Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs

Autores
Margarit, David Hipólito; Reale, Marcela Verónica; Coolen, Anthony C.C.
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Metastasis is a complex, stochastic systemic process characterised by non-random spreadpatterns to secondary organs. Conventional dyadic projections fragment multi-organ interactionsinto independent events, effectively discarding irreducible higher-order topological informationand the synergistic nature of clonal dissemination. This limitation often leads to conflatingfirst-order topological properties with intrinsic biological affinities, particularly in the absenceof validation against structure-aware null models. To address this methodological gap, we present a higher-order hypergraph frameworkthat preserves simultaneous colonisation patterns and accounts for emergent architectures thatcannot be reduced to simple pairwise interactions. Our approach incorporates: (1) a functionalpartition of the network nodes (, , ) to distinguish organs with documented reseedingcapacity; and (2) a directed association matrix S quantifying the non-linear information fluxand dependencies among source organs.We implement a rigorous validation protocol by exploring the constrained null-space ofthe metastatic hypergraph using a Metropolis–Hastings MCMC algorithm. By contrasting theempirical data against a degree-preserving Canonical Ensemble (Configuration Model), thisframework acts as a structural filter, demonstrating that many observed metastatic hotspots arespurious correlations emergent from nodal degree heterogeneity rather than genuine biologicalco-selection. By de-convoluting effective affinities from degree-driven effects, our methodidentifies a robust structural backbone that remains invariant under topological perturbations.This work shifts the focus from counting isolated events to quantifying the statistical mechanicsof non-linear disease progression in complex biological networks.
Fil: Margarit, David Hipólito. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Reale, Marcela Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Coolen, Anthony C.C.. Radboud Universiteit Nijmegen; Países Bajos
Materia
Hypergraphs
Complex networks
Metastatic organotropism
Null models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/291783
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/291783
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphsMargarit, David HipólitoReale, Marcela VerónicaCoolen, Anthony C.C.HypergraphsComplex networksMetastatic organotropismNull modelshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Metastasis is a complex, stochastic systemic process characterised by non-random spreadpatterns to secondary organs. Conventional dyadic projections fragment multi-organ interactionsinto independent events, effectively discarding irreducible higher-order topological informationand the synergistic nature of clonal dissemination. This limitation often leads to conflatingfirst-order topological properties with intrinsic biological affinities, particularly in the absenceof validation against structure-aware null models. To address this methodological gap, we present a higher-order hypergraph frameworkthat preserves simultaneous colonisation patterns and accounts for emergent architectures thatcannot be reduced to simple pairwise interactions. Our approach incorporates: (1) a functionalpartition of the network nodes (, , ) to distinguish organs with documented reseedingcapacity; and (2) a directed association matrix S quantifying the non-linear information fluxand dependencies among source organs.We implement a rigorous validation protocol by exploring the constrained null-space ofthe metastatic hypergraph using a Metropolis–Hastings MCMC algorithm. By contrasting theempirical data against a degree-preserving Canonical Ensemble (Configuration Model), thisframework acts as a structural filter, demonstrating that many observed metastatic hotspots arespurious correlations emergent from nodal degree heterogeneity rather than genuine biologicalco-selection. By de-convoluting effective affinities from degree-driven effects, our methodidentifies a robust structural backbone that remains invariant under topological perturbations.This work shifts the focus from counting isolated events to quantifying the statistical mechanicsof non-linear disease progression in complex biological networks.Fil: Margarit, David Hipólito. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; ArgentinaFil: Reale, Marcela Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; ArgentinaFil: Coolen, Anthony C.C.. Radboud Universiteit Nijmegen; Países BajosElsevier2026-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/291783Margarit, David Hipólito; Reale, Marcela Verónica; Coolen, Anthony C.C.; Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs; Elsevier; Nonlinear Science; 8; 5-2026; 1-203050-5178CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S305051782600050Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.nls.2026.100155info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:48:31Zoai:ri.conicet.gov.ar:11336/291783instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:48:32.08CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
title Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
spellingShingle Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
Margarit, David Hipólito
Hypergraphs
Complex networks
Metastatic organotropism
Null models
title_short Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
title_full Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
title_fullStr Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
title_full_unstemmed Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
title_sort Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs
dc.creator.none.fl_str_mv Margarit, David Hipólito
Reale, Marcela Verónica
Coolen, Anthony C.C.
author Margarit, David Hipólito
author_facet Margarit, David Hipólito
Reale, Marcela Verónica
Coolen, Anthony C.C.
author_role author
author2 Reale, Marcela Verónica
Coolen, Anthony C.C.
author2_role author
author
dc.subject.none.fl_str_mv Hypergraphs
Complex networks
Metastatic organotropism
Null models
topic Hypergraphs
Complex networks
Metastatic organotropism
Null models
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Metastasis is a complex, stochastic systemic process characterised by non-random spreadpatterns to secondary organs. Conventional dyadic projections fragment multi-organ interactionsinto independent events, effectively discarding irreducible higher-order topological informationand the synergistic nature of clonal dissemination. This limitation often leads to conflatingfirst-order topological properties with intrinsic biological affinities, particularly in the absenceof validation against structure-aware null models. To address this methodological gap, we present a higher-order hypergraph frameworkthat preserves simultaneous colonisation patterns and accounts for emergent architectures thatcannot be reduced to simple pairwise interactions. Our approach incorporates: (1) a functionalpartition of the network nodes (, , ) to distinguish organs with documented reseedingcapacity; and (2) a directed association matrix S quantifying the non-linear information fluxand dependencies among source organs.We implement a rigorous validation protocol by exploring the constrained null-space ofthe metastatic hypergraph using a Metropolis–Hastings MCMC algorithm. By contrasting theempirical data against a degree-preserving Canonical Ensemble (Configuration Model), thisframework acts as a structural filter, demonstrating that many observed metastatic hotspots arespurious correlations emergent from nodal degree heterogeneity rather than genuine biologicalco-selection. By de-convoluting effective affinities from degree-driven effects, our methodidentifies a robust structural backbone that remains invariant under topological perturbations.This work shifts the focus from counting isolated events to quantifying the statistical mechanicsof non-linear disease progression in complex biological networks.
Fil: Margarit, David Hipólito. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Reale, Marcela Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de General Sarmiento. Instituto de Ciencias; Argentina
Fil: Coolen, Anthony C.C.. Radboud Universiteit Nijmegen; Países Bajos
description Metastasis is a complex, stochastic systemic process characterised by non-random spreadpatterns to secondary organs. Conventional dyadic projections fragment multi-organ interactionsinto independent events, effectively discarding irreducible higher-order topological informationand the synergistic nature of clonal dissemination. This limitation often leads to conflatingfirst-order topological properties with intrinsic biological affinities, particularly in the absenceof validation against structure-aware null models. To address this methodological gap, we present a higher-order hypergraph frameworkthat preserves simultaneous colonisation patterns and accounts for emergent architectures thatcannot be reduced to simple pairwise interactions. Our approach incorporates: (1) a functionalpartition of the network nodes (, , ) to distinguish organs with documented reseedingcapacity; and (2) a directed association matrix S quantifying the non-linear information fluxand dependencies among source organs.We implement a rigorous validation protocol by exploring the constrained null-space ofthe metastatic hypergraph using a Metropolis–Hastings MCMC algorithm. By contrasting theempirical data against a degree-preserving Canonical Ensemble (Configuration Model), thisframework acts as a structural filter, demonstrating that many observed metastatic hotspots arespurious correlations emergent from nodal degree heterogeneity rather than genuine biologicalco-selection. By de-convoluting effective affinities from degree-driven effects, our methodidentifies a robust structural backbone that remains invariant under topological perturbations.This work shifts the focus from counting isolated events to quantifying the statistical mechanicsof non-linear disease progression in complex biological networks.
publishDate 2026
dc.date.none.fl_str_mv 2026-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/291783
Margarit, David Hipólito; Reale, Marcela Verónica; Coolen, Anthony C.C.; Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs; Elsevier; Nonlinear Science; 8; 5-2026; 1-20
3050-5178
CONICET Digital
CONICET
url http://hdl.handle.net/11336/291783
identifier_str_mv Margarit, David Hipólito; Reale, Marcela Verónica; Coolen, Anthony C.C.; Higher-order architecture of metastatic networks: A statistical mechanics approach via validated hypergraphs; Elsevier; Nonlinear Science; 8; 5-2026; 1-20
3050-5178
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S305051782600050X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nls.2026.100155
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1874774703234613248
score 13.265058

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