--- _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 day: '01' ddc: - '006' - '576' department: - _id: ToHe - _id: CaGu - _id: NiBa doi: 10.1007/s00236-016-0278-x ec_funded: 1 external_id: isi: - '000414343200003' file: - access_level: open_access checksum: 4e661d9135d7f8c342e8e258dee76f3e content_type: application/pdf creator: dernst date_created: 2019-01-17T15:57:29Z date_updated: 2020-07-14T12:44:46Z file_id: '5841' file_name: 2017_ActaInformatica_Giacobbe.pdf file_size: 755241 relation: main_file file_date_updated: 2020-07-14T12:44:46Z has_accepted_license: '1' intvolume: ' 54' isi: 1 issue: '8' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: 765 - 787 project: - _id: 25EE3708-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '267989' name: Quantitative Reactive Modeling - _id: 25832EC2-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S 11407_N23 name: Rigorous Systems Engineering - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize - _id: 25B1EC9E-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '618091' name: Speed of Adaptation in Population Genetics and Evolutionary Computation - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 25B07788-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '250152' name: Limits to selection in biology and in evolutionary computation publication: Acta Informatica publication_identifier: issn: - '00015903' publication_status: published publisher: Springer publist_id: '5898' pubrep_id: '649' quality_controlled: '1' related_material: record: - id: '1835' relation: earlier_version status: public scopus_import: '1' status: public title: Model checking the evolution of gene regulatory networks 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: 54 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' file: - access_level: open_access checksum: 7873f665a0c598ac747c908f34cb14b9 content_type: application/pdf 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 has_accepted_license: '1' 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 external_id: isi: - '000392229100011' intvolume: ' 118' isi: 1 language: - iso: eng main_file_link: - open_access: '1' 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' related_material: record: - id: '9710' relation: research_data status: public scopus_import: '1' status: public title: How does epistasis influence the response to selection? type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 118 year: '2017' ...