title: Inferring epigenetic dynamics from kin correlations.
abstract: Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity that arises from intrinsic stochastic dynamics of cells. The phenotypic state of a cell can be transmitted epigenetically in cell division, leading to correlations in the states of cells related by descent. The extent of these correlations is determined by the rates of transitions between the phenotypic states. Therefore, a snapshot of the phenotypes 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 that allows extraction of an approximate probabilistic description of the dynamics from observed phenotype correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in Pseudomonas aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but nontrivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal--a symmetry characteristic of critical behavior in physical systems. The proposed inference method is sufficiently general to be applied in any system where lineage information is available.
authors.affiliation: Kavli Institute for Theoretical Physics and.
authors.forename: Sahand
authors.initials: S
authors.lastname: Hormoz
authors.affiliation: Laboratoire de Physique Statistique, UMR8550, Ecole Normale Supérieure, 75005 Paris, France; Institut de Biologie, UMR 8197, Ecole Normale Supérieure, 75230 Paris, France; and Université Paris Diderot, 75205 Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Desprat
authors.affiliation: Kavli Institute for Theoretical Physics and Department of Physics, University of California, Santa Barbara, CA 93106; shraiman@kitp.ucsb.edu.
authors.forename: Boris I
authors.initials: BI
authors.lastname: Shraiman
mesh_terms: D000465:Algorithms; D000818:Animals; D053595:Embryonic Stem Cells / Q000166:cytology; D044127:Epigenesis, Genetic; D015233:Models, Statistical; D010641:Phenotype; D011336:Probability; D011550:Pseudomonas aeruginosa / Q000235:genetics*; D013234:Stem Cells / Q000166:cytology; D013269:Stochastic Processes
keywords: Bethe lattice; conformal symmetry; correlation functions; stochastic dynamics