---
OA_place: repository
OA_type: green
_id: '1320'
abstract:
- lang: eng
text: 'In recent years, several biomolecular systems have been shown to be scale-invariant
(SI), i.e. to show the same output dynamics when exposed to geometrically scaled
input signals (u → pu, p > 0) after pre-adaptation to accordingly scaled constant
inputs. In this article, we show that SI systems-as well as systems invariant
with respect to other input transformations-can realize nonlinear differential
operators: when excited by inputs obeying functional forms characteristic for
a given class of invariant systems, the systems'' outputs converge to constant
values directly quantifying the speed of the input.'
acknowledgement: The research leading to these results has received funding from the
People Programme (Marie Curie Actions) of the European Union's Seventh Framework
Programme (FP7/2007-2013) under REA grant agreement n° [291734]. Work supported
in part by grants AFOSR FA9550-14-1-0060 and NIH 1R01GM100473.
article_number: '7526722'
article_processing_charge: No
author:
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Eduardo
full_name: Sontag, Eduardo
last_name: Sontag
citation:
ama: 'Lang M, Sontag E. Scale-invariant systems realize nonlinear differential operators.
In: Vol 2016-July. IEEE; 2016. doi:10.1109/ACC.2016.7526722'
apa: 'Lang, M., & Sontag, E. (2016). Scale-invariant systems realize nonlinear
differential operators (Vol. 2016–July). Presented at the ACC: American Control
Conference, Boston, MA, USA: IEEE. https://doi.org/10.1109/ACC.2016.7526722'
chicago: Lang, Moritz, and Eduardo Sontag. “Scale-Invariant Systems Realize Nonlinear
Differential Operators,” Vol. 2016–July. IEEE, 2016. https://doi.org/10.1109/ACC.2016.7526722.
ieee: 'M. Lang and E. Sontag, “Scale-invariant systems realize nonlinear differential
operators,” presented at the ACC: American Control Conference, Boston, MA, USA,
2016, vol. 2016–July.'
ista: 'Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential
operators. ACC: American Control Conference vol. 2016–July, 7526722.'
mla: Lang, Moritz, and Eduardo Sontag. Scale-Invariant Systems Realize Nonlinear
Differential Operators. Vol. 2016–July, 7526722, IEEE, 2016, doi:10.1109/ACC.2016.7526722.
short: M. Lang, E. Sontag, in:, IEEE, 2016.
conference:
end_date: 2016-07-08
location: Boston, MA, USA
name: 'ACC: American Control Conference'
start_date: 2016-07-06
date_created: 2018-12-11T11:51:21Z
date_published: 2016-07-28T00:00:00Z
date_updated: 2025-06-25T11:46:37Z
day: '28'
ddc:
- '003'
- '621'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1109/ACC.2016.7526722
ec_funded: 1
file:
- access_level: open_access
checksum: 7219432b43defc62a0d45f48d4ce6a19
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:17Z
date_updated: 2025-06-25T11:46:36Z
file_id: '5203'
file_name: IST-2017-810-v1+1_root.pdf
file_size: 539166
relation: main_file
file_date_updated: 2025-06-25T11:46:36Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: IEEE
publist_id: '5950'
pubrep_id: '810'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scale-invariant systems realize nonlinear differential operators
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2016-July
year: '2016'
...
---
_id: '1332'
abstract:
- lang: eng
text: Antibiotic-sensitive and -resistant bacteria coexist in natural environments
with low, if detectable, antibiotic concentrations. Except possibly around localized
antibiotic sources, where resistance can provide a strong advantage, bacterial
fitness is dominated by stresses unaffected by resistance to the antibiotic. How
do such mixed and heterogeneous conditions influence the selective advantage or
disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels
of tetracyclines potentiate selection for or against tetracycline resistance around
localized sources of almost any toxin or stress. Furthermore, certain stresses
generate alternating rings of selection for and against resistance around a localized
source of the antibiotic. In these conditions, localized antibiotic sources, even
at high strengths, can actually produce a net selection against resistance to
the antibiotic. Our results show that interactions between the effects of an antibiotic
and other stresses in inhomogeneous environments can generate pervasive, complex
patterns of selection both for and against antibiotic resistance.
acknowledgement: This work was partially supported by US National Institutes of Health
grant R01-GM081617, Israeli Centers of Research Excellence I-CORE Program ISF Grant
No. 152/11, and the European Research Council FP7 ERC Grant 281891.
article_number: '10333'
article_processing_charge: No
author:
- first_name: Remy P
full_name: Chait, Remy P
id: 3464AE84-F248-11E8-B48F-1D18A9856A87
last_name: Chait
orcid: 0000-0003-0876-3187
- first_name: Adam
full_name: Palmer, Adam
last_name: Palmer
- first_name: Idan
full_name: Yelin, Idan
last_name: Yelin
- first_name: Roy
full_name: Kishony, Roy
last_name: Kishony
citation:
ama: Chait RP, Palmer A, Yelin I, Kishony R. Pervasive selection for and against
antibiotic resistance in inhomogeneous multistress environments. Nature Communications.
2016;7. doi:10.1038/ncomms10333
apa: Chait, R. P., Palmer, A., Yelin, I., & Kishony, R. (2016). Pervasive selection
for and against antibiotic resistance in inhomogeneous multistress environments.
Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms10333
chicago: Chait, Remy P, Adam Palmer, Idan Yelin, and Roy Kishony. “Pervasive Selection
for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.”
Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms10333.
ieee: R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for
and against antibiotic resistance in inhomogeneous multistress environments,”
Nature Communications, vol. 7. Nature Publishing Group, 2016.
ista: Chait RP, Palmer A, Yelin I, Kishony R. 2016. Pervasive selection for and
against antibiotic resistance in inhomogeneous multistress environments. Nature
Communications. 7, 10333.
mla: Chait, Remy P., et al. “Pervasive Selection for and against Antibiotic Resistance
in Inhomogeneous Multistress Environments.” Nature Communications, vol.
7, 10333, Nature Publishing Group, 2016, doi:10.1038/ncomms10333.
short: R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016).
date_created: 2018-12-11T11:51:25Z
date_published: 2016-01-20T00:00:00Z
date_updated: 2025-09-22T08:22:19Z
day: '20'
ddc:
- '570'
- '579'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1038/ncomms10333
external_id:
isi:
- '000369021700002'
file:
- access_level: open_access
checksum: ef147bcbb8bd37e9079cf3ce06f5815d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:13:52Z
date_updated: 2020-07-14T12:44:44Z
file_id: '5039'
file_name: IST-2016-662-v1+1_ncomms10333.pdf
file_size: 1844107
relation: main_file
file_date_updated: 2020-07-14T12:44:44Z
has_accepted_license: '1'
intvolume: ' 7'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_status: published
publisher: Nature Publishing Group
publist_id: '5936'
pubrep_id: '662'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Pervasive selection for and against antibiotic resistance in inhomogeneous
multistress environments
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 7
year: '2016'
...
---
_id: '1342'
abstract:
- lang: eng
text: A key aspect of bacterial survival is the ability to evolve while migrating
across spatially varying environmental challenges. Laboratory experiments, however,
often study evolution in well-mixed systems. Here, we introduce an experimental
device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria
spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that
allowed visual observation of mutation and selection in a migrating bacterial
front.While resistance increased consistently, multiple coexisting lineages diversified
both phenotypically and genotypically. Analyzing mutants at and behind the propagating
front,we found that evolution is not always led by the most resistant mutants;
highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate
provides a versatile platformfor studying microbial adaption and directly visualizing
evolutionary dynamics.
article_processing_charge: No
author:
- first_name: Michael
full_name: Baym, Michael
last_name: Baym
- first_name: Tami
full_name: Lieberman, Tami
last_name: Lieberman
- first_name: Eric
full_name: Kelsic, Eric
last_name: Kelsic
- first_name: Remy P
full_name: Chait, Remy P
id: 3464AE84-F248-11E8-B48F-1D18A9856A87
last_name: Chait
orcid: 0000-0003-0876-3187
- first_name: Rotem
full_name: Gross, Rotem
last_name: Gross
- first_name: Idan
full_name: Yelin, Idan
last_name: Yelin
- first_name: Roy
full_name: Kishony, Roy
last_name: Kishony
citation:
ama: Baym M, Lieberman T, Kelsic E, et al. Spatiotemporal microbial evolution on
antibiotic landscapes. Science. 2016;353(6304):1147-1151. doi:10.1126/science.aag0822
apa: Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., &
Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes.
Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aag0822
chicago: Baym, Michael, Tami Lieberman, Eric Kelsic, Remy P Chait, Rotem Gross,
Idan Yelin, and Roy Kishony. “Spatiotemporal Microbial Evolution on Antibiotic
Landscapes.” Science. American Association for the Advancement of Science,
2016. https://doi.org/10.1126/science.aag0822.
ieee: M. Baym et al., “Spatiotemporal microbial evolution on antibiotic landscapes,”
Science, vol. 353, no. 6304. American Association for the Advancement of
Science, pp. 1147–1151, 2016.
ista: Baym M, Lieberman T, Kelsic E, Chait RP, Gross R, Yelin I, Kishony R. 2016.
Spatiotemporal microbial evolution on antibiotic landscapes. Science. 353(6304),
1147–1151.
mla: Baym, Michael, et al. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.”
Science, vol. 353, no. 6304, American Association for the Advancement of
Science, 2016, pp. 1147–51, doi:10.1126/science.aag0822.
short: M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony,
Science 353 (2016) 1147–1151.
date_created: 2018-12-11T11:51:29Z
date_published: 2016-09-09T00:00:00Z
date_updated: 2025-09-22T08:17:11Z
day: '09'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1126/science.aag0822
external_id:
isi:
- '000382626800052'
intvolume: ' 353'
isi: 1
issue: '6304'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/
month: '09'
oa: 1
oa_version: Preprint
page: 1147 - 1151
publication: Science
publication_status: published
publisher: American Association for the Advancement of Science
publist_id: '5911'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Spatiotemporal microbial evolution on antibiotic landscapes
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 353
year: '2016'
...
---
_id: '1358'
abstract:
- lang: eng
text: 'Gene regulation relies on the specificity of transcription factor (TF)–DNA
interactions. Limited specificity may lead to crosstalk: a regulatory state in
which a gene is either incorrectly activated due to noncognate TF–DNA interactions
or remains erroneously inactive. As each TF can have numerous interactions with
noncognate cis-regulatory elements, crosstalk is inherently a global problem,
yet has previously not been studied as such. We construct a theoretical framework
to analyse the effects of global crosstalk on gene regulation. We find that crosstalk
presents a significant challenge for organisms with low-specificity TFs, such
as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting
at equilibrium, including variants of cooperativity and combinatorial regulation.
Our results suggest that crosstalk imposes a previously unexplored global constraint
on the functioning and evolution of regulatory networks, which is qualitatively
distinct from the known constraints that act at the level of individual gene regulatory
elements.'
article_number: '12307'
article_processing_charge: No
author:
- first_name: Tamar
full_name: Friedlander, Tamar
id: 36A5845C-F248-11E8-B48F-1D18A9856A87
last_name: Friedlander
- first_name: Roshan
full_name: Prizak, Roshan
id: 4456104E-F248-11E8-B48F-1D18A9856A87
last_name: Prizak
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to
gene regulation by global crosstalk. Nature Communications. 2016;7. doi:10.1038/ncomms12307
apa: Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., & Tkačik, G. (2016).
Intrinsic limits to gene regulation by global crosstalk. Nature Communications.
Nature Publishing Group. https://doi.org/10.1038/ncomms12307
chicago: Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and
Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature
Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms12307.
ieee: T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkačik, “Intrinsic
limits to gene regulation by global crosstalk,” Nature Communications,
vol. 7. Nature Publishing Group, 2016.
ista: Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits
to gene regulation by global crosstalk. Nature Communications. 7, 12307.
mla: Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.”
Nature Communications, vol. 7, 12307, Nature Publishing Group, 2016, doi:10.1038/ncomms12307.
short: T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications
7 (2016).
corr_author: '1'
date_created: 2018-12-11T11:51:34Z
date_published: 2016-08-04T00:00:00Z
date_updated: 2026-04-08T13:54:24Z
day: '04'
ddc:
- '576'
department:
- _id: GaTk
- _id: NiBa
- _id: CaGu
doi: 10.1038/ncomms12307
ec_funded: 1
external_id:
isi:
- '000380858400001'
file:
- access_level: open_access
checksum: fe3f3a1526d180b29fe691ab11435b78
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:01Z
date_updated: 2020-07-14T12:44:46Z
file_id: '4919'
file_name: IST-2016-627-v1+1_ncomms12307.pdf
file_size: 861805
relation: main_file
- access_level: open_access
checksum: 164864a1a675f3ad80e9917c27aba07f
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:02Z
date_updated: 2020-07-14T12:44:46Z
file_id: '4920'
file_name: IST-2016-627-v1+2_ncomms12307-s1.pdf
file_size: 1084703
relation: main_file
file_date_updated: 2020-07-14T12:44:46Z
has_accepted_license: '1'
intvolume: ' 7'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publication: Nature Communications
publication_status: published
publisher: Nature Publishing Group
publist_id: '5887'
pubrep_id: '627'
quality_controlled: '1'
related_material:
record:
- id: '6071'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Intrinsic limits to gene regulation by global crosstalk
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 7
year: '2016'
...
---
_id: '1394'
abstract:
- lang: eng
text: "The solution space of genome-scale models of cellular metabolism provides
a map between physically\r\nviable flux configurations and cellular metabolic
phenotypes described, at the most basic level, by the\r\ncorresponding growth
rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat
empirical growth rate distributions recently obtained in experiments at single-cell
resolution can\r\nbe explained in terms of a trade-off between the higher fitness
of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based
on this, we propose a minimal model for the evolution of\r\na large bacterial
population that captures this trade-off. The scaling relationships observed in\r\nexperiments
encode, in such frameworks, for the same distance from the maximum achievable
growth\r\nrate, the same degree of growth rate maximization, and/or the same rate
of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions,
these results allow for multiple\r\nimplications and extensions in spite of the
underlying conceptual simplicity."
acknowledgement: "The research leading to these results has received funding from
the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038."
article_number: '036005'
article_processing_charge: No
arxiv: 1
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Fabrizio
full_name: Capuani, Fabrizio
last_name: Capuani
- first_name: Andrea
full_name: De Martino, Andrea
last_name: De Martino
citation:
ama: 'De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005'
apa: 'De Martino, D., Capuani, F., & De Martino, A. (2016). Growth against entropy
in bacterial metabolism: the phenotypic trade-off behind empirical growth rate
distributions in E. coli. Physical Biology. IOP Publishing. https://doi.org/10.1088/1478-3975/13/3/036005'
chicago: 'De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth
against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical
Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing,
2016. https://doi.org/10.1088/1478-3975/13/3/036005.'
ieee: 'D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in
bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing, 2016.'
ista: 'De Martino D, Capuani F, De Martino A. 2016. Growth against entropy in bacterial
metabolism: the phenotypic trade-off behind empirical growth rate distributions
in E. coli. Physical Biology. 13(3), 036005.'
mla: 'De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism:
The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.”
Physical Biology, vol. 13, no. 3, 036005, IOP Publishing, 2016, doi:10.1088/1478-3975/13/3/036005.'
short: D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).
date_created: 2018-12-11T11:51:46Z
date_published: 2016-05-27T00:00:00Z
date_updated: 2025-09-18T14:31:33Z
day: '27'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/3/036005
ec_funded: 1
external_id:
arxiv:
- '1601.03243'
isi:
- '000380148200014'
intvolume: ' 13'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1601.03243
month: '05'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Biology
publication_status: published
publisher: IOP Publishing
publist_id: '5815'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Growth against entropy in bacterial metabolism: the phenotypic trade-off behind
empirical growth rate distributions in E. coli'
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2016'
...
---
_id: '1420'
abstract:
- lang: eng
text: 'Selection, mutation, and random drift affect the dynamics of allele frequencies
and consequently of quantitative traits. While the macroscopic dynamics of quantitative
traits can be measured, the underlying allele frequencies are typically unobserved.
Can we understand how the macroscopic observables evolve without following these
microscopic processes? This problem has been studied previously by analogy with
statistical mechanics: the allele frequency distribution at each time point is
approximated by the stationary form, which maximizes entropy. We explore the limitations
of this method when mutation is small (4Nμ < 1) so that populations are typically
close to fixation, and we extend the theory in this regime to account for changes
in mutation strength. We consider a single diallelic locus either under directional
selection or with overdominance and then generalize to multiple unlinked biallelic
loci with unequal effects. We find that the maximum-entropy approximation is remarkably
accurate, even when mutation and selection change rapidly. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of
quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127
apa: Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation
for the dynamics of quantitative traits. Genetics. Genetics Society of
America. https://doi.org/10.1534/genetics.115.184127
chicago: Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation
for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of
America, 2016. https://doi.org/10.1534/genetics.115.184127.
ieee: K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics
of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of
America, pp. 1523–1548, 2016.
ista: Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics
of quantitative traits. Genetics. 202(4), 1523–1548.
mla: Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative
Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016,
pp. 1523–48, doi:10.1534/genetics.115.184127.
short: K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.
corr_author: '1'
date_created: 2018-12-11T11:51:55Z
date_published: 2016-04-06T00:00:00Z
date_updated: 2025-09-18T14:22:05Z
day: '06'
department:
- _id: GaTk
- _id: NiBa
doi: 10.1534/genetics.115.184127
ec_funded: 1
external_id:
arxiv:
- '1510.08344'
isi:
- '000373959100022'
intvolume: ' 202'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1510.08344
month: '04'
oa: 1
oa_version: Preprint
page: 1523 - 1548
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '5787'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A general approximation for the dynamics of quantitative traits
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 202
year: '2016'
...
---
_id: '1485'
abstract:
- lang: eng
text: In this article the notion of metabolic turnover is revisited in the light
of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo
methods we perform an exact sampling of the enzymatic fluxes in a genome scale
metabolic network of E. Coli in stationary growth conditions from which we infer
the metabolites turnover times. However the latter are inferred from net fluxes,
and we argue that this approximation is not valid for enzymes working nearby thermodynamic
equilibrium. We recalculate turnover times from total fluxes by performing an
energy balance analysis of the network and recurring to the fluctuation theorem.
We find in many cases values one of order of magnitude lower, implying a faster
picture of intermediate metabolism.
article_number: '016003'
article_processing_charge: No
arxiv: 1
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
citation:
ama: De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from
the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003
apa: De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E.
Coli from the energy balance analysis. Physical Biology. IOP Publishing.
https://doi.org/10.1088/1478-3975/13/1/016003
chicago: De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of
E. Coli from the Energy Balance Analysis.” Physical Biology. IOP Publishing,
2016. https://doi.org/10.1088/1478-3975/13/1/016003.
ieee: D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli
from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP
Publishing, 2016.
ista: De Martino D. 2016. Genome-scale estimate of the metabolic turnover of E.
Coli from the energy balance analysis. Physical Biology. 13(1), 016003.
mla: De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E.
Coli from the Energy Balance Analysis.” Physical Biology, vol. 13, no.
1, 016003, IOP Publishing, 2016, doi:10.1088/1478-3975/13/1/016003.
short: D. De Martino, Physical Biology 13 (2016).
corr_author: '1'
date_created: 2018-12-11T11:52:18Z
date_published: 2016-01-29T00:00:00Z
date_updated: 2025-09-18T11:34:17Z
day: '29'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/1/016003
ec_funded: 1
external_id:
arxiv:
- '1505.04613'
isi:
- '000371585200003'
intvolume: ' 13'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1505.04613
month: '01'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Physical Biology
publication_status: published
publisher: IOP Publishing
publist_id: '5702'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Genome-scale estimate of the metabolic turnover of E. Coli from the energy
balance analysis
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2016'
...
---
_id: '8094'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. The idea is that the controller controls the world---the body plus
its environment---as reliably as possible. This paper focuses on new lines of
self-organization for developmental robotics. We apply the recently developed
differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
system from the Myorobotics toolkit. In the experiments, we observe a vast variety
of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
sequences of different poses. By applying physical forces, the system can be entrained
into definite motion patterns like wiping a table. Most interestingly, after attaching
an object, the controller gets in a functional resonance with the object''s internal
dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
driving an attached pendulum into a circular mode. When attached to the crank
of a wheel the neural system independently discovers how to rotate it. In this
way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Rafael
full_name: Hostettler, Rafael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
driven arm by differential extrinsic plasticity. In: 15th International Conference
on the Synthesis and Simulation of Living Systems. Vol 28. MIT Press; 2016:142-143.
doi:10.7551/978-0-262-33936-0-ch029'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized
control of an tendon driven arm by differential extrinsic plasticity. In 15th
International Conference on the Synthesis and Simulation of Living Systems
(Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029'
chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In 15th
International Conference on the Synthesis and Simulation of Living Systems,
28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.
ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
of an tendon driven arm by differential extrinsic plasticity,” in 15th International
Conference on the Synthesis and Simulation of Living Systems, Cancun, Mexico,
2016, vol. 28, pp. 142–143.
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
an tendon driven arm by differential extrinsic plasticity. 15th International
Conference on the Synthesis and Simulation of Living Systems. ALIFE 2016: Conference
on the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
Differential Extrinsic Plasticity.” 15th International Conference on the Synthesis
and Simulation of Living Systems, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, 15th International Conference
on the Synthesis and Simulation of Living Systems, MIT Press, 2016, pp. 142–143.
conference:
end_date: 2016-07-08
location: Cancun, Mexico
name: 'ALIFE 2016: Conference on the Synthesis and Simulation of Living Systems'
start_date: 2016-07-04
corr_author: '1'
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2025-07-10T11:55:05Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
checksum: cff63e7a4b8ac466ba51a9c84153a940
content_type: application/pdf
creator: cziletti
date_created: 2020-07-06T12:59:09Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8096'
file_name: 2016_ProcALIFE_Martius.pdf
file_size: 678670
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: 15th International Conference on the Synthesis and Simulation of Living
Systems
publication_identifier:
isbn:
- '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic plasticity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 28
year: '2016'
...
---
_id: '948'
abstract:
- lang: eng
text: Experience constantly shapes neural circuits through a variety of plasticity
mechanisms. While the functional roles of some plasticity mechanisms are well-understood,
it remains unclear how changes in neural excitability contribute to learning.
Here, we develop a normative interpretation of intrinsic plasticity (IP) as a
key component of unsupervised learning. We introduce a novel generative mixture
model that accounts for the class-specific statistics of stimulus intensities,
and we derive a neural circuit that learns the input classes and their intensities.
We will analytically show that inference and learning for our generative model
can be achieved by a neural circuit with intensity-sensitive neurons equipped
with a specific form of IP. Numerical experiments verify our analytical derivations
and show robust behavior for artificial and natural stimuli. Our results link
IP to non-trivial input statistics, in particular the statistics of stimulus intensities
for classes to which a neuron is sensitive. More generally, our work paves the
way toward new classification algorithms that are robust to intensity variations.
acknowledgement: DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1
(JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement
no. 291734 (CS)
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
author:
- first_name: Travis
full_name: Monk, Travis
last_name: Monk
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Jörg
full_name: Lücke, Jörg
last_name: Lücke
citation:
ama: 'Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn
stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems
Foundation; 2016:4285-4293.'
apa: 'Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic
plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented
at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural
Information Processing Systems Foundation.'
chicago: Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic
Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information
Processing Systems Foundation, 2016.
ieee: 'T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics,” presented at the NIPS: Neural Information
Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.'
ista: 'Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity
learn stimulus intensity statistics. NIPS: Neural Information Processing Systems,
Advances in Neural Information Processing Systems, vol. 29, 4285–4293.'
mla: Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus
Intensity Statistics. Vol. 29, Neural Information Processing Systems Foundation,
2016, pp. 4285–93.
short: T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems Foundation,
2016, pp. 4285–4293.
conference:
end_date: 2016-12-10
location: Barcelona, Spaine
name: 'NIPS: Neural Information Processing Systems'
start_date: 2016-12-05
date_created: 2018-12-11T11:49:21Z
date_published: 2016-01-01T00:00:00Z
date_updated: 2025-06-03T11:18:32Z
day: '01'
department:
- _id: GaTk
ec_funded: 1
intvolume: ' 29'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics
month: '01'
oa: 1
oa_version: None
page: 4285 - 4293
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Neural Information Processing Systems Foundation
publist_id: '6469'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Neurons equipped with intrinsic plasticity learn stimulus intensity statistics
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 29
year: '2016'
...
---
_id: '9869'
abstract:
- lang: eng
text: A lower bound on the error of a positional estimator with limited positional
information is derived.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position.
2016. doi:10.1371/journal.pone.0163628.s001
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator
of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on
an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of
position.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position,
Public Library of Science, 10.1371/journal.pone.0163628.s001.
mla: Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position.
Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T08:53:48Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2025-09-22T08:46:14Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s001
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Error bound on an estimator of position
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9870'
abstract:
- lang: eng
text: The effect of noise in the input field on an Ising model is approximated.
Furthermore, methods to compute positional information in an Ising model by transfer
matrices and Monte Carlo sampling are outlined.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in an Ising model.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in an
Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:23:45Z
date_published: 2016-09-27T00:00:00Z
date_updated: 2025-09-22T08:46:14Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s002
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in an Ising model
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
_id: '9871'
abstract:
- lang: eng
text: The positional information in a discrete morphogen field with Gaussian noise
is computed.
article_processing_charge: No
author:
- first_name: Patrick
full_name: Hillenbrand, Patrick
last_name: Hillenbrand
- first_name: Ulrich
full_name: Gerland, Ulrich
last_name: Gerland
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
citation:
ama: Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in
a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003
apa: Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional
information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003
chicago: Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of
Positional Information in a Discrete Morphogen Field.” Public Library of Science,
2016. https://doi.org/10.1371/journal.pone.0163628.s003.
ieee: P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information
in a discrete morphogen field.” Public Library of Science, 2016.
ista: Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information
in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.
mla: Hillenbrand, Patrick, et al. Computation of Positional Information in a
Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003.
short: P. Hillenbrand, U. Gerland, G. Tkačik, (2016).
date_created: 2021-08-10T09:27:35Z
date_updated: 2025-09-22T08:46:14Z
day: '27'
department:
- _id: GaTk
doi: 10.1371/journal.pone.0163628.s003
month: '09'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1270'
relation: used_in_publication
status: public
status: public
title: Computation of positional information in a discrete morphogen field
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2016'
...
---
OA_place: repository
OA_type: green
_id: '1170'
abstract:
- lang: eng
text: The increasing complexity of dynamic models in systems and synthetic biology
poses computational challenges especially for the identification of model parameters.
While modularization of the corresponding optimization problems could help reduce
the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit
a simple decomposition of most biomolecular networks into subnetworks, or modules.
Drawing on ideas from network modularization and multiple-shooting optimization,
we present here a modular parameter identification approach that explicitly allows
for such interdependencies. Interfaces between our modules are given by the experimentally
measured molecular species. This definition allows deriving good (initial) estimates
for the inter-module communication directly from the experimental data. Given
these estimates, the states and parameter sensitivities of different modules can
be integrated independently. To achieve consistency between modules, we iteratively
adjust the estimates for inter-module communication while optimizing the parameters.
After convergence to an optimal parameter set---but not during earlier iterations---the
intermodule communication as well as the individual modules\' state dynamics agree
with the dynamics of the nonmodularized network. Our modular parameter identification
approach allows for easy parallelization; it can reduce the computational complexity
for larger networks and decrease the probability to converge to suboptimal local
minima. We demonstrate the algorithm\'s performance in parameter estimation for
two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling
pathway.
article_processing_charge: No
article_type: original
author:
- first_name: Moritz
full_name: Lang, Moritz
id: 29E0800A-F248-11E8-B48F-1D18A9856A87
last_name: Lang
- first_name: Jörg
full_name: Stelling, Jörg
last_name: Stelling
citation:
ama: Lang M, Stelling J. Modular parameter identification of biomolecular networks.
SIAM Journal on Scientific Computing. 2016;38(6):B988-B1008. doi:10.1137/15M103306X
apa: 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
chicago: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing. Society for Industrial
and Applied Mathematics, 2016. https://doi.org/10.1137/15M103306X.
ieee: M. Lang and J. Stelling, “Modular parameter identification of biomolecular
networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society
for Industrial and Applied Mathematics, pp. B988–B1008, 2016.
ista: Lang M, Stelling J. 2016. Modular parameter identification of biomolecular
networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008.
mla: Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular
Networks.” SIAM Journal on Scientific Computing, vol. 38, no. 6, Society
for Industrial and Applied Mathematics, 2016, pp. B988–1008, doi:10.1137/15M103306X.
short: M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.
corr_author: '1'
das_tickbox: '1'
date_created: 2018-12-11T11:50:31Z
date_published: 2016-11-15T00:00:00Z
date_updated: 2026-07-06T14:00:20Z
day: '15'
ddc:
- '003'
- '518'
- '570'
- '621'
department:
- _id: CaGu
- _id: GaTk
doi: 10.1137/15M103306X
external_id:
isi:
- '000391853100010'
file:
- access_level: open_access
checksum: 781bc3ffd30b2dd65b7727c5a285fc78
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:41Z
date_updated: 2025-06-25T11:26:45Z
file_id: '5095'
file_name: IST-2017-811-v1+1_modular_parameter_identification.pdf
file_size: 871964
relation: main_file
file_date_updated: 2025-06-25T11:26:45Z
has_accepted_license: '1'
intvolume: ' 38'
isi: 1
issue: '6'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Submitted Version
page: B988 - B1008
publication: SIAM Journal on Scientific Computing
publication_status: published
publisher: Society for Industrial and Applied Mathematics
publist_id: '6186'
pubrep_id: '811'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Modular parameter identification of biomolecular networks
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2016'
...
---
_id: '1188'
abstract:
- lang: eng
text: "We consider a population dynamics model coupling cell growth to a diffusion
in the space of metabolic phenotypes as it can be obtained from realistic constraints-based
modelling. \r\nIn the asymptotic regime of slow\r\ndiffusion, that coincides with
the relevant experimental range, the resulting\r\nnon-linear Fokker–Planck equation
is solved for the steady state in the WKB\r\napproximation that maps it into the
ground state of a quantum particle in an\r\nAiry potential plus a centrifugal
term. We retrieve scaling laws for growth rate\r\nfluctuations and time response
with respect to the distance from the maximum\r\ngrowth rate suggesting that suboptimal
populations can have a faster response\r\nto perturbations."
acknowledgement: D De Martino is supported by the People Programme (Marie Curie Actions)
of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant
agreement no. [291734]. D Masoero is supported by the FCT scholarship, number SFRH/BPD/75908/2011.
D De Martino thanks the Grupo de Física Matemática of the Universidade de Lisboa
for the kind hospitality. We also wish to thank Matteo Osella, Vincenzo Vitagliano
and Vera Luz Masoero for useful discussions, also late at night.
article_number: '123502'
article_processing_charge: No
arxiv: 1
author:
- first_name: Daniele
full_name: De Martino, Daniele
id: 3FF5848A-F248-11E8-B48F-1D18A9856A87
last_name: De Martino
orcid: 0000-0002-5214-4706
- first_name: Davide
full_name: Masoero, Davide
last_name: Masoero
citation:
ama: 'De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth. Journal of Statistical Mechanics: Theory
and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f'
apa: 'De Martino, D., & Masoero, D. (2016). Asymptotic analysis of noisy fitness
maximization, applied to metabolism & growth. Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/aa4e8f'
chicago: 'De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy
Fitness Maximization, Applied to Metabolism & Growth.” Journal of Statistical
Mechanics: Theory and Experiment. IOP Publishing, 2016. https://doi.org/10.1088/1742-5468/aa4e8f.'
ieee: 'D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth,” Journal of Statistical Mechanics:
Theory and Experiment, vol. 2016, no. 12. IOP Publishing, 2016.'
ista: 'De Martino D, Masoero D. 2016. Asymptotic analysis of noisy fitness maximization,
applied to metabolism & growth. Journal of Statistical Mechanics: Theory
and Experiment. 2016(12), 123502.'
mla: 'De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness
Maximization, Applied to Metabolism & Growth.” Journal of Statistical
Mechanics: Theory and Experiment, vol. 2016, no. 12, 123502, IOP Publishing,
2016, doi:10.1088/1742-5468/aa4e8f.'
short: 'D. De Martino, D. Masoero, Journal of Statistical Mechanics: Theory and
Experiment 2016 (2016).'
das_tickbox: '1'
date_created: 2018-12-11T11:50:37Z
date_published: 2016-12-30T00:00:00Z
date_updated: 2026-07-07T13:11:22Z
day: '30'
department:
- _id: GaTk
doi: 10.1088/1742-5468/aa4e8f
ec_funded: 1
external_id:
arxiv:
- '1606.09048'
isi:
- '000391973900001'
intvolume: ' 2016'
isi: 1
issue: '12'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1606.09048
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: 'Journal of Statistical Mechanics: Theory and Experiment'
publication_status: published
publisher: IOP Publishing
publist_id: '6165'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Asymptotic analysis of noisy fitness maximization, applied to metabolism &
growth
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2016
year: '2016'
...
---
OA_place: publisher
_id: '1128'
abstract:
- lang: eng
text: "The process of gene expression is central to the modern understanding of
how cellular systems\r\nfunction. In this process, a special kind of regulatory
proteins, called transcription factors,\r\nare important to determine how much
protein is produced from a given gene. As biological\r\ninformation is transmitted
from transcription factor concentration to mRNA levels to amounts of\r\nprotein,
various sources of noise arise and pose limits to the fidelity of intracellular
signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene
expression: (i) the mathematical\r\ndescription of complex promoters responsible
for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information
processing the cell faces due to the interference from multiple\r\nfluctuating
signals, (iii) how the presence of gene expression noise influences the evolution\r\nof
regulatory sequences, (iv) and tools for the experimental study of origins and
consequences\r\nof cell-cell heterogeneity, including an application to bacterial
stress response systems."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Georg
full_name: Rieckh, Georg
id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87
last_name: Rieckh
citation:
ama: Rieckh G. Studying the complexities of transcriptional regulation. 2016.
apa: Rieckh, G. (2016). Studying the complexities of transcriptional regulation.
Institute of Science and Technology Austria.
chicago: Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.”
Institute of Science and Technology Austria, 2016.
ieee: G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute
of Science and Technology Austria, 2016.
ista: Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute
of Science and Technology Austria.
mla: Rieckh, Georg. Studying the Complexities of Transcriptional Regulation.
Institute of Science and Technology Austria, 2016.
short: G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute
of Science and Technology Austria, 2016.
corr_author: '1'
date_created: 2018-12-11T11:50:18Z
date_published: 2016-08-01T00:00:00Z
date_updated: 2026-07-29T11:29:22Z
day: '01'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: GaTk
- _id: GradSch
doi_confirm: '1'
file:
- access_level: closed
checksum: ec453918c3bf8e6f460fd1156ef7b493
content_type: application/pdf
creator: dernst
date_created: 2019-08-13T11:46:25Z
date_updated: 2019-08-13T11:46:25Z
file_id: '6815'
file_name: Thesis_Georg_Rieckh_w_signature_page.pdf
file_size: 2614660
relation: main_file
- access_level: open_access
checksum: 51ae398166370d18fd22478b6365c4da
content_type: application/pdf
creator: dernst
date_created: 2020-09-21T11:30:40Z
date_updated: 2020-09-21T11:30:40Z
file_id: '8542'
file_name: Thesis_Georg_Rieckh.pdf
file_size: 6096178
relation: main_file
success: 1
file_date_updated: 2020-09-21T11:30:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
page: '114'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '6232'
status: public
supervisor:
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
title: Studying the complexities of transcriptional regulation
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2016'
...
---
_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'
article_processing_charge: No
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: 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.
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. American Physical Society. 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. American Physical Society, 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, vol. 93, no. 2. American Physical Society, 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. 93(2), 022404.
mla: Sokolowski, Thomas R., et al. “Extending the Dynamic Range of Transcription
Factor Action by Translational Regulation.” Physical Review E, vol. 93,
no. 2, 022404, American Physical Society, 2016, doi:10.1103/PhysRevE.93.022404.
short: T.R. Sokolowski, A. Walczak, W. Bialek, G. Tkačik, Physical Review E 93 (2016).
date_created: 2018-12-11T11:50:54Z
date_published: 2016-02-04T00:00:00Z
date_updated: 2026-08-12T14:28:33Z
day: '04'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.93.022404
external_id:
arxiv:
- '1507.02562'
isi:
- '000369439100005'
intvolume: ' 93'
isi: 1
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
publication_status: published
publisher: American Physical Society
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 93
year: '2016'
...
---
_id: '1538'
abstract:
- lang: eng
text: Systems biology rests on the idea that biological complexity can be better
unraveled through the interplay of modeling and experimentation. However, the
success of this approach depends critically on the informativeness of the chosen
experiments, which is usually unknown a priori. Here, we propose a systematic
scheme based on iterations of optimal experiment design, flow cytometry experiments,
and Bayesian parameter inference to guide the discovery process in the case of
stochastic biochemical reaction networks. To illustrate the benefit of our methodology,
we apply it to the characterization of an engineered light-inducible gene expression
circuit in yeast and compare the performance of the resulting model with models
identified from nonoptimal experiments. In particular, we compare the parameter
posterior distributions and the precision to which the outcome of future experiments
can be predicted. Moreover, we illustrate how the identified stochastic model
can be used to determine light induction patterns that make either the average
amount of protein or the variability in a population of cells follow a desired
profile. Our results show that optimal experiment design allows one to derive
models that are accurate enough to precisely predict and regulate the protein
expression in heterogeneous cell populations over extended periods of time.
acknowledgement: 'J.R., F.P., and J.L. acknowledge support from the European Commission
under the Network of Excellence HYCON2 (highly-complex and networked control systems)
and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People
Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme
FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges
support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). '
article_processing_charge: No
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Francesca
full_name: Parise, Francesca
last_name: Parise
- first_name: Andreas
full_name: Milias Argeitis, Andreas
last_name: Milias Argeitis
- first_name: Mustafa
full_name: Khammash, Mustafa
last_name: Khammash
- first_name: John
full_name: Lygeros, John
last_name: Lygeros
citation:
ama: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment
design guides the characterization of a light-inducible gene expression circuit.
PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112
apa: Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J.
(2015). Iterative experiment design guides the characterization of a light-inducible
gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112
chicago: Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash,
and John Lygeros. “Iterative Experiment Design Guides the Characterization of
a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1423947112.
ieee: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153,
2015.
ista: Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative
experiment design guides the characterization of a light-inducible gene expression
circuit. PNAS. 112(26), 8148–8153.
mla: Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization
of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26,
National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112.
short: J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112
(2015) 8148–8153.
date_created: 2018-12-11T11:52:36Z
date_published: 2015-06-30T00:00:00Z
date_updated: 2025-09-23T09:24:24Z
day: '30'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1073/pnas.1423947112
ec_funded: 1
external_id:
isi:
- '000357079400070'
pmid:
- '26085136'
intvolume: ' 112'
isi: 1
issue: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/
month: '06'
oa: 1
oa_version: Submitted Version
page: 8148 - 8153
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5633'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Iterative experiment design guides the characterization of a light-inducible
gene expression circuit
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 112
year: '2015'
...
---
_id: '1539'
abstract:
- lang: eng
text: 'Many stochastic models of biochemical reaction networks contain some chemical
species for which the number of molecules that are present in the system can only
be finite (for instance due to conservation laws), but also other species that
can be present in arbitrarily large amounts. The prime example of such networks
are models of gene expression, which typically contain a small and finite number
of possible states for the promoter but an infinite number of possible states
for the amount of mRNA and protein. One of the main approaches to analyze such
models is through the use of equations for the time evolution of moments of the
chemical species. Recently, a new approach based on conditional moments of the
species with infinite state space given all the different possible states of the
finite species has been proposed. It was argued that this approach allows one
to capture more details about the full underlying probability distribution with
a smaller number of equations. Here, I show that the result that less moments
provide more information can only stem from an unnecessarily complicated description
of the system in the classical formulation. The foundation of this argument will
be the derivation of moment equations that describe the complete probability distribution
over the finite state space but only low-order moments over the infinite state
space. I will show that the number of equations that is needed is always less
than what was previously claimed and always less than the number of conditional
moment equations up to the same order. To support these arguments, a symbolic
algorithm is provided that can be used to derive minimal systems of unconditional
moment equations for models with partially finite state space. '
article_number: '244103'
article_processing_charge: No
author:
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Ruess J. Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space. Journal of Chemical Physics.
2015;143(24). doi:10.1063/1.4937937
apa: Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
American Institute of Physics. https://doi.org/10.1063/1.4937937
chicago: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics.
American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937.
ieee: J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction
networks with partially finite state space,” Journal of Chemical Physics,
vol. 143, no. 24. American Institute of Physics, 2015.
ista: Ruess J. 2015. Minimal moment equations for stochastic models of biochemical
reaction networks with partially finite state space. Journal of Chemical Physics.
143(24), 244103.
mla: Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical
Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics,
vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937.
short: J. Ruess, Journal of Chemical Physics 143 (2015).
corr_author: '1'
date_created: 2018-12-11T11:52:36Z
date_published: 2015-12-22T00:00:00Z
date_updated: 2025-09-23T09:34:48Z
day: '22'
ddc:
- '000'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1063/1.4937937
ec_funded: 1
external_id:
isi:
- '000370412900068'
file:
- access_level: open_access
checksum: 838657118ae286463a2b7737319f35ce
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:07:43Z
date_updated: 2020-07-14T12:45:01Z
file_id: '4641'
file_name: IST-2016-593-v1+1_Minimal_moment_equations.pdf
file_size: 605355
relation: main_file
file_date_updated: 2020-07-14T12:45:01Z
has_accepted_license: '1'
intvolume: ' 143'
isi: 1
issue: '24'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: Formal methods for the design and analysis of complex systems
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Journal of Chemical Physics
publication_status: published
publisher: American Institute of Physics
publist_id: '5632'
pubrep_id: '593'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Minimal moment equations for stochastic models of biochemical reaction networks
with partially finite state space
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 143
year: '2015'
...
---
_id: '1564'
article_number: '145'
article_processing_charge: No
author:
- first_name: Matthieu
full_name: Gilson, Matthieu
last_name: Gilson
- first_name: Cristina
full_name: Savin, Cristina
id: 3933349E-F248-11E8-B48F-1D18A9856A87
last_name: Savin
- first_name: Friedemann
full_name: Zenke, Friedemann
last_name: Zenke
citation:
ama: 'Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the
interaction of different forms of plasticity. Frontiers in Computational Neuroscience.
2015;9(11). doi:10.3389/fncom.2015.00145'
apa: 'Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural
computation from the interaction of different forms of plasticity. Frontiers
in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145'
chicago: 'Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent
Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers
in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.'
ieee: 'M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation
from the interaction of different forms of plasticity,” Frontiers in Computational
Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.'
ista: 'Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation
from the interaction of different forms of plasticity. Frontiers in Computational
Neuroscience. 9(11), 145.'
mla: 'Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the
Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience,
vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.'
short: M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9
(2015).
corr_author: '1'
date_created: 2018-12-11T11:52:45Z
date_published: 2015-11-30T00:00:00Z
date_updated: 2025-09-23T08:35:56Z
day: '30'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.3389/fncom.2015.00145
ec_funded: 1
external_id:
isi:
- '000365824800002'
file:
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creator: system
date_created: 2018-12-12T10:12:09Z
date_updated: 2020-07-14T12:45:02Z
file_id: '4927'
file_name: IST-2016-479-v1+1_fncom-09-00145.pdf
file_size: 187038
relation: main_file
file_date_updated: 2020-07-14T12:45:02Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
issue: '11'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Frontiers in Computational Neuroscience
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '5607'
pubrep_id: '479'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Editorial: Emergent neural computation from the interaction of different forms
of plasticity'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 9
year: '2015'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, self-organized behavioral development provides
more questions than answers. Are there special functional units for curiosity,
motivation, and creativity? This paper argues that these features can be grounded
in synaptic plasticity itself, without requiring any higher-level constructs.
We propose differential extrinsic plasticity (DEP) as a new synaptic rule for
self-learning systems and apply it to a number of complex robotic systems as a
test case. Without specifying any purpose or goal, seemingly purposeful and adaptive
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
article_processing_charge: No
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
corr_author: '1'
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2025-09-23T09:41:37Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
isi:
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isi: 1
issue: '45'
language:
- iso: eng
main_file_link:
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url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 112
year: '2015'
...