Opponent Shaping for Antibody Development
Anti-viral therapies are typically designed to target only the current strains of a virus. Game theoretically, this corresponds to a short-sighted, or myopic, response. However, therapy-induced selective pressures act on viruses to drive the emergence of mutated strains, against which initial therap...
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creator | Towers, Sebastian Kalisz, Aleksandra Robert, Philippe A Higueruelo, Alicia Vianello, Francesca Tsai, Ming-Han Chloe Steel, Harrison Foerster, Jakob N |
description | Anti-viral therapies are typically designed to target only the current
strains of a virus. Game theoretically, this corresponds to a short-sighted, or
myopic, response. However, therapy-induced selective pressures act on viruses
to drive the emergence of mutated strains, against which initial therapies have
reduced efficacy. Building on a computational model of binding between
antibodies and viral antigens (the Absolut! framework), we design and implement
a genetic simulation of viral evolutionary escape. Crucially, this allows our
antibody optimisation algorithm to consider and influence the entire escape
curve of the virus, i.e. to guide (or "shape") the viral evolution. This is
inspired by opponent shaping which, in general-sum learning, accounts for the
adaptation of the co-player rather than playing a myopic best response. Hence
we call the optimised antibodies shapers. Within our simulations, we
demonstrate that our shapers target both current and simulated future viral
variants, outperforming the antibodies chosen in a myopic way. Furthermore, we
show that shapers exert specific evolutionary pressure on the virus compared to
myopic antibodies. Altogether, shapers modify the evolutionary trajectories of
viral strains and minimise the viral escape compared to their myopic
counterparts. While this is a simplified model, we hope that our proposed
paradigm will facilitate the discovery of better long-lived vaccines and
antibody therapies in the future, enabled by rapid advancements in the
capabilities of simulation tools. Our code is available at
https://github.com/olakalisz/antibody-shapers. |
doi_str_mv | 10.48550/arxiv.2409.10588 |
format | Article |
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strains of a virus. Game theoretically, this corresponds to a short-sighted, or
myopic, response. However, therapy-induced selective pressures act on viruses
to drive the emergence of mutated strains, against which initial therapies have
reduced efficacy. Building on a computational model of binding between
antibodies and viral antigens (the Absolut! framework), we design and implement
a genetic simulation of viral evolutionary escape. Crucially, this allows our
antibody optimisation algorithm to consider and influence the entire escape
curve of the virus, i.e. to guide (or "shape") the viral evolution. This is
inspired by opponent shaping which, in general-sum learning, accounts for the
adaptation of the co-player rather than playing a myopic best response. Hence
we call the optimised antibodies shapers. Within our simulations, we
demonstrate that our shapers target both current and simulated future viral
variants, outperforming the antibodies chosen in a myopic way. Furthermore, we
show that shapers exert specific evolutionary pressure on the virus compared to
myopic antibodies. Altogether, shapers modify the evolutionary trajectories of
viral strains and minimise the viral escape compared to their myopic
counterparts. While this is a simplified model, we hope that our proposed
paradigm will facilitate the discovery of better long-lived vaccines and
antibody therapies in the future, enabled by rapid advancements in the
capabilities of simulation tools. Our code is available at
https://github.com/olakalisz/antibody-shapers.</description><identifier>DOI: 10.48550/arxiv.2409.10588</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computer Science and Game Theory ; Computer Science - Multiagent Systems ; Quantitative Biology - Populations and Evolution</subject><creationdate>2024-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2409.10588$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2409.10588$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Towers, Sebastian</creatorcontrib><creatorcontrib>Kalisz, Aleksandra</creatorcontrib><creatorcontrib>Robert, Philippe A</creatorcontrib><creatorcontrib>Higueruelo, Alicia</creatorcontrib><creatorcontrib>Vianello, Francesca</creatorcontrib><creatorcontrib>Tsai, Ming-Han Chloe</creatorcontrib><creatorcontrib>Steel, Harrison</creatorcontrib><creatorcontrib>Foerster, Jakob N</creatorcontrib><title>Opponent Shaping for Antibody Development</title><description>Anti-viral therapies are typically designed to target only the current
strains of a virus. Game theoretically, this corresponds to a short-sighted, or
myopic, response. However, therapy-induced selective pressures act on viruses
to drive the emergence of mutated strains, against which initial therapies have
reduced efficacy. Building on a computational model of binding between
antibodies and viral antigens (the Absolut! framework), we design and implement
a genetic simulation of viral evolutionary escape. Crucially, this allows our
antibody optimisation algorithm to consider and influence the entire escape
curve of the virus, i.e. to guide (or "shape") the viral evolution. This is
inspired by opponent shaping which, in general-sum learning, accounts for the
adaptation of the co-player rather than playing a myopic best response. Hence
we call the optimised antibodies shapers. Within our simulations, we
demonstrate that our shapers target both current and simulated future viral
variants, outperforming the antibodies chosen in a myopic way. Furthermore, we
show that shapers exert specific evolutionary pressure on the virus compared to
myopic antibodies. Altogether, shapers modify the evolutionary trajectories of
viral strains and minimise the viral escape compared to their myopic
counterparts. While this is a simplified model, we hope that our proposed
paradigm will facilitate the discovery of better long-lived vaccines and
antibody therapies in the future, enabled by rapid advancements in the
capabilities of simulation tools. Our code is available at
https://github.com/olakalisz/antibody-shapers.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computer Science and Game Theory</subject><subject>Computer Science - Multiagent Systems</subject><subject>Quantitative Biology - Populations and Evolution</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjGw1DM0MLWw4GTQ9C8oyM9LzStRCM5ILMjMS1dIyy9ScMwryUzKT6lUcEktS83JL8gFKuBhYE1LzClO5YXS3Azybq4hzh66YEPjC4oycxOLKuNBhseDDTcmrAIA37AubQ</recordid><startdate>20240916</startdate><enddate>20240916</enddate><creator>Towers, Sebastian</creator><creator>Kalisz, Aleksandra</creator><creator>Robert, Philippe A</creator><creator>Higueruelo, Alicia</creator><creator>Vianello, Francesca</creator><creator>Tsai, Ming-Han Chloe</creator><creator>Steel, Harrison</creator><creator>Foerster, Jakob N</creator><scope>AKY</scope><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20240916</creationdate><title>Opponent Shaping for Antibody Development</title><author>Towers, Sebastian ; Kalisz, Aleksandra ; Robert, Philippe A ; Higueruelo, Alicia ; Vianello, Francesca ; Tsai, Ming-Han Chloe ; Steel, Harrison ; Foerster, Jakob N</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2409_105883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computer Science and Game Theory</topic><topic>Computer Science - Multiagent Systems</topic><topic>Quantitative Biology - Populations and Evolution</topic><toplevel>online_resources</toplevel><creatorcontrib>Towers, Sebastian</creatorcontrib><creatorcontrib>Kalisz, Aleksandra</creatorcontrib><creatorcontrib>Robert, Philippe A</creatorcontrib><creatorcontrib>Higueruelo, Alicia</creatorcontrib><creatorcontrib>Vianello, Francesca</creatorcontrib><creatorcontrib>Tsai, Ming-Han Chloe</creatorcontrib><creatorcontrib>Steel, Harrison</creatorcontrib><creatorcontrib>Foerster, Jakob N</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Towers, Sebastian</au><au>Kalisz, Aleksandra</au><au>Robert, Philippe A</au><au>Higueruelo, Alicia</au><au>Vianello, Francesca</au><au>Tsai, Ming-Han Chloe</au><au>Steel, Harrison</au><au>Foerster, Jakob N</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Opponent Shaping for Antibody Development</atitle><date>2024-09-16</date><risdate>2024</risdate><abstract>Anti-viral therapies are typically designed to target only the current
strains of a virus. Game theoretically, this corresponds to a short-sighted, or
myopic, response. However, therapy-induced selective pressures act on viruses
to drive the emergence of mutated strains, against which initial therapies have
reduced efficacy. Building on a computational model of binding between
antibodies and viral antigens (the Absolut! framework), we design and implement
a genetic simulation of viral evolutionary escape. Crucially, this allows our
antibody optimisation algorithm to consider and influence the entire escape
curve of the virus, i.e. to guide (or "shape") the viral evolution. This is
inspired by opponent shaping which, in general-sum learning, accounts for the
adaptation of the co-player rather than playing a myopic best response. Hence
we call the optimised antibodies shapers. Within our simulations, we
demonstrate that our shapers target both current and simulated future viral
variants, outperforming the antibodies chosen in a myopic way. Furthermore, we
show that shapers exert specific evolutionary pressure on the virus compared to
myopic antibodies. Altogether, shapers modify the evolutionary trajectories of
viral strains and minimise the viral escape compared to their myopic
counterparts. While this is a simplified model, we hope that our proposed
paradigm will facilitate the discovery of better long-lived vaccines and
antibody therapies in the future, enabled by rapid advancements in the
capabilities of simulation tools. Our code is available at
https://github.com/olakalisz/antibody-shapers.</abstract><doi>10.48550/arxiv.2409.10588</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Computer Science - Multiagent Systems Quantitative Biology - Populations and Evolution |
title | Opponent Shaping for Antibody Development |
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