Topology in Biology

Blevins, Ann S.; Bassett, Danielle S.

doi:10.1007/978-3-319-57072-3_87

Cited by 2 publications

(2 citation statements)

References 99 publications

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“…Computational topology and higher-order networks have proven successful for analyzing the full spectrum of brain data ranging from functional networks [270], to morphology of branching neurons [167], to structural (synaptic) connectivity [246], to place cells [127], to the C. elegans connectome [147], to imaging of brain disease [90,51]. Rather than an exhaustive list of research in topological neuroscience, we refer the reader to a few recent survey articles [91,149,43].…”

Section: Applications Of Persistent Homologymentioning

confidence: 99%

“…Higher-order networks have also proven useful in genomics and evolutionary biology [243,65], structural biology [302], as well as for the analysis of structures such as vascular networks [28,280,54]. References overviewing the potential of topological techniques (e.g., to biology) include [43,9,243,281]. Recent studies suggest that higher-order network structures and computational topology can be helpful for analyzing complicated mathematical models of biological systems [289,175,221,207].…”

Section: Applications Of Persistent Homologymentioning

confidence: 99%

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What Are Higher-Order Networks?

Bick,

Gross,

Harrington

et al. 2023

SIAM Rev.

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Network-based modeling of complex systems and data using the language of graphs has become an essential topic across a range of different disciplines. Arguably, this graph-based perspective derives its success from the relative simplicity of graphs: A graph consists of nothing more than a set of vertices and a set of edges, describing relationships between pairs of such vertices. This simple combinatorial structure makes graphs interpretable and flexible modeling tools. The simplicity of graphs as system models, however, has been scrutinized in the literature recently. Specifically, it has been argued from a variety of different angles that there is a need for higher-order networks, which go beyond the paradigm of modeling pairwise relationships, as encapsulated by graphs. In this survey article we take stock of these recent developments. Our goals are to clarify (i) what higherorder networks are, (ii) why these are interesting objects of study, and (iii) how they can be used in applications.

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