---
_id: '7553'
abstract:
- lang: eng
  text: Normative theories and statistical inference provide complementary approaches
    for the study of biological systems. A normative theory postulates that organisms
    have adapted to efficiently solve essential tasks, and proceeds to mathematically
    work out testable consequences of such optimality; parameters that maximize the
    hypothesized organismal function can be derived ab initio, without reference to
    experimental data. In contrast, statistical inference focuses on efficient utilization
    of data to learn model parameters, without reference to any a priori notion of
    biological function, utility, or fitness. Traditionally, these two approaches
    were developed independently and applied separately. Here we unify them in a coherent
    Bayesian framework that embeds a normative theory into a family of maximum-entropy
    “optimization priors.” This family defines a smooth interpolation between a data-rich
    inference regime (characteristic of “bottom-up” statistical models), and a data-limited
    ab inito prediction regime (characteristic of “top-down” normative theory). We
    demonstrate the applicability of our framework using data from the visual cortex,
    and argue that the flexibility it affords is essential to address a number of
    fundamental challenges relating to inference and prediction in complex, high-dimensional
    biological problems.
acknowledgement: The authors thank Dario Ringach for providing the V1 receptive fields
  and Olivier Marre for providing the retinal receptive fields. W.M. was funded by
  the European Union’s Horizon 2020 research and innovation programme under the Marie
  Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers
  Science grant no. HFSP RGP0032/2018.
article_processing_charge: No
author:
- first_name: Wiktor F
  full_name: Mlynarski, Wiktor F
  id: 358A453A-F248-11E8-B48F-1D18A9856A87
  last_name: Mlynarski
- first_name: Michal
  full_name: Hledik, Michal
  id: 4171253A-F248-11E8-B48F-1D18A9856A87
  last_name: Hledik
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
citation:
  ama: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality
    of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href="https://doi.org/10.1016/j.neuron.2021.01.020">10.1016/j.neuron.2021.01.020</a>
  apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical
    analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href="https://doi.org/10.1016/j.neuron.2021.01.020">https://doi.org/10.1016/j.neuron.2021.01.020</a>
  chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik.
    “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press,
    2021. <a href="https://doi.org/10.1016/j.neuron.2021.01.020">https://doi.org/10.1016/j.neuron.2021.01.020</a>.
  ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical
    analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell
    Press, p. 1227–1241.e5, 2021.
  ista: Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis
    and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.
  mla: Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural
    Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a
    href="https://doi.org/10.1016/j.neuron.2021.01.020">10.1016/j.neuron.2021.01.020</a>.
  short: W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021)
    1227–1241.e5.
date_created: 2020-02-28T11:00:12Z
date_published: 2021-04-07T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '07'
department:
- _id: GaTk
doi: 10.1016/j.neuron.2021.01.020
ec_funded: 1
external_id:
  isi:
  - '000637809600006'
intvolume: '       109'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1101/848374
month: '04'
oa: 1
oa_version: Preprint
page: 1227-1241.e5
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '754411'
  name: ISTplus - Postdoctoral Fellowships
publication: Neuron
publication_status: published
publisher: Cell Press
quality_controlled: '1'
related_material:
  link:
  - description: News on IST Homepage
    relation: press_release
    url: https://ist.ac.at/en/news/can-evolution-be-predicted/
  record:
  - id: '15020'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: Statistical analysis and optimality of neural systems
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 109
year: '2021'
...
---
_id: '7422'
abstract:
- lang: eng
  text: Biochemical reactions often occur at low copy numbers but at once in crowded
    and diverse environments. Space and stochasticity therefore play an essential
    role in biochemical networks. Spatial-stochastic simulations have become a prominent
    tool for understanding how stochasticity at the microscopic level influences the
    macroscopic behavior of such systems. While particle-based models guarantee the
    level of detail necessary to accurately describe the microscopic dynamics at very
    low copy numbers, the algorithms used to simulate them typically imply trade-offs
    between computational efficiency and biochemical accuracy. eGFRD (enhanced Green’s
    Function Reaction Dynamics) is an exact algorithm that evades such trade-offs
    by partitioning the N-particle system into M ≤ N analytically tractable one- and
    two-particle systems; the analytical solutions (Green’s functions) then are used
    to implement an event-driven particle-based scheme that allows particles to make
    large jumps in time and space while retaining access to their state variables
    at arbitrary simulation times. Here we present “eGFRD2,” a new eGFRD version that
    implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based
    simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on
    2D planes representing membranes, and on 1D elongated cylinders representative
    of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion
    used to model active transport. We find that, for low particle densities, eGFRD2
    is up to 6 orders of magnitude faster than conventional Brownian dynamics. We
    exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1
    gradient formation, which involves 3D diffusion, active transport on microtubules,
    and autophosphorylation on the membrane, confirming recent experimental and theoretical
    results on this system to hold under genuinely stochastic conditions.
article_number: '054108'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Joris
  full_name: Paijmans, Joris
  last_name: Paijmans
- first_name: Laurens
  full_name: Bossen, Laurens
  last_name: Bossen
- first_name: Thomas
  full_name: Miedema, Thomas
  last_name: Miedema
- first_name: Martijn
  full_name: Wehrens, Martijn
  last_name: Wehrens
- first_name: Nils B.
  full_name: Becker, Nils B.
  last_name: Becker
- first_name: Kazunari
  full_name: Kaizu, Kazunari
  last_name: Kaizu
- first_name: Koichi
  full_name: Takahashi, Koichi
  last_name: Takahashi
- first_name: Marileen
  full_name: Dogterom, Marileen
  last_name: Dogterom
- first_name: Pieter Rein
  full_name: ten Wolde, Pieter Rein
  last_name: ten Wolde
citation:
  ama: Sokolowski TR, Paijmans J, Bossen L, et al. eGFRD in all dimensions. <i>The
    Journal of Chemical Physics</i>. 2019;150(5). doi:<a href="https://doi.org/10.1063/1.5064867">10.1063/1.5064867</a>
  apa: Sokolowski, T. R., Paijmans, J., Bossen, L., Miedema, T., Wehrens, M., Becker,
    N. B., … ten Wolde, P. R. (2019). eGFRD in all dimensions. <i>The Journal of Chemical
    Physics</i>. AIP Publishing. <a href="https://doi.org/10.1063/1.5064867">https://doi.org/10.1063/1.5064867</a>
  chicago: Sokolowski, Thomas R, Joris Paijmans, Laurens Bossen, Thomas Miedema, Martijn
    Wehrens, Nils B. Becker, Kazunari Kaizu, Koichi Takahashi, Marileen Dogterom,
    and Pieter Rein ten Wolde. “EGFRD in All Dimensions.” <i>The Journal of Chemical
    Physics</i>. AIP Publishing, 2019. <a href="https://doi.org/10.1063/1.5064867">https://doi.org/10.1063/1.5064867</a>.
  ieee: T. R. Sokolowski <i>et al.</i>, “eGFRD in all dimensions,” <i>The Journal
    of Chemical Physics</i>, vol. 150, no. 5. AIP Publishing, 2019.
  ista: Sokolowski TR, Paijmans J, Bossen L, Miedema T, Wehrens M, Becker NB, Kaizu
    K, Takahashi K, Dogterom M, ten Wolde PR. 2019. eGFRD in all dimensions. The Journal
    of Chemical Physics. 150(5), 054108.
  mla: Sokolowski, Thomas R., et al. “EGFRD in All Dimensions.” <i>The Journal of
    Chemical Physics</i>, vol. 150, no. 5, 054108, AIP Publishing, 2019, doi:<a href="https://doi.org/10.1063/1.5064867">10.1063/1.5064867</a>.
  short: T.R. Sokolowski, J. Paijmans, L. Bossen, T. Miedema, M. Wehrens, N.B. Becker,
    K. Kaizu, K. Takahashi, M. Dogterom, P.R. ten Wolde, The Journal of Chemical Physics
    150 (2019).
date_created: 2020-01-30T10:34:36Z
date_published: 2019-02-07T00:00:00Z
date_updated: 2023-09-06T14:59:28Z
day: '07'
department:
- _id: GaTk
doi: 10.1063/1.5064867
external_id:
  arxiv:
  - '1708.09364'
  isi:
  - '000458109300009'
intvolume: '       150'
isi: 1
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1708.09364
month: '02'
oa: 1
oa_version: Preprint
publication: The Journal of Chemical Physics
publication_identifier:
  eissn:
  - 1089-7690
  issn:
  - 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
status: public
title: eGFRD in all dimensions
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 150
year: '2019'
...
---
_id: '7606'
abstract:
- lang: eng
  text: We derive a tight lower bound on equivocation (conditional entropy), or equivalently
    a tight upper bound on mutual information between a signal variable and channel
    outputs. The bound is in terms of the joint distribution of the signals and maximum
    a posteriori decodes (most probable signals given channel output). As part of
    our derivation, we describe the key properties of the distribution of signals,
    channel outputs and decodes, that minimizes equivocation and maximizes mutual
    information. This work addresses a problem in data analysis, where mutual information
    between signals and decodes is sometimes used to lower bound the mutual information
    between signals and channel outputs. Our result provides a corresponding upper
    bound.
article_number: '8989292'
article_processing_charge: No
arxiv: 1
author:
- first_name: Michal
  full_name: Hledik, Michal
  id: 4171253A-F248-11E8-B48F-1D18A9856A87
  last_name: Hledik
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
citation:
  ama: 'Hledik M, Sokolowski TR, Tkačik G. A tight upper bound on mutual information.
    In: <i>IEEE Information Theory Workshop, ITW 2019</i>. IEEE; 2019. doi:<a href="https://doi.org/10.1109/ITW44776.2019.8989292">10.1109/ITW44776.2019.8989292</a>'
  apa: 'Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2019). A tight upper bound
    on mutual information. In <i>IEEE Information Theory Workshop, ITW 2019</i>. Visby,
    Sweden: IEEE. <a href="https://doi.org/10.1109/ITW44776.2019.8989292">https://doi.org/10.1109/ITW44776.2019.8989292</a>'
  chicago: Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper
    Bound on Mutual Information.” In <i>IEEE Information Theory Workshop, ITW 2019</i>.
    IEEE, 2019. <a href="https://doi.org/10.1109/ITW44776.2019.8989292">https://doi.org/10.1109/ITW44776.2019.8989292</a>.
  ieee: M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual
    information,” in <i>IEEE Information Theory Workshop, ITW 2019</i>, Visby, Sweden,
    2019.
  ista: Hledik M, Sokolowski TR, Tkačik G. 2019. A tight upper bound on mutual information.
    IEEE Information Theory Workshop, ITW 2019. Information Theory Workshop, 8989292.
  mla: Hledik, Michal, et al. “A Tight Upper Bound on Mutual Information.” <i>IEEE
    Information Theory Workshop, ITW 2019</i>, 8989292, IEEE, 2019, doi:<a href="https://doi.org/10.1109/ITW44776.2019.8989292">10.1109/ITW44776.2019.8989292</a>.
  short: M. Hledik, T.R. Sokolowski, G. Tkačik, in:, IEEE Information Theory Workshop,
    ITW 2019, IEEE, 2019.
conference:
  end_date: 2019-08-28
  location: Visby, Sweden
  name: Information Theory Workshop
  start_date: 2019-08-25
date_created: 2020-03-22T23:00:47Z
date_published: 2019-08-01T00:00:00Z
date_updated: 2025-06-30T13:21:05Z
day: '01'
department:
- _id: GaTk
doi: 10.1109/ITW44776.2019.8989292
ec_funded: 1
external_id:
  arxiv:
  - '1812.01475'
  isi:
  - '000540384500015'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1812.01475
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '665385'
  name: International IST Doctoral Program
publication: IEEE Information Theory Workshop, ITW 2019
publication_identifier:
  isbn:
  - '9781538669006'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
  record:
  - id: '15020'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: A tight upper bound on mutual information
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '1242'
abstract:
- lang: eng
  text: A crucial step in the regulation of gene expression is binding of transcription
    factor (TF) proteins to regulatory sites along the DNA. But transcription factors
    act at nanomolar concentrations, and noise due to random arrival of these molecules
    at their binding sites can severely limit the precision of regulation. Recent
    work on the optimization of information flow through regulatory networks indicates
    that the lower end of the dynamic range of concentrations is simply inaccessible,
    overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain
    proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest
    a scheme in which transcription factors also act as indirect translational regulators,
    binding to the mRNA of other regulatory proteins. Intuitively, each mRNA molecule
    acts as an independent sensor of the input concentration, and averaging over these
    multiple sensors reduces the noise. We analyze information flow through this scheme
    and identify conditions under which it outperforms direct transcriptional regulation.
    Our results suggest that the dual role of homeodomain proteins is not just a historical
    accident, but a solution to a crucial physics problem in the regulation of gene
    expression.
acknowledgement: "We thank T. Gregor, A. Prochaintz, and others for\r\nhelpful discussions.
  This work was supported in part by\r\nGrants No. PHY-1305525 and No. CCF-0939370
  from the\r\nUS National Science Foundation and by the W.M. Keck\r\nFoundation. A.M.W.
  acknowledges the support by European\r\nResearch Council (ERC) Grant No. MCCIG PCIG10–GA-\r\n2011–303561.
  G.T. and T.R.S. were supported by Austrian\r\nScience Fund (FWF) Grant No. P28844S."
article_number: '022404'
author:
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Aleksandra
  full_name: Walczak, Aleksandra
  last_name: Walczak
- first_name: William
  full_name: Bialek, William
  last_name: Bialek
- first_name: Gasper
  full_name: Tkacik, Gasper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkacik
  orcid: 0000-0002-6699-1455
citation:
  ama: Sokolowski TR, Walczak A, Bialek W, Tkačik G. Extending the dynamic range of
    transcription factor action by translational regulation. <i>Physical Review E
    Statistical Nonlinear and Soft Matter Physics</i>. 2016;93(2). doi:<a href="https://doi.org/10.1103/PhysRevE.93.022404">10.1103/PhysRevE.93.022404</a>
  apa: Sokolowski, T. R., Walczak, A., Bialek, W., &#38; Tkačik, G. (2016). Extending
    the dynamic range of transcription factor action by translational regulation.
    <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American
    Institute of Physics. <a href="https://doi.org/10.1103/PhysRevE.93.022404">https://doi.org/10.1103/PhysRevE.93.022404</a>
  chicago: Sokolowski, Thomas R, Aleksandra Walczak, William Bialek, and Gašper Tkačik.
    “Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.”
    <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American
    Institute of Physics, 2016. <a href="https://doi.org/10.1103/PhysRevE.93.022404">https://doi.org/10.1103/PhysRevE.93.022404</a>.
  ieee: T. R. Sokolowski, A. Walczak, W. Bialek, and G. Tkačik, “Extending the dynamic
    range of transcription factor action by translational regulation,” <i>Physical
    Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 93, no. 2. American
    Institute of Physics, 2016.
  ista: Sokolowski TR, 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. 93(2), 022404.
  mla: Sokolowski, Thomas R., et al. “Extending the Dynamic Range of Transcription
    Factor Action by Translational Regulation.” <i>Physical Review E Statistical Nonlinear
    and Soft Matter Physics</i>, vol. 93, no. 2, 022404, American Institute of Physics,
    2016, doi:<a href="https://doi.org/10.1103/PhysRevE.93.022404">10.1103/PhysRevE.93.022404</a>.
  short: T.R. Sokolowski, A. Walczak, W. Bialek, G. Tkačik, Physical Review E Statistical
    Nonlinear and Soft Matter Physics 93 (2016).
date_created: 2018-12-11T11:50:54Z
date_published: 2016-02-04T00:00:00Z
date_updated: 2021-01-12T06:49:20Z
day: '04'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.93.022404
intvolume: '        93'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1507.02562
month: '02'
oa: 1
oa_version: Preprint
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: P28844-B27
  name: Biophysics of information processing in gene regulation
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '6088'
quality_controlled: '1'
scopus_import: 1
status: public
title: Extending the dynamic range of transcription factor action by translational
  regulation
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 93
year: '2016'
...
---
_id: '1244'
abstract:
- lang: eng
  text: Cell polarity refers to a functional spatial organization of proteins that
    is crucial for the control of essential cellular processes such as growth and
    division. To establish polarity, cells rely on elaborate regulation networks that
    control the distribution of proteins at the cell membrane. In fission yeast cells,
    a microtubule-dependent network has been identified that polarizes the distribution
    of signaling proteins that restricts growth to cell ends and targets the cytokinetic
    machinery to the middle of the cell. Although many molecular components have been
    shown to play a role in this network, it remains unknown which molecular functionalities
    are minimally required to establish a polarized protein distribution in this system.
    Here we show that a membrane-binding protein fragment, which distributes homogeneously
    in wild-type fission yeast cells, can be made to concentrate at cell ends by attaching
    it to a cytoplasmic microtubule end-binding protein. This concentration results
    in a polarized pattern of chimera proteins with a spatial extension that is very
    reminiscent of natural polarity patterns in fission yeast. However, chimera levels
    fluctuate in response to microtubule dynamics, and disruption of microtubules
    leads to disappearance of the pattern. Numerical simulations confirm that the
    combined functionality of membrane anchoring and microtubule tip affinity is in
    principle sufficient to create polarized patterns. Our chimera protein may thus
    represent a simple molecular functionality that is able to polarize the membrane,
    onto which additional layers of molecular complexity may be built to provide the
    temporal robustness that is typical of natural polarity patterns.
acknowledgement: "We thank Sophie Martin, Ken Sawin, Stephen Huisman,\r\nand Damian
  Brunner for strains; Julianne\r\nTeapal, Marcel Janson, Sergio Rincon,\r\nand Phong
  Tran for technical assistance; Andrew Mugler and Bela Mulder for\r\ndiscussions;
  and Sander Tans, Phong Tran,\r\nand Anne Paoletti for critical reading\r\nof the
  manuscript. This work is part of the research program of the\r\n“\r\nStichting\r\nvoor
  Fundamenteel Onderzoek de Materie,\r\n”\r\nwhich is financially supported by\r\nthe\r\n“\r\nNederlandse
  organisatie voor Wete\r\nnschappelijk Onderzoek (NWO).\r\n”"
author:
- first_name: Pierre
  full_name: Recouvreux, Pierre
  last_name: Recouvreux
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Aristea
  full_name: Grammoustianou, Aristea
  last_name: Grammoustianou
- first_name: Pieter
  full_name: Tenwolde, Pieter
  last_name: Tenwolde
- first_name: Marileen
  full_name: Dogterom, Marileen
  last_name: Dogterom
citation:
  ama: Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. Chimera
    proteins with affinity for membranes and microtubule tips polarize in the membrane
    of fission yeast cells. <i>PNAS</i>. 2016;113(7):1811-1816. doi:<a href="https://doi.org/10.1073/pnas.1419248113">10.1073/pnas.1419248113</a>
  apa: Recouvreux, P., Sokolowski, T. R., Grammoustianou, A., Tenwolde, P., &#38;
    Dogterom, M. (2016). Chimera proteins with affinity for membranes and microtubule
    tips polarize in the membrane of fission yeast cells. <i>PNAS</i>. National Academy
    of Sciences. <a href="https://doi.org/10.1073/pnas.1419248113">https://doi.org/10.1073/pnas.1419248113</a>
  chicago: Recouvreux, Pierre, Thomas R Sokolowski, Aristea Grammoustianou, Pieter
    Tenwolde, and Marileen Dogterom. “Chimera Proteins with Affinity for Membranes
    and Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” <i>PNAS</i>.
    National Academy of Sciences, 2016. <a href="https://doi.org/10.1073/pnas.1419248113">https://doi.org/10.1073/pnas.1419248113</a>.
  ieee: 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,” <i>PNAS</i>, vol. 113, no. 7. National Academy
    of Sciences, pp. 1811–1816, 2016.
  ista: Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. 2016.
    Chimera proteins with affinity for membranes and microtubule tips polarize in
    the membrane of fission yeast cells. PNAS. 113(7), 1811–1816.
  mla: Recouvreux, Pierre, et al. “Chimera Proteins with Affinity for Membranes and
    Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” <i>PNAS</i>,
    vol. 113, no. 7, National Academy of Sciences, 2016, pp. 1811–16, doi:<a href="https://doi.org/10.1073/pnas.1419248113">10.1073/pnas.1419248113</a>.
  short: P. Recouvreux, T.R. Sokolowski, A. Grammoustianou, P. Tenwolde, M. Dogterom,
    PNAS 113 (2016) 1811–1816.
date_created: 2018-12-11T11:50:55Z
date_published: 2016-02-16T00:00:00Z
date_updated: 2021-01-12T06:49:21Z
day: '16'
department:
- _id: GaTk
doi: 10.1073/pnas.1419248113
intvolume: '       113'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763754/
month: '02'
oa: 1
oa_version: Submitted Version
page: 1811 - 1816
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '6085'
quality_controlled: '1'
scopus_import: 1
status: public
title: Chimera proteins with affinity for membranes and microtubule tips polarize
  in the membrane of fission yeast cells
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 113
year: '2016'
...
---
_id: '1940'
abstract:
- lang: eng
  text: We typically think of cells as responding to external signals independently
    by regulating their gene expression levels, yet they often locally exchange information
    and coordinate. Can such spatial coupling be of benefit for conveying signals
    subject to gene regulatory noise? Here we extend our information-theoretic framework
    for gene regulation to spatially extended systems. As an example, we consider
    a lattice of nuclei responding to a concentration field of a transcriptional regulator
    (the &quot;input&quot;) by expressing a single diffusible target gene. When input
    concentrations are low, diffusive coupling markedly improves information transmission;
    optimal gene activation functions also systematically change. A qualitatively
    new regulatory strategy emerges where individual cells respond to the input in
    a nearly step-like fashion that is subsequently averaged out by strong diffusion.
    While motivated by early patterning events in the Drosophila embryo, our framework
    is generically applicable to spatially coupled stochastic gene expression models.
article_number: '062710'
author:
- first_name: Thomas R
  full_name: Sokolowski, Thomas R
  id: 3E999752-F248-11E8-B48F-1D18A9856A87
  last_name: Sokolowski
  orcid: 0000-0002-1287-3779
- first_name: Gasper
  full_name: Tkacik, Gasper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkacik
  orcid: 0000-0002-6699-1455
citation:
  ama: Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks.
    IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter
    Physics</i>. 2015;91(6). doi:<a href="https://doi.org/10.1103/PhysRevE.91.062710">10.1103/PhysRevE.91.062710</a>
  apa: Sokolowski, T. R., &#38; Tkačik, G. (2015). Optimizing information flow in
    small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical
    Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href="https://doi.org/10.1103/PhysRevE.91.062710">https://doi.org/10.1103/PhysRevE.91.062710</a>
  chicago: Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in
    Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical
    Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2015. <a
    href="https://doi.org/10.1103/PhysRevE.91.062710">https://doi.org/10.1103/PhysRevE.91.062710</a>.
  ieee: T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic
    networks. IV. Spatial coupling,” <i>Physical Review E Statistical Nonlinear and
    Soft Matter Physics</i>, vol. 91, no. 6. American Institute of Physics, 2015.
  ista: Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic
    networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft
    Matter Physics. 91(6), 062710.
  mla: Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small
    Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear
    and Soft Matter Physics</i>, vol. 91, no. 6, 062710, American Institute of Physics,
    2015, doi:<a href="https://doi.org/10.1103/PhysRevE.91.062710">10.1103/PhysRevE.91.062710</a>.
  short: T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft
    Matter Physics 91 (2015).
date_created: 2018-12-11T11:54:49Z
date_published: 2015-06-15T00:00:00Z
date_updated: 2021-01-12T06:54:13Z
day: '15'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.91.062710
intvolume: '        91'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://arxiv.org/abs/1501.04015
month: '06'
oa: 1
oa_version: Preprint
publication: Physical Review E Statistical Nonlinear and Soft Matter Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5145'
quality_controlled: '1'
scopus_import: 1
status: public
title: Optimizing information flow in small genetic networks. IV. Spatial coupling
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 91
year: '2015'
...