Paused in translation: A model for the transcript length-dependent impact of ribosome-targeting antibiotics

Johannes Keisers; Luca Ciandrini; Norbert Kern

REVIEW 3 major objections 4 minor 56 references

Paused in translation: A model for the transcript length-dependent impact of ribosome-targeting antibiotics

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Ribosome-targeting antibiotics inhibit longer mRNAs more than short ones, and reducing ribosome initiation offsets the damage.

desk verdict The paper's core length-dependence mechanism is real and the analytics largely hold up, but the Fig. 8 quantitative match rests on an unvalidated mean-field replacement of the random pause time. read the letter →

arxiv 2507.19395 v1 pith:AVUWU444 submitted 2025-07-25 q-bio.SC cond-mat.stat-mechphysics.bio-ph

Johannes Keisers , Norbert Kern , Luca Ciandrini This is my paper · ORCID

classification q-bio.SCcond-mat.stat-mechphysics.bio-ph

keywords TotallyAsymmetricSimpleExclusionProcess(TASEP)ribosomepausingribosome-targetingantibioticschloramphenicoltranscriptlengthdependencemRNAdegradationtranslationinhibitioncollectivedynamics

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

The paper sets out to show that the cellular impact of ribosome-targeting antibiotics cannot be read off from what happens to a single ribosome: because ribosomes queue on an mRNA, one antibiotic-induced pause nucleates a traffic jam that grows backward toward the start codon, so the damage scales with transcript length and with how often new ribosomes start. The authors build a TASEP with stochastic pausing and experimentally measured binding and unbinding rates, and they introduce a Single-Cluster approximation that yields closed-form expressions for the time until the first pause, the time to dissolve the resulting jam, and the resulting density and protein-synthesis current. Their analytical results match Gillespie simulations, and the model quantitatively reproduces the measured short-to-long expression ratio for gfp and lacZ under chloramphenicol. A sympathetic reader would care because the model turns a generic "antibiotics stall ribosomes" statement into concrete, testable predictions: long genes are more vulnerable, and slowing initiation protects them.

What carries the argument

The carrying object is the Single-Cluster Approximation: after the first antibiotic-induced pause at position $X_f$, a single contiguous cluster of ribosomes forms and extends back to the initiation site, then dissolves in discrete batches of average size $B = \frac{k_u}{k_p}[1-(\frac{k_p}{k_p+k_u})^N] + 1$ before any additional distinct cluster appears. This approximation splits the dynamics into two states: an unpaused state obeying standard TASEP with extended particles, and a paused state whose lifetime is $T_p = n_B/k_u$ with $n_B = \lceil N_i/B \rceil$ batches to clear. All analytical expressions for density, current, and the fractions of paused, jammed, and mobile ribosomes follow from the probabilities $P_0$ and $P_p$ of being in each state, making the approximation the bridge from a many-body exclusion process to closed-form formulas.

What would settle it

Watch a single mRNA under fluorophore-tagged ribosomes with chloramphenicol at concentrations where $k_p$ approaches $k_u$: if a second spatially separate ribosome cluster appears before the first one dissolves, or if the measured first-pause time and jam-clearing time deviate from $T_0$ and $T_p$, the central mechanism fails. A population-level test is equally direct: measure fold-change expression of a long and a short reporter driven by identical promoters over a range of antibiotic concentrations and initiation rates; the model's short-to-long ratio must rise with concentration and flatten when initiation is lowered.

Watch

Extended reading notes

Core claim

The central discovery is that translation inhibition by elongation-stalling antibiotics is a collective, length-dependent phenomenon. In the model, an antibiotic molecule binds a ribosome at rate $k_p$, pausing it for an average time $1/k_u$ that is long compared with elongation, mRNA lifetime, and initiation intervals. That single paused ribosome blocks all ribosomes behind it, forming one contiguous cluster that reaches back to the initiation site; the jam then dissolves in discrete batches as paused ribosomes unpause. Because longer transcripts hold more ribosomes at steady state, they are hit sooner, their jam takes longer to clear, and their protein output per mRNA drops more steeply with antibiotic concentration. The model's key derived quantities, the unpaused-state lifetime $T_0$, the paused-state lifetime $T_p$, and the density $\rho$ and current $J$ as weighted averages of the two states, reproduce Gillespie simulations and, when mRNA degradation is added, the experimental gfp/lacZ expression ratio under chloramphenicol.

Load-bearing premise

The whole calculation rests on the Single-Cluster Approximation: after the first antibiotic-induced pause, exactly one contiguous jam forms and dissolves completely before any second distinct cluster can appear, an assumption the paper states is valid for slow-binding antibiotics but never quantifies.

Editorial extensions

If this is right

If correct, chloramphenicol-like antibiotics should reduce protein output per mRNA more for long genes than for short genes even when promoter strength, mRNA abundance, and initiation and elongation rates are identical.
Lowering ribosome initiation rate should measurably reduce a transcript's antibiotic vulnerability, by lengthening the time to the first pause and shrinking the jam that forms once a pause occurs.
Estimates of the active-ribosome fraction that count only paused versus non-paused ribosomes overstate protein synthesis under antibiotic stress, because jammed but unpaused ribosomes contribute to density but not to current.
The short-to-long expression ratio $R$ should rise monotonically with antibiotic concentration, and the model gives a quantitative curve for that rise once initiation rate, mRNA lifetime $\tau$, $k_{\text{on}}$, and $k_u$ are fixed.
The same two-state pausing-TASEP picture extends to other driven biological traffic with rare, long pauses, notably transcription by RNA polymerases.

Reading between the lines

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

The paper's mechanism implies a design principle: cells can buffer long genes against stochastic pausing by lowering their initiation rates, so the observed anticorrelation between gene length and initiation rate in bacteria and yeast may be a consequence of such buffering rather than an unrelated trend.
Because the first-pause time is set by the collective number of ribosomes on the transcript, reporter-gene comparisons under antibiotics should control for initiation rate as well as length; otherwise differences in ribosome loading would masquerade as length effects.
A single-molecule translation assay should reveal a bimodal protein-output distribution per mRNA under antibiotic stress: either a near-normal yield if the message degrades before the first pause, or near zero if it is hit, with the fraction of zero-yield transcripts growing with transcript length.
At high antibiotic binding rates the Single-Cluster Approximation is expected to fail, but the length-dependence should persist; an extension treating coagulation and decoagulation of multiple clusters could sharpen the quantitative predictions precisely in the regime where the analytical formulas start to break down.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Johannes,

If you only have time for one thing: the paper's central mechanism is real and its analytics mostly check out, but the 'quantitative agreement' in Fig. 8 rests on a mean-field replacement of a random first-pause time that is not validated anywhere in the paper.

What is genuinely new: the combination of finite-length TASEP, stochastic pausing, and explicit mRNA degradation, analyzed via a Single-Cluster Approximation, is a fresh and biologically motivated package. The prediction that longer transcripts are disproportionately vulnerable, and that lowering initiation rates mitigates that vulnerability, is a clean, testable consequence of collective ribosome traffic rather than an input. The analytical expressions match the paper's own Gillespie simulations across a broad parameter range—that is real evidence of internal consistency. The paper also makes a fair point against the single-ribosome hitting probability in Eq. (31); the collective version in Eq. (32) is simple and correct in spirit.

Now the soft spots, in proportion. The load-bearing issue is Eq. (38) in Sec. IV C. The derivation treats T0—the expected unpaused lifetime—as if every mRNA either degrades by T0 or pauses exactly at T0. The actual first-pause time F(t) has a broad distribution, and the correct expectation is an integral over that distribution weighted by the mRNA lifetime distribution. The paper never tests this mean replacement against simulations, and it creates a hard cutoff: for lacZ at high chloramphenicol concentrations, T0 approaches t_L, so the formula can predict zero output while the stochastic process still produces protein from mRNAs whose real pause time is later. That could easily steepen the short-to-long ratio R and manufacture quantitative agreement. This is a real flaw, not a quibble.

Second, the experimental validation in Fig. 8 uses two fitted parameters (alpha = 0.12 s^-1, tau = 170 s), and no competing single-ribosome model is fit to the same data, so the 'collective dynamics govern' claim is not yet tested against a simpler alternative. Third, the Single-Cluster Approximation's validity conditions—kp much less than epsilon, beta approximately epsilon, lattice not too long—are stated qualitatively and never quantified; the regime used for the experimental comparison is exactly where the approximation is most strained.

All that said, the central length-dependence direction is robust, the paper is honest about its limitations, and the framework is a useful stepping stone. The typos are fixable. This paper deserves a serious referee; the referee should ask for a simulation-based check of Eq. (38) and a comparison against a single-ribosome baseline.

Best, [Your name]

Referee Report

3 major / 4 minor

Summary. The paper develops a TASEP model of ribosome translation with stochastic antibiotic-induced pausing and unpausing, and introduces a Single-Cluster Approximation in which the first pause creates a single contiguous jam that fills back to the initiation site and dissolves in discrete batches. The authors derive analytical expressions for the unpaused-state lifetime, first-pause position, paused-state lifetime, density, current, particle state fractions, and hitting probability, and validate these against Gillespie simulations. They then extend the model to include finite mRNA lifetimes and use it to predict the short-to-long expression ratio for gfp and lacZ under chloramphenicol, reporting quantitative agreement with the experimental data of Zhang et al. (Fig. 8). The central claims are that antibiotic-induced inhibition is strongly transcript-length dependent, that lowering initiation rates mitigates vulnerability, and that collective ribosome dynamics, rather than single-ribosome properties, govern the response.

Significance. If the results hold, this is a valuable contribution to the modeling of translation under antibiotic stress. The paper's strengths are its explicit analytical treatment, the use of experimentally measured pausing/unpausing rates, and the systematic Gillespie validation of the core observables (lifetimes, densities, currents, and fractions) in Figs. 2-7. The length-dependence prediction and the initiation-rate mitigation effect are concrete and falsifiable, and the model gives a mechanistic explanation for why longer transcripts are more vulnerable. The comparison with Zhang et al. is an ambitious step toward quantitative validation, but it is the least secure part of the paper because it relies on an unvalidated mean-field replacement and on fitted parameters whose number is reported inconsistently. The core modeling framework is nevertheless defensible and the analytical results for the pre-validation part are credible.

major comments (3)

The protein-output formula replaces the random first-pause time by its mean T0. The actual first-pause time has the broad distribution F(t) in Eq. (11), and mRNA degradation is exponential with density in Eq. (33); the correct expected output is an integral over the stopping time min(T_pause, T_degradation), not an evaluation at the mean T0. This mean replacement is never validated against Gillespie simulations, and it creates a hard cutoff: Eq. (38) sets P=0 whenever T0 <= tL. For lacZ at high chloramphenicol concentrations, e.g. kp ~ 0.027 s^-1 with the parameters in Fig. 8, one obtains T0 ~ 23 s while tL ~ 51 s, so Eq. (38) predicts zero lacZ output even though stochastic realizations with first-pause times beyond tL, and ribosomes that have already passed the pause site, would still produce finite protein. This could artificially steepen the short-to-long ratio R and create the appearance of quantitative agreement. This is the load-bearing step for the central validation claim, so it must be corrected by integrating over F(t) and the mRNA lifetime distribution, or otherwise rigorously justified against simulations, and the impact on Fig. 8 must be reassessed.
The validity conditions of the Single-Cluster Approximation are stated only qualitatively: kp << epsilon, beta ~ epsilon, and 'the segment cannot be too long'. The approximation is used in Fig. 8 at antibiotic concentrations where kp is not small compared to ku; for example, at 10 uM chloramphenicol kp ~ 5.4e-3 s^-1 and ku = 1.4e-3 s^-1, so the batch size B = ku/kp + 1 is about 1.26, meaning the 'clusters' are small and multiple pauses can occur before a cluster dissolves. The authors should quantify the regime of validity, ideally with a phase diagram or simulation-based boundary, and demonstrate that the experimental comparison in Fig. 8 lies inside that regime. Without this, the analytical formulas for Tp, density, and current, and therefore the protein-output prediction, are not guaranteed to apply in the very regime used for the quantitative claim.
The number of fitted parameters in the experimental comparison is reported inconsistently. The Fig. 8 caption states that a fitted initiation rate alpha = 0.12 s^-1 and a mean mRNA lifetime tau = 170 s are used, while the Discussion states that the model 'quantitatively reproduces experimental differences ... by fitting only the degradation time tau'. This discrepancy matters because the 'quantitative agreement' is not parameter-free: it depends on two biologically plausible but adjustable parameters, and no confidence intervals, sensitivity analysis, or goodness-of-fit measure is reported. The authors should clarify which parameters were fitted, show how the fit was performed, and report the sensitivity of the short-to-long ratio R to alpha and tau, especially given the hard cutoff issue in Eq. (38).

minor comments (4)

The quantity tL is defined in Eq. (5) as the domain-wall traversal time L/(epsilon-alpha), but in Sec. IV B it is called 'the time required for a ribosome to translate the full mRNA', which for a single ribosome should be L/epsilon. This notational reuse should be clarified, since the comparison with the single-ribosome hitting probability relies on the same symbol.
The binding rate constant kon is given as 5.6 x 10^-4 uM^-1 s^-1 in the text and 5.4 x 10^-4 uM^-1 s^-1 in the Fig. 8 caption. Please reconcile the values and cite the corresponding uncertainty.
Some figure labels appear truncated or orphaned: Fig. 4 contains a floating heading 'Paused-state fraction,' and the legend entries in Fig. 6 are cut off ('(mobile+jammed)' and 'this work (mobile)'). The figures should be readable without referring to the main text.
The text has a minor typo ('mRNAS' for 'mRNAs'), and the notation P(t) for protein number is easily confused with the probability P0 and Pp from Eq. (21). A different symbol or a brief reminder would improve readability.

Circularity Check

1 steps flagged · score 3.0 of 10

The analytical derivation is self-contained; the only circularity-adjacent step is the 'quantitative match' to Zhang et al., which uses two parameters fitted to that same dataset rather than an independent prediction.

fitted input called prediction [Sec. IV C, Eq. (38)-(39), Fig. 8 caption; Discussion (Sec. V)]
"The parameters used are kon = 5.4 × 10−4 µM−1s−1, ku = 1.4 × 10−3 s−1 (giving KD ≈ 2.5 µM), with a fitted initiation rate of 0.12 s−1 and a mean mRNA lifetime τ = 170 s. ... The predictions from our model quantitatively match the experimental data, as shown in Fig. 8."

The claimed quantitative agreement with the experimental short-to-long expression ratio R is not an independent prediction: the two free parameters entering Eq. (38) (the initiation rate α and the mRNA lifetime τ) are fitted to the same Zhang et al. dataset that R is then said to match. The curve in Fig. 8 is therefore calibrated, not predicted, and the Discussion's statement that the model reproduces the data 'by fitting only the degradation time τ' omits the separately fitted initiation rate, understating the degrees of freedom. The qualitative length-dependence is still derived from the model's assumptions, so this is partial circularity in the validation step rather than in the core derivation.

full rationale

The core model derivation is not circular: the length-dependence follows from the stated per-ribosome independent pausing hazard λ(t)=kpN(t) and the finite-lattice filling dynamics, and the Single-Cluster Approximation is an explicit assumption tested against Gillespie simulations, not a conclusion smuggled in through self-citation. The analytical expressions for density, current, hitting probability, and protein output are derived from the model equations; the reported agreement with simulations supports the internal consistency of the derivation. No load-bearing uniqueness theorem or ansatz is imported from the authors' prior work; the self-citation [30] is used only for a mapping remark and is not needed for the central claims. The only circularity-adjacent element is the experimental validation: the 'quantitative match' in Fig. 8 is obtained after fitting both α and τ to the same dataset, and the text sometimes presents this as prediction. This does not invalidate the model's qualitative predictions, but it means the quantitative agreement is partly constructed by the fit rather than being an out-of-sample test.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The model's analytical machinery rests on standard TASEP mean-field results and on a purpose-built Single-Cluster Approximation, plus a domain-specific assumption that mRNA degradation truncates protein output after the first antibiotic-induced pause. Two fitted parameters (alpha and tau) are used to match the experimental data, which weakens the claim of quantitative prediction. No new physical entities are introduced.

free parameters (3)

alpha (ribosome initiation rate) = 0.12 s^-1
Fitted to the Zhang et al. experimental data in Fig. 8; the abstract's claim of quantitative agreement does not mention this fit.
tau (mean mRNA lifetime) = 170 s
Fitted to the same experimental data in Fig. 8; the discussion says only tau is fitted, while the caption lists both alpha and tau.
transcriptional efficiency threshold (Appendix C) = approximately 0.95 fraction of time in unpaused state
Chosen so that the model's inferred initiation rates match the experimental data in Fig. 10; this is an appendix hypothesis, not a central claim.

assumptions (5)

standard math Standard TASEP mean-field expressions for extended particles, J0 = alpha(epsilon - alpha)/(epsilon + alpha(ell - 1)) and rho0 = alpha/(epsilon + alpha(ell - 1)), are valid (Eq. 6).
Used for the unpaused-state current and density; this is a standard result from the prior TASEP literature.
domain assumption mRNA lifetime is exponentially distributed with mean tau (Eq. 33).
Used to compute the expected protein output per transcript; no experimental justification for the exponential form is provided here.
domain assumption Once a ribosome pauses, the mRNA is degraded before the antibiotic unbinds, so no further protein is produced after the first pause (Sec. IV C).
This makes the first pause time T0 the effective protein production window; based on the comparison between a roughly 12-minute antibiotic unbinding time and a 1 to 5-minute mRNA lifetime.
ad hoc to paper Single-Cluster Approximation: after the first pause, a single contiguous jam fills back to the initiation site and dissolves in batches of size B, with no second distinct cluster forming (Sec. II B).
Central simplification that enables all analytical derivations; validity conditions are stated qualitatively and not quantified.
domain assumption Termination is fast relative to other translation events, effectively beta approximately epsilon (condition ii in Sec. II B).
Required for the standard TASEP steady-state and the domain-wall picture; plausible for E. coli but not directly verified in this paper.

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Cite this review

Pith. "Pith review of Paused in translation: A model for the transcript length-dependent impact of ribosome-targeting antibiotics." pith.science (2026). https://pith.science/paper/AVUWU444

@misc{pith2026250719395,
  author       = {Pith},
  title        = {Pith review of: Paused in translation: A model for the transcript length-dependent impact of ribosome-targeting antibiotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AVUWU444}},
  note         = {Machine review of arXiv:2507.19395}
}

read the original abstract

Ribosome-targeting antibiotics, such as chloramphenicol, stall elongating ribosomes during protein synthesis, disrupting mRNA translation. These antibiotic-induced pauses occur stochastically, alter collective ribosome dynamics and transiently block protein production on the affected transcript. Existing models of ribosome traffic often rely on idealized assumptions, such as infinitely long mRNAs and simplified pausing dynamics, overlooking key biological constraints. Here, we develop a Totally Asymmetric Simple Exclusion Process (TASEP) that incorporates stochastic particle pausing, using experimentally determined pausing and unpausing rates to model the effects of ribosome-targeting antibiotics. We introduce a Single-Cluster approximation, which is analytically treatable, tailored to capture the biologically relevant regime of rare and long antibiotic-induced pauses. This biologically constrained model reveals three key insights: (i) the inhibition of antibiotic-induced translation strongly depends on transcript length, with longer transcripts being disproportionately affected; (ii) reducing ribosome initiation rates significantly mitigates antibiotic vulnerability; and (iii) inhibition of translation is governed more by collective ribosome dynamics than by single-ribosome properties. Our analytical predictions match Gillespie simulations, align quantitatively with experimental observations, and yield testable hypotheses for future experiments. These findings may have broader implications for the mechanistic modeling of other biological transport processes (e.g., RNAP dynamics), and more generally for the community studying traffic models.

Figures

Figures reproduced from arXiv: 2507.19395 by the authors.

**Figure 2.** Lifetime of the unpaused state, i.e. expected time until the first pausing event, as a function of the pausing rate, for different lattice lengths L. Analytical results (lines) closely match simulation data (markers). The simulation parameters here are α = 0.2, ϵ = 20, ku = 1.4 × 10−3 1. Entering the paused state and expected first pausing position In order to quantify the length of the jam in the paused state we mu… view at source ↗

**Figure 3.** Relative position (Xf /L) of the expected first pausing event for various lattice lengths as a function of the pausing rate kp. The relative pausing position shifts towards the lattice midpoint 0.5 as lattice length increases or pausing rate decreases. Analytical results (lines) closely match the simulation (markers). The parameters are the same as in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 5.** Figure 5: (a) Particle density as a function of the pausing rate [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: (a) shows how the fractions of jammed, mobile, and [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: (a) Probability of completing translation with [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Comparison between model predictions and ex [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: (a) Paused-state density ρp for L = 100, comparing the finite-cluster batch size B against the infinite-cluster approximation B = ku/kp + 1. The finite-cluster density ρp is obtained by numerically evaluating Eq. (23) with the batch size depending on cluster size in m…

**Figure 10.** Figure 10: (a) the Unpaused state fraction P0 as a function of initiation rates, for different system sizes L. Here, we choose the efficiency level (green vertical line) so that the predicted initiation rate for a certain gene length matches the experimentally observed one, sho…

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Particles pause independently following a stochastic Poisson process with rate kp

Lifetime Distribution of the Unpaused State The random time T0 until the first pausing event can be analysed by viewing pauses as a simple point process, in which times at which pauses occur form a random set of points on the time axis. Particles pause independently following a stochastic Poisson process with rate kp. How- ever, the number of particles in...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Particles pause independently following a stochastic Poisson process with rate kp

Lifetime Distribution of the Unpaused State The random time T0 until the first pausing event can be analysed by viewing pauses as a simple point process, in which times at which pauses occur form a random set of points on the time axis. Particles pause independently following a stochastic Poisson process with rate kp. How- ever, the number of particles in...

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[2] [2]

(11) to weight the average: T0 = Z tL 0 t F(t) dt Note that F (t) is the probability density function for the distribution of the first pausing time

Expected Lifetime of the Unpaused State The expected duration of the unpaused state, denoted as T0, now follows as the average time when the first pausing event occurs, thus using the function F (t) in Eq. (11) to weight the average: T0 = Z tL 0 t F(t) dt Note that F (t) is the probability density function for the distribution of the first pausing time. S...

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[3] [3]

To this end we first consider the instantaneous position of the domain wall, xs(t), as de- fined in Eq

Entering the paused state and expected first pausing position In order to quantify the length of the jam in the paused state we must identify the position at which the first pausing event occurs. To this end we first consider the instantaneous position of the domain wall, xs(t), as de- fined in Eq. (4). Any particle behind this domain wall has an equal pr...

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[4] [4]

Lifetime of the paused state To compute the lifetime of the paused state we need to evaluate the time to dissolve it by successive release of discrete batches of particles (see Fig. 1). The number of particles in the initial cluster is, on average, Ni := Xf /ℓ when considering the filling of the initial cluster as a fast process. This means that the loadi...

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[5] [5]

(21), and the steady-state density in the unpaused state ρ0 is given by the standard TASEP expression, Eq

Particle density The total particle density, ρ, is obtained as a weighted average of the paused and unpaused states, ρ = P0ρ0 + Ppρp, (22) where the probabilities P0 and Pp are defined in Eq. (21), and the steady-state density in the unpaused state ρ0 is given by the standard TASEP expression, Eq. (6). To analytically determine the density in the paused s...

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[6] [6]

(26) 8 The contribution from the unpaused state is given simply by the zero-paused particle current J0, as described by the standard TASEP expression for extended particles, Eq

Current Similarly to the density, we can also compute the av- erage current, J, as a weighted sum: J = P0 J0 + Pp Jp. (26) 8 The contribution from the unpaused state is given simply by the zero-paused particle current J0, as described by the standard TASEP expression for extended particles, Eq. (6). Conversely, the current contribution Jp from the paused ...

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[7] [7]

free” (unpaused) particles preceding a paused one. Equivalently, this is a Bernoulli process in which a “success

Microscopic particle states Having characterized the global properties of the sys- tem in terms of density and current, we now turn our attention to the microscopic dynamics by categorizing the states of individual particles. In order to do that, we divide particles in three distinct categories: paused, mobile, and jammed (not paused, but blocked by exclu...

work page

[8] [8]

Klumpp and T

S. Klumpp and T. Hwa 105, 10.1073/pnas.0806084105

work page doi:10.1073/pnas.0806084105