model of antibiotic action on bacterial growth · plan: model of antibiotic action on bacterial...
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Model of antibiotic action on bacterialgrowth
Martin R. Evans
SUPA, School of Physics and Astronomy, University of Edinburgh, U.K.
November 15, 2016
Collaborators:Philip Greulich (Edinburgh then Cambridge now Southampton),Matt Scott (Waterloo, Ontario), Rosalind Allen (Edinburgh)
M. R. Evans Model of antibiotic action on bacterial growth
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Plan: Model of Antibiotic Action on BacterialGrowth
PlanI Antibiotics: the challenges
II Simple model of dynamics in bacterial cellIII Comparison with experimentsIV Conclusions and thoughts
Reference:P. Greulich, M. Scott, M. R. Evans, R. J. Allen (Molecular Systems Biology2015)
M. R. Evans Model of antibiotic action on bacterial growth
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Antibiotics in the 20th Century
Alexander Fleming (1928) :“Fleming was a Scottish bacteriologist and Nobel Prize winner(Medicine, 1945), best known for his discovery of penicillin”
By 1940s penicillin was being mass-produced by the Americandrugs industry.
Leading causes of US deaths:
1900: pneumonia, tuberculosis, diarrhoea1997: heart disease, cancer, strokeMajor contributors to global health in the past century
Major challenges challenges: rise of antibiotic resistance, declinein new drug discoveriesHow a bacterial population acquires resistance is a problemsuitable for physics approachesTo combat resistance need good models for how antibiotics work
M. R. Evans Model of antibiotic action on bacterial growth
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Antibiotics in the 20th Century
Alexander Fleming (1928) :“Fleming was a Scottish bacteriologist and Nobel Prize winner(Medicine, 1945), best known for his discovery of penicillin”
By 1940s penicillin was being mass-produced by the Americandrugs industry.
Leading causes of US deaths:
1900: pneumonia, tuberculosis, diarrhoea1997: heart disease, cancer, strokeMajor contributors to global health in the past century
Major challenges challenges: rise of antibiotic resistance, declinein new drug discoveriesHow a bacterial population acquires resistance is a problemsuitable for physics approaches
To combat resistance need good models for how antibiotics work
M. R. Evans Model of antibiotic action on bacterial growth
![Page 5: Model of antibiotic action on bacterial growth · Plan: Model of Antibiotic Action on Bacterial Growth Plan I Antibiotics: the challenges II Simple model of dynamics in bacterial](https://reader033.vdocuments.mx/reader033/viewer/2022042220/5ec6265bd157cf4ed0730328/html5/thumbnails/5.jpg)
Antibiotics in the 20th Century
Alexander Fleming (1928) :“Fleming was a Scottish bacteriologist and Nobel Prize winner(Medicine, 1945), best known for his discovery of penicillin”
By 1940s penicillin was being mass-produced by the Americandrugs industry.
Leading causes of US deaths:
1900: pneumonia, tuberculosis, diarrhoea1997: heart disease, cancer, strokeMajor contributors to global health in the past century
Major challenges challenges: rise of antibiotic resistance, declinein new drug discoveriesHow a bacterial population acquires resistance is a problemsuitable for physics approachesTo combat resistance need good models for how antibiotics work
M. R. Evans Model of antibiotic action on bacterial growth
![Page 6: Model of antibiotic action on bacterial growth · Plan: Model of Antibiotic Action on Bacterial Growth Plan I Antibiotics: the challenges II Simple model of dynamics in bacterial](https://reader033.vdocuments.mx/reader033/viewer/2022042220/5ec6265bd157cf4ed0730328/html5/thumbnails/6.jpg)
How do antibiotics work?
Target processes that differ between bacteria and human cells
Inhibition of Cell Wall Synthesisbeta-lactams, vancomycinInhibition of Protein Synthesisaminoglycosides, tetracyclines, chloramphenicol, macrolidesAlteration of Cell MembranespolymyxinsInhibition of Nucleic Acid Synthesisquinolones, metronidazole, rifampicinInterference with metabolismsulfonamides, trimethoprim
Rough Classification: Bacteriostatic and Bactericidal
M. R. Evans Model of antibiotic action on bacterial growth
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Bacteria for physicists I
Unicellular prokaryotic organism (no nucleus or organelles)
Active unit - spherocylinder often with flagella acting as propellor
Lots of them!“There are approximately ten times as many bacterial cells in thehuman flora as there are human cells in the body, with the largestnumber of the human flora being in the gut flora, and a largenumber on the skin”.- wikipedia
Form dense aggregates such as biofilmsFrom statistical physics perspective ideal candidate for ‘activematter’ microconstituent: driven, noisy, profuse
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Bacteria for physicists II
Exponential growth of bacteria: N(t) = N02t/td = N0eλ0t
λ0 is the growth rate; td is the doubling time
Growth-inhibition curve is λ(a)
IC50 is antibiotic dose required to half the growth rate
Standard wisdom has it that slower growing bacteria (e.g.biofilms) are more resilient to antibiotics.
M. R. Evans Model of antibiotic action on bacterial growth
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Our Experiments (Matt Scott)
Grow bacteria under different nutrient conditionsE. coli MG1655, MOPS media with 6 different nutrients and differentgrowth rates.
Glucose (glu) 0.64 ; Glucose + amino acids 1.09; Glucose + aminoacids + nucleotides 1.68Glycerol (gly) 0.40; Glycerol + amino acids 0.85; Glycerol + aminoacids + nucleotides 1.35
Measure the efficacy (IC50) of 4 different antibiotics as afunction of growth rate
Chloramphenicol: bacteriostaticTetracycline: bacteriostaticStreptomycin: bactericidalKanamycin: bactercidal
M. R. Evans Model of antibiotic action on bacterial growth
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Growth inhibition for ‘bacteriostatic’ antibiotics
M. R. Evans Model of antibiotic action on bacterial growth
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Growth inhibition for ‘bactericidal’ antibiotics
M. R. Evans Model of antibiotic action on bacterial growth
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Summary of Experimental Results
Bacteriostatic antibiotics (chlor, tet)
Fast-growing bacteria are more susceptibleGrowth inhibition curve shows gradual decrease
Bactericidal antibiotics (strep, kan)
Fast-growing bacteria are less susceptibleGrowth inhibition curve shows sharp, threshold-like decrease
Why do different antibiotics behave differently?
M. R. Evans Model of antibiotic action on bacterial growth
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Summary of Experimental Results
Bacteriostatic antibiotics (chlor, tet)
Fast-growing bacteria are more susceptibleGrowth inhibition curve shows gradual decrease
Bactericidal antibiotics (strep, kan)
Fast-growing bacteria are less susceptibleGrowth inhibition curve shows sharp, threshold-like decrease
Why do different antibiotics behave differently?
M. R. Evans Model of antibiotic action on bacterial growth
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A simple model
Antibiotic crosses membrane; net inflow rate J.Antibiotic binds ribosomes at rate kon, unbinds at rate koff
Cell grows at rate λ, diluting cell contents (division not explicitlyincluded)New ribosomes (unbound) are synthesized at rate s
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Mathematical model IThree variables a, ru, rb describe the state of the cell.Dynamics:
dadt
= −λ(?)a + f (ru, rb,a) + J(aex ,a)
dru
dt= −λ(?)ru + f (ru, rb,a) + s(?)
drb
dt= −λ(?)rb − f (ru, rb,a)
where unbinding/binding rate
f (ru, rb,a) = koffrb − kona(ru − rmin)
and influxJ(aex ,a) = Pinaex − Pouta
How do we model synthesis rate s and growth rate λ?
M. R. Evans Model of antibiotic action on bacterial growth
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Mathematical model IThree variables a, ru, rb describe the state of the cell.Dynamics:
dadt
= −λ(?)a + f (ru, rb,a) + J(aex ,a)
dru
dt= −λ(?)ru + f (ru, rb,a) + s(?)
drb
dt= −λ(?)rb − f (ru, rb,a)
where unbinding/binding rate
f (ru, rb,a) = koffrb − kona(ru − rmin)
and influxJ(aex ,a) = Pinaex − Pouta
How do we model synthesis rate s and growth rate λ?
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Empirical Growth Laws
H. Bremer and P. Dennis, in E. coli and Salmonella (1996)
M. Scott, C. W. Gunderson, E. M. Mateescu, Z. Zhang and T. HwaScience (2010) 330, 1099:
1 measured ribosome concentration as function of growth rate (inabsence of antibiotic) and found linear relation
ru = λ0/κt + rmin
i.e. growth rate increases linearly with unbound ribosomeconcentration
2 measured total ribosome concentration as function of growthrate, in presence of antibiotic and found another linear relation
ru + rb = rmax − λC
i.e. The cell upregulates ribosome synthesis to compensate forantibiotic challenge
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Mathematical model II: steady state
0 =dadt
= −λa + f (ru, rb,a) + J(aex ,a)(1)
0 =dru
dt= −λru + f (ru, rb,a) + s(2)
0 =drb
dt= −λrb − f (ru, rb,a)(3)
In steady state (2) + (3) s = λ(rb + ru) = λ(rmax − λC)⇒ s(λ)
using second growth law
Then solve (1–3) plus first growth law
ru = λ/κt + rmin
for the four variables λ, a, ru, rb in terms of parameters of cell and aex
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Mathematical model III: predictions
(λ
λ0
)3
−(λ
λ0
)2
+
(λ
λ0
)[aex
2 IC∗50
(λ∗0λ0
)+
14
(λ∗0λ0
)2]− 1
4
(λ∗0λ0
)2
= 0
where λ∗0 = 2√
Poutκtkoff/kon is the ‘reversibility parameter’
and IC∗50 =
λ∗0(rmax − rmin)
2Pinis another parameter combination
Setλ
λ0=
12
and solve for aex :
IC50
IC∗50
=12
[(λ0
λ∗0
)+
(λ∗0λ0
)]
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Predictions for inhibition curves
Forλ∗0λ0
< 0.3 get hysteresis curve→ discontinuity
Limitλ∗0λ0� 1 λ
λ0=
12
[1−
√1− aex
IC50
]For
λ∗0λ0
> 0.3 get gradual decrease
Limitλ∗0λ0� 1
λ
λ0=
[1 +
aex
IC50
]−1
langmuir-like
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Comparison with experiment I
Collapse of data onto limiting growth-inhibition curves - no freeparameters
λ
λ0=
12
[1−
√1− aex
IC50
]λ
λ0=
[1 +
aex
IC50
]−1
Tetracycline and ChloramphenicolStreptomycin and Kanamycin
A B
0 2Relative drug concentration a
ex/IC
50
0
0.2
0.4
0.6
0.8
1
Rela
tive g
row
th r
ate
/0
0 1 2
Relative drug concentration aex
/IC50
0
0.2
0.4
0.6
0.8
1
Rela
tive g
row
th r
ate
/0
Gly Glu GlyCAA
GluCAA
GlyRDM
GluRDM
0.40 0.64 0.85 1.09 1.350(/h) 1.68
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Comparison with experiment II
Fit three glycerol curves with two parameters λ∗0 and IC∗50
Chloramphenicol
Kanamycin
A B
DC
Tetracycline
Streptomycin
0 10 20Concentration (µM)
0
0.2
0.4
0.6
0.8
1
Re
lativ
e g
row
th r
ate
λ/λ
0
0 0.1 0.2 0.3 0.4Concentration (µg/ml)
0
0.2
0.4
0.6
0.8
1
Re
lative
gro
wth
ra
teλ
/λ0
0 0.2 0.4Concentration (µg/ml)
0
0.2
0.4
0.6
0.8
1
Re
lative
gro
wth
ra
teλ
/λ0
0 0.5 1 1.5 2Concentration (µM)
0
0.2
0.4
0.6
0.8
1
Re
lativ
e g
row
th r
ate
λ/λ
0
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Comparison with experiment III
Universal Curve IC50
IC∗50
=12
[(λ0
λ∗0
)+
(λ∗0λ0
)]
0.1 1 10
λ0/λ
0*
1
10
IC50/C
*
Streptomycin
Kanamycin
Tetracycline
Chloramphenicol
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Conclusions
Complex relation between susceptibility and growth environmentcaptured by simple model in which growth and ribosomesynthesis rates depend on ribosome concentration
‘reversibility rate’ λ∗0 provides natural spectrum classification ofribosome-targetting antibiotic actionChloramphenicol and tetracycline (bacteriostatic) ->Fast-growing cells are more susceptible
Streptomycin and kanamycin (bactericidal) ->Fast-growing cells are less susceptible
Further tests of theory by using mutants to vary κt and hence thereduced parameters
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M. R. Evans Model of antibiotic action on bacterial growth