Tkačik G
Thesis, Princeton University (2007)
[ pdf ]

5. Faster solutions to the inverse pairwise Ising problem
Broderick T, Dudik M, Tkačik G, Schapire RE, Bialek W
arXiv.org (2007): 0712.2437
[ site ] [ pdf ]

4. The role of input noise in transcriptional regulation
Tkačik G, Gregor T, Bialek W
PLOS One 3 (2007): e2774
[ site ] [ pdf ]

3. Precise physical models of protein-DNA interaction from high-throughput data
Kinney JB, Tkačik G, Callan CG
Proc Nat’l Acad Sci USA 104 (2007): 501-506
[ site ] [ pdf ]

2. Information-based clustering
Slonim N, Atwal GS, Tkačik G, Bialek W
Proc Nat’l Acad Sci USA 102 (2005): 18297
[ site ] [ pdf ]

1. Estimating mutual and multi-information in large networks
Slonim N, Atwal GS, Tkačik G, Bialek W
arXiv.org (2005): cs.it/0502017
[ site ] [ pdf ]