---
_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. Neuron. 2021;109(7):1227-1241.e5. doi:10.1016/j.neuron.2021.01.020
apa: Mlynarski, W. F., Hledik, M., Sokolowski, T. R., & Tkačik, G. (2021). Statistical
analysis and optimality of neural systems. Neuron. Cell Press. https://doi.org/10.1016/j.neuron.2021.01.020
chicago: Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik.
“Statistical Analysis and Optimality of Neural Systems.” Neuron. Cell Press,
2021. https://doi.org/10.1016/j.neuron.2021.01.020.
ieee: W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical
analysis and optimality of neural systems,” Neuron, 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.” Neuron, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:10.1016/j.neuron.2021.01.020.
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: 2024-03-06T14:22:51Z
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
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. The
Journal of Chemical Physics. 2019;150(5). doi:10.1063/1.5064867
apa: Sokolowski, T. R., Paijmans, J., Bossen, L., Miedema, T., Wehrens, M., Becker,
N. B., … ten Wolde, P. R. (2019). eGFRD in all dimensions. The Journal of Chemical
Physics. AIP Publishing. https://doi.org/10.1063/1.5064867
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.” The Journal of Chemical
Physics. AIP Publishing, 2019. https://doi.org/10.1063/1.5064867.
ieee: T. R. Sokolowski et al., “eGFRD in all dimensions,” The Journal
of Chemical Physics, 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.” The Journal of
Chemical Physics, vol. 150, no. 5, 054108, AIP Publishing, 2019, doi:10.1063/1.5064867.
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
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: IEEE Information Theory Workshop, ITW 2019. IEEE; 2019. doi:10.1109/ITW44776.2019.8989292'
apa: 'Hledik, M., Sokolowski, T. R., & Tkačik, G. (2019). A tight upper bound
on mutual information. In IEEE Information Theory Workshop, ITW 2019. Visby,
Sweden: IEEE. https://doi.org/10.1109/ITW44776.2019.8989292'
chicago: Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper
Bound on Mutual Information.” In IEEE Information Theory Workshop, ITW 2019.
IEEE, 2019. https://doi.org/10.1109/ITW44776.2019.8989292.
ieee: M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual
information,” in IEEE Information Theory Workshop, ITW 2019, 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.” IEEE
Information Theory Workshop, ITW 2019, 8989292, IEEE, 2019, doi:10.1109/ITW44776.2019.8989292.
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: 2024-03-06T14:22:51Z
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. Physical Review E
Statistical Nonlinear and Soft Matter Physics. 2016;93(2). doi:10.1103/PhysRevE.93.022404
apa: 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
chicago: Sokolowski, Thomas R, Aleksandra Walczak, William Bialek, and Gašper Tkačik.
“Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.”
Physical Review E Statistical Nonlinear and Soft Matter Physics. American
Institute of Physics, 2016. https://doi.org/10.1103/PhysRevE.93.022404.
ieee: T. R. Sokolowski, A. Walczak, W. Bialek, and G. Tkačik, “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.
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.” Physical Review E Statistical Nonlinear
and Soft Matter Physics, vol. 93, no. 2, 022404, American Institute of Physics,
2016, doi:10.1103/PhysRevE.93.022404.
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. PNAS. 2016;113(7):1811-1816. doi:10.1073/pnas.1419248113
apa: Recouvreux, P., Sokolowski, T. R., 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. National Academy
of Sciences. https://doi.org/10.1073/pnas.1419248113
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.” PNAS.
National Academy of Sciences, 2016. https://doi.org/10.1073/pnas.1419248113.
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,” PNAS, 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.” PNAS,
vol. 113, no. 7, National Academy of Sciences, 2016, pp. 1811–16, doi:10.1073/pnas.1419248113.
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 "input") 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. Physical Review E Statistical Nonlinear and Soft Matter
Physics. 2015;91(6). doi:10.1103/PhysRevE.91.062710
apa: Sokolowski, T. R., & Tkačik, G. (2015). Optimizing information flow in
small genetic networks. IV. Spatial coupling. Physical Review E Statistical
Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.91.062710
chicago: Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in
Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical
Nonlinear and Soft Matter Physics. American Institute of Physics, 2015. https://doi.org/10.1103/PhysRevE.91.062710.
ieee: T. R. Sokolowski and G. Tkačik, “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.
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.” Physical Review E Statistical Nonlinear
and Soft Matter Physics, vol. 91, no. 6, 062710, American Institute of Physics,
2015, doi:10.1103/PhysRevE.91.062710.
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'
...