---
_id: '14901'
abstract:
- lang: eng
text: Global services like navigation, communication, and Earth observation have
increased dramatically in the 21st century due to advances in outer space industries.
But as orbits become increasingly crowded with both satellites and inevitable
space debris pollution, continued operations become endangered by the heightened
risks of debris collisions in orbit. Kessler Syndrome is the term for when a critical
threshold of orbiting debris triggers a runaway positive feedback loop of debris
collisions, creating debris congestion that can render orbits unusable. As this
potential tipping point becomes more widely recognized, there have been renewed
calls for debris mitigation and removal. Here, we combine complex systems and
social-ecological systems approaches to study how these efforts may affect space
debris accumulation and the likelihood of reaching Kessler Syndrome. Specifically,
we model how debris levels are affected by future launch rates, cleanup activities,
and collisions between extant debris. We contextualize and interpret our dynamic
model within a discussion of existing space debris governance and other social,
economic, and geopolitical factors that may influence effective collective management
of the orbital commons. In line with previous studies, our model finds that debris
congestion may be reached in less than 200 years, though a holistic management
strategy combining removal and mitigation actions can avoid such outcomes while
continuing space activities. Moreover, although active debris removal may be particularly
effective, the current lack of market and governance support may impede its implementation.
Research into these critical dynamics and the multi-faceted variables that influence
debris outcomes can support policymakers in curating impactful governance strategies
and realistic transition pathways to sustaining debris-free orbits. Overall, our
study is useful for communicating about space debris sustainability in policy
and education settings by providing an exploration of policy portfolio options
supported by a simple and clear social-ecological modeling approach.
acknowledgement: The authors would like to thank the special issue co-editors, Marco
Janssen and Xiao-Shan Yap, and the anonymous reviewers for their comments that helped
improve the manuscript. The paper also benefited from suggestions by other author
participants in this special issue. We would also like to thank the 2022 Santa Fe
Institute Complex Systems Summer School for providing space to initiate this study.
article_processing_charge: Yes
article_type: original
author:
- first_name: Keiko
full_name: Nomura, Keiko
last_name: Nomura
- first_name: Simon
full_name: Rella, Simon
id: B4765ACA-AA38-11E9-AC9A-0930E6697425
last_name: Rella
- first_name: Haily
full_name: Merritt, Haily
last_name: Merritt
- first_name: Mathieu
full_name: Baltussen, Mathieu
last_name: Baltussen
- first_name: Darcy
full_name: Bird, Darcy
last_name: Bird
- first_name: Annika
full_name: Tjuka, Annika
last_name: Tjuka
- first_name: Dan
full_name: Falk, Dan
last_name: Falk
citation:
ama: Nomura K, Rella S, Merritt H, et al. Tipping points of space debris in low
earth orbit. International Journal of the Commons. 2024;18(1). doi:10.5334/ijc.1275
apa: Nomura, K., Rella, S., Merritt, H., Baltussen, M., Bird, D., Tjuka, A., &
Falk, D. (2024). Tipping points of space debris in low earth orbit. International
Journal of the Commons. Ubiquity Press. https://doi.org/10.5334/ijc.1275
chicago: Nomura, Keiko, Simon Rella, Haily Merritt, Mathieu Baltussen, Darcy Bird,
Annika Tjuka, and Dan Falk. “Tipping Points of Space Debris in Low Earth Orbit.”
International Journal of the Commons. Ubiquity Press, 2024. https://doi.org/10.5334/ijc.1275.
ieee: K. Nomura et al., “Tipping points of space debris in low earth orbit,”
International Journal of the Commons, vol. 18, no. 1. Ubiquity Press, 2024.
ista: Nomura K, Rella S, Merritt H, Baltussen M, Bird D, Tjuka A, Falk D. 2024.
Tipping points of space debris in low earth orbit. International Journal of the
Commons. 18(1).
mla: Nomura, Keiko, et al. “Tipping Points of Space Debris in Low Earth Orbit.”
International Journal of the Commons, vol. 18, no. 1, Ubiquity Press, 2024,
doi:10.5334/ijc.1275.
short: K. Nomura, S. Rella, H. Merritt, M. Baltussen, D. Bird, A. Tjuka, D. Falk,
International Journal of the Commons 18 (2024).
date_created: 2024-01-30T11:58:02Z
date_published: 2024-01-11T00:00:00Z
date_updated: 2024-02-05T10:10:27Z
day: '11'
ddc:
- '550'
department:
- _id: GradSch
- _id: GaTk
doi: 10.5334/ijc.1275
file:
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checksum: b80ebc889033c365d8f8c05a0c655382
content_type: application/pdf
creator: dernst
date_created: 2024-02-05T10:06:35Z
date_updated: 2024-02-05T10:06:35Z
file_id: '14939'
file_name: 2023_IntJourCommons_Nomura.pdf
file_size: 1305786
relation: main_file
success: 1
file_date_updated: 2024-02-05T10:06:35Z
has_accepted_license: '1'
intvolume: ' 18'
issue: '1'
keyword:
- Sociology and Political Science
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '01'
oa: 1
oa_version: Published Version
publication: International Journal of the Commons
publication_identifier:
issn:
- 1875-0281
publication_status: published
publisher: Ubiquity Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Tipping points of space debris in low earth orbit
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: 18
year: '2024'
...
---
_id: '15020'
abstract:
- lang: eng
text: "This thesis consists of four distinct pieces of work within theoretical biology,
with two themes in common: the concept of optimization in biological systems,
and the use of information-theoretic tools to quantify biological stochasticity
and statistical uncertainty.\r\nChapter 2 develops a statistical framework for
studying biological systems which we believe to be optimized for a particular
utility function, such as retinal neurons conveying information about visual stimuli.
We formalize such beliefs as maximum-entropy Bayesian priors, constrained by the
expected utility. We explore how such priors aid inference of system parameters
with limited data and enable optimality hypothesis testing: is the utility higher
than by chance?\r\nChapter 3 examines the ultimate biological optimization process:
evolution by natural selection. As some individuals survive and reproduce more
successfully than others, populations evolve towards fitter genotypes and phenotypes.
We formalize this as accumulation of genetic information, and use population genetics
theory to study how much such information can be accumulated per generation and
maintained in the face of random mutation and genetic drift. We identify the population
size and fitness variance as the key quantities that control information accumulation
and maintenance.\r\nChapter 4 reuses the concept of genetic information from Chapter
3, but from a different perspective: we ask how much genetic information organisms
actually need, in particular in the context of gene regulation. For example, how
much information is needed to bind transcription factors at correct locations
within the genome? Population genetics provides us with a refined answer: with
an increasing population size, populations achieve higher fitness by maintaining
more genetic information. Moreover, regulatory parameters experience selection
pressure to optimize the fitness-information trade-off, i.e. minimize the information
needed for a given fitness. This provides an evolutionary derivation of the optimization
priors introduced in Chapter 2.\r\nChapter 5 proves an upper bound on mutual information
between a signal and a communication channel output (such as neural activity).
Mutual information is an important utility measure for biological systems, but
its practical use can be difficult due to the large dimensionality of many biological
channels. Sometimes, a lower bound on mutual information is computed by replacing
the high-dimensional channel outputs with decodes (signal estimates). Our result
provides a corresponding upper bound, provided that the decodes are the maximum
posterior estimates of the signal."
acknowledged_ssus:
- _id: ScienComp
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Michal
full_name: Hledik, Michal
id: 4171253A-F248-11E8-B48F-1D18A9856A87
last_name: Hledik
citation:
ama: Hledik M. Genetic information and biological optimization. 2024. doi:10.15479/at:ista:15020
apa: Hledik, M. (2024). Genetic information and biological optimization.
Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:15020
chicago: Hledik, Michal. “Genetic Information and Biological Optimization.” Institute
of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:15020.
ieee: M. Hledik, “Genetic information and biological optimization,” Institute of
Science and Technology Austria, 2024.
ista: Hledik M. 2024. Genetic information and biological optimization. Institute
of Science and Technology Austria.
mla: Hledik, Michal. Genetic Information and Biological Optimization. Institute
of Science and Technology Austria, 2024, doi:10.15479/at:ista:15020.
short: M. Hledik, Genetic Information and Biological Optimization, Institute of
Science and Technology Austria, 2024.
date_created: 2024-02-23T14:02:04Z
date_published: 2024-02-23T00:00:00Z
date_updated: 2024-03-06T14:22:52Z
day: '23'
ddc:
- '576'
- '519'
degree_awarded: PhD
department:
- _id: GradSch
- _id: NiBa
- _id: GaTk
doi: 10.15479/at:ista:15020
ec_funded: 1
file:
- access_level: open_access
checksum: b2d3da47c98d481577a4baf68944fe41
content_type: application/pdf
creator: mhledik
date_created: 2024-02-23T13:50:53Z
date_updated: 2024-02-23T13:50:53Z
file_id: '15021'
file_name: hledik thesis pdfa 2b.pdf
file_size: 7102089
relation: main_file
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content_type: application/zip
creator: mhledik
date_created: 2024-02-23T13:50:54Z
date_updated: 2024-02-23T14:20:16Z
file_id: '15022'
file_name: hledik thesis source.zip
file_size: 14014790
relation: source_file
file_date_updated: 2024-02-23T14:20:16Z
has_accepted_license: '1'
keyword:
- Theoretical biology
- Optimality
- Evolution
- Information
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '158'
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 2665AAFE-B435-11E9-9278-68D0E5697425
grant_number: RGP0034/2018
name: Can evolution minimize spurious signaling crosstalk to reach optimal performance?
- _id: bd6958e0-d553-11ed-ba76-86eba6a76c00
grant_number: '101055327'
name: Understanding the evolution of continuous genomes
publication_identifier:
issn:
- 2663 - 337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7553'
relation: part_of_dissertation
status: public
- id: '12081'
relation: part_of_dissertation
status: public
- id: '7606'
relation: part_of_dissertation
status: public
status: public
supervisor:
- 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: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
title: Genetic information and biological optimization
type: dissertation
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '13127'
abstract:
- lang: eng
text: Cooperative disease defense emerges as group-level collective behavior, yet
how group members make the underlying individual decisions is poorly understood.
Using garden ants and fungal pathogens as an experimental model, we derive the
rules governing individual ant grooming choices and show how they produce colony-level
hygiene. Time-resolved behavioral analysis, pathogen quantification, and probabilistic
modeling reveal that ants increase grooming and preferentially target highly-infectious
individuals when perceiving high pathogen load, but transiently suppress grooming
after having been groomed by nestmates. Ants thus react to both, the infectivity
of others and the social feedback they receive on their own contagiousness. While
inferred solely from momentary ant decisions, these behavioral rules quantitatively
predict hour-long experimental dynamics, and synergistically combine into efficient
colony-wide pathogen removal. Our analyses show that noisy individual decisions
based on only local, incomplete, yet dynamically-updated information on pathogen
threat and social feedback can lead to potent collective disease defense.
acknowledged_ssus:
- _id: LifeSc
acknowledgement: We thank Mike Bidochka for the fungal strains, the ISTA Social Immunity
Team for ant collection, Hanna Leitner for experimental and molecular support, Jennifer
Robb and Lukas Lindorfer for microscopy, and the LabSupport Facility at ISTA for
general laboratory support. We further thank Victor Mireles, Iain Couzin, Fabian
Theis and the Social Immunity Team for continued feedback throughout, and Michael
Sixt, Yuko Ulrich, Koos Boomsma, Erika Dawson, Megan Kutzer and Hinrich Schulenburg
for comments on the manuscript. This project has received funding from the European
Research Council (ERC) under the European Union’s Horizon 2020 research and innovation
program (Grant No. 771402; EPIDEMICSonCHIP) to SC, from the Scientific Grant Agency
of the Slovak Republic (Grant No. 1/0521/20) to KB, and the Human Frontier Science
Program (Grant No. RGP0065/2012) to GT.
article_number: '3232'
article_processing_charge: Yes
article_type: original
author:
- first_name: Barbara E
full_name: Casillas Perez, Barbara E
id: 351ED2AA-F248-11E8-B48F-1D18A9856A87
last_name: Casillas Perez
- 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: Anna V
full_name: Grasse, Anna V
id: 406F989C-F248-11E8-B48F-1D18A9856A87
last_name: Grasse
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Sylvia
full_name: Cremer, Sylvia
id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87
last_name: Cremer
orcid: 0000-0002-2193-3868
citation:
ama: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. Dynamic pathogen
detection and social feedback shape collective hygiene in ants. Nature Communications.
2023;14. doi:10.1038/s41467-023-38947-y
apa: Casillas Perez, B. E., Bodova, K., Grasse, A. V., Tkačik, G., & Cremer,
S. (2023). Dynamic pathogen detection and social feedback shape collective hygiene
in ants. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-38947-y
chicago: Casillas Perez, Barbara E, Katarina Bodova, Anna V Grasse, Gašper Tkačik,
and Sylvia Cremer. “Dynamic Pathogen Detection and Social Feedback Shape Collective
Hygiene in Ants.” Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-38947-y.
ieee: B. E. Casillas Perez, K. Bodova, A. V. Grasse, G. Tkačik, and S. Cremer, “Dynamic
pathogen detection and social feedback shape collective hygiene in ants,” Nature
Communications, vol. 14. Springer Nature, 2023.
ista: Casillas Perez BE, Bodova K, Grasse AV, Tkačik G, Cremer S. 2023. Dynamic
pathogen detection and social feedback shape collective hygiene in ants. Nature
Communications. 14, 3232.
mla: Casillas Perez, Barbara E., et al. “Dynamic Pathogen Detection and Social Feedback
Shape Collective Hygiene in Ants.” Nature Communications, vol. 14, 3232,
Springer Nature, 2023, doi:10.1038/s41467-023-38947-y.
short: B.E. Casillas Perez, K. Bodova, A.V. Grasse, G. Tkačik, S. Cremer, Nature
Communications 14 (2023).
date_created: 2023-06-11T22:00:40Z
date_published: 2023-06-03T00:00:00Z
date_updated: 2023-08-07T13:09:09Z
day: '03'
ddc:
- '570'
department:
- _id: SyCr
- _id: GaTk
doi: 10.1038/s41467-023-38947-y
ec_funded: 1
external_id:
isi:
- '001002562700005'
pmid:
- '37270641'
file:
- access_level: open_access
checksum: 4af0393e3ed47b3fc46e68b81c3c1007
content_type: application/pdf
creator: dernst
date_created: 2023-06-13T08:05:46Z
date_updated: 2023-06-13T08:05:46Z
file_id: '13132'
file_name: 2023_NatureComm_CasillasPerez.pdf
file_size: 2358167
relation: main_file
success: 1
file_date_updated: 2023-06-13T08:05:46Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 2649B4DE-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '771402'
name: Epidemics in ant societies on a chip
- _id: 255008E4-B435-11E9-9278-68D0E5697425
grant_number: RGP0065/2012
name: Information processing and computation in fish groups
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12945'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Dynamic pathogen detection and social feedback shape collective hygiene in
ants
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: 14
year: '2023'
...
---
_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
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: '14515'
abstract:
- lang: eng
text: Most natural and engineered information-processing systems transmit information
via signals that vary in time. Computing the information transmission rate or
the information encoded in the temporal characteristics of these signals requires
the mutual information between the input and output signals as a function of time,
i.e., between the input and output trajectories. Yet, this is notoriously difficult
because of the high-dimensional nature of the trajectory space, and all existing
techniques require approximations. We present an exact Monte Carlo technique called
path weight sampling (PWS) that, for the first time, makes it possible to compute
the mutual information between input and output trajectories for any stochastic
system that is described by a master equation. The principal idea is to use the
master equation to evaluate the exact conditional probability of an individual
output trajectory for a given input trajectory and average this via Monte Carlo
sampling in trajectory space to obtain the mutual information. We present three
variants of PWS, which all generate the trajectories using the standard stochastic
simulation algorithm. While direct PWS is a brute-force method, Rosenbluth-Rosenbluth
PWS exploits the analogy between signal trajectory sampling and polymer sampling,
and thermodynamic integration PWS is based on a reversible work calculation in
trajectory space. PWS also makes it possible to compute the mutual information
between input and output trajectories for systems with hidden internal states
as well as systems with feedback from output to input. Applying PWS to the bacterial
chemotaxis system, consisting of 182 coupled chemical reactions, demonstrates
not only that the scheme is highly efficient but also that the number of receptor
clusters is much smaller than hitherto believed, while their size is much larger.
acknowledgement: "We thank Bela Mulder, Tom Shimizu, Fotios Avgidis, Peter Bolhuis,
and Daan Frenkel for useful discussions and a careful reading of the manuscript,
and we thank Age Tjalma for support with obtaining the Gaussian approximation of
the chemotaxis system. This work is part of the Dutch Research Council (NWO) and
was performed at the research institute AMOLF. This project has received funding
from the European Research Council (ERC) under the European Union’s Horizon 2020
research and innovation program (Grant Agreement No. 885065) and was\r\nfinancially
supported by NWO through the “Building a Synthetic Cell (BaSyC)” Gravitation Grant
(024.003.019)."
article_number: '041017'
article_processing_charge: Yes
article_type: original
author:
- first_name: Manuel
full_name: Reinhardt, Manuel
last_name: Reinhardt
- first_name: Gašper
full_name: Tkačik, Gašper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkačik
orcid: 0000-0002-6699-1455
- first_name: Pieter Rein
full_name: Ten Wolde, Pieter Rein
last_name: Ten Wolde
citation:
ama: 'Reinhardt M, Tkačik G, Ten Wolde PR. Path weight sampling: Exact Monte Carlo
computation of the mutual information between stochastic trajectories. Physical
Review X. 2023;13(4). doi:10.1103/PhysRevX.13.041017'
apa: 'Reinhardt, M., Tkačik, G., & Ten Wolde, P. R. (2023). Path weight sampling:
Exact Monte Carlo computation of the mutual information between stochastic trajectories.
Physical Review X. American Physical Society. https://doi.org/10.1103/PhysRevX.13.041017'
chicago: 'Reinhardt, Manuel, Gašper Tkačik, and Pieter Rein Ten Wolde. “Path Weight
Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic
Trajectories.” Physical Review X. American Physical Society, 2023. https://doi.org/10.1103/PhysRevX.13.041017.'
ieee: 'M. Reinhardt, G. Tkačik, and P. R. Ten Wolde, “Path weight sampling: Exact
Monte Carlo computation of the mutual information between stochastic trajectories,”
Physical Review X, vol. 13, no. 4. American Physical Society, 2023.'
ista: 'Reinhardt M, Tkačik G, Ten Wolde PR. 2023. Path weight sampling: Exact Monte
Carlo computation of the mutual information between stochastic trajectories. Physical
Review X. 13(4), 041017.'
mla: 'Reinhardt, Manuel, et al. “Path Weight Sampling: Exact Monte Carlo Computation
of the Mutual Information between Stochastic Trajectories.” Physical Review
X, vol. 13, no. 4, 041017, American Physical Society, 2023, doi:10.1103/PhysRevX.13.041017.'
short: M. Reinhardt, G. Tkačik, P.R. Ten Wolde, Physical Review X 13 (2023).
date_created: 2023-11-12T23:00:55Z
date_published: 2023-10-26T00:00:00Z
date_updated: 2023-11-13T09:03:30Z
day: '26'
ddc:
- '530'
department:
- _id: GaTk
doi: 10.1103/PhysRevX.13.041017
external_id:
arxiv:
- '2203.03461'
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intvolume: ' 13'
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publication: Physical Review X
publication_identifier:
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title: 'Path weight sampling: Exact Monte Carlo computation of the mutual information
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