Stochastic Turing patterns of trichomes in Arabidopsis leaves

Author:

Di Patti Francesca12ORCID,Ugartechea Chirino Yamel3,Arbel-Goren Rinat4ORCID,Sharon Tom4ORCID,Castillo Aaron3ORCID,Alvarez–Buylla Elena3,Fanelli Duccio56,Stavans Joel4ORCID

Affiliation:

1. Dipartimento di Matematica e Informatica, Universitá degli Studi di Perugia, Perugia 06123, Italia

2. Istituto Nazionale di Fisica Nucleare - Sezione di Perugia, Perugia 06123, Italia

3. Instituto de Ecología, Universidad Nacional Autónoma de México Ciudad, Universitaria 3er Circuito Interior Coyoacán, Ciudad de México 04510, México

4. Faculty of Physics, Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel

5. Dipartimento di Fisica e Astronomia, Università degli Studi di Firenze, Sesto Fiorentino, Firenze 50019, Italia

6. Centro Interdipartimentale per lo Studio delle Dinamiche Complesse and Istituto Nazionale di Fisica Nucleare Sezione di Firenze, Sesto Fiorentino, Firenze 50019, Italia

Abstract

Biological patterns that emerge during the morphogenesis of multicellular organisms can display high precision at large scales, while at cellular scales, cells exhibit large fluctuations stemming from cell–cell differences in molecular copy numbers also called demographic noise. We study the conflicting interplay between high precision and demographic noise in trichome patterns on the epidermis of wild-type Arabidopsis thaliana leaves, as a two-dimensional model system. We carry out a statistical characterization of these patterns and show that their power spectra display fat tails—a signature compatible with noise-driven stochastic Turing patterns—which are absent in power spectra of patterns driven by deterministic instabilities. We then present a theoretical model that includes demographic noise stemming from birth–death processes of genetic regulators which we study analytically and by stochastic simulations. The model captures the observed experimental features of trichome patterns.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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