---
_id: '10939'
abstract:
- lang: eng
text: Understanding and characterising biochemical processes inside single cells
requires experimental platforms that allow one to perturb and observe the dynamics
of such processes as well as computational methods to build and parameterise models
from the collected data. Recent progress with experimental platforms and optogenetics
has made it possible to expose each cell in an experiment to an individualised
input and automatically record cellular responses over days with fine time resolution.
However, methods to infer parameters of stochastic kinetic models from single-cell
longitudinal data have generally been developed under the assumption that experimental
data is sparse and that responses of cells to at most a few different input perturbations
can be observed. Here, we investigate and compare different approaches for calculating
parameter likelihoods of single-cell longitudinal data based on approximations
of the chemical master equation (CME) with a particular focus on coupling the
linear noise approximation (LNA) or moment closure methods to a Kalman filter.
We show that, as long as cells are measured sufficiently frequently, coupling
the LNA to a Kalman filter allows one to accurately approximate likelihoods and
to infer model parameters from data even in cases where the LNA provides poor
approximations of the CME. Furthermore, the computational cost of filtering-based
iterative likelihood evaluation scales advantageously in the number of measurement
times and different input perturbations and is thus ideally suited for data obtained
from modern experimental platforms. To demonstrate the practical usefulness of
these results, we perform an experiment in which single cells, equipped with an
optogenetic gene expression system, are exposed to various different light-input
sequences and measured at several hundred time points and use parameter inference
based on iterative likelihood evaluation to parameterise a stochastic model of
the system.
acknowledgement: We thank Virgile Andreani for useful discussions about the model
and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts
of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002).
The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
article_number: e1009950
article_processing_charge: No
article_type: original
author:
- first_name: Anđela
full_name: Davidović, Anđela
last_name: Davidović
- 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: Gregory
full_name: Batt, Gregory
last_name: Batt
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
citation:
ama: Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 2022;18(3). doi:10.1371/journal.pcbi.1009950
apa: Davidović, A., Chait, R. P., Batt, G., & Ruess, J. (2022). Parameter inference
for stochastic biochemical models from perturbation experiments parallelised at
the single cell level. PLoS Computational Biology. Public Library of Science.
https://doi.org/10.1371/journal.pcbi.1009950
chicago: Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter
Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised
at the Single Cell Level.” PLoS Computational Biology. Public Library of
Science, 2022. https://doi.org/10.1371/journal.pcbi.1009950.
ieee: A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for
stochastic biochemical models from perturbation experiments parallelised at the
single cell level,” PLoS Computational Biology, vol. 18, no. 3. Public
Library of Science, 2022.
ista: Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic
biochemical models from perturbation experiments parallelised at the single cell
level. PLoS Computational Biology. 18(3), e1009950.
mla: Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models
from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS
Computational Biology, vol. 18, no. 3, e1009950, Public Library of Science,
2022, doi:10.1371/journal.pcbi.1009950.
short: A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18
(2022).
date_created: 2022-04-03T22:01:42Z
date_published: 2022-03-18T00:00:00Z
date_updated: 2022-04-04T10:21:53Z
day: '18'
ddc:
- '570'
- '000'
department:
- _id: CaGu
doi: 10.1371/journal.pcbi.1009950
file:
- access_level: open_access
checksum: 458ef542761fb714ced214f240daf6b2
content_type: application/pdf
creator: dernst
date_created: 2022-04-04T10:14:39Z
date_updated: 2022-04-04T10:14:39Z
file_id: '10947'
file_name: 2022_PLoSCompBio_Davidovic.pdf
file_size: 2958642
relation: main_file
success: 1
file_date_updated: 2022-04-04T10:14:39Z
has_accepted_license: '1'
intvolume: ' 18'
issue: '3'
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://gitlab.pasteur.fr/adavidov/inferencelnakf
scopus_import: '1'
status: public
title: Parameter inference for stochastic biochemical models from perturbation experiments
parallelised at the single cell level
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: '2022'
...
---
_id: '10535'
abstract:
- lang: eng
text: Realistic models of biological processes typically involve interacting components
on multiple scales, driven by changing environment and inherent stochasticity.
Such models are often analytically and numerically intractable. We revisit a dynamic
maximum entropy method that combines a static maximum entropy with a quasi-stationary
approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed
by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics,
without the need to track microscopic details. Although the method has been previously
applied to a few (rather complicated) applications in population genetics, our
main goal here is to explain and to better understand how the method works. We
demonstrate the usefulness of the method for two widely studied stochastic problems,
highlighting its accuracy in capturing important macroscopic quantities even in
rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process,
the method recovers the exact dynamics whilst for a stochastic island model with
migration from other habitats, the approximation retains high macroscopic accuracy
under a wide range of scenarios in a dynamic environment.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "Computational resources for the study were provided by the Institute
of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant
Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20."
article_number: e1009661
article_processing_charge: No
article_type: original
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: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- 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, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation
of structured population dynamics. PLoS Computational Biology. 2021;17(12).
doi:10.1371/journal.pcbi.1009661
apa: Bodova, K., Szep, E., & Barton, N. H. (2021). Dynamic maximum entropy provides
accurate approximation of structured population dynamics. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009661
chicago: Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy
Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational
Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009661.
ieee: K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate
approximation of structured population dynamics,” PLoS Computational Biology,
vol. 17, no. 12. Public Library of Science, 2021.
ista: Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate
approximation of structured population dynamics. PLoS Computational Biology. 17(12),
e1009661.
mla: Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation
of Structured Population Dynamics.” PLoS Computational Biology, vol. 17,
no. 12, e1009661, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009661.
short: K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).
date_created: 2021-12-12T23:01:27Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-08-01T10:48:04Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1371/journal.pcbi.1009661
external_id:
arxiv:
- '2102.03669'
pmid:
- '34851948'
file:
- access_level: open_access
checksum: dcd185d4f7e0acee25edf1d6537f447e
content_type: application/pdf
creator: dernst
date_created: 2022-05-16T08:53:11Z
date_updated: 2022-05-16T08:53:11Z
file_id: '11383'
file_name: 2021_PLOsComBio_Bodova.pdf
file_size: 2299486
relation: main_file
success: 1
file_date_updated: 2022-05-16T08:53:11Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '12'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: PLoS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: Dynamic maximum entropy provides accurate approximation of structured population
dynamics
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: 17
year: '2021'
...
---
_id: '8767'
abstract:
- lang: eng
text: Resources are rarely distributed uniformly within a population. Heterogeneity
in the concentration of a drug, the quality of breeding sites, or wealth can all
affect evolutionary dynamics. In this study, we represent a collection of properties
affecting the fitness at a given location using a color. A green node is rich
in resources while a red node is poorer. More colors can represent a broader spectrum
of resource qualities. For a population evolving according to the birth-death
Moran model, the first question we address is which structures, identified by
graph connectivity and graph coloring, are evolutionarily equivalent. We prove
that all properly two-colored, undirected, regular graphs are evolutionarily equivalent
(where “properly colored” means that no two neighbors have the same color). We
then compare the effects of background heterogeneity on properly two-colored graphs
to those with alternative schemes in which the colors are permuted. Finally, we
discuss dynamic coloring as a model for spatiotemporal resource fluctuations,
and we illustrate that random dynamic colorings often diminish the effects of
background heterogeneity relative to a proper two-coloring.
acknowledgement: 'We thank Igor Erovenko for many helpful comments on an earlier version
of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the
Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation
(A.M.). The funders had no role in study design, data collection and analysis, decision
to publish, or preparation of the manuscript.'
article_number: e1008402
article_processing_charge: No
article_type: original
author:
- first_name: Kamran
full_name: Kaveh, Kamran
last_name: Kaveh
- first_name: Alex
full_name: McAvoy, Alex
last_name: McAvoy
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin A.
full_name: Nowak, Martin A.
last_name: Nowak
citation:
ama: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 2020;16(11). doi:10.1371/journal.pcbi.1008402
apa: Kaveh, K., McAvoy, A., Chatterjee, K., & Nowak, M. A. (2020). The Moran
process on 2-chromatic graphs. PLOS Computational Biology. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1008402
chicago: Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak.
“The Moran Process on 2-Chromatic Graphs.” PLOS Computational Biology.
Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1008402.
ieee: K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on
2-chromatic graphs,” PLOS Computational Biology, vol. 16, no. 11. Public
Library of Science, 2020.
ista: Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic
graphs. PLOS Computational Biology. 16(11), e1008402.
mla: Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” PLOS Computational
Biology, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:10.1371/journal.pcbi.1008402.
short: K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology
16 (2020).
date_created: 2020-11-18T07:20:23Z
date_published: 2020-11-05T00:00:00Z
date_updated: 2023-08-22T12:49:18Z
day: '05'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.1371/journal.pcbi.1008402
external_id:
isi:
- '000591317200004'
file:
- access_level: open_access
checksum: 555456dd0e47bcf9e0994bcb95577e88
content_type: application/pdf
creator: dernst
date_created: 2020-11-18T07:26:10Z
date_updated: 2020-11-18T07:26:10Z
file_id: '8768'
file_name: 2020_PlosCompBio_Kaveh.pdf
file_size: 2498594
relation: main_file
success: 1
file_date_updated: 2020-11-18T07:26:10Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
issue: '11'
keyword:
- Ecology
- Modelling and Simulation
- Computational Theory and Mathematics
- Genetics
- Ecology
- Evolution
- Behavior and Systematics
- Molecular Biology
- Cellular and Molecular Neuroscience
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: PLOS Computational Biology
publication_identifier:
eissn:
- 1553-7358
issn:
- 1553-734X
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
status: public
title: The Moran process on 2-chromatic graphs
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: 16
year: '2020'
...