Inferring Epigenetic Dynamics from Kin Correlations

TL;DR

This paper introduces a method to infer phenotypic dynamics from kinship correlations in cell populations, revealing conformal symmetry indicative of critical behavior, validated on bacterial colonies.

Contribution

The paper presents a novel inference approach that extracts probabilistic phenotypic dynamics from genealogical correlations, applicable across various biological systems with lineage data.

Findings

Correlations among relatives exhibit a simple, non-trivial structure.

Phenotypic dynamics are approximately conformal, indicating criticality.

Method validated on P. aeruginosa colonies.

Abstract

Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity which arises from stochastic intrinsic dynamics of cells. The internal state of the cell can be transmitted epigenetically in cell division, leading to correlations in the phenotypic states of cells related by descent. Therefore, a phenotypic snapshot of a collection of cells with known genealogical structure, contains information on phenotypic dynamics. Here we use a model of phenotypic dynamics on a genealogical tree to define an inference method which allows to extract an approximate probabilistic description of phenotypic dynamics based on measured correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in P. aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but non-trivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal - a symmetry characteristic of critical behavior in physical systems. Proposed inference method is sufficiently general to be applied in any system where lineage information is available.