This document summarizes how antibiotics work, how antibiotic resistance evolves, and how physicists can help address the growing problem of antibiotic resistance. It discusses how antibiotics target bacterial processes like cell wall synthesis and protein synthesis. It also describes simple models that link the molecular mechanisms of antibiotic action to whole-cell physiology and growth. The document outlines pathways to antibiotic resistance, and how resistance can emerge more quickly in drug gradients due to strong selection at the wave front of expanding bacterial populations. It concludes by discussing opportunities for physicists to better understand biofilm infections and help design strategies to avoid antibiotic resistance.

1. Rosalind Allen
School of Physics and Astronomy, University of Edinburgh
QLSB II, Como, June 21st 2016
How do antibiotics work?
…. and can physicists help?

2. Antibiotics: molecules that inhibit bacteria
Alexander Fleming 1928
Wikipedia
Y. G. Song, Infect. Chemother. 2012, 44, 263-268

3. Antibiotics have revolutionised global health
Leading causes of US deaths
1900: pneumonia, tuberculosis, diarrhoea
1997: heart disease, cancer, stroke
Source: www.cdc.gov

4. But there is a looming crisis of antibiotic resistance
Evolution.berkeley.edu www.cdc.gov

5. What to do about this?
• Understand how antibiotics work and how resistance evolves
can we develop smart strategies to avoid resistance?
• Discover new antibiotics
eg screen environmental samples for new compounds
• Reduce antibiotic use
eg improve diagnostics to distinguish bacterial and viral infections

6. What can physicists do to help?
• Help design tools for better diagnosis
eg chips to detect DNA of bacterial pathogens
• Help improve basic understanding
simple lab model systems
mathematical and computational models
R. J. Allen and B. Waclaw
Antibiotic resistance: a physicist’s view
Arxiv 1605.06086

7. How do antibiotics work?
Target processes that differ between bacteria and human cells
• Cell Wall Synthesis
beta-lactams, vancomycin
• Protein Synthesis
aminoglycosides, tetracyclines,
chloramphenicol, macrolides
• Nucleic Acid Synthesis
quinolones, metronidazole, rifampicin
• Cell Membrane
polymyxins
• Metabolism
sulfonamides, trimethoprim
Bactericidal drugs kill bacteria
Bacteriostatic drugs stop bacterial growth
www.tnmanning.com

8. How to quantify antibiotic efficacy?
Minimum inhibitory concentration (MIC):
concentration that prevents visible growth of bacteria
Small MIC
-> high efficacy
Vads.vetmed.vt.edu
IC50
IC50: concentration needed to halve the growth rate
Small IC50
-> high efficacy

9. From lab assays to clinical use
Pharmacokinetics: predict antibiotic concentration in the human body
www.biologicaltestcenter.com

10. Pharmacodynamics: what concentration is needed to treat an infection?
Time-dependent drugs:
what matters is time above MIC
eg penicillins, cephalosporins
Concentration-dependent drugs:
what matters is concentration
peak/MIC or AUC:MIC
eg quinolones, aminoglycosides
Also need to avoid antibiotic resistance
“After more than 50 years of study, the shape of drug concentration-time curve that
is needed at the site of infection for optimum antimicrobial effects is still not known”
D. Greenwood in “Antimicrobial chemotherapy”, 4th Ed.

11. Real infections are complicated
Urinary tract infection
• Bacteria stick to bladder wall
• Damage epithelium, trigger
immune response
• Colonise and damage
kidneys
• Eventually spread to
bloodstream
A. L. Flores-Mireles et al, Nature Reviews Microbiology 13, 269-294 (2015)

12. But simple models can help
H. Kuwahara et al, Plos computational biology 6 e1000723 (2010)
e.g. Urinary tract infection
E. coli switch stochastically between
fibriated and non-fimbriated states
Fimbriated bacteria stick to walls
But also activate immune system
Statistical physics model:
• Population grows
• Switches between states A and B
• Environmental catastrophe wipes out
A cells, triggered by population
What is the optimum switching rate?
P. Visco et al Biophysical Journal 98, 1099-1108 (2010)
FractionofcellsinAstate
time
See also
M. Thattai & A. van Oudenaarden
Genetics 167, 523-530 (2004)
E. Kussell & S. Leibler
Science 309, 2075-2078 (2005)

13. More detailed example:
how does growth rate affect antibiotic efficacy?
Virulent infections: fast-growing bacteria
Chronic infections: slow-growing bacteria
Do antibiotics work differently for virulent versus chronic infections?
Growth-dependent bacterial sensitivity to ribosome-targeting antibiotics
P. Greulich, M. Scott, M. R. Evans & R. J. Allen, Mol. Syst. Biol. 11, 796 (2015)
Philip Greulich
Matt Scott
Martin Evans

14. A simple test:
grow E. coli bacteria in the lab on different nutrients
Do fast-growing bacteria respond better or worse to antibiotics than slow-
growing bacteria?
N(t) = N0el0t
6 growth media
4 antibiotics: tetracycline, chloramphenicol, streptomycin, kanamycin
All target the ribosome; cell’s protein synthesis machinery

15. Result: some antibiotics work better on fast-growing cells
Tetracycline Chloramphenicol
Kanamycin
But others work better on slow-growing cells
Streptomycin
why?

16. A simple model
Ribosomes are needed to
make new ribosomes
Ribosomes are needed
for growth
• Antibiotic crosses membrane; net inflow rate J.
• Antibiotic binds ribosomes at rate kon, unbinds at rate koff
• Cell grows at rate l, diluting cell contents
• New ribosomes are synthesized at rate s
l and s depend
on the ribosome
concentration!

17. Model variables
a(t): intracellular antibiotic concentration
ru(t): free (unbound) ribosome concentration
rb(t): antibiotic-bound ribosome concentration
Model equations
Constraints: Free ribosomes are needed for growth l = l(ru)
Ribosome synthesis rate is regulated s =s(l)
Dilution due to growth
Antibiotic-ribosome binding
Ribosome synthesis
Antibiotic inflow

18. Constraint 1: ribosomes are needed for growth
M. Scott, et al Science (2010) 330, 1099
Constraints can be obtained from experimental data
Constraint 2: up-regulation of ribosome synthesis
s = lrtot = l ru +rb( )
Steady-state growth, synthesis balances dilution

19. Result: cubic equation linking growth rate and antibiotic concentration
l*
0 = 2 Poutkt
koff
kon
æ
è
ç
ö
ø
÷
Measures the reversibility
of membrane transport
and ribosome binding
One key parameter
Good fits to experimental data

20. Simple prediction for the IC50
Large l0*: IC50 decreases with nutrient richness:
Fast-growing cells are more susceptible
Small l0*: IC50 increases with nutrient richness:
Fast-growing cells are less susceptible
Scaled drug-free growth rate
Scaledsusceptibility
Outcomes:
It’s all about reversibility
Link molecular mechanism
to whole-cell physiology

21. Related work:
Rebecca Brouwers
Linking mechanism to physiology for cell-wall targeting antibiotics
Dan Taylor
How do bacteria respond to antibiotics in small populations?

22. What about antibiotic resistance?

23. Antibiotic resistance
Emergence of bacterial strains that are not inhibited by antibiotic
• Gain of a degrading enzyme
e.g. beta-lactamases
• Alteration of the bacterial target
e.g. changes in ribosome structure
• Change in permeability or transport
e.g. increased expression of efflux pumps
Can happen by
• Gain of extra DNA (eg plasmids by horizontal gene transfer)
• Mutations in genome
• Changes in gene expression
www.reactgroup.org

24. How does an infection become antibiotic resistant?
An individual bacterium arises that is resistant
e.g. through genetic mutation
It proliferates in competition with sensitive bacteria
typically wins in presence of antibiotic, loses otherwise
It spreads beyond the initial infection
e.g. to other people
Usually a multistep process, several mutations

25. Pathways to drug resistance
D. M. Weinreich et al, Science 312, 111-114 (2006)
Usually several mutations needed for clinically relevant antibiotic resistance
Does evolution always follow the same pathway?
Example: Weinreich et al (2006)
Construct all combinations of 5
mutations in a b-lactamase
enzyme
Measure MIC of all mutants
Attempt to infer possible
evolutionary pathways
-> Only a few are feasible

26. Morbidostat: a smart device for tracking evolutionary pathways in time
E. Toprak et al, Nature Genet. 44, 101-105 (2011)
E. Toprak et al, Nature Protocols 8, 555-567 (2013)
Grow bacteria at constant volume
Add nutrients, remove waste
If growth rate is positive, add drug
-> maintains constant selection for
resistance
Trimethoprim: stepped trajectories,
mutations only in target protein
(dihydrofolate reductase)
Chloramphenicol: smooth trajectories,
many mutations involved (translation,
transcription, transport)

27. But real infections can be spatially structured
How does a spatial drug
gradient affect evolution of
resistance?

28. Qiucen Zhang et al. Science 2011;333:1764-1767
Experiments in microfluidic “death galaxy” (Bob Austin’s group):
E. coli resistance to ciprofloxacin emerges much faster in a drug gradient

29. Our simulations:
bacterial population invades a drug gradient
-> model by chain of connected microhabitats
• Population well-mixed within habitats
• Migration between habitats
• Mutation between genotypes
• Growth rate depends on local drug
concentration
genotype m cannot grow if c>bm
• Exponential drug gradient
Microhabitat i
Genotype m
Philip Greulich
Bartek Waclaw

30. Result: population expands in a series of waves
P. Greulich , B. Waclaw & R. J. Allen, PRL 109, 088101 (2012)
Why?
• Strong selection at the wave front
• No need to compete with neighbours
• Very steep gradient:
fronts too narrow to produce mutants
Steepness of gradient
Timetofullresistance
populationdensity
Time to resistance depends on steepness of gradient

31. Experiments: Bartek Waclaw
Track evolution in drug gradients
Directly mimic the model
Preliminary results (E. coli in ciprofloxacin)
• We do see evolution of resistance
• Mutation rate depends on drug concentration

32. Conclusion
Antibiotic resistance: how can we help?
Try to understand the basics
• how antibiotics work
• how resistance evolves
Using simple experimental and mathematical models
Can we connect it to “real biology”?
It remains to be seen….

33. Postdoctoral positions in the physics of antibiotic
resistance
University of Edinburgh
soft matter, biological and statistical physics group
www.vacancies.ed.ac.uk ref number 036372
closing date 1st July 2016
Enquiries to Rosalind Allen or Bartek Waclaw
rosalind.allen@ed.ac.uk bwaclaw@staffmail.ed.ac.uk

35. e.g. biofilm infections: bacteria colonising surfaces
L. Hall-Stoodley et al, Nat Rev Microbiol. 2, 95 (2004)
gum
disease
catheter
contaminatio
n
implant
contaminatio
n
R. J. Broomfield et al, J. Medical Microbiology
58, 1367-1375 (2009)
How relevant is this to real infections?
responsible for chronic infections
bacteria experience different chemical
environments
How does antibiotic resistance evolve in
these infections?

36. no antibiotic
with antibiotic
How does biofilm structure affect evolution of antibiotic resistance?
Courtesy of R. McKenzie & G. Melaugh
How does antibiotic change biofilm structure?
Can we predict rate of resistance evolution?
Computer simulations
• Simulation tracks individual bacteria
• Bacteria interact via physical forces
• Bacteria consume nutrient, grow and divide
• Nutrients and drugs diffuse from above
www.sharklet.com
Can we design smart surfaces to avoid resistant
biofilms?