Symmetry, gauge freedoms, and the interpretability of sequence-function relationships

Quantitative models that describe how biological sequences encode functional activities are ubiquitous in modern biology. One important aspect of these models is that they commonly exhibit gauge freedoms, i.e., directions in parameter space that do not affect model predictions. In physics, gauge freedoms arise when physical theories are formulated in ways that respect fundamental symmetries. However, the connections that gauge freedoms in models of sequence-function relationships have to the symmetries of sequence space have yet to be systematically studied. Here we study the gauge freedoms of models that respect a specific symmetry of sequence space: the group of position-specific character permutations. We find that gauge freedoms arise when model parameters transform under redundant irreducible matrix representations of this group. Based on this finding, we describe an "embedding distillation" procedure that enables analytic calculation of the number of independent gauge freedoms, as well as efficient computation of a sparse basis for the space of gauge freedoms. We also study how parameter transformation behavior affects parameter interpretability. We find that in many (and possibly all) nontrivial models, the ability to interpret individual model parameters as quantifying intrinsic allelic effects requires that gauge freedoms be present. This finding establishes an incompatibility between two distinct notions of parameter interpretability. Our work thus advances the understanding of symmetries, gauge freedoms, and parameter interpretability in sequence-function relationships.

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Publication Details

Journal
Physical Review Research
Published
2025-04-02
DOI
https://doi.org/10.1103/physrevresearch.7.023005
Citations
1
Primary Topic
Evolution and Genetic Dynamics
Type
article
Field-Weighted Citation Impact
1.82

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article

Symmetry, gauge freedoms, and the interpretability of sequence-function relationships

David M. McCandlish, Anna Posfai, Justin B. Kinney
1 citations
Physical Review Research
Evolution and Genetic Dynamics
1.82
article

Symmetry, gauge freedoms, and the interpretability of sequence-function relationships

David M. McCandlish, Anna Posfai, Justin B. Kinney
article en
1 citations

Abstract

Quantitative models that describe how biological sequences encode functional activities are ubiquitous in modern biology. One important aspect of these models is that they commonly exhibit gauge freedoms, i.e., directions in parameter space that do not affect model predictions. In physics, gauge freedoms arise when physical theories are formulated in ways that respect fundamental symmetries. However, the connections that gauge freedoms in models of sequence-function relationships have to the symmetries of sequence space have yet to be systematically studied. Here we study the gauge freedoms of models that respect a specific symmetry of sequence space: the group of position-specific character permutations. We find that gauge freedoms arise when model parameters transform under redundant irreducible matrix representations of this group. Based on this finding, we describe an "embedding distillation" procedure that enables analytic calculation of the number of independent gauge freedoms, as well as efficient computation of a sparse basis for the space of gauge freedoms. We also study how parameter transformation behavior affects parameter interpretability. We find that in many (and possibly all) nontrivial models, the ability to interpret individual model parameters as quantifying intrinsic allelic effects requires that gauge freedoms be present. This finding establishes an incompatibility between two distinct notions of parameter interpretability. Our work thus advances the understanding of symmetries, gauge freedoms, and parameter interpretability in sequence-function relationships.

Physical Review ResearchVol. 7(2)
Cold Spring Harbor Laboratory (US)
Alfred P. Sloan Foundation, Cold Spring Harbor Laboratory, National Institutes of Health, Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory
Openalex Percentile: Top 17%
Evolution and Genetic Dynamics
1.82
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