@article{vittadello2021a,

title = {Model comparison via simplicial complexes and persistent homology},

author = {Sean T. Vittadello and Michael P.H. Stumpf},

doi = {10.1098/rsos.211361},

issn = {2054-5703},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Royal Society Open Science},

volume = {8},

number = {10},

pages = {211361},

abstract = {In many scientific and technological contexts, we have only a poor understanding of the structure and details of appropriate mathematical models. We often, therefore, need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. There is, however, a lack of rigorous methods to compare different models a priori. Here, we develop and illustrate two such approaches that allow us to compare model structures in a systematic way by representing models as simplicial complexes. Using well-developed concepts from simplicial algebraic topology, we define a distance between models based on their simplicial representations. Employing persistent homology with a flat filtration provides for alternative representations of the models as persistence intervals, which represent model structure, from which the model distances are also obtained. We then expand on this measure of model distance to study the concept of model equivalence to determine the conceptual similarity of models. We apply our methodology for model comparison to demonstrate an equivalence between a positional-information model and a Turing-pattern model from developmental biology, constituting a novel observation for two classes of models that were previously regarded as unrelated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}