The lab focuses on understanding multicellular bacterial behaviors,
using
biofilm formation and swarming as model
systems. Bacterial biofilms are
surface-associated bacterial communities that are held together by an
extracellular matrix. Cells within these communities are highly
tolerant to antibiotics and display strong phenotypic heterogeneity.
By combining microscopy, molecular biology techniques, advanced data
analysis, machine learning, and mathematical
modeling, we study how bacteria form complex multicellular communities,
how these communities function, and how these communities affect
bacterial ecology.
Biofilms in Ecology and Evolution
Why do bacteria form biofilms? Bacteria that are bound in biofilms are
highly resistant against antibiotics and other chemical insults of the
environment, which is a clear evolutionary advantage of forming
biofilms. Remarkably, the mechanisms underlying the biofilm-antibiotic interaction is poorly understood, and we are
investigating unicellular and multicellular responses to antibiotics in biofilms [Diaz-Pascual et al. 2019].
Apart from providing protection against toxins, evolutionary advantages to biofilm formation are vague.
However, we recently found the mechanisms underlying the most
important selective advantage of making a biofilm: predation avoidance
by bacteriophages.
[Vidakovic,
et al. 2018; Simmons,
et al. 2018; Simmons et al. 2019]
We also recently discovered another reason for why
bacteria may want to form biofilms: physical aspects of the biofilm
life style strongly favor the evolution of simple social behaviors,
such as the production of shared resources or "public goods"
[Drescher,
et al. 2014; Nadell,
et al. 2013].
In addition, we are investigation social interactions in spatially
structured biofilm communities [Dragos, et al. 2018; Nadell, et al.
2016].
Biofilm Dynamics: From Growth to Dispersal
What determines the biofilm architecture, and how do cells decide when
they should disperse from biofilms? We recently developed novel imaging
techniques that allow us to track all individual cells in biofilms,
revealing beautiful internal cellular arrangements, and the different
stages of biofilm growth.
Drescher,
et al. 2016]
We are now using these (and improved) imaging techniques to identify
key cell-cell interactions in biofilms that determine the multicellular
community growth [Hartmann, et al. 2019]. Based on this single-cell
imaging, we also revealed how biofilms interact with fluid flow
[Pearce, et al. 2019] and how biofilms respond to antibiotics
[Diaz-Pascual, et al. 2019].
Cells need not stay in a biofilm forever. Yet it is unclear how cells
reach a decision for when they should decide to disperse. We recently
discovered that cells monitor a self-secreted quorum sensing signal,
and the local nutrient concentration, to reach robust decisions about
dispersal as a collective. [Singh,
et al. 2017]
Biophysics of Collective Behaviors
What can we learn about collective bacterial behaviors from physics?
Many aspects of bacterial interactions are inherently physical. Some
examples: During biofilm growth, cells push and pull on each other,
while being embedded in an elastic matrix. Understanding the molecular
transport of nutrients and metabolites through the biofilm also relies
on physics. Before bacteria form biofilms, their swimming motility
creates fluid flows that lead to physical interactions with surfaces
and other bacteria.
[Jeckel,
et al. 2019;
Drescher,
et al. 2011; Wensink,
et al. 2012; Dunkel, et al.
2014;]
Biofilm Interactions with Flow
How do biofilms grow in realistic physical and chemical environments?
Biofilms are often thought to occur as surface-attached films. However,
in environmental conditions that mimic their natural habitats, biofilms
of P. aeruginosa and S. aureus are deformed into
string-like structures. We discovered that these structures have a
mesh-like architecture that captures other cells that are flowing past
to grow explosively fast and cause rapid clogging of various
industrial, environmental, and medical flow systems.
[Pearce, et al. 2019; Drescher,
et al. 2013; Kim, et
al. 2014]