Pablo Bravo
PhD Candidate in Quantitative Biosciences at GeorgiaTech. I am interested in biofilms, and how their topography evolves and what we could learn about their development by looking at them
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
- 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.
