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Inhibition of bacterial growth by antibiotics : A minimal model

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Pith's one-line read This paper claims that the two regimes of antibiotic susceptibility previously tied to ribosomes are generic to any inhibitor of an autocatalytic cycle, and derives the growth laws behind them from a minimal model rather than assuming them.

desk verdict Solid autocatalytic-cycle derivation of growth laws and Greulich regimes, but the 'generic two-regime' claim overreaches and the empirical support is thinner than advertised. read the letter →

arxiv 2501.02944 v7 pith:I6EWB3PZ submitted 2025-01-06 physics.bio-ph

Barnabe Ledoux , David Lacoste This is my paper · ORCID
classification physics.bio-ph MSC 92C4092C3737N25
keywords bacterialgrowthbacteriostaticantibioticsautocatalyticcycleslawsreversibleandirreversibleinhibitiongrowth-ratebistabilityhalf-inhibitoryconcentrationantibioticriskproxy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that bacteriostatic antibiotics slow bacterial growth by inhibiting one essential autocatalytic cycle in cell metabolism, modeled as two coupled cycles that make ribosomes and RNA polymerase. From that minimal picture, the authors derive the known bacterial growth laws instead of taking them as inputs, and they show that the reversible and irreversible regimes of growth-dependent susceptibility previously found only for ribosome-targeting drugs arise generically for any inhibitor of an autocatalytic cycle. The model also predicts a window of antibiotic concentration in which two growth rates coexist, a bistability that has been seen experimentally, and introduces a simple risk proxy: the ratio of drug-bound to free active enzymes. If the central claim is right, drug dose-response data for many antibiotic classes should collapse onto one generic curve relating half-inhibitory concentration to pre-exposure growth rate, with the drug's reversible or irreversible binding deciding which side of the curve it occupies.

What carries the argument

The machinery is a minimal network of two coupled autocatalytic cycles, one for ribosomes and one for RNA polymerase, where joint production steps obey a Leontief minimum rule: the rate of any step using two resources is set by the scarcer of the two. When the drug targets the currently limiting cycle, the model reduces to a single cycle with an arbitrary number of assembly steps, and the central object is the self-consistent equation for the growth rate lambda written through the fraction of active, unbound autocatalysts Q(lambda). Equating two expressions for the bound fraction B1,b/Btot gives Eq. (49), whose roots are the accessible growth rates; in the fast-assembly, long-lifetime limit it simplifies to a cubic that reproduces the earlier ribosome-specific equation, from which the reversible and irreversible limits are read off. The same self-consistent relation carries the two growth laws, the IC50 curve, the bistable window, and the risk proxy B1,b/B1,u, so the argument rides on this single equation.

What would settle it

Find an antibiotic whose target is an essential autocatalytic cycle but whose half-inhibitory concentration, measured across growth media that change the inhibitor-free growth rate, cannot be brought onto the model's U-shaped IC50 curve (Eq. 15) with any choice of the model's rates; alternatively, observe a dose-response curve in the irreversible regime that is smooth with no branch jump near the predicted threshold concentration. Either observation would falsify the claim that the reversible/irreversible classification is generic.

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Extended reading notes

Core claim

The paper's central claim is that antibiotic action does not need to be described ribosome by ribosome: any toxic agent that inhibits an essential autocatalytic cycle should produce one of two generic responses. In the reversible regime, where drug molecules leave the cell and unbind quickly, the growth rate declines smoothly with external drug concentration; in the irreversible regime, where drug accumulates and remains bound, a discontinuous transition to near-zero growth appears. Both behaviors follow from a single self-consistent equation for the growth rate, written through the fraction of unbound active autocatalysts. The derived growth laws, ribosome fraction increasing with growth rate in clean medium and decreasing when translation is inhibited, match the empirical relations that earlier work used as assumptions. The same equation yields a U-shaped dependence of the half-inhibitory concentration on the inhibitor-free growth rate, and measured values for several antibiotics, including one usually classified as bactericidal, are shown to collapse onto this curve. The authors conclude that the reversible/irreversible distinction and the growth-rate heterogeneity it produces should be expected generically for any inhibitor targeting an autocatalytic cycle.

Load-bearing premise

The argument holds only if the antibiotic slows growth by throttling one particular self-reproducing production loop that is the bottleneck, with ribosome density held fixed; if several loops limit growth together, the drug hits more than one loop, or ribosome density responds to the drug, the single-equation reduction and the generic two-regime conclusion do not follow.

Editorial extensions

If this is right

  • Diverse bacteriostatic drugs, regardless of their molecular target, should fall into either the reversible or the irreversible class, with faster growth making the cell more susceptible in the reversible class and less susceptible in the irreversible class.
  • The known bacterial growth laws are consequences of autocatalytic-cycle balance rather than empirical constraints, and they should show slight curvature; departures from linearity in growth-law data are therefore expected, not noise.
  • Below a threshold external drug concentration, the model permits two coexisting growth rates, so growth-rate heterogeneity is an intrinsic property of the deterministic dynamics rather than a single-cell noise effect.
  • The ratio of drug-bound to free target molecules is a drug-class-independent risk proxy that rises sharply near IC50 and jumps discontinuously in the irreversible regime, making it a candidate measure for comparing antibiotic lethality.
  • Two drugs that target different coupled cycles act antagonistically: only the drug acting on the currently limiting cycle reduces growth, producing a dose-response surface that switches sharply between the two drugs.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the model is formulated in molecule numbers and uses only autocatalytic stoichiometry plus a minimum rule, the same reversible/irreversible dichotomy should appear in other autocatalytic systems exposed to poisons, such as ecological autocatalytic loops or economic production networks; the authors mention but do not develop this generality.
  • The constant ribosome density assumption ties cell volume to total mature ribosome count; if experiments show that ribosome density itself changes with drug concentration, the universal IC50 curve should bend or split, giving a testable boundary for the claim.
  • A stochastic version of this deterministic model, which the authors call for, would likely convert the coexistence window into a bimodal single-cell growth-rate distribution; measuring single-cell growth rates near the predicted threshold concentration could distinguish true bistability from population averaging.
  • The risk proxy suggests a quantitative ranking of antibiotics by early growth suppression independent of the bactericidal/bacteriostatic label, but testing it would require time-resolved measurements of bound target fractions, not just growth-rate dose-response curves.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents a minimal biophysical model of bacteriostatic antibiotic action in which cell metabolism is represented as two coupled autocatalytic cycles (ribosome and RNA-polymerase production) interacting through a Leontief minimum function. The drug is assumed to bind to the autocatalyst of one cycle. The authors derive the two empirical growth laws rather than postulating them, recover the reversible and irreversible regimes of growth-dependent susceptibility previously found by Greulich et al., propose a risk proxy B1,b/B1,u, fit literature dose-response data for several antibiotics, and extend the framework to two-drug combinations and self-inhibiting waste. The central claims are that the two susceptibility regimes are generic for inhibitors of any autocatalytic cycle and that the growth laws follow from the model.

Significance. If the central claims hold, the model provides a mechanistic unification of bacterial growth laws and the empirical classification of antibiotics into reversible and irreversible binders, and it connects these to the hypothesis of autocatalytic cycles as the basic modules of cell metabolism. The derivation of the growth laws from the cycle equations (rather than imposing them phenomenologically) is a genuine methodological advance, and the model makes falsifiable predictions (universal IC50 curve, growth-rate bistability above a threshold concentration, antagonistic interactions for two drugs acting on separate cycles). The paper also ships explicit derivations in Appendices A-C, which I verified for internal consistency. The significance is, however, conditional on closing the gap between the isolated-cycle analysis and the full coupled system, because the genericity claim is the main novel message.

major comments (3)
  1. [Modified model based on autocatalytic cycles; Conclusion] The genericity claim ('the two regimes ... should in fact be expected generically for any inhibitors targeting an autocatalytic cycle') is derived from the isolated-cycle equation (Eq. 49), which is obtained by assuming that the drug-targeted cycle is the limiting cycle ('we assume the cycle targeted by the toxic agent becomes limiting'). In the full coupled system (Eq. 20), the cycles interact through min(kB1 B1u, kC1 C1u); if the targeted cycle is not the one setting the min, or if the two branches are close to equality, the growth rate is set by the other cycle and Eq. 49 does not describe the dose-response. Appendix C.2 still assumes B is limiting, and the two-drug system (Eq. 60) is not used to check the balanced-cycles regime for a single drug. To support the central claim, the authors should either prove that any bacteriostatic inhibitor makes its target cycle limiting, add a numerical analysis of the full system showing the same two regimes for the balanced case, or explicitly restrict the genericity statement.
  2. [Main text Eq. (12); Appendix A.5.2 Eq. (54)] The irreversible limit is written inconsistently: main-text Eq. (12) gives Q(lambda) = 1 + P_in a_ex/lambda, whereas the derivation from Eq. (49) in Appendix A.5.2 gives Q(lambda) = 1 - P_in a_ex/lambda, which is also consistent with the root of Eq. (38), q = (1 + sqrt(1 - 4 P_in a_ex/lambda0))/2 approximately equal to 1 - P_in a_ex/lambda0. The plus sign would mean that increasing antibiotic concentration increases the fraction of active ribosomes, contradicting the model's purpose. This appears to be a typographical error, but it appears in a central display equation and must be corrected, with the surrounding text checked for further sign inconsistencies.
  3. [Experimental test of the model; Appendix B.2] The statement that the model 'describes well' a large panel of antibiotics is not quantitatively supported. Each dose-response curve is fitted with four free parameters (Table 2) plus two globally hand-set values (KD = 1/50, kof = 5 h^-1), and no confidence intervals, goodness-of-fit measures, or model-comparison tests are reported. Without these, the reader cannot judge whether the observed concavities and the data collapse in Fig. 4b are meaningful evidence for the two-regime classification or merely a consequence of the fitting flexibility. A sensitivity analysis of the hand-set parameters is also needed.
minor comments (6)
  1. [Eq. (2)] The text reads 'where where r_b is the concentration' - the word 'where' is duplicated.
  2. [Throughout] Several instances of 'k of f' should be 'k_of' (subscripts are corrupted in the text; e.g., Eq. (4) and surrounding text).
  3. [Appendix A.5, Eq. (40)] In the last equation for dA/dt, the term '+k_of f B_1,u' should presumably read '+k_of f B_1,b', as in the simplified system Eq. (21); as written, the binding/unbinding balance is incorrect.
  4. [References] Reference [28] is an incomplete self-reference ('Supp. Mat. at ...; 2025'); provide a working link or a proper title for the supplementary material.
  5. [Cell risk induced by the antibiotics] The text says 'we rescale the risk by P_in a_ex/(K_D P_out) to obtain a collapse of the experimental data in the reversible limit', but Fig. 3b shows a collapse of model curves, not of experimental data; please clarify the wording.
  6. [Appendix A.5.4] The parameter m ('the number of limiting intermediate steps') is introduced rather quickly; please define it explicitly at first occurrence and state how it relates to the number of intermediate steps N.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the growth laws, dose-response equations, and two-regime distinction are derived from the model ODEs, with fitted parameters entering only at the data-comparison stage.

full rationale

The paper's derivation chain is self-contained. The self-consistent growth-rate equation (Eq. 49) is obtained by algebraic elimination of the intracellular antibiotic abundance and the bound-ribosome fraction from the coupled autocatalytic-cycle ODEs (Eq. 20), not by assuming the target result. The first and second growth laws are derived from the model (Eqs. 9, 33) rather than imposed as in Ref. [13]. The reversible and irreversible limits follow explicitly from the stated limits on the kinetic rates (Pout, koff relative to lambda; Eqs. 36-39 and 50-54), and the IC50 expression (Eq. 15) is an analytic substitution into the self-consistent equation. Fitted parameters (Table 2) are used only to compare the model with experimental dose-response curves; they do not enter the derivation of the regimes or the IC50 curve. The central genericity claim rests on the explicit modeling assumption that the drug-targeted cycle becomes limiting ('we assume the cycle targeted by the toxic agent becomes limiting'), which is a substantive scope condition rather than a circular step; it limits the claim's domain but does not make the derivation equivalent to its inputs. The constant-ribosome-density assumption in Appendix A.1 is likewise a stated modeling premise, not a conclusion reintroduced as a premise. No load-bearing self-citation occurs: the cited prior models [13] and [18] are by other authors and serve as starting points, and no uniqueness theorem is imported from the present authors' prior work. The paper even transparently notes when the same experimental data as Ref. [13] is used for a collapse. For completeness, the sign mismatch between Eq. 12 (Q = 1 + Pin aex/lambda) and Eq. 54 (Q = 1 - Pin aex/lambda) appears to be a typo and is a correctness issue, not a circularity.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The model is built on several stated assumptions that do the load-bearing work: balanced growth, constant ribosome density, the Leontief minimum law, and the reduction to a single limiting cycle. The dose-response fits introduce 4 free parameters per antibiotic plus two hand-set global constants; these are the empirical price of the 'confirmation' claims. No new physical entities are introduced.

free parameters (6)
  • Pin (antibiotic influx rate) = Per antibiotic, e.g., Triclosan 2.85, Chloramphenicol (0) 55.4, Rifampicin 0.022, in mL·µg^-1·h^-1
    Fitted to dose-response data for each antibiotic in Table 2; centrally determines the reversible/irreversible classification.
  • Pout (antibiotic outflux rate) = Per antibiotic, e.g., Triclosan 4.33, Chloramphenicol (0) 44.4, Rifampicin 3.46, in h^-1
    Fitted per antibiotic; the ratio Pin/Pout and the comparison with lambda0 set which binding regime applies.
  • kB1 (rate of the autocatalytic step of the ribosome cycle) = Per antibiotic, around 1 h^-1 (e.g., 1.28, 1.87, 1.32, 1.16)
    Fitted per antibiotic; essentially sets the basal growth rate lambda0 and enters the growth-law slope.
  • kB,N+1 (deactivation rate of active ribosomes) = Per antibiotic between 1e-3 and 1e-1 h^-1
    Fitted per antibiotic within an a priori biological range; small values keep the long-lifetime assumption valid.
  • KD = kof/kon (dissociation constant) = 1/50 (dimensionless)
    Chosen by hand globally for all antibiotics to reduce the number of free parameters; affects binding kinetics in every fit.
  • kof (antibiotic unbinding rate) = 5 h^-1 globally
    Chosen by hand for all antibiotics; fixes the residence time of the drug and enters the reversible/irreversible separation.
assumptions (7)
  • domain assumption Balanced growth: all molecular species and the cell volume grow exponentially with the same rate lambda.
    Invoked in the model section and Appendix A; converts the ODE system into algebraic equations in lambda.
  • domain assumption Constant ribosome density rho = Btot/Omega, independent of antibiotic concentration.
    Appendix A.1 states: 'This ribosome concentration is assumed to be a constant [48], which does not depend on the antibiotic concentration.' This lets the model switch between abundances and concentrations and is load-bearing for the growth-law identifications.
  • domain assumption Leontief law of the minimum: production rates are set by the scarcest complementary resource.
    Adopted from [18,29,30] and used to couple the two autocatalytic cycles; this nonlinearity produces the antagonism result in the two-drug extension.
  • ad hoc to paper The cycle targeted by the toxic agent becomes limiting, so the other coupled cycle can be discarded.
    Main text: 'we assume the cycle targeted by the toxic agent becomes limiting... Consequently, we isolate the inhibited cycle and study its growth.' If both cycles are simultaneously limiting, the single-cycle self-consistent equation does not hold.
  • domain assumption Fast assembly, fast activation, and long ribosome lifetime (kB2, kB3 >> lambda0; kB4 << kB1; 1/tau_life << lambda).
    Used to obtain the linear growth laws (Eqs 9-10) and the cubic equation (Eq 35); claimed to hold for ribosomes in intermediate and high growth regimes following [18].
  • domain assumption Fast binding in the irreversible limit (kon >> lambda0).
    Used to derive Eq 38 and the irreversible dose-response relation Eq 39.
  • domain assumption The antibiotic binds only to active ribosomes B1, not to precursors or other cycle species.
    Main text: 'we suppose that toxic inhibiting agents in numbers A can bind to one of the autocatalysts (chosen here to be B1 for simplicity)'. This excludes off-target binding and transport effects beyond passive/active influx-outflux.

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Pith. "Pith review of Inhibition of bacterial growth by antibiotics : A minimal model." pith.science (2026). https://pith.science/paper/I6EWB3PZ

@misc{pith2026250102944,
  author       = {Pith},
  title        = {Pith review of: Inhibition of bacterial growth by antibiotics : A minimal model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I6EWB3PZ}},
  note         = {Machine review of arXiv:2501.02944}
}
read the original abstract

Growth in bacterial populations generally depends on the environment (availability and quality of nutrients, presence of a toxic inhibitor, product inhibition..). Here, we build a model to describe the action of a bacteriostatic antibiotic, assuming that this drug inhibits an essential autocatalytic cycle involved in the cell metabolism. The model recovers known growth laws, can describe various types of antibiotics and confirms the existence of two distinct regimes of growth-dependent susceptibility, previously identified only for ribosome targeting antibiotics. Interestingly, below a certain threshold in terms of antibiotic concentration, a coexistence between two values of the growth rate is possible, which has also been observed experimentally. Interesting extensions of the model include the antagonistic effect of two drugs targeting different autocatalytic cycles or the production of an inhibiting waste.

Figures

Figures reproduced from arXiv: 2501.02944 by the authors.

Figure 1
Figure 1. (a) Scheme of coupled autocatalytic networks interacting with a toxic agent. The blue box linking two arrows represents a coupling through a min function [29, 30]. (b) The first growth law is the increase of the ribosome fraction with the growth rate (solid curve), the second law corresponds to the colored lines obtained by varying the amount of antibiotics. The pre-exposure growth rate λ0 displayed on the right sca… view at source ↗
Figure 2
Figure 2. Comparison with experiments for two drugs affecting bacterial growth, namely (a) Chloramphenicol (data from [9] and [13]) and (b) Kanamycin (data from [13]). The solid line shows the growth rate as a function of the fraction of inhibitors, while the dotted line shows a measure of the risk faced by the cell defined in the text. The data were fitted by constraining the parameters as explained in Supplementary material… view at source ↗
Figure 3
Figure 3. Normalized risk versus antibiotic concentration. (a) Risk faced by the system in the presence of a toxic agent. We compare the reversible case (dotted lines) and the irreversible case (full lines). (b) Rescaled risk depending on the growth rate. We compare the reversible case (dotted lines) and the irreversible case (full lines). We observe a complete collapse of the curves in the reversible limit. The risk is resca… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: (a) Normalized growth rate versus the normalized antibiotic concentration. In dotted lines we represent the reversible regime kof f , Pout ≥ kon, Pin, in full lines the irreversible regime kof f , Pout ≪ kon, Pin. For the irreversible case (full lines), we observe two …
Figure 5
Figure 5. Figure 5: (a) Two antibiotics A1 and A2 targeting two different but coupled autocatalytic cycles (coupled through a min function represented with the blue box). (b) Predominance diagram of the two drugs, when B1,u (resp. C1,u) gets small, the associated cycle is limiting and the…
Figure 6
Figure 6. Figure 6: Dose response surface of two antibiotics targeting two coupled autocatalytic cycles. On (a), the drug 1 is in the reversible regime with parameters: Pin(B) = 40mL.µg.h−1 , Pout(B) = 30h −1 , kB,1 = 2h −1 , τlif e(B) = 102h, while drug 2 is in the irreversible regime wi…
Figure 7
Figure 7. Figure 7: (a) Network where self inhibiting waste is produced. (b) Risk related to growth in a regime where risk can be increasing with λ. The full lines corresponds to a higher value of kw (kw = 0.01h −1 ) compared to the dotted lines (kw = 1h −1 ). model describes well the eff…
Figure 8
Figure 8. Figure 8: Self-consistent function, the roots of which define the growth rate. The dotted lines represent the function with increasing values of aex. September 5, 2025 21/31 [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: Comparison with experiments for various drugs. In solid lines, we show the growth rate as a function of the fraction of inhibitors. In dotted lines, we show a measure of the risk B1,b B1,u . This measure compares the abundance of bound individuals B1,b to that of unbou…
Figure 10
Figure 10. Figure 10: Fraction of autocatalysts in the second cycle when the first cycle is targeted by inhibitors. C.3 Closed compartment and inhibiting waste For a closed compartment Pin = Pout = 0 and waste W produced at rate kw, the equations are  λ + kon W Btot + kB4 + kw  B1,u = kB…

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Works this paper leans on

51 extracted references · 50 canonical work pages

  1. [13]

    Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics

    Greulich P, Scott M, Evans MR, Allen RJ. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Molecular systems biology;11(3):796. doi:10.15252/msb.20145949

    work page doi:10.15252/msb.20145949

  2. [1]

    Origins and Evolution of Antibiotic Resistance

    Davies J DD. Origins and Evolution of Antibiotic Resistance. Microbiology and Molecular Biology Reviews;(74). doi:https://doi.org/10.1128/mmbr.00016-10

    work page doi:10.1128/mmbr.00016-10

  3. [2]

    Antibiotic interactions that select against resistance

    Chait R, Craney A, Kishony R. Antibiotic interactions that select against resistance. Nature. 2007;446(7136):668–671. doi:10.1038/nature05685

    work page doi:10.1038/nature05685 2007

  4. [3]

    Bacteriostatic Antibiotics

    Loree J, Lappin SL. Bacteriostatic Antibiotics. StatPearls Publishing, Treasure Island (FL);. Available from: http://europepmc.org/abstract/MED/31613458

    work page arXiv

  5. [4]

    Ribosome-Targeting Antibiotics: Modes of Action, Mechanisms of Resistance, and Implications for Drug Design

    Lin J, Zhou D, Steitz TA, Polikanov YS, Gagnon MG. Ribosome-Targeting Antibiotics: Modes of Action, Mechanisms of Resistance, and Implications for Drug Design. Annual review of biochemistry;87:451–478. doi:10.1146/annurev-biochem-062917-011942. September 5, 2025 27/31

    work page doi:10.1146/annurev-biochem-062917-011942

  6. [5]

    Binding to ribosomes and mode of action of chloramphenicol analogues

    Contreras A, Barbacid M, Vazquez D. Binding to ribosomes and mode of action of chloramphenicol analogues. Biochimica et Biophysica Acta (BBA) - Nucleic Acids and Protein Synthesis;349(3):376–388. doi:10.1016/0005-2787(74)90124-5

    work page doi:10.1016/0005-2787(74)90124-5

  7. [6]

    Impact of P-Site tRNA and Antibiotics on Ribosome Mediated Protein Folding: Studies Using the Escherichia coli Ribosome

    Mondal S, Pathak BK, Ray S, Barat C. Impact of P-Site tRNA and Antibiotics on Ribosome Mediated Protein Folding: Studies Using the Escherichia coli Ribosome. PLOS ONE;9(7):e101293. doi:10.1371/journal.pone.0101293

    work page doi:10.1371/journal.pone.0101293

  8. [7]

    Mechanisms of antibiotics inhibiting bacterial RNA polymerase

    Mosaei H, Harbottle J. Mechanisms of antibiotics inhibiting bacterial RNA polymerase. Biochemical Society Transactions;47(1):339–350. doi:10.1042/BST20180499

    work page doi:10.1042/bst20180499

Show all 51 references
  1. [8]

    Mistranslation of Membrane Proteins and Two-Component System Activation Trigger Antibiotic-Mediated Cell Death

    Kohanski MA, Dwyer DJ, Wierzbowski J, Cottarel G, Collins JJ. Mistranslation of Membrane Proteins and Two-Component System Activation Trigger Antibiotic-Mediated Cell Death. Cell;135(4):679–690. doi:10.1016/j.cell.2008.09.038

    2008 doi

  2. [9]

    Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli

    Si F, Li D, Cox SE, Sauls JT, Azizi O, Sou C, et al. Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli. Current Biology;27(9):1278–1287. doi:10.1016/j.cub.2017.03.022

    2017 doi

  3. [10]

    A Numbers Game: Ribosome Densities, Bacterial Growth, and Antibiotic-Mediated Stasis and Death

    Levin BR, McCall IC, Perrot V, Weiss H, Ovesepian A, Baquero F. A Numbers Game: Ribosome Densities, Bacterial Growth, and Antibiotic-Mediated Stasis and Death. mBio;8(1). doi:10.1128/mBio.02253-16

    doi

  4. [11]

    The rate of killing of Escherichia coli by beta-lactam antibiotics is strictly proportional to the rate of bacterial growth

    Tuomanen E, Cozens R, Tosch W, Zak O, Tomasz A. The rate of killing of Escherichia coli by beta-lactam antibiotics is strictly proportional to the rate of bacterial growth. Journal of general microbiology;132(5):1297–1304. doi:10.1099/00221287-132-5-1297

    doi

  5. [12]

    Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate

    Lopatkin AJ, Stokes JM, Zheng EJ, Yang JH, Takahashi MK, You L, et al. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. Nature Microbiology;4(12):2109–2117. doi:10.1038/s41564-019-0536-0

    doi

  6. [14]

    Kinetics of drug–ribosome interactions defines the cidality of macrolide antibiotics

    Svetlov MS, V´ azquez-Laslop N, Mankin AS. Kinetics of drug–ribosome interactions defines the cidality of macrolide antibiotics. Proceedings of the National Academy of Sciences;114(52):13673–13678. doi:10.1073/pnas.1717168115

    doi

  7. [15]

    Bacterial growth laws and their applications

    Scott M, Hwa T. Bacterial growth laws and their applications. Nanobiotechnology and Systems Biology;22(4):559–565. doi:10.1016/j.copbio.2011.04.014

    2011 doi

  8. [16]

    Cellular perception of growth rate and the mechanistic origin of bacterial growth law

    Wu C, Balakrishnan R, Braniff N, Mori M, Manzanarez G, Zhang Z, et al. Cellular perception of growth rate and the mechanistic origin of bacterial growth law. Proceedings of the National Academy of Sciences;119(20):e2201585119. doi:10.1073/pnas.2201585119

    doi

  9. [17]

    Interdependence of Cell Growth and Gene Expression: Origins and Consequences

    Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. Interdependence of Cell Growth and Gene Expression: Origins and Consequences. Science;330(6007):1099–1102. doi:10.1126/science.1192588

    doi

  10. [18]

    A unifying autocatalytic network-based framework for bacterial growth laws

    Roy A, Goberman D, Pugatch R. A unifying autocatalytic network-based framework for bacterial growth laws. Proceedings of the National Academy of Sciences. 2021;118(33):e2107829118. doi:10.1073/pnas.2107829118. September 5, 2025 28/31

    2021 doi

  11. [19]

    How total mRNA influences cell growth

    Calabrese L, Ciandrini L, Cosentino Lagomarsino M. How total mRNA influences cell growth. Proceedings of the National Academy of Sciences;121(21):e2400679121. doi:10.1073/pnas.2400679121

    doi

  12. [20]

    The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria

    Deris JB, Kim M, Zhang Z, Okano H, Hermsen R, Groisman A, et al. The Innate Growth Bistability and Fitness Landscapes of Antibiotic-Resistant Bacteria. Science;342(6162):1237435. doi:10.1126/science.1237435

    doi

  13. [21]

    Antibiotic resistance: a physicist’s view

    Allen R, Waclaw B. Antibiotic resistance: a physicist’s view. Phys Biol. 2016;13(4):045001

    2016

  14. [22]

    Minimal biophysical model of combined antibiotic action

    Kavˇ ciˇ c B, Tkaˇ cik G, Bollenbach T. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology;17(1):e1008529. doi:10.1371/journal.pcbi.1008529

    doi

  15. [23]

    Mechanisms of drug interactions between translation-inhibiting antibiotics

    Kavˇ ciˇ c B, Tkaˇ cik G, Bollenbach T. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nat Commun. 2020;11(1):4013

    2020

  16. [24]

    Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions

    Bollenbach T, Quan S, Chait R, Kishony R. Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions. Cell. 2009;139(4):707–718. doi:10.1016/j.cell.2009.10.025

    2009 doi

  17. [25]

    Molecular mechanisms involved in the transport of antibiotics into bacteria

    Chopra I. Molecular mechanisms involved in the transport of antibiotics into bacteria. Parasitology;96:S25–S44. doi:10.1017/S0031182000085966

    doi

  18. [26]

    The role of bacterial membrane vesicles in antibiotic resistance

    MacNair CR, Tan MW. The role of bacterial membrane vesicles in antibiotic resistance. Annals of the New York Academy of Sciences;1519(1):63–73. doi:10.1111/nyas.14932

    doi

  19. [27]

    Microeconomics of Metabolism: The Warburg Effect as Giffen Behaviour

    Yamagishi JF, Hatakeyama TS. Microeconomics of Metabolism: The Warburg Effect as Giffen Behaviour. Bulletin of mathematical biology;83(12):120. doi:10.1007/s11538-021-00952-x

    doi

  20. [28]

    Details about the model and the calculations are provided in Supp. Mat. at ...; 2025

    2025

  21. [29]

    A Dynamic Leontief Model with Non-renewable Resources

    Dobos I, Floriska A. A Dynamic Leontief Model with Non-renewable Resources. Economic Systems Research;17(3):317–326. doi:10.1080/09535310500221856

    doi

  22. [30]

    Multiple nutrient limitations in ecological models

    O’Neill R V, DeAngelis DL, Pastor JJ, Jackson BJ, Post WM. Multiple nutrient limitations in ecological models. Ecological Modelling;46(3):147–163. doi:10.1016/0304-3800(89)90015-X

    doi

  23. [31]

    Analytic derivation of bacterial growth laws from a simple model of intracellular chemical dynamics

    Pandey PP, Jain S. Analytic derivation of bacterial growth laws from a simple model of intracellular chemical dynamics. Theory in Biosciences;135(3):121–130. doi:10.1007/s12064-016-0227-9

    doi

  24. [32]

    Sources, propagation and consequences of stochasticity in cellular growth

    Thomas P, Terradot G, Danos V, Weiße AY. Sources, propagation and consequences of stochasticity in cellular growth. Nature Communications;9(1):4528. doi:10.1038/s41467-018-06912-9

    doi

  25. [33]

    Bistable Bacterial Growth Rate in Response to Antibiotics with Low Membrane Permeability

    Elf J, Nilsson K, Tenson T, Ehrenberg M. Bistable Bacterial Growth Rate in Response to Antibiotics with Low Membrane Permeability. Physical Review Letters;97(25):258104. doi:10.1103/PhysRevLett.97.258104

    doi

  26. [34]

    Evidence for a bimodal distribution of Escherichia coli doubling times below a threshold initial cell concentration

    Irwin PL, Nguyen LHT, Paoli GC, Chen CY. Evidence for a bimodal distribution of Escherichia coli doubling times below a threshold initial cell concentration. BMC Microbiology;10(1):207. doi:10.1186/1471-2180-10-207. September 5, 2025 29/31

    doi

  27. [35]

    On the mechanism of rifampicin inhibition of RNA synthesis

    McClure WR, Cech CL. On the mechanism of rifampicin inhibition of RNA synthesis. Journal of Biological Chemistry;253(24):8949–8956. doi:10.1016/S0021-9258(17)34269-2

    doi

  28. [36]

    Structural Mechanism for Rifampicin Inhibition of Bacterial RNA Polymerase

    Campbell EA, Korzheva N, Mustaev A, Murakami K, Nair S, Goldfarb A, et al. Structural Mechanism for Rifampicin Inhibition of Bacterial RNA Polymerase. Cell;104(6):901–912. doi:10.1016/S0092-8674(01)00286-0

    doi

  29. [37]

    Protein misfolding and aggregation: Mechanism, factors and detection

    Chaturvedi SK, Siddiqi MK, Alam P, Khan RH. Protein misfolding and aggregation: Mechanism, factors and detection. Process Biochemistry;51(9):1183–1192. doi:10.1016/j.procbio.2016.05.015

    2016 doi

  30. [38]

    Mechanism of Triclosan Inhibition of Bacterial Fatty Acid Synthesis*

    Heath RJ, Rubin JR, Holland DR, Zhang E, Snow ME, Rock CO. Mechanism of Triclosan Inhibition of Bacterial Fatty Acid Synthesis*. Journal of Biological Chemistry;274(16):11110–11114. doi:10.1074/jbc.274.16.11110

    doi

  31. [39]

    Triclosan targets lipid synthesis

    McMurry LM, Oethinger M, Levy SB. Triclosan targets lipid synthesis. Nature;394(6693):531–532. doi:10.1038/28970

    doi

  32. [40]

    Triclosan inhibition of fatty acid synthesis and its effect on growth of Escherichia coli and Pseudomonas aeruginosa

    Escalada MG, Harwood JL, Maillard JY, Ochs D. Triclosan inhibition of fatty acid synthesis and its effect on growth of Escherichia coli and Pseudomonas aeruginosa. Journal of Antimicrobial Chemotherapy;55(6):879–882. doi:10.1093/jac/dki123

    doi

  33. [41]

    Proximate and ultimate causes of the bactericidal action of antibiotics

    Baquero F, Levin BR. Proximate and ultimate causes of the bactericidal action of antibiotics. Nature Reviews Microbiology;19(2):123–132. doi:10.1038/s41579-020-00443-1

    doi

  34. [42]

    Stochasticity of metabolism and growth at the single-cell level

    Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ. Stochasticity of metabolism and growth at the single-cell level. Nature;514(7522):376–379. doi:10.1038/nature13582

    doi

  35. [43]

    Ribosome Modulation Factor: Stationary Growth Phase-Specific Inhibitor of Ribosome Functions from Escherichia coli

    Wada A, Igarashi K, Yoshimura S, Aimoto S, Ishihama A. Ribosome Modulation Factor: Stationary Growth Phase-Specific Inhibitor of Ribosome Functions from Escherichia coli. Biochemical and Biophysical Research Communications;214(2):410–417. doi:10.1006/bbrc.1995.2302

  36. [44]

    A New Model of Alcoholic Fermentation under a Byproduct Inhibitory Effect

    Zentou H, Zainal Abidin Z, Yunus R, Awang Biak DR, Abdullah Issa M, Yahaya Pudza M. A New Model of Alcoholic Fermentation under a Byproduct Inhibitory Effect. ACS Omega;6(6):4137–4146. doi:10.1021/acsomega.0c04025

    doi

  37. [45]

    Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

    Marrec L, Bitbol AF. Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial. PLOS Computational Biology;16(4):e1007798. doi:10.1371/journal.pcbi.1007798

    doi

  38. [46]

    Measuring single-cell susceptibility to antibiotics within monoclonal bacterial populations

    Le Quellec L, Aristov A, Guti´ errez Ramos S, Amselem G, Bos J, Baharoglu Z, et al. Measuring single-cell susceptibility to antibiotics within monoclonal bacterial populations. PLOS ONE;19(8):e0303630. doi:10.1371/journal.pone.0303630

    doi

  39. [47]

    Ecological autocatalysis: a central principle in ecosystem organization? Ecological Monographs;88(3):304–319

    Veldhuis MP, Berg MP, Loreau M, Olff H. Ecological autocatalysis: a central principle in ecosystem organization? Ecological Monographs;88(3):304–319. doi:10.1002/ecm.1292

    doi

  40. [48]

    Relevance and Regulation of Cell Density

    Neurohr GE, Amon A. Relevance and Regulation of Cell Density. Trends in Cell Biology;30(3):213–225. doi:10.1016/j.tcb.2019.12.006. September 5, 2025 30/31

    2019 doi

  41. [49]

    Chemistry and mode of action of macrolides

    Mazzei T, Mini E, Novelli A, Periti P. Chemistry and mode of action of macrolides. Journal of Antimicrobial Chemotherapy;31:1–9. doi:10.1093/jac/31.suppl

    doi

  42. [50]

    Growth Conditions and Rifampin Susceptibility

    Koch Arthur L , Gross Gayle H . Growth Conditions and Rifampin Susceptibility. Antimicrobial Agents and Chemotherapy;15(2):220–228. doi:10.1128/aac.15.2.220

    doi

  43. [51]

    Mathematical modelling of growth of Escherichia coli at subinhibitory levels of chloramphenicol or tetracyclines

    Comby S, Flandrois JP, Carret G, Pichat C. Mathematical modelling of growth of Escherichia coli at subinhibitory levels of chloramphenicol or tetracyclines. Research in Microbiology;140(3):243–254. doi:10.1016/0923-2508(89)90079-X. September 5, 2025 31/31

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.