---
_id: '12762'
abstract:
- lang: eng
text: Neurons in the brain are wired into adaptive networks that exhibit collective
dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches.
Although existing models account for oscillations and avalanches separately, they
typically do not explain both phenomena, are too complex to analyze analytically
or intractable to infer from data rigorously. Here we propose a feedback-driven
Ising-like class of neural networks that captures avalanches and oscillations
simultaneously and quantitatively. In the simplest yet fully microscopic model
version, we can analytically compute the phase diagram and make direct contact
with human brain resting-state activity recordings via tractable inference of
the model’s two essential parameters. The inferred model quantitatively captures
the dynamics over a broad range of scales, from single sensor oscillations to
collective behaviors of extreme events and neuronal avalanches. Importantly, the
inferred parameters indicate that the co-existence of scale-specific (oscillations)
and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical
point at the onset of self-sustained oscillations.
acknowledgement: This research was funded in whole, or in part, by the Austrian Science
Fund (FWF) (grant no. PT1013M03318 to F.L. and no. P34015 to G.T.). For the purpose
of open access, the author has applied a CC BY public copyright licence to any Author
Accepted Manuscript version arising from this submission. The study was supported
by the European Union Horizon 2020 research and innovation program under the Marie
Sklodowska-Curie action (grant agreement No. 754411 to F.L.).
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Fabrizio
full_name: Lombardi, Fabrizio
id: A057D288-3E88-11E9-986D-0CF4E5697425
last_name: Lombardi
orcid: 0000-0003-2623-5249
- first_name: Selver
full_name: Pepic, Selver
id: F93245C4-C3CA-11E9-B4F0-C6F4E5697425
last_name: Pepic
- first_name: Oren
full_name: Shriki, Oren
last_name: Shriki
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- 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: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. Statistical modeling
of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain. Nature Computational Science. 2023;3:254-263. doi:10.1038/s43588-023-00410-9
apa: Lombardi, F., Pepic, S., Shriki, O., Tkačik, G., & De Martino, D. (2023).
Statistical modeling of adaptive neural networks explains co-existence of avalanches
and oscillations in resting human brain. Nature Computational Science.
Springer Nature. https://doi.org/10.1038/s43588-023-00410-9
chicago: Lombardi, Fabrizio, Selver Pepic, Oren Shriki, Gašper Tkačik, and Daniele
De Martino. “Statistical Modeling of Adaptive Neural Networks Explains Co-Existence
of Avalanches and Oscillations in Resting Human Brain.” Nature Computational
Science. Springer Nature, 2023. https://doi.org/10.1038/s43588-023-00410-9.
ieee: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, and D. De Martino, “Statistical
modeling of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain,” Nature Computational Science, vol. 3. Springer
Nature, pp. 254–263, 2023.
ista: Lombardi F, Pepic S, Shriki O, Tkačik G, De Martino D. 2023. Statistical modeling
of adaptive neural networks explains co-existence of avalanches and oscillations
in resting human brain. Nature Computational Science. 3, 254–263.
mla: Lombardi, Fabrizio, et al. “Statistical Modeling of Adaptive Neural Networks
Explains Co-Existence of Avalanches and Oscillations in Resting Human Brain.”
Nature Computational Science, vol. 3, Springer Nature, 2023, pp. 254–63,
doi:10.1038/s43588-023-00410-9.
short: F. Lombardi, S. Pepic, O. Shriki, G. Tkačik, D. De Martino, Nature Computational
Science 3 (2023) 254–263.
date_created: 2023-03-26T22:01:08Z
date_published: 2023-03-20T00:00:00Z
date_updated: 2023-08-16T12:41:53Z
day: '20'
ddc:
- '570'
department:
- _id: GaTk
- _id: GradSch
doi: 10.1038/s43588-023-00410-9
ec_funded: 1
external_id:
arxiv:
- '2108.06686'
file:
- access_level: open_access
checksum: 7c63b2b2edfd68aaffe96d70ca6a865a
content_type: application/pdf
creator: dernst
date_created: 2023-08-16T12:39:57Z
date_updated: 2023-08-16T12:39:57Z
file_id: '14073'
file_name: 2023_NatureCompScience_Lombardi.pdf
file_size: 4474284
relation: main_file
success: 1
file_date_updated: 2023-08-16T12:39:57Z
has_accepted_license: '1'
intvolume: ' 3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
page: 254-263
project:
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
- _id: eb943429-77a9-11ec-83b8-9f471cdf5c67
grant_number: M03318
name: Functional Advantages of Critical Brain Dynamics
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
publication: Nature Computational Science
publication_identifier:
eissn:
- 2662-8457
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Statistical modeling of adaptive neural networks explains co-existence of avalanches
and oscillations in resting human brain
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2023'
...
---
_id: '6049'
abstract:
- lang: eng
text: 'In this article it is shown that large systems with many interacting units
endowing multiple phases display self-oscillations in the presence of linear feedback
between the control and order parameters, where an Andronov–Hopf bifurcation takes
over the phase transition. This is simply illustrated through the mean field Landau
theory whose feedback dynamics turn out to be described by the Van der Pol equation
and it is then validated for the fully connected Ising model following heat bath
dynamics. Despite its simplicity, this theory accounts potentially for a rich
range of phenomena: here it is applied to describe in a stylized way (i) excess
demand-price cycles due to strong herding in a simple agent-based market model;
(ii) congestion waves in queuing networks triggered by user feedback to delays
in overloaded conditions; and (iii) metabolic network oscillations resulting from
cell growth control in a bistable phenotypic landscape.'
article_number: '045002'
article_processing_charge: Yes (in subscription journal)
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. Feedback-induced self-oscillations in large interacting systems
subjected to phase transitions. Journal of Physics A: Mathematical and Theoretical.
2019;52(4). doi:10.1088/1751-8121/aaf2dd'
apa: 'De Martino, D. (2019). Feedback-induced self-oscillations in large interacting
systems subjected to phase transitions. Journal of Physics A: Mathematical
and Theoretical. IOP Publishing. https://doi.org/10.1088/1751-8121/aaf2dd'
chicago: 'De Martino, Daniele. “Feedback-Induced Self-Oscillations in Large Interacting
Systems Subjected to Phase Transitions.” Journal of Physics A: Mathematical
and Theoretical. IOP Publishing, 2019. https://doi.org/10.1088/1751-8121/aaf2dd.'
ieee: 'D. De Martino, “Feedback-induced self-oscillations in large interacting systems
subjected to phase transitions,” Journal of Physics A: Mathematical and Theoretical,
vol. 52, no. 4. IOP Publishing, 2019.'
ista: 'De Martino D. 2019. Feedback-induced self-oscillations in large interacting
systems subjected to phase transitions. Journal of Physics A: Mathematical and
Theoretical. 52(4), 045002.'
mla: 'De Martino, Daniele. “Feedback-Induced Self-Oscillations in Large Interacting
Systems Subjected to Phase Transitions.” Journal of Physics A: Mathematical
and Theoretical, vol. 52, no. 4, 045002, IOP Publishing, 2019, doi:10.1088/1751-8121/aaf2dd.'
short: 'D. De Martino, Journal of Physics A: Mathematical and Theoretical 52 (2019).'
date_created: 2019-02-24T22:59:19Z
date_published: 2019-01-07T00:00:00Z
date_updated: 2023-08-24T14:49:23Z
day: '07'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1088/1751-8121/aaf2dd
ec_funded: 1
external_id:
isi:
- '000455379500001'
file:
- access_level: open_access
checksum: 1112304ad363a6d8afaeccece36473cf
content_type: application/pdf
creator: kschuh
date_created: 2019-04-19T12:18:57Z
date_updated: 2020-07-14T12:47:17Z
file_id: '6344'
file_name: 2019_IOP_DeMartino.pdf
file_size: 1804557
relation: main_file
file_date_updated: 2020-07-14T12:47:17Z
has_accepted_license: '1'
intvolume: ' 52'
isi: 1
issue: '4'
language:
- iso: eng
month: '01'
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: 'Journal of Physics A: Mathematical and Theoretical'
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Feedback-induced self-oscillations in large interacting systems subjected to
phase transitions
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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 52
year: '2019'
...
---
_id: '306'
abstract:
- lang: eng
text: A cornerstone of statistical inference, the maximum entropy framework is being
increasingly applied to construct descriptive and predictive models of biological
systems, especially complex biological networks, from large experimental data
sets. Both its broad applicability and the success it obtained in different contexts
hinge upon its conceptual simplicity and mathematical soundness. Here we try to
concisely review the basic elements of the maximum entropy principle, starting
from the notion of ‘entropy’, and describe its usefulness for the analysis of
biological systems. As examples, we focus specifically on the problem of reconstructing
gene interaction networks from expression data and on recent work attempting to
expand our system-level understanding of bacterial metabolism. Finally, we highlight
some extensions and potential limitations of the maximum entropy approach, and
point to more recent developments that are likely to play a key role in the upcoming
challenges of extracting structures and information from increasingly rich, high-throughput
biological data.
article_number: e00596
author:
- first_name: Andrea
full_name: De Martino, Andrea
last_name: De Martino
- 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 A, De Martino D. An introduction to the maximum entropy approach
and its application to inference problems in biology. Heliyon. 2018;4(4).
doi:10.1016/j.heliyon.2018.e00596
apa: De Martino, A., & De Martino, D. (2018). An introduction to the maximum
entropy approach and its application to inference problems in biology. Heliyon.
Elsevier. https://doi.org/10.1016/j.heliyon.2018.e00596
chicago: De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum
Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon.
Elsevier, 2018. https://doi.org/10.1016/j.heliyon.2018.e00596.
ieee: A. De Martino and D. De Martino, “An introduction to the maximum entropy approach
and its application to inference problems in biology,” Heliyon, vol. 4,
no. 4. Elsevier, 2018.
ista: De Martino A, De Martino D. 2018. An introduction to the maximum entropy approach
and its application to inference problems in biology. Heliyon. 4(4), e00596.
mla: De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum
Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon,
vol. 4, no. 4, e00596, Elsevier, 2018, doi:10.1016/j.heliyon.2018.e00596.
short: A. De Martino, D. De Martino, Heliyon 4 (2018).
date_created: 2018-12-11T11:45:44Z
date_published: 2018-04-01T00:00:00Z
date_updated: 2021-01-12T07:40:46Z
day: '01'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1016/j.heliyon.2018.e00596
ec_funded: 1
file:
- access_level: open_access
checksum: 67010cf5e3b3e0637c659371714a715a
content_type: application/pdf
creator: dernst
date_created: 2019-02-06T07:36:24Z
date_updated: 2020-07-14T12:45:59Z
file_id: '5929'
file_name: 2018_Heliyon_DeMartino.pdf
file_size: 994490
relation: main_file
file_date_updated: 2020-07-14T12:45:59Z
has_accepted_license: '1'
intvolume: ' 4'
issue: '4'
language:
- iso: eng
month: '04'
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: Heliyon
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: 1
status: public
title: An introduction to the maximum entropy approach and its application to inference
problems in biology
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 4
year: '2018'
...
---
_id: '5587'
abstract:
- lang: eng
text: "Supporting material to the article \r\nSTATISTICAL MECHANICS FOR METABOLIC
NETWORKS IN STEADY-STATE GROWTH\r\n\r\nboundscoli.dat\r\nFlux Bounds of the E.
coli catabolic core model iAF1260 in a glucose limited minimal medium. \r\n\r\npolcoli.dat\r\nMatrix
enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose
limited minimal medium, \r\nobtained from the soichiometric matrix by standard
linear algebra (reduced row echelon form).\r\n\r\nellis.dat\r\nApproximate Lowner-John
ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in
a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\npoint0.dat\r\nCenter
of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli
catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with
the Lovasz method.\r\n\r\nlovasz.cpp \r\nThis c++ code file receives in input
the polytope of the feasible steady states of a metabolic network, \r\n(matrix
and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding
the polytope\r\nwith the Lovasz method \r\nNB inputs are referred by defaults
to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we
refer to PLoS ONE 10.4 e0122670 (2015).\r\n\r\nsampleHRnew.cpp \r\nThis c++
code file receives in input the polytope of the feasible steady states of a metabolic
network, \r\n(matrix and bounds), the ellipsoid rounding the polytope, a point
inside and \r\nit gives in output a max entropy sampling at fixed average growth
rate \r\nof the steady states by performing an Hit-and-Run Monte Carlo Markov
chain.\r\nNB inputs are referred by defaults to the catabolic core of the E.Coli
network iAF1260. \r\nFor further details we refer to PLoS ONE 10.4 e0122670 (2015)."
article_processing_charge: No
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: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC
NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:10.15479/AT:ISTA:62
apa: De Martino, D., & Tkačik, G. (2018). Supporting materials “STATISTICAL
MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:62
chicago: De Martino, Daniele, and Gašper Tkačik. “Supporting Materials ‘STATISTICAL
MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science
and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:62.
ieee: D. De Martino and G. Tkačik, “Supporting materials ‘STATISTICAL MECHANICS
FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology
Austria, 2018.
ista: De Martino D, Tkačik G. 2018. Supporting materials ‘STATISTICAL MECHANICS
FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH’, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:62.
mla: De Martino, Daniele, and Gašper Tkačik. Supporting Materials “STATISTICAL
MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science
and Technology Austria, 2018, doi:10.15479/AT:ISTA:62.
short: D. De Martino, G. Tkačik, (2018).
datarep_id: '111'
date_created: 2018-12-12T12:31:41Z
date_published: 2018-09-21T00:00:00Z
date_updated: 2024-02-21T13:45:39Z
day: '21'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.15479/AT:ISTA:62
ec_funded: 1
file:
- access_level: open_access
checksum: 97992e3e8cf8544ec985a48971708726
content_type: application/zip
creator: system
date_created: 2018-12-12T13:05:13Z
date_updated: 2020-07-14T12:47:08Z
file_id: '5641'
file_name: IST-2018-111-v1+1_CODES.zip
file_size: 14376
relation: main_file
file_date_updated: 2020-07-14T12:47:08Z
has_accepted_license: '1'
keyword:
- metabolic networks
- e.coli core
- maximum entropy
- monte carlo markov chain sampling
- ellipsoidal rounding
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '09'
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: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '161'
relation: research_paper
status: public
status: public
title: Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE
GROWTH"
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '161'
abstract:
- lang: eng
text: 'Which properties of metabolic networks can be derived solely from stoichiometry?
Predictive results have been obtained by flux balance analysis (FBA), by postulating
that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization
of FBA to single-cell level using maximum entropy modeling, which we extend and
test experimentally. Specifically, we define for Escherichia coli metabolism a
flux distribution that yields the experimental growth rate: the model, containing
FBA as a limit, provides a better match to measured fluxes and it makes a wide
range of predictions: on flux variability, regulation, and correlations; on the
relative importance of stoichiometry vs. optimization; on scaling relations for
growth rate distributions. We validate the latter here with single-cell data at
different sub-inhibitory antibiotic concentrations. The model quantifies growth
optimization as emerging from the interplay of competitive dynamics in the population
and regulation of metabolism at the level of single cells.'
article_number: '2988'
article_processing_charge: No
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: Andersson Anna
full_name: Mc, Andersson Anna
last_name: Mc
- first_name: Tobias
full_name: Bergmiller, Tobias
id: 2C471CFA-F248-11E8-B48F-1D18A9856A87
last_name: Bergmiller
orcid: 0000-0001-5396-4346
- 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: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics
for metabolic networks during steady state growth. Nature Communications.
2018;9(1). doi:10.1038/s41467-018-05417-9
apa: De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., & Tkačik, G. (2018).
Statistical mechanics for metabolic networks during steady state growth. Nature
Communications. Springer Nature. https://doi.org/10.1038/s41467-018-05417-9
chicago: De Martino, Daniele, Andersson Anna Mc, Tobias Bergmiller, Calin C Guet,
and Gašper Tkačik. “Statistical Mechanics for Metabolic Networks during Steady
State Growth.” Nature Communications. Springer Nature, 2018. https://doi.org/10.1038/s41467-018-05417-9.
ieee: D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical
mechanics for metabolic networks during steady state growth,” Nature Communications,
vol. 9, no. 1. Springer Nature, 2018.
ista: De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. 2018. Statistical mechanics
for metabolic networks during steady state growth. Nature Communications. 9(1),
2988.
mla: De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during
Steady State Growth.” Nature Communications, vol. 9, no. 1, 2988, Springer
Nature, 2018, doi:10.1038/s41467-018-05417-9.
short: D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications
9 (2018).
date_created: 2018-12-11T11:44:57Z
date_published: 2018-07-30T00:00:00Z
date_updated: 2024-02-21T13:45:39Z
day: '30'
ddc:
- '570'
department:
- _id: GaTk
- _id: CaGu
doi: 10.1038/s41467-018-05417-9
ec_funded: 1
external_id:
isi:
- '000440149300021'
file:
- access_level: open_access
checksum: 3ba7ab27b27723c7dcf633e8fc1f8f18
content_type: application/pdf
creator: dernst
date_created: 2018-12-17T16:44:28Z
date_updated: 2020-07-14T12:45:06Z
file_id: '5728'
file_name: 2018_NatureComm_DeMartino.pdf
file_size: 1043205
relation: main_file
file_date_updated: 2020-07-14T12:45:06Z
has_accepted_license: '1'
intvolume: ' 9'
isi: 1
issue: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P28844-B27
name: Biophysics of information processing in gene regulation
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Nature Communications
publication_status: published
publisher: Springer Nature
publist_id: '7760'
quality_controlled: '1'
related_material:
record:
- id: '5587'
relation: popular_science
status: public
scopus_import: '1'
status: public
title: Statistical mechanics for metabolic networks during steady state growth
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: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 9
year: '2018'
...
---
_id: '823'
abstract:
- lang: eng
text: The resolution of a linear system with positive integer variables is a basic
yet difficult computational problem with many applications. We consider sparse
uncorrelated random systems parametrised by the density c and the ratio α=N/M
between number of variables N and number of constraints M. By means of ensemble
calculations we show that the space of feasible solutions endows a Van-Der-Waals
phase diagram in the plane (c, α). We give numerical evidence that the associated
computational problems become more difficult across the critical point and in
particular in the coexistence region.
article_number: '093404'
article_processing_charge: No
author:
- first_name: Simona
full_name: Colabrese, Simona
last_name: Colabrese
- 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: Luca
full_name: Leuzzi, Luca
last_name: Leuzzi
- first_name: Enzo
full_name: Marinari, Enzo
last_name: Marinari
citation:
ama: 'Colabrese S, De Martino D, Leuzzi L, Marinari E. Phase transitions in integer
linear problems. Journal of Statistical Mechanics: Theory and Experiment.
2017;2017(9). doi:10.1088/1742-5468/aa85c3'
apa: 'Colabrese, S., De Martino, D., Leuzzi, L., & Marinari, E. (2017). Phase
transitions in integer linear problems. Journal of Statistical Mechanics:
Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa85c3'
chicago: 'Colabrese, Simona, Daniele De Martino, Luca Leuzzi, and Enzo Marinari.
“Phase Transitions in Integer Linear Problems.” Journal of Statistical Mechanics:
Theory and Experiment. IOPscience, 2017. https://doi.org/10.1088/1742-5468/aa85c3.'
ieee: 'S. Colabrese, D. De Martino, L. Leuzzi, and E. Marinari, “Phase transitions
in integer linear problems,” Journal of Statistical Mechanics: Theory and
Experiment, vol. 2017, no. 9. IOPscience, 2017.'
ista: 'Colabrese S, De Martino D, Leuzzi L, Marinari E. 2017. Phase transitions
in integer linear problems. Journal of Statistical Mechanics: Theory and Experiment.
2017(9), 093404.'
mla: 'Colabrese, Simona, et al. “Phase Transitions in Integer Linear Problems.”
Journal of Statistical Mechanics: Theory and Experiment, vol. 2017, no.
9, 093404, IOPscience, 2017, doi:10.1088/1742-5468/aa85c3.'
short: 'S. Colabrese, D. De Martino, L. Leuzzi, E. Marinari, Journal of Statistical
Mechanics: Theory and Experiment 2017 (2017).'
date_created: 2018-12-11T11:48:41Z
date_published: 2017-09-26T00:00:00Z
date_updated: 2023-09-26T16:18:12Z
day: '26'
department:
- _id: GaTk
doi: 10.1088/1742-5468/aa85c3
ec_funded: 1
external_id:
isi:
- '000411842900001'
intvolume: ' 2017'
isi: 1
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1705.06303
month: '09'
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: ' Journal of Statistical Mechanics: Theory and Experiment'
publication_identifier:
issn:
- '17425468'
publication_status: published
publisher: IOPscience
publist_id: '6826'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Phase transitions in integer linear problems
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 2017
year: '2017'
...
---
_id: '548'
abstract:
- lang: eng
text: In this work maximum entropy distributions in the space of steady states of
metabolic networks are considered upon constraining the first and second moments
of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux
distributions, emerges upon considering control on the average growth (optimization)
and its fluctuations (heterogeneity). This is applied to the carbon catabolic
core of Escherichia coli where it quantifies the metabolic activity of slow growing
phenotypes and it provides a quantitative map with metabolic fluxes, opening the
possibility to detect coexistence from flux data. A preliminary analysis on data
for E. coli cultures in standard conditions shows degeneracy for the inferred
parameters that extend in the coexistence region.
alternative_title:
- Rapid Communications
article_number: '060401'
article_processing_charge: No
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. Maximum entropy modeling of metabolic networks by constraining
growth-rate moments predicts coexistence of phenotypes. Physical Review E.
2017;96(6). doi:10.1103/PhysRevE.96.060401
apa: De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining
growth-rate moments predicts coexistence of phenotypes. Physical Review E.
American Physical Society. https://doi.org/10.1103/PhysRevE.96.060401
chicago: De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by
Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” Physical
Review E. American Physical Society, 2017. https://doi.org/10.1103/PhysRevE.96.060401.
ieee: D. De Martino, “Maximum entropy modeling of metabolic networks by constraining
growth-rate moments predicts coexistence of phenotypes,” Physical Review E,
vol. 96, no. 6. American Physical Society, 2017.
ista: De Martino D. 2017. Maximum entropy modeling of metabolic networks by constraining
growth-rate moments predicts coexistence of phenotypes. Physical Review E. 96(6),
060401.
mla: De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining
Growth-Rate Moments Predicts Coexistence of Phenotypes.” Physical Review E,
vol. 96, no. 6, 060401, American Physical Society, 2017, doi:10.1103/PhysRevE.96.060401.
short: D. De Martino, Physical Review E 96 (2017).
date_created: 2018-12-11T11:47:06Z
date_published: 2017-12-21T00:00:00Z
date_updated: 2023-10-10T13:29:38Z
day: '21'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.96.060401
ec_funded: 1
intvolume: ' 96'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1707.00320
month: '12'
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: Physical Review E
publication_identifier:
issn:
- 2470-0045
publication_status: published
publisher: American Physical Society
publist_id: '7266'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Maximum entropy modeling of metabolic networks by constraining growth-rate
moments predicts coexistence of phenotypes
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 96
year: '2017'
...
---
_id: '947'
abstract:
- lang: eng
text: Viewing the ways a living cell can organize its metabolism as the phase space
of a physical system, regulation can be seen as the ability to reduce the entropy
of that space by selecting specific cellular configurations that are, in some
sense, optimal. Here we quantify the amount of regulation required to control
a cell's growth rate by a maximum-entropy approach to the space of underlying
metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern
as described by genome-scale models. We link the mean growth rate achieved by
a population of cells to the minimal amount of metabolic regulation needed to
achieve it through a phase diagram that highlights how growth suppression can
be as costly (in regulatory terms) as growth enhancement. Moreover, we provide
an interpretation of the inverse temperature β controlling maximum-entropy distributions
based on the underlying growth dynamics. Specifically, we show that the asymptotic
value of β for a cell population can be expected to depend on (i) the carrying
capacity of the environment, (ii) the initial size of the colony, and (iii) the
probability distribution from which the inoculum was sampled. Results obtained
for E. coli and human cells are found to be remarkably consistent with empirical
evidence.
article_number: '010401'
article_processing_charge: No
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. Quantifying the entropic cost of cellular
growth control. Physical Review E Statistical Nonlinear and Soft Matter Physics
. 2017;96(1). doi:10.1103/PhysRevE.96.010401
apa: De Martino, D., Capuani, F., & De Martino, A. (2017). Quantifying the entropic
cost of cellular growth control. Physical Review E Statistical Nonlinear and
Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.96.010401
chicago: De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Quantifying
the Entropic Cost of Cellular Growth Control.” Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.96.010401.
ieee: D. De Martino, F. Capuani, and A. De Martino, “Quantifying the entropic cost
of cellular growth control,” Physical Review E Statistical Nonlinear and Soft
Matter Physics , vol. 96, no. 1. American Institute of Physics, 2017.
ista: De Martino D, Capuani F, De Martino A. 2017. Quantifying the entropic cost
of cellular growth control. Physical Review E Statistical Nonlinear and Soft
Matter Physics . 96(1), 010401.
mla: De Martino, Daniele, et al. “Quantifying the Entropic Cost of Cellular Growth
Control.” Physical Review E Statistical Nonlinear and Soft Matter Physics
, vol. 96, no. 1, 010401, American Institute of Physics, 2017, doi:10.1103/PhysRevE.96.010401.
short: D. De Martino, F. Capuani, A. De Martino, Physical Review E Statistical
Nonlinear and Soft Matter Physics 96 (2017).
date_created: 2018-12-11T11:49:21Z
date_published: 2017-07-10T00:00:00Z
date_updated: 2023-09-22T10:03:50Z
day: '10'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.96.010401
ec_funded: 1
external_id:
isi:
- '000405194200002'
intvolume: ' 96'
isi: 1
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.00219
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: ' Physical Review E Statistical Nonlinear and Soft Matter Physics '
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Institute of Physics
publist_id: '6470'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying the entropic cost of cellular growth control
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 96
year: '2017'
...
---
_id: '959'
abstract:
- lang: eng
text: In this work it is shown that scale-free tails in metabolic flux distributions
inferred in stationary models are an artifact due to reactions involved in thermodynamically
unfeasible cycles, unbounded by physical constraints and in principle able to
perform work without expenditure of free energy. After implementing thermodynamic
constraints by removing such loops, metabolic flux distributions scale meaningfully
with the physical limiting factors, acquiring in turn a richer multimodal structure
potentially leading to symmetry breaking while optimizing for objective functions.
article_processing_charge: No
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. Scales and multimodal flux distributions in stationary metabolic
network models via thermodynamics. Physical Review E Statistical Nonlinear
and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419
apa: De Martino, D. (2017). Scales and multimodal flux distributions in stationary
metabolic network models via thermodynamics. Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.95.062419
chicago: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary
Metabolic Network Models via Thermodynamics.” Physical Review E Statistical
Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.95.062419.
ieee: D. De Martino, “Scales and multimodal flux distributions in stationary metabolic
network models via thermodynamics,” Physical Review E Statistical Nonlinear
and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p.
062419, 2017.
ista: De Martino D. 2017. Scales and multimodal flux distributions in stationary
metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear
and Soft Matter Physics . 95(6), 062419.
mla: De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary
Metabolic Network Models via Thermodynamics.” Physical Review E Statistical
Nonlinear and Soft Matter Physics , vol. 95, no. 6, American Institute of
Physics, 2017, p. 062419, doi:10.1103/PhysRevE.95.062419.
short: D. De Martino, Physical Review E Statistical Nonlinear and Soft Matter Physics 95
(2017) 062419.
date_created: 2018-12-11T11:49:25Z
date_published: 2017-06-28T00:00:00Z
date_updated: 2023-09-22T09:59:01Z
day: '28'
department:
- _id: GaTk
doi: 10.1103/PhysRevE.95.062419
ec_funded: 1
external_id:
isi:
- '000404546400004'
intvolume: ' 95'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1703.00853.pdf
month: '06'
oa: 1
oa_version: Submitted Version
page: '062419'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: ' Physical Review E Statistical Nonlinear and Soft Matter Physics '
publication_identifier:
issn:
- '24700045'
publication_status: published
publisher: American Institute of Physics
publist_id: '6446'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scales and multimodal flux distributions in stationary metabolic network models
via thermodynamics
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 95
year: '2017'
...
---
_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'
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
Ltd. 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
Ltd., 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 Ltd., 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 Ltd., 2016, doi:10.1088/1478-3975/13/1/016003.
short: D. De Martino, Physical Biology 13 (2016).
date_created: 2018-12-11T11:52:18Z
date_published: 2016-01-29T00:00:00Z
date_updated: 2021-01-12T06:51:04Z
day: '29'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/1/016003
ec_funded: 1
intvolume: ' 13'
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 Ltd.
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
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'
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 Ltd. 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
Ltd., 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 Ltd., 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 Ltd., 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: 2021-01-12T06:50:23Z
day: '27'
department:
- _id: GaTk
doi: 10.1088/1478-3975/13/3/036005
ec_funded: 1
intvolume: ' 13'
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 Ltd.
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 13
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'
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. IOPscience. 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. IOPscience, 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. IOPscience, 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, IOPscience, 2016,
doi:10.1088/1742-5468/aa4e8f.'
short: 'D. De Martino, D. Masoero, Journal of Statistical Mechanics: Theory and
Experiment 2016 (2016).'
date_created: 2018-12-11T11:50:37Z
date_published: 2016-12-30T00:00:00Z
date_updated: 2021-01-12T06:48:57Z
day: '30'
department:
- _id: GaTk
doi: 10.1088/1742-5468/aa4e8f
ec_funded: 1
intvolume: ' 2016'
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: IOPscience
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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016
year: '2016'
...
---
_id: '1260'
abstract:
- lang: eng
text: In this work, the Gardner problem of inferring interactions and fields for
an Ising neural network from given patterns under a local stability hypothesis
is addressed under a dual perspective. By means of duality arguments, an integer
linear system is defined whose solution space is the dual of the Gardner space
and whose solutions represent mutually unstable patterns. We propose and discuss
Monte Carlo methods in order to find and remove unstable patterns and uniformly
sample the space of interactions thereafter. We illustrate the problem on a set
of real data and perform ensemble calculation that shows how the emergence of
phase dominated by unstable patterns can be triggered in a nonlinear discontinuous
way.
article_number: '1650067'
article_processing_charge: No
article_type: original
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. The dual of the space of interactions in neural network models.
International Journal of Modern Physics C. 2016;27(6). doi:10.1142/S0129183116500674
apa: De Martino, D. (2016). The dual of the space of interactions in neural network
models. International Journal of Modern Physics C. World Scientific Publishing.
https://doi.org/10.1142/S0129183116500674
chicago: De Martino, Daniele. “The Dual of the Space of Interactions in Neural Network
Models.” International Journal of Modern Physics C. World Scientific Publishing,
2016. https://doi.org/10.1142/S0129183116500674.
ieee: D. De Martino, “The dual of the space of interactions in neural network models,”
International Journal of Modern Physics C, vol. 27, no. 6. World Scientific
Publishing, 2016.
ista: De Martino D. 2016. The dual of the space of interactions in neural network
models. International Journal of Modern Physics C. 27(6), 1650067.
mla: De Martino, Daniele. “The Dual of the Space of Interactions in Neural Network
Models.” International Journal of Modern Physics C, vol. 27, no. 6, 1650067,
World Scientific Publishing, 2016, doi:10.1142/S0129183116500674.
short: D. De Martino, International Journal of Modern Physics C 27 (2016).
date_created: 2018-12-11T11:51:00Z
date_published: 2016-06-01T00:00:00Z
date_updated: 2021-01-12T06:49:28Z
day: '01'
department:
- _id: GaTk
doi: 10.1142/S0129183116500674
external_id:
arxiv:
- '1505.02963'
intvolume: ' 27'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1505.02963
month: '06'
oa: 1
oa_version: Preprint
publication: International Journal of Modern Physics C
publication_status: published
publisher: World Scientific Publishing
publist_id: '6065'
quality_controlled: '1'
scopus_import: 1
status: public
title: The dual of the space of interactions in neural network models
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2016'
...