The biophysics of biofilm surfaces

PhD Research Proposal

Doctoral Program in Quantitative Biosciences

Pablo Bravo

Advisor: Dr. Peter Yunker

Committee Members:

Dr. Sam Brown; School of Biological Sciences; Georgia Institute of Technology

Dr. Jennifer Curtis; School of Physics; Georgia Institute of Technology

Dr. Brian Hammer; School of Biological Sciences; Georgia Institute of Technology

Biofilms in context

Hall-Stoodley, L., et al., Nat Rev Microbiol (2012)

Lacy, D. E., et al. Journal of Infection (1993).

McConoughey, S., et al. F. Microbiology (2014)

Enning, D., et al.  Appl. and env. microbiology (2014).

Biomedical

Industry

Ecological

Biofilms are complex heterogeneous structures

Boudarel, H., et al. Biofilms Microbiomes (2018)

Inside the biofilms: confocal/light-sheet

Hartmann, R., et al. Nature Microbiology (2021)

Images by Dr. Gabi Steinbach

Unprocessed

Processed

"Structures" in the biofilm surface

\(1 cm\)

\(1 cm\)

Dietrich, L., et al. Journal of Bacteriology (2013)

Boudarel, H., et al. Biofilms Microbiomes (2018)

Biofilm-air interface

Interfaces

Kazantsev, D. et al. J. Phys: Conf (2017)

Interferometry

  • Multiple light sources in the instrument
  • Super-resolution measurements
  • Non-invasive
  • No preparation needed

\( 0.5 mm\)

0

2

4

6

8

10

\(\Delta z\) (\( \mu m\))

Central region of a vibrio cholerae biofilm

Surface topography + intensity!

Biofilm topographies & Yunkerlab

Kalziqi, A., et al. PRL (2018)

Kalziqi, A., et al. ArXiv preprint (2019)

  • Detect the amount of killing (T6SS)
  • Viscosity through topography
  • Rapid heteroresistance identification

Interferometry

  • Fast, high-resolution
  • Non-invasive
  • Surface measurements

Microbiology applications

Specific aims

I. Develop a biophysical understanding of biofilm interface growth

II. Characterize topographic fluctuations

III. Elucidate the effects of bacterial interactions

Specific aims

I. Develop a biophysical understanding of biofilm interface growth

II. Characterize topographic fluctuations

III. Elucidate the effects of bacterial interactions

Fancy isolation chamber

New old trick: timelapses

  • Sample going out of focus from evaporation

New old trick: timelapses

First attempts on a timelapse, not so good

Time (hours)

0

24

Surface profiles of a growing V. Cholerae biofilm

Wow, incredible, amazing!

Quick language slide

\( \langle h \rangle = \langle h \rangle_{\text{homeland}}\)

I. Biophysics of biofilm growth

Bad on longer timescales

Logistic can do well on certain sections,

but not in all of them!

Data         Model

Look at how height growth (z') as a function of height (z).

I. Biophysics of biofilm growth

\( K \)

Logistic 

\( z\cdot (1-\frac{z}{K}) \)

Exploring + visualization of height growth

Missing first 7h

~2M points

Weights

\(z' = \alpha G(z) z - \beta z \)

Fit

What is the minimum time we need to predict the long time behavior?

SN503 B

\frac {\partial h}{\partial t} =

G(h)

h

h*

Let's build the model!

\( h \)

\( c\)

\frac {\partial c}{\partial t} =
- \alpha \epsilon h \frac{c}{K+c}
\alpha h \frac{c}{K+c}
\beta h \quad

Growth 

Decay 

Depletion

-

Add geometry constraint!

Biofilm

Nutrient-rich media

Interface

\alpha G(h) \frac{c}{K+c} \, \,

Nutrients are not depleted (in lab conditions)

Column

Membrane

Colony

\( 1.5 \mu L \) on membrane

 48h @37C

  1. There's enough nutrients to keep growing.
\frac {\partial h}{\partial t} =

G(h)

h

h*

Let's build the model!

\( h \)

\( c\)

\frac {\partial c}{\partial t} =
- \alpha \epsilon h \frac{c}{K+c}
\alpha G(h) \frac{c}{K+c}
\frac {\partial h}{\partial t} = \alpha G (h, h^*) - \beta h
\beta h \quad

Growth 

Decay 

Depletion

-

Add geometry constraint!

1

Collected fish

Fishers

Lake with infinite fish

Saturation

Nutrient uptake

Colony Height

Saturation

Pluses and minuses

Let's build the model!

\( h \)

\( c\)

\frac {\partial h}{\partial t} = \alpha G (h, h^*) - \beta h

Add geometry constraint!

h^*
-\beta
\alpha-\beta

Does it do any better?

I. Biophysics of biofilm growth

h_{\text{max}} = \frac{\alpha h^*}{\beta}
  • Equilibrium prediction:
h_{\text{max}} \approx 300 \mu m
h_{\text{max}} \approx 230 \mu m

Thinking about spatial dependence

Fit parameters \(\alpha, \beta, h^*\) to each trajectory

\( h_{\text{max}} = \frac{\alpha h^*}{\beta} \)

Spatial interactions?

Less nutrient availability?

I. Captures height growth of multiple species

I. Biophysics of biofilm growth - Summary

  • Logistic models do not fit the data!
  • Proposed model with geometry
    • Agrees with data
    • Useful predictions
  • How universal is this? Meaning of each parameter?

Specific aims

I. Develop a biophysical understanding of biofilm interface growth

II. Characterize topographic fluctuations

III. Elucidate the effects of bacterial interactions

Fluctuations are not just noise!

Fluctuations are not just noise!

Fluctuations in:

  • Between replicates
  • In replicates (forces, spatial structure)

Height differences

Different rates \( (\alpha, \beta, h^*) \)

Force

Individual cells are quite unresponsive to external perturbations

Force 

Biofilms as composites of colloids in a cross-linked polymer gel

Force 

Hammer BK, et al. Mol Microbiol. (2003)

II. Extracellular Polymeric Substances (EPS)

EPS-         EPS+(wt)      EPS++(rugose)

Fei, C, et al. PNAS. (2020)

  • EPS is composed by proteins and polysaccharides
  • Responsible for the mechanical and viscoelastic properties of biofilms 
  • How does EPS affect the surface topography?
  • Controlled EPS production through HapR

Time (hours)

0

24

  • Width of perpendicular fluctuations in the height profile:

 

 

w_l(\mathbf{r}, t) = \langle (h (\mathbf{r}, t) - \langle h(\mathbf{r}, t) \rangle_l )^2 \rangle_l^{1/2}

\(\alpha\)

II. Quantifying fluctuations

Self similar systems link the fractal dimension \(FD\) and roughness \(\alpha\) with:

FD + \alpha = n + 1

Fractal Dimension FD

Measure of how  complexity  changes with scale

Surface roughness \(\alpha\)

Not all surfaces are self-similar

\(100 \mu m\)

Egg shell

Tomato

Leaf bottom

Egg in

Self similarity

Random interface growth predictions

II. Topography characterization

3 samples each

100 simulations

\(D=1, \lambda=0.5, \eta=1\)

100 simulations

\( \eta \propto\) growth

Can we get to a similar configuration if we add viscoelasticity? stress?

\(FD\)-\(\alpha\) phase space

S. Similarity

II. Topographic fluctuations - Summary

  • Characterize fluctuations across 4 orders of magnitude \( (0.177 \mu m - 7 mm ) \)
  • EPS production greatly influences topography
  • Can we extend our interface model to do a better job than random interface growth?

Specific aims

I. Develop a biophysical understanding of biofilm interface growth

II. Characterize topographic fluctuations

III. Elucidate the effects of bacterial interactions

III. Spatial structure in biofilms

Yanni, D., et al. ArXiv preprint. (2017)

Nadell, C., et al. Nat. Rev. Microbiology. (2016)

  • Developed biofilms have a well-defined spatial structure

 

  • Interaction in this inner structure can be reflected in the surface topography

III. Multi-species biofilms

\frac {\partial h}{\partial t} = \alpha G (h, h^*) - \beta h
\alpha_A, \beta_A, h_A^*
\alpha_B, \beta_B, h_B^*
A+B

Can we predict the multi-strain dynamics from the single-strain parameters?

Find interaction terms by varying the \(A+B\) initial composition

III. Heteroresistance and topography

\frac {\partial h}{\partial t} = D \nabla^2 h + \alpha G (h, h^*) - \beta h
  • Applied interface model with constant lateral diffusion
  • Tolerant and susceptible populations
    • \(\alpha_{\text{sus}} = 0.2 \alpha_{\text{tol}}\)
  • A mixed (1:1) population showed larger surface fluctuations than a full tolerant population

III. Biofilm surface memory

  • Signature in the topography from mutual killing after 24h
  • Most mutual killing stops after ~4 hours.
  • Does the killing signature persist on time? Or does growth+death erase it? What's the timescale?

Kalziqi, A., et al. PRL. (2018)

Steinbach, G., et al. J.R.Soc. Interface. (2020)

Interferometry

  • Fast, high-resolution
  • Non-invasive
  • Surface measurements

Microbiology applications

Framework

  • Experimental
  • Theoretical

I. Surface Growth

II. Height fluctuations

III. Interactions and surface

Interferometry

My PhD

Hopefully...

Acknowledgements

NIH-NIMS

NSF BMAT

Biolocity

Dr. Peter Yunker

Dr. Gabi Steinbach

Thomas Day

Aawaz Pokhrel

Emma Bingham

Adam Krueger

Raymond Copeland

Hammer Lab @ GT

Weiss Lab @ Emory

QBioS @ GT

Funding

Thanks!

QBioSProposal-BiophysicsBiofilmSurfaces

By Pablo Bravo

QBioSProposal-BiophysicsBiofilmSurfaces

Quantitative Biosciences PhD Proposal @ GT.

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