The physics of higher-order interactions in complex systems

Battiston, Federico; Amico, Enrico; Barrat, Alain; Bianconi, Ginestra; Arruda, Guilherme Ferraz de; Franceschiello, Benedetta; Iacopini, Iacopo; Kéfi, Sonia; Latora, Vito; Moreno, Yamir; Murray, Micah M.; Peixoto, Tiago P.; Vaccarino, Francesco; Petri, Giovanni

doi:10.1038/s41567-021-01371-4

Cited by 439 publications

(262 citation statements)

References 105 publications

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“…We used hypernetworks to investigate the relationships between MCTs and ECMs and tease out the functional connectivity among the individual elements. Hypernetworks summarise the conditional independence between genes in a network model by using all the interaction data, not just pairwise [ 28 ]. This defines a roadmap of the relationships between genes where the “width” of the roadways is recognised as defining the importance of the higher-order organisation that represents the generation of the mechanism [ 72 ].…”

Section: Discussionmentioning

confidence: 99%

“…The causal relationship between MCTs and ECMs was evaluated using a hypernetwork modelling approach as previously described [ 29 , 31 ]. Briefly, hypernetworks represent network structures where edges define a relationship between nodes (e.g., transcripts) and can be shared by many nodes ( Appendix A , Figure A1 ); this is the definition of “higher order interactions” [ 28 ]. In this way, hypernetwork structures are used to delineate complex relationships that connect multiple omic elements.…”

Section: Methodsmentioning

confidence: 99%

“…Omics approaches are now widely used in the field of cancer to identify dysregulated molecular mechanisms, provide insights into biological pathways, and propose potential biomarkers for different cancer types. The incorporation of higher-order interactions driven from omic data is now seen as an essential part of modelling biological systems [ 28 ]. Hypernetworks model higher-order interactions or relationships between omic elements (represented by nodes) based on large numbers of shared correlations (represented by edges) [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Ufuk

Garner

Stevens

et al. 2022

Cancers

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Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival rate of <8%. PDAC is characterised by desmoplasia with an abundant extracellular matrix (ECM) rendering current therapies ineffective. Monocarboxylate transporters (MCTs) are key regulators of cellular metabolism and are upregulated in different cancers; however, their role in PDAC desmoplasia is little understood. Here, we investigated MCT and ECM gene expression in primary PDAC patient biopsies using RNA-sequencing data obtained from Gene Expression Omnibus. We generated a hypernetwork model from these data to investigate whether a causal relationship exists between MCTs and ECMs. Our analysis of stromal and epithelial tissues (n = 189) revealed nine differentially expressed MCTs, including the upregulation of SLC16A2/6/10 and the non-coding SLC16A1-AS1, and 502 ECMs, including collagens, laminins, and ECM remodelling enzymes (false discovery rate < 0.05). A causal hypernetwork analysis demonstrated a bidirectional relationship between MCTs and ECMs; four MCT and 255 ECM-related transcripts correlated with 90% of the differentially expressed ECMs (n = 376) and MCTs (n = 7), respectively. The hypernetwork model was robust, established by iterated sampling, direct path analysis, validation by an independent dataset, and random forests. This transcriptomic analysis highlights the role of MCTs in PDAC desmoplasia via associations with ECMs, opening novel treatment pathways to improve patient survival.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Ufuk

Garner

Stevens

et al. 2022

Cancers

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“…Yet, the problem remains unsolved for complex networked systems. Finally, the nodes in a complex system can have higher-order, beyond pairwise, couplings and such higher-order interactions may significantly impact the dynamics of networked systems 49,50 . Hence it is an interesting direction to extend the approach to inferring higher-order network dynamics.…”

Section: Discussionmentioning

confidence: 99%

Autonomous inference of complex network dynamics from incomplete and noisy data

Gao,

Yan

2022

Preprint

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The availability of empirical data that capture the structure and behavior of complex networked systems has been greatly increased in recent years, however a versatile computational toolbox for unveiling a complex system's nodal and interaction dynamics from data remains elusive. Here we develop a two-phase approach for autonomous inference of complex network dynamics, and its effectiveness is demonstrated by the tests of inferring neuronal, genetic, social, and coupled oscillators dynamics on various synthetic and real networks. Importantly, the approach is robust to incompleteness and noises, including low resolution, observational and dynamical noises, missing and spurious links, and dynamical heterogeneity. We apply the two-phase approach to inferring the early spreading dynamics of H1N1 flu upon the worldwide airline network, and the inferred dynamical equation can also capture the spread of SARS and COVID-19 diseases. These findings together offer an avenue to discover the hidden microscopic mechanisms of a broad array of real networked systems.

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“…In some ecosystems, multiple species may mutually compete for food and territory and affect each other. Those interactions are known as high-order interactions, which are about influence or similarity at the level of groups of agents [16,17]. A high-order interaction can not only correspond to a specific semantic attribute or behavior of agents, but it can also be an interplay among agents in a process or an overall similarity of agents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Hypergraph Signals via High-Order Total Variation

Feng

et al. 2022

Symmetry

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Beyond pairwise relationships, interactions among groups of agents do exist in many real-world applications, but they are difficult to capture by conventional graph models. Generalized from graphs, hypergraphs have been introduced to describe such high-order group interactions. Inspired by graph signal processing (GSP) theory, an existing hypergraph signal processing (HGSP) method presented a spectral analysis framework relying on the orthogonal CP decomposition of adjacency tensors. However, such decomposition may not exist even for supersymmetric tensors. In this paper, we propose a high-order total variation (HOTV) form of a hypergraph signal (HGS) as its smoothness measure, which is a hyperedge-wise measure aggregating all signal values in each hyperedge instead of a pairwise one in most existing work. Further, we propose an HGS analysis framework based on the Tucker decomposition of the hypergraph Laplacian induced by the aforementioned HOTV. We construct an orthonormal basis from the HOTV, by which a new spectral transformation of the HGS is introduced. Then, we design hypergraph filters in both vertex and spectral domains correspondingly. Finally, we illustrate the advantages of the proposed framework by applications in label learning.

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The physics of higher-order interactions in complex systems

Cited by 439 publications

References 105 publications

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Autonomous inference of complex network dynamics from incomplete and noisy data

Analysis of Hypergraph Signals via High-Order Total Variation

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