---
_id: '626'
abstract:
- lang: eng
text: 'Our focus here is on the infinitesimal model. In this model, one or several
quantitative traits are described as the sum of a genetic and a non-genetic component,
the first being distributed within families as a normal random variable centred
at the average of the parental genetic components, and with a variance independent
of the parental traits. Thus, the variance that segregates within families is
not perturbed by selection, and can be predicted from the variance components.
This does not necessarily imply that the trait distribution across the whole population
should be Gaussian, and indeed selection or population structure may have a substantial
effect on the overall trait distribution. One of our main aims is to identify
some general conditions on the allelic effects for the infinitesimal model to
be accurate. We first review the long history of the infinitesimal model in quantitative
genetics. Then we formulate the model at the phenotypic level in terms of individual
trait values and relationships between individuals, but including different evolutionary
processes: genetic drift, recombination, selection, mutation, population structure,
…. We give a range of examples of its application to evolutionary questions related
to stabilising selection, assortative mating, effective population size and response
to selection, habitat preference and speciation. We provide a mathematical justification
of the model as the limit as the number M of underlying loci tends to infinity
of a model with Mendelian inheritance, mutation and environmental noise, when
the genetic component of the trait is purely additive. We also show how the model
generalises to include epistatic effects. We prove in particular that, within
each family, the genetic components of the individual trait values in the current
generation are indeed normally distributed with a variance independent of ancestral
traits, up to an error of order 1∕M. Simulations suggest that in some cases the
convergence may be as fast as 1∕M.'
author:
- 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: Alison
full_name: Etheridge, Alison
last_name: Etheridge
- first_name: Amandine
full_name: Véber, Amandine
last_name: Véber
citation:
ama: 'Barton NH, Etheridge A, Véber A. The infinitesimal model: Definition derivation
and implications. Theoretical Population Biology. 2017;118:50-73. doi:10.1016/j.tpb.2017.06.001'
apa: 'Barton, N. H., Etheridge, A., & Véber, A. (2017). The infinitesimal model:
Definition derivation and implications. Theoretical Population Biology.
Academic Press. https://doi.org/10.1016/j.tpb.2017.06.001'
chicago: 'Barton, Nicholas H, Alison Etheridge, and Amandine Véber. “The Infinitesimal
Model: Definition Derivation and Implications.” Theoretical Population Biology.
Academic Press, 2017. https://doi.org/10.1016/j.tpb.2017.06.001.'
ieee: 'N. H. Barton, A. Etheridge, and A. Véber, “The infinitesimal model: Definition
derivation and implications,” Theoretical Population Biology, vol. 118.
Academic Press, pp. 50–73, 2017.'
ista: 'Barton NH, Etheridge A, Véber A. 2017. The infinitesimal model: Definition
derivation and implications. Theoretical Population Biology. 118, 50–73.'
mla: 'Barton, Nicholas H., et al. “The Infinitesimal Model: Definition Derivation
and Implications.” Theoretical Population Biology, vol. 118, Academic Press,
2017, pp. 50–73, doi:10.1016/j.tpb.2017.06.001.'
short: N.H. Barton, A. Etheridge, A. Véber, Theoretical Population Biology 118 (2017)
50–73.
date_created: 2018-12-11T11:47:34Z
date_published: 2017-12-01T00:00:00Z
date_updated: 2021-01-12T08:06:50Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
doi: 10.1016/j.tpb.2017.06.001
ec_funded: 1
file:
- access_level: open_access
checksum: 7dd02bfcfe8f244f4a6c19091aedf2c8
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:45Z
date_updated: 2020-07-14T12:47:25Z
file_id: '4964'
file_name: IST-2017-908-v1+1_1-s2.0-S0040580917300886-main_1_.pdf
file_size: 1133924
relation: main_file
file_date_updated: 2020-07-14T12:47:25Z
has_accepted_license: '1'
intvolume: ' 118'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 50 - 73
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Theoretical Population Biology
publication_identifier:
issn:
- '00405809'
publication_status: published
publisher: Academic Press
publist_id: '7169'
pubrep_id: '908'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'The infinitesimal model: Definition derivation and implications'
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: 118
year: '2017'
...
---
_id: '9849'
abstract:
- lang: eng
text: This text provides additional information about the model, a derivation of
the analytic results in Eq (4), and details about simulations of an additional
parameter set.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Modelling and simulation details. 2017.
doi:10.1371/journal.pcbi.1005609.s001
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Modelling and simulation
details. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s001
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Modelling and
Simulation Details.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s001.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Modelling and simulation details.”
Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Modelling and simulation details,
Public Library of Science, 10.1371/journal.pcbi.1005609.s001.
mla: Lukacisinova, Marta, et al. Modelling and Simulation Details. Public
Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s001.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:02:34Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: NiBa
- _id: CaGu
doi: 10.1371/journal.pcbi.1005609.s001
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Modelling and simulation details
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '9850'
abstract:
- lang: eng
text: In this text, we discuss how a cost of resistance and the possibility of lethal
mutations impact our model.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Extensions of the model. 2017. doi:10.1371/journal.pcbi.1005609.s002
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Extensions of the model.
Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s002
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Extensions of
the Model.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s002.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Extensions of the model.” Public
Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Extensions of the model, Public Library
of Science, 10.1371/journal.pcbi.1005609.s002.
mla: Lukacisinova, Marta, et al. Extensions of the Model. Public Library
of Science, 2017, doi:10.1371/journal.pcbi.1005609.s002.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:05:24Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s002
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Extensions of the model
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '9851'
abstract:
- lang: eng
text: Based on the intuitive derivation of the dynamics of SIM allele frequency
pM in the main text, we present a heuristic prediction for the long-term SIM allele
frequencies with χ > 1 stresses and compare it to numerical simulations.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Heuristic prediction for multiple stresses.
2017. doi:10.1371/journal.pcbi.1005609.s003
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Heuristic prediction
for multiple stresses. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s003
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Heuristic Prediction
for Multiple Stresses.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s003.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Heuristic prediction for multiple
stresses.” Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Heuristic prediction for multiple
stresses, Public Library of Science, 10.1371/journal.pcbi.1005609.s003.
mla: Lukacisinova, Marta, et al. Heuristic Prediction for Multiple Stresses.
Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s003.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:08:14Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s003
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Heuristic prediction for multiple stresses
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '9852'
abstract:
- lang: eng
text: We show how different combination strategies affect the fraction of individuals
that are multi-resistant.
article_processing_charge: No
author:
- first_name: Marta
full_name: Lukacisinova, Marta
id: 4342E402-F248-11E8-B48F-1D18A9856A87
last_name: Lukacisinova
orcid: 0000-0002-2519-8004
- first_name: Sebastian
full_name: Novak, Sebastian
id: 461468AE-F248-11E8-B48F-1D18A9856A87
last_name: Novak
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
citation:
ama: Lukacisinova M, Novak S, Paixao T. Resistance frequencies for different combination
strategies. 2017. doi:10.1371/journal.pcbi.1005609.s004
apa: Lukacisinova, M., Novak, S., & Paixao, T. (2017). Resistance frequencies
for different combination strategies. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609.s004
chicago: Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Resistance Frequencies
for Different Combination Strategies.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.s004.
ieee: M. Lukacisinova, S. Novak, and T. Paixao, “Resistance frequencies for different
combination strategies.” Public Library of Science, 2017.
ista: Lukacisinova M, Novak S, Paixao T. 2017. Resistance frequencies for different
combination strategies, Public Library of Science, 10.1371/journal.pcbi.1005609.s004.
mla: Lukacisinova, Marta, et al. Resistance Frequencies for Different Combination
Strategies. Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.s004.
short: M. Lukacisinova, S. Novak, T. Paixao, (2017).
date_created: 2021-08-09T14:11:40Z
date_published: 2017-07-18T00:00:00Z
date_updated: 2023-02-23T12:55:39Z
day: '18'
department:
- _id: ToBo
- _id: CaGu
- _id: NiBa
doi: 10.1371/journal.pcbi.1005609.s004
month: '07'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '696'
relation: used_in_publication
status: public
status: public
title: Resistance frequencies for different combination strategies
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '6291'
abstract:
- lang: eng
text: Bacteria and their pathogens – phages – are the most abundant living entities
on Earth. Throughout their coevolution, bacteria have evolved multiple immune
systems to overcome the ubiquitous threat from the phages. Although the molecu-
lar details of these immune systems’ functions are relatively well understood,
their epidemiological consequences for the phage-bacterial communities have been
largely neglected. In this thesis we employed both experimental and theoretical
methods to explore whether herd and social immunity may arise in bacterial popu-
lations. Using our experimental system consisting of Escherichia coli strains
with a CRISPR based immunity to the T7 phage we show that herd immunity arises
in phage-bacterial communities and that it is accentuated when the populations
are spatially structured. By fitting a mathematical model, we inferred expressions
for the herd immunity threshold and the velocity of spread of a phage epidemic
in partially resistant bacterial populations, which both depend on the bacterial
growth rate, phage burst size and phage latent period. We also investigated the
poten- tial for social immunity in Streptococcus thermophilus and its phage 2972
using a bioinformatic analysis of potentially coding short open reading frames
with a signalling signature, encoded within the CRISPR associated genes. Subsequently,
we tested one identified potentially signalling peptide and found that its addition
to a phage-challenged culture increases probability of survival of bacteria two
fold, although the results were only marginally significant. Together, these results
demonstrate that the ubiquitous arms races between bacteria and phages have further
consequences at the level of the population.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Pavel
full_name: Payne, Pavel
id: 35F78294-F248-11E8-B48F-1D18A9856A87
last_name: Payne
orcid: 0000-0002-2711-9453
citation:
ama: Payne P. Bacterial herd and social immunity to phages. 2017.
apa: Payne, P. (2017). Bacterial herd and social immunity to phages. Institute
of Science and Technology Austria.
chicago: Payne, Pavel. “Bacterial Herd and Social Immunity to Phages.” Institute
of Science and Technology Austria, 2017.
ieee: P. Payne, “Bacterial herd and social immunity to phages,” Institute of Science
and Technology Austria, 2017.
ista: Payne P. 2017. Bacterial herd and social immunity to phages. Institute of
Science and Technology Austria.
mla: Payne, Pavel. Bacterial Herd and Social Immunity to Phages. Institute
of Science and Technology Austria, 2017.
short: P. Payne, Bacterial Herd and Social Immunity to Phages, Institute of Science
and Technology Austria, 2017.
date_created: 2019-04-09T15:16:45Z
date_published: 2017-02-01T00:00:00Z
date_updated: 2023-09-07T12:00:00Z
day: '01'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: NiBa
- _id: JoBo
file:
- access_level: closed
checksum: a0fc5c26a89c0ea759947ffba87d0d8f
content_type: application/pdf
creator: dernst
date_created: 2019-04-09T15:15:32Z
date_updated: 2020-07-14T12:47:27Z
file_id: '6292'
file_name: thesis_pavel_payne_final_w_signature_page.pdf
file_size: 3025175
relation: main_file
- access_level: open_access
checksum: af531e921a7f64a9e0af4cd8783b2226
content_type: application/pdf
creator: dernst
date_created: 2021-02-22T13:45:59Z
date_updated: 2021-02-22T13:45:59Z
file_id: '9187'
file_name: 2017_Payne_Thesis.pdf
file_size: 3111536
relation: main_file
success: 1
file_date_updated: 2021-02-22T13:45:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '83'
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Jonathan P
full_name: Bollback, Jonathan P
id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87
last_name: Bollback
orcid: 0000-0002-4624-4612
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
title: Bacterial herd and social immunity to phages
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2017'
...
---
_id: '9842'
abstract:
- lang: eng
text: Mathematica notebooks used to generate figures.
article_processing_charge: No
author:
- first_name: Alison
full_name: Etheridge, Alison
last_name: Etheridge
- 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: 'Etheridge A, Barton NH. Data for: Establishment in a new habitat by polygenic
adaptation. 2017. doi:10.17632/nw68fxzjpm.1'
apa: 'Etheridge, A., & Barton, N. H. (2017). Data for: Establishment in a new
habitat by polygenic adaptation. Mendeley Data. https://doi.org/10.17632/nw68fxzjpm.1'
chicago: 'Etheridge, Alison, and Nicholas H Barton. “Data for: Establishment in
a New Habitat by Polygenic Adaptation.” Mendeley Data, 2017. https://doi.org/10.17632/nw68fxzjpm.1.'
ieee: 'A. Etheridge and N. H. Barton, “Data for: Establishment in a new habitat
by polygenic adaptation.” Mendeley Data, 2017.'
ista: 'Etheridge A, Barton NH. 2017. Data for: Establishment in a new habitat by
polygenic adaptation, Mendeley Data, 10.17632/nw68fxzjpm.1.'
mla: 'Etheridge, Alison, and Nicholas H. Barton. Data for: Establishment in a
New Habitat by Polygenic Adaptation. Mendeley Data, 2017, doi:10.17632/nw68fxzjpm.1.'
short: A. Etheridge, N.H. Barton, (2017).
date_created: 2021-08-09T13:18:55Z
date_published: 2017-12-29T00:00:00Z
date_updated: 2023-09-11T13:41:21Z
day: '29'
department:
- _id: NiBa
doi: 10.17632/nw68fxzjpm.1
main_file_link:
- open_access: '1'
url: https://doi.org/10.17632/nw68fxzjpm.1
month: '12'
oa: 1
oa_version: Published Version
publisher: Mendeley Data
related_material:
record:
- id: '564'
relation: used_in_publication
status: public
status: public
title: 'Data for: Establishment in a new habitat by polygenic adaptation'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2017'
...
---
_id: '1351'
abstract:
- lang: eng
text: The behaviour of gene regulatory networks (GRNs) is typically analysed using
simulation-based statistical testing-like methods. In this paper, we demonstrate
that we can replace this approach by a formal verification-like method that gives
higher assurance and scalability. We focus on Wagner’s weighted GRN model with
varying weights, which is used in evolutionary biology. In the model, weight parameters
represent the gene interaction strength that may change due to genetic mutations.
For a property of interest, we synthesise the constraints over the parameter space
that represent the set of GRNs satisfying the property. We experimentally show
that our parameter synthesis procedure computes the mutational robustness of GRNs—an
important problem of interest in evolutionary biology—more efficiently than the
classical simulation method. We specify the property in linear temporal logic.
We employ symbolic bounded model checking and SMT solving to compute the space
of GRNs that satisfy the property, which amounts to synthesizing a set of linear
constraints on the weights.
article_processing_charge: No
author:
- first_name: Mirco
full_name: Giacobbe, Mirco
id: 3444EA5E-F248-11E8-B48F-1D18A9856A87
last_name: Giacobbe
orcid: 0000-0001-8180-0904
- 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: Ashutosh
full_name: Gupta, Ashutosh
id: 335E5684-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Tatjana
full_name: Petrov, Tatjana
id: 3D5811FC-F248-11E8-B48F-1D18A9856A87
last_name: Petrov
orcid: 0000-0002-9041-0905
citation:
ama: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. Model checking
the evolution of gene regulatory networks. Acta Informatica. 2017;54(8):765-787.
doi:10.1007/s00236-016-0278-x
apa: Giacobbe, M., Guet, C. C., Gupta, A., Henzinger, T. A., Paixao, T., & Petrov,
T. (2017). Model checking the evolution of gene regulatory networks. Acta Informatica.
Springer. https://doi.org/10.1007/s00236-016-0278-x
chicago: Giacobbe, Mirco, Calin C Guet, Ashutosh Gupta, Thomas A Henzinger, Tiago
Paixao, and Tatjana Petrov. “Model Checking the Evolution of Gene Regulatory Networks.”
Acta Informatica. Springer, 2017. https://doi.org/10.1007/s00236-016-0278-x.
ieee: M. Giacobbe, C. C. Guet, A. Gupta, T. A. Henzinger, T. Paixao, and T. Petrov,
“Model checking the evolution of gene regulatory networks,” Acta Informatica,
vol. 54, no. 8. Springer, pp. 765–787, 2017.
ista: Giacobbe M, Guet CC, Gupta A, Henzinger TA, Paixao T, Petrov T. 2017. Model
checking the evolution of gene regulatory networks. Acta Informatica. 54(8), 765–787.
mla: Giacobbe, Mirco, et al. “Model Checking the Evolution of Gene Regulatory Networks.”
Acta Informatica, vol. 54, no. 8, Springer, 2017, pp. 765–87, doi:10.1007/s00236-016-0278-x.
short: M. Giacobbe, C.C. Guet, A. Gupta, T.A. Henzinger, T. Paixao, T. Petrov, Acta
Informatica 54 (2017) 765–787.
date_created: 2018-12-11T11:51:32Z
date_published: 2017-12-01T00:00:00Z
date_updated: 2023-09-20T11:06:03Z
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call_identifier: FP7
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grant_number: S 11407_N23
name: Rigorous Systems Engineering
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call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
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call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
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call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
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call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Acta Informatica
publication_identifier:
issn:
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publisher: Springer
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status: public
title: Model checking the evolution of gene regulatory networks
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year: '2017'
...
---
_id: '1336'
abstract:
- lang: eng
text: Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired
by natural evolution. In recent years the field of evolutionary computation has
developed a rigorous analytical theory to analyse the runtimes of EAs on many
illustrative problems. Here we apply this theory to a simple model of natural
evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the
time between occurrences of new mutations is much longer than the time it takes
for a mutated genotype to take over the population. In this situation, the population
only contains copies of one genotype and evolution can be modelled as a stochastic
process evolving one genotype by means of mutation and selection between the resident
and the mutated genotype. The probability of accepting the mutated genotype then
depends on the change in fitness. We study this process, SSWM, from an algorithmic
perspective, quantifying its expected optimisation time for various parameters
and investigating differences to a similar evolutionary algorithm, the well-known
(1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at
crossing fitness valleys and study an example where SSWM outperforms the (1+1)
EA by taking advantage of information on the fitness gradient.
article_processing_charge: No
author:
- first_name: Tiago
full_name: Paixao, Tiago
id: 2C5658E6-F248-11E8-B48F-1D18A9856A87
last_name: Paixao
orcid: 0000-0003-2361-3953
- first_name: Jorge
full_name: Pérez Heredia, Jorge
last_name: Pérez Heredia
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
citation:
ama: Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. Towards a runtime comparison
of natural and artificial evolution. Algorithmica. 2017;78(2):681-713.
doi:10.1007/s00453-016-0212-1
apa: Paixao, T., Pérez Heredia, J., Sudholt, D., & Trubenova, B. (2017). Towards
a runtime comparison of natural and artificial evolution. Algorithmica.
Springer. https://doi.org/10.1007/s00453-016-0212-1
chicago: Paixao, Tiago, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenova.
“Towards a Runtime Comparison of Natural and Artificial Evolution.” Algorithmica.
Springer, 2017. https://doi.org/10.1007/s00453-016-0212-1.
ieee: T. Paixao, J. Pérez Heredia, D. Sudholt, and B. Trubenova, “Towards a runtime
comparison of natural and artificial evolution,” Algorithmica, vol. 78,
no. 2. Springer, pp. 681–713, 2017.
ista: Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. 2017. Towards a runtime
comparison of natural and artificial evolution. Algorithmica. 78(2), 681–713.
mla: Paixao, Tiago, et al. “Towards a Runtime Comparison of Natural and Artificial
Evolution.” Algorithmica, vol. 78, no. 2, Springer, 2017, pp. 681–713,
doi:10.1007/s00453-016-0212-1.
short: T. Paixao, J. Pérez Heredia, D. Sudholt, B. Trubenova, Algorithmica 78 (2017)
681–713.
date_created: 2018-12-11T11:51:27Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2023-09-20T11:14:42Z
day: '01'
ddc:
- '576'
department:
- _id: NiBa
- _id: CaGu
doi: 10.1007/s00453-016-0212-1
ec_funded: 1
external_id:
isi:
- '000400379500013'
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creator: system
date_created: 2018-12-12T10:10:19Z
date_updated: 2020-07-14T12:44:44Z
file_id: '4805'
file_name: IST-2016-658-v1+1_s00453-016-0212-1.pdf
file_size: 710206
relation: main_file
file_date_updated: 2020-07-14T12:44:44Z
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intvolume: ' 78'
isi: 1
issue: '2'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 681 - 713
project:
- _id: 25B1EC9E-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '618091'
name: Speed of Adaptation in Population Genetics and Evolutionary Computation
publication: Algorithmica
publication_identifier:
issn:
- '01784617'
publication_status: published
publisher: Springer
publist_id: '5931'
pubrep_id: '658'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards a runtime comparison of natural and artificial evolution
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: 78
year: '2017'
...
---
_id: '1199'
abstract:
- lang: eng
text: Much of quantitative genetics is based on the ‘infinitesimal model’, under
which selection has a negligible effect on the genetic variance. This is typically
justified by assuming a very large number of loci with additive effects. However,
it applies even when genes interact, provided that the number of loci is large
enough that selection on each of them is weak relative to random drift. In the
long term, directional selection will change allele frequencies, but even then,
the effects of epistasis on the ultimate change in trait mean due to selection
may be modest. Stabilising selection can maintain many traits close to their optima,
even when the underlying alleles are weakly selected. However, the number of traits
that can be optimised is apparently limited to ~4Ne by the ‘drift load’, and this
is hard to reconcile with the apparent complexity of many organisms. Just as for
the mutation load, this limit can be evaded by a particular form of negative epistasis.
A more robust limit is set by the variance in reproductive success. This suggests
that selection accumulates information most efficiently in the infinitesimal regime,
when selection on individual alleles is weak, and comparable with random drift.
A review of evidence on selection strength suggests that although most variance
in fitness may be because of alleles with large Nes, substantial amounts of adaptation
may be because of alleles in the infinitesimal regime, in which epistasis has
modest effects.
article_processing_charge: No
author:
- 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: Barton NH. How does epistasis influence the response to selection? Heredity.
2017;118:96-109. doi:10.1038/hdy.2016.109
apa: Barton, N. H. (2017). How does epistasis influence the response to selection?
Heredity. Nature Publishing Group. https://doi.org/10.1038/hdy.2016.109
chicago: Barton, Nicholas H. “How Does Epistasis Influence the Response to Selection?”
Heredity. Nature Publishing Group, 2017. https://doi.org/10.1038/hdy.2016.109.
ieee: N. H. Barton, “How does epistasis influence the response to selection?,” Heredity,
vol. 118. Nature Publishing Group, pp. 96–109, 2017.
ista: Barton NH. 2017. How does epistasis influence the response to selection? Heredity.
118, 96–109.
mla: Barton, Nicholas H. “How Does Epistasis Influence the Response to Selection?”
Heredity, vol. 118, Nature Publishing Group, 2017, pp. 96–109, doi:10.1038/hdy.2016.109.
short: N.H. Barton, Heredity 118 (2017) 96–109.
date_created: 2018-12-11T11:50:40Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2023-09-20T11:17:47Z
day: '01'
department:
- _id: NiBa
doi: 10.1038/hdy.2016.109
ec_funded: 1
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isi:
- '000392229100011'
intvolume: ' 118'
isi: 1
language:
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url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5176114/
month: '01'
oa: 1
oa_version: Submitted Version
page: 96 - 109
project:
- _id: 25B07788-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '250152'
name: Limits to selection in biology and in evolutionary computation
publication: Heredity
publication_status: published
publisher: Nature Publishing Group
publist_id: '6151'
quality_controlled: '1'
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title: How does epistasis influence the response to selection?
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volume: 118
year: '2017'
...