Abstract

Bacteria are ubiquitous in our daily lives, either as motile planktonic cells or as immobilized surface-attached biofilms. These different phenotypic states play key roles in agriculture, environment, industry, and medicine; hence, it is critically important to be able to predict the conditions under which bacteria transition from one state to the other. Unfortunately, these transitions depend on a dizzyingly complex array of factors that are determined by the intrinsic properties of the individual cells as well as those of their surrounding environments, and are thus challenging to describe. To address this issue, here, we develop a generally-applicable biophysical model of the interplay between motility-mediated dispersal and biofilm formation under positive quorum sensing control. Using this model, we establish a universal rule predicting how the onset and extent of biofilm formation depend collectively on cell concentration and motility, nutrient diffusion and consumption, chemotactic sensing, and autoinducer production. Our work thus provides a key step toward quantitatively predicting and controlling biofilm formation in diverse and complex settings.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for all Figures

Article and author information

Author details

  1. Jenna Anne Moore-Ott

    Department of Chemical and Biological Engineering, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6832-0658
  2. Selena Chiu

    Department of Chemical and Biological Engineering, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Daniel B Amchin

    Department of Chemical and Biological Engineering, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Tapomoy Bhattacharjee

    Andlinger Center for Energy and the Environment, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Sujit Sankar Datta

    Department of Chemical and Biological Engineering, Princeton University, Princeton, United States
    For correspondence
    ssdatta@princeton.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2400-1561

Funding

National Science Foundation (CBET-1941716)

  • Sujit Sankar Datta

National Science Foundation (EF-2124863)

  • Sujit Sankar Datta

National Science Foundation (DMR-2011750)

  • Sujit Sankar Datta

Pew Charitable Trusts (Pew Biomedical Scholars Program)

  • Sujit Sankar Datta

National Science Foundation (DGE-1656466)

  • Jenna Anne Moore-Ott

Princeton University (Eric and Wendy 708 Schmidt Transformative Technology Fund)

  • Sujit Sankar Datta

Princeton University (Princeton Catalysis Initiative)

  • Sujit Sankar Datta

Princeton University (Reiner G. Stoll Undergraduate Summer Fellowship)

  • Selena Chiu

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Moore-Ott et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,353
    views
  • 749
    downloads
  • 18
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jenna Anne Moore-Ott
  2. Selena Chiu
  3. Daniel B Amchin
  4. Tapomoy Bhattacharjee
  5. Sujit Sankar Datta
(2022)
A biophysical threshold for biofilm formation
eLife 11:e76380.
https://doi.org/10.7554/eLife.76380

Share this article

https://doi.org/10.7554/eLife.76380

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Juan Manuel Gomez, Hendrik Nolte ... Maria Leptin
    Research Article Updated

    The initially homogeneous epithelium of the early Drosophila embryo differentiates into regional subpopulations with different behaviours and physical properties that are needed for morphogenesis. The factors at top of the genetic hierarchy that control these behaviours are known, but many of their targets are not. To understand how proteins work together to mediate differential cellular activities, we studied in an unbiased manner the proteomes and phosphoproteomes of the three main cell populations along the dorso-ventral axis during gastrulation using mutant embryos that represent the different populations. We detected 6111 protein groups and 6259 phosphosites of which 3398 and 3433 were differentially regulated, respectively. The changes in phosphosite abundance did not correlate with changes in host protein abundance, showing phosphorylation to be a regulatory step during gastrulation. Hierarchical clustering of protein groups and phosphosites identified clusters that contain known fate determinants such as Doc1, Sog, Snail, and Twist. The recovery of the appropriate known marker proteins in each of the different mutants we used validated the approach, but also revealed that two mutations that both interfere with the dorsal fate pathway, Toll10B and serpin27aex do this in very different manners. Diffused network analyses within each cluster point to microtubule components as one of the main groups of regulated proteins. Functional studies on the role of microtubules provide the proof of principle that microtubules have different functions in different domains along the DV axis of the embryo.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Natanael Spisak, Gabriel Athènes ... Aleksandra M Walczak
    Tools and Resources Updated

    B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B-cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution, and dynamics. We present HILARy (high-precision inference of lineages in antibody repertoires), an efficient, fast, and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.