On the Role of Vesicle Transport in Neurite Growth: Modelling and Experiments

Andreas W. P\"uschel; Danila Di Meo; Ina Humpert; Jan-Frederik Pietschmann

REVIEW 3 major objections 5 minor 46 references

On the Role of Vesicle Transport in Neurite Growth: Modelling and Experiments

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Vesicle transport alone, the model predicts, turns a length lead into a growth-cone advantage.

desk verdict A simple two-neurite vesicle-transport model that predicts length-dependent axon selection, but the discrete boundary conditions don't conserve mass and may produce the central asymmetry as an artifact. read the letter →

arxiv 1908.02055 v1 pith:D4BTVIVG submitted 2019-08-06 q-bio.SC

Ina Humpert , Danila Di Meo , Andreas W. Püschel , Jan-Frederik Pietschmann This is my paper · ORCID

classification q-bio.SC MSC 92C3735Q92

keywords neuritegrowthvesicletransportneuronalpolarizationsymmetrybreakinglattice-basedmodelasymmetricexclusionprocesscross-diffusionconepool

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 combines live imaging of Vamp2-labelled vesicles in unpolarized hippocampal neurons with a lattice model of bidirectional vesicle transport to ask whether transport alone can explain why one neurite becomes the axon. In the model, two neurites share a finite soma pool and exchange anterograde and retrograde vesicles with growth-cone pools at their tips. Live imaging found 37% more anterograde than retrograde vesicle movements in extending neurites, motivating a transport-based account. When the neurites have very different lengths, the longer one accumulates a higher vesicle concentration in its growth-cone pool, while nearly equal lengths show almost no asymmetry. The authors read the growth-cone pool as growth potential: more vesicles at the tip means more membrane material for extension, so an initial length advantage becomes self-reinforcing.

What carries the argument

The load-bearing object is a discrete lattice model of two vesicle species with size exclusion. Each neurite is a one-dimensional lattice of cells connected to a soma pool at one end and a growth-cone pool at the other; anterograde vesicles move toward the tip under potential $V_a(x)=1.75x$ and retrograde vesicles move back under $V_r(x)=-1.5x$, with jumps allowed only into cells that are not full. The rate of leaving the soma or a pool is proportional to occupancy, and the rate of entering is proportional to free space, so the shared finite soma couples the two neurites and makes their transport competitive. A formal continuum limit gives a cross-diffusion system for anterograde and retrograde densities, with boundary fluxes carrying exactly the soma and pool exchange terms. The mechanism carrying the argument is that a longer neurite presents a larger total potential drop and more vesicle capacity, pulling more anterograde vesicles out of the soma and leaving fewer for the shorter neurite.

What would settle it

Run the same two-neurite simulation with the anterograde and retrograde potentials scaled by each neurite's own length, for example $V_a(x)=1.75x/L$, and with equal pool capacities per neurite; if the longer neurite no longer maintains a higher growth-cone pool concentration, the predicted length advantage is an artifact of giving longer domains larger total potential drops rather than a property of the transport dynamics.

Watch

Extended reading notes

Core claim

The central claim is that bidirectional vesicle transport through a common soma pool converts an initial difference in neurite length into a difference in growth potential, favoring the longer neurite. In simulations with otherwise symmetric initial data, the growth-cone pool concentration $\Lambda_{N,1}$ rises above $\Lambda_{N,2}$ when the domains are $\Omega_1=[0,1]$ and $\Omega_2=[0,0.3]$, but the asymmetry is nearly absent when $\Omega_2=[0,0.9]$. The authors interpret the growth-cone vesicle concentration as the neurite's growth potential because vesicles fused at the membrane supply the surface area for extension. They also report a sudden drop in the shorter neurite's pool concentration after a metastable plateau between $t=26$ and $t=36$ in the strongly asymmetric run, and they compare this threshold-like event to behavior seen in polarizing neurons. Their conclusion is that length-dependent vesicle supply, mediated by bidirectional transport, can serve as a mechanical explanation for axon selection without requiring a separate molecular length sensor.

Load-bearing premise

The model's result assumes that the transport potential and vesicle capacity are proportional to neurite length, so the longer neurite starts with a larger share of the shared vesicle supply; if those quantities were instead held equal per neurite, the predicted advantage of the longer neurite could vanish.

Editorial extensions

If this is right

If the model is right, a neurite that has crossed a critical length will automatically receive a larger share of soma-derived vesicles, providing a transport-level explanation for the experimental finding that a neurite must exceed a minimal length to become an axon.
Nearly equal initial lengths should not break symmetry by themselves; the simulations with $\Omega_2=[0,0.9]$ show almost no growth-cone asymmetry, implying a threshold length gap is required for reliable axon specification.
Oscillations in growth-cone vesicle pools should appear during polarization, matching observed cycles of neurite extension and retraction and giving a dynamical signature that live imaging could look for.
The coupling through the soma means interventions that change soma capacity, retrograde return, or pool size should shift which neurite wins, making transport rates a control point for axon selection.

Reading between the lines

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

Editorial inference: the model's symmetry breaking is driven in part by domain size, since the longer neurite has a larger total potential drop and more vesicle capacity; running the same two-neurite computation with potentials and pool capacities normalized per unit length would separate genuine dynamical competition from a static bias.
Editorial inference: the same soma-pool coupling should apply to other bidirectional cargo systems sharing a resource pool, such as mitochondrial or mRNA distribution in dendrites, so the mechanism may be a general principle for competitive cellular transport.
Editorial inference: because the growth-cone pool is treated as a well-mixed compartment, adding explicit exocytosis and endocytosis delays or stochastic vesicle numbers could reveal whether the metastable plateau and sudden drop survive noise; this is testable by stochastic simulation.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Colleague — this one is worth a look, but it's not as solid as it presents. The new thing here is simple: a two-species exclusion process on a lattice, with both neurites fed from the same soma pool, and a numerical observation that a longer neurite ends up with a higher growth-cone pool concentration. That's a plausible mechanism for axon selection, and the live-imaging data (Vamp2-GFP vesicle counts during extension vs. retraction) give at least some empirical motivation. The paper is clearly written and doesn't oversell the biology too much.

But the central numerical result rests on an inconsistency in the discrete equations. The boundary conditions in (2) and (8) write the fluxes as, e.g., +Ch α_a(1-ρ1) and -Ch β_a a_N, without the occupancy factors Λ_som/Λ_max_som and 1-Λ_N/Λ_max_N that appear in the pool ODEs (3)-(4) and in the macroscopic boundary conditions (11). The rates α_q(Λ), β_q(Λ) are defined just above (2) but then not used. So as written, the lattice and the pools don't exchange the same amount of mass. The code likely does exactly this, since the numerical section says they multiply by h but never mentions restoring the omitted factors. Mass is not conserved, and the leak is not neutral: the missing factors are order-one (e.g., Λ_som/Λ_max_som ≈ 0.7), and because the two domains are discretized with the same number of points, the boundary structure differs between them. It's entirely plausible that the asymmetry in Figure 8 is an artifact of this leak. That's a load-bearing flaw, because the paper's main claim is that this asymmetry is a vesicle-transport feedback. I'd want to see the corrected scheme, a mass-conservation test, and a sensitivity analysis before believing it.

Other concerns: the potentials V_a=1.75x and V_r=-1.5x are the same slope on both neurites, so the longer neurite gets a bigger total potential drop and can hold more vesicles — that structural advantage may be doing the work. The diffusion coefficient ε=0.05 is admitted to be 'purely estimated,' and there's no sensitivity analysis. Lemma 5.1's proof uses an inequality that's tautological as written. The experiments are descriptive and don't test the pool-concentration prediction.

None of these secondary issues kill the paper on their own. The core idea — length-dependent symmetry breaking through shared-pool transport — is interesting and testable. But the mass-nonconservation issue has to be fixed before the simulation result means anything.

My recommendation: send it to peer review, yes. It's an important problem and the flaw is technical rather than fatal to the concept, but referees should ask for a corrected, mass-conserving implementation and proper sensitivity checks. I wouldn't cite it in its current form.

Referee Report

3 major / 5 minor

Summary. The paper combines live-imaging experiments on Vamp2-GFP vesicle transport in cultured hippocampal neurons with a two-species lattice model of anterograde/retrograde vesicle transport along neurites, coupled to a soma pool and growth-cone pools. The central modelling claim is that an initial difference in neurite length creates a self-reinforcing asymmetry in growth-cone vesicle pools, so that the longer neurite acquires a higher growth potential; this is presented as a possible mechanism for axon specification. The authors also derive a formal cross-diffusion PDE limit and state an existence/regularity caveat. The experiments show a significant increase in anterograde vesicle transport during neurite extension.

Significance. If the central claim survives scrutiny, the model would provide a mechanism linking vesicle transport to axon selection and makes a falsifiable prediction about length thresholds. The experimental data are carefully quantified and the modelling framework is novel in coupling size-exclusion transport to finite-capacity pools. However, the numerical support is currently undermined by a boundary-flux inconsistency and by structural choices that favour the longer neurite; the qualitative conclusion should be re-established with a mass-conservative scheme and neutral parameter normalization.

major comments (3)

[Section 3, Eqs. (2)-(4); Section 3.1, Eq. (8); Section 5, Eq. (14)] The discrete boundary fluxes are inconsistent with the pool ODEs and with the PDE boundary conditions (11). Specifically, the anterograde influx into cell 1 in (2) is written as +Ch α_a(1−ρ_1) without the factor Λ_som/Λmax_som that appears in (4); the anterograde outflux at cell N is −Ch β_a a_N without (1−Λ_N/Λmax_N) from (3); and analogous factors are missing in the r_1 and r_N equations. The same omissions appear in the scaled scheme (8) and in the boundary update (14). Because both domains are discretized with 400 points, the missing factors multiply terms proportional to Ch, so the longer neurite has proportionally larger spurious sources and sinks; the mass imbalance therefore contaminates the comparison in Section 5.2 and Figure 8. The statement in Section 5 that multiplying in- and outflux terms by h ensures mass conservation does not address this inconsistency.
[Section 5.1, Table 1] The potentials V_a(x)=1.75x and V_r(x)=−1.5x give the longer neurite a larger total potential drop (1.75 for Ω1=[0,1] versus 0.525 for Ω2=[0,0.3]) and, with the stated per-length maximal density, a larger total vesicle capacity; with uniform initial concentration 0.1, the longer neurite also contains more initial vesicles. The reported symmetry breaking may therefore reflect an initial structural advantage rather than a dynamic feedback. The authors should either normalize the potentials per neurite length, hold total capacity and initial vesicle number equal, or justify the chosen scaling from the biology.
[Section 5, Lemma 5.1, Eq. (17)] The proof of the upper bound ρ≤1 is circular: inequality (17) states (1−ρ^{k+1}_i) ≥ (1−4τHV_max)(1−ρ^{k+1}_i), which is a tautology and gives no bound in terms of the previous time step. The CFL condition may still be correct, but the lemma as written is not proven; since the simulations rely on this scheme, the box constraints should be verified numerically or the proof corrected.

minor comments (5)

[Section 2.2] The heading "Exterimental Methods" contains a typo; it should read "Experimental Methods".
[Section 3, bullet list] The bullet text says anterograde vesicles enter with rate α_a(Λ_som)(1−ρ_N), but equation (2) and the intended coupling require (1−ρ_1); similarly, retrograde vesicles should enter from the pool with rate α_r(Λ_N)(1−ρ_N), not (1−ρ_1).
[Table 1] The Ω2 row lists [0,3] while the text and simulations use [0,0.3] and [0,0.9]; also the β entries repeat β_a,2, which appears to be a typographical error.
[Section 5.3, Figure 9] The text refers to a rapid change "at the tip of the longer neurite (Λ_N,2)", but in the simulations neurite 1 is the longer domain; the labels for Λ_N,1 and Λ_N,2 appear to be swapped in this discussion.
[Section 3, Eq. (2)] In the r_1 equation, the notation "Vr,x1" should be "Vr,1" for consistency with the other potential terms.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the longer-neurite advantage in growth-cone pool concentration is an emergent numerical outcome of the specified transport model, not a rename of its inputs or a fitted prediction.

full rationale

The central claim—that a length difference raises the growth-cone pool concentration of the longer neurite—is obtained by solving the coupled lattice/pool model numerically, and the paper never fits the pool asymmetry to data. The linear potentials Va(x)=1.75x and Vr(x)=-1.5x in Section 5.1 are a stated modeling ansatz based on measured vesicle velocity ranges; although a longer neurite then has a larger total potential drop, this does not make the pool ordering an identity: the boundary fluxes, exclusion factors, and pool ODEs must still be solved, and the direction and magnitude of the asymmetry are not fixed by the potential alone. Interpreting the pool concentration Lambda_N as 'growth potential' (Section 5.2) is a definition, but the numerical result Lambda_N,1 > Lambda_N,2 for Omega2=[0,0.3] is computed rather than assumed. Self-citations ([6],[7],[8],[34]) support background cross-diffusion analysis and are not load-bearing for the Section 5 simulations. The paper's own limitations (Remark 4.1 on missing regularity, Section 6.2 on constant total mass, and the statement that epsilon is 'purely estimated') are scope warnings, not admissions of circularity. A possible mass-conservation discrepancy between boundary conditions (2)/(8) and pool ODEs (3)-(4) is a numerical-correctness concern, not a circularity, and is therefore not counted in this score.

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

The central claim rests on a small number of modeling assumptions and hand-picked parameters. The free parameters are the potential slopes, the diffusion coefficient, the influx and outflux rates, the initial densities, and the pool capacities; none are fitted to the live-imaging data with error bars. The axioms are standard for this modeling family (size exclusion, well-mixed pools, mass conservation) plus the biologically motivated but unproven identification of growth-cone pool size with growth potential. The linear potentials with equal per-length slope on both neurites are the most consequential ad hoc choice, because they give the longer neurite a larger potential drop and a larger reservoir, which may pre-dispose the reported symmetry breaking. No new entities are introduced.

free parameters (6)

Potential slopes V_a and V_r = V_a=1.75, V_r=-1.5 (dimensionless)
Chosen in Section 5.1 to match measured velocity ranges (1-2.5 µm/s anterograde, 1-2 µm/s retrograde), but the linear-in-x form and same slope per unit length on both neurites are modeling choices that bias longer neurites.
Diffusion coefficient ε = 0.05 (dimensionless; 5e-3 µm²/s)
Section 5.1 states ε is 'not biologically meaningful' and 'purely estimated'; it controls the strength of the diffusive term in the cross-diffusion limit.
Influx rates α_a, α_r = 0.8 (dimensionless)
Chosen in Table 1 without sensitivity analysis; these rates set how fast vesicles enter the neurites from soma and growth-cone pools.
Outflux velocities β_a, β_r = 15 (dimensionless, equivalent to 1.5 µm/s)
Chosen in Table 1 from velocity ranges; combined with pool capacities they control depletion of the growth-cone pool.
Initial vesicle densities a0, r0 = 0.1 (dimensionless, 1.5 vesicles/µm)
Set to 'half filled neurite' in Section 3.1; not measured for the specific cells imaged.
Pool capacities Λ_max_som, Λ_max_N = 0.175 and 0.0029 (dimensionless)
Estimated from assumed soma volume and vesicle size in Section 5.1; the coupling strength between neurites depends on these values.

assumptions (6)

domain assumption Vesicle fusion at the growth cone drives neurite extension, so the number of vesicles in the growth-cone pool is a measure of growth potential.
Used throughout, explicitly in Section 5.2: 'if the concentration in the pools Λ_N,1 and Λ_N,2 are not equal, the neurite with the higher concentration in the pool has a higher growing potential.' Supported by cited biology but not tested here.
domain assumption Neurites can be treated as one-dimensional lattices with a maximal vesicle density (size exclusion).
Sections 2.3 and 3; the model's foundation. The estimated maximal density of about 8 vesicles per 1000 nm neurite is an order-of-magnitude estimate.
domain assumption The soma and growth-cone pools are well-mixed reservoirs with fast internal dynamics; vesicles leaving one neurite can immediately enter another.
Remark 3.1(b) states this explicitly; it affects how strongly the two neurites compete for the shared soma pool.
ad hoc to paper Total vesicle number is conserved; no vesicle production or degradation occurs during the simulated time.
The authors state in the Outlook that 'a production term of vesicles in the soma is necessary since at present the total mass of vesicles is constant which prevents the neurite from intensive growth.' This is a simplifying assumption that may limit the model's biological validity.
ad hoc to paper The potentials V_a and V_r are linear in x with the same slope on both neurites, so a longer domain has a larger total potential drop.
Section 5.1: 'The potentials Va(x)=1.75x and Vr(x)=-1.5x translate to the fact that particles of type a move anterograde and particles of type r move retrograde with different velocities.' This choice is not derived from data and biases longer neurites.
standard math The formal Taylor expansion from the lattice model to the cross-diffusion PDE system is valid.
Section 4; the same formal limit is standard for exclusion processes and is the basis for the macroscopic equations (10) and boundary conditions (11).

how reviews work

0 comments

Cite this review

Pith. "Pith review of On the Role of Vesicle Transport in Neurite Growth: Modelling and Experiments." pith.science (2026). https://pith.science/paper/D4BTVIVG

@misc{pith2026190802055,
  author       = {Pith},
  title        = {Pith review of: On the Role of Vesicle Transport in Neurite Growth: Modelling and Experiments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D4BTVIVG}},
  note         = {Machine review of arXiv:1908.02055}
}

read the original abstract

The processes that determine the establishment of the complex morphology of neurons during development are still poorly understood. We present experiments that use live imaging to examine the role of vesicle transport and propose a lattice-based model that shows symmetry breaking features similar to a neuron during its polarization. In a otherwise symmetric situation our model predicts that a difference in neurite length increases the growth potential of the longer neurite indicating that vesicle transport can be regarded as a major factor in neurite growth.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 46 canonical work pages

[1]

Albus, Ida Rishal, and Mike Fainzilber

Christin A. Albus, Ida Rishal, and Mike Fainzilber. Cell length sensing for neuronal growth control. Trends in Cell Biology , 23(7):305–310, July 2013. 21

work page 2013
[2]

Modeling, simulation and visual analysis of crowds: a multidisciplinary perspective

Saad Ali, Ko Nishino, Dinesh Manocha, and Mubarak Shah. Modeling, simulation and visual analysis of crowds: a multidisciplinary perspective. In Modeling, simulation and visual analysis of crowds, pages 1–19. Springer, 2013

work page 2013
[3]

Bradke and C

F. Bradke and C. G. Dotti. Neuronal polarity: vectorial cytoplasmic ﬂow precedes axon formation. Neuron, 19(6):1175–1186, December 1997

work page 1997
[4]

Bressloﬀ

Paul C. Bressloﬀ. Stochastic model of protein receptor traﬃcking prior to synaptogenesis. Phys. Rev. E , 74:031910, 9 2006

work page 2006
[5]

Bressloﬀ and Ethan Levien

Paul C. Bressloﬀ and Ethan Levien. Synaptic democracy and vesicular transport in axons. Phys. Rev. Lett., 114:168101, 4 2015

work page 2015
[6]

Burger, M

M. Burger, M. Di Francesco, J.F. Pietschmann, and B. Schlake. Nonlinear cross-diﬀusion with size exclusion. SIAM J. Math. Anal. , 42(6):2842 – 2871, 2010

work page 2010
[7]

Nonlinear cross-diﬀusion with size exclusion

Martin Burger, Marco Di Francesco, Jan-Frederik Pietschmann, and B¨ arbel Schlake. Nonlinear cross-diﬀusion with size exclusion. SIAM J. Math. Anal. , 42(6):2842–2871, 2010

work page 2010
[8]

Flow characteristics in a crowded transport model

Martin Burger and Jan-Frederik Pietschmann. Flow characteristics in a crowded transport model. Nonlinearity, 29(11):3528, 2016

work page 2016

Show all 46 references

[9]

Jonathan A. Cooper. Cell biology in neuroscience: mechanisms of cell migration in the nervous system. The Journal of Cell Biology , 202(5):725–734, September 2013

2013
[10]

B. Derrida. An exactly soluble non-equilibrium system: The asymmetric simple exclusion process. Physics Reports, 301(1):65 – 83, 1998

1998
[11]

Dotti and Gary A

Carlos G. Dotti and Gary A. Banker. Experimentally induced alteration in the polarity of developing neurons. Nature, 330(6145):254–256, November 1987

1987
[12]

Cross-diﬀusion systems with non-zero ﬂux and moving boundary conditions

Virginie Ehrlacher and Athmane Bakhta. Cross-diﬀusion systems with non-zero ﬂux and moving boundary conditions. Mathematical Modelling and Numerical Analysis , 2018

2018
[13]

Asymmetric exclusion model with two species: spontaneous symmetry breaking

MR Evans, DP Foster, C Godreche, and D Mukamel. Asymmetric exclusion model with two species: spontaneous symmetry breaking. Journal of statistical physics , 80(1-2):69–102, 1995

1995
[14]

Franco, Isabel Martinez-Garay, Cristina Gil-Sanz, Sarah R

Santos J. Franco, Isabel Martinez-Garay, Cristina Gil-Sanz, Sarah R. Harkins-Perry, and Ulrich M¨ uller. Reelin regulates cadherin function via Dab1/Rap1 to control neuronal migration and lamination in the neocortex. Neuron, 69(3):482–497, February 2011

2011
[15]

Goslin and G

K. Goslin and G. Banker. Experimental observations on the development of polarity by hippocampal neurons in culture. The Journal of Cell Biology , 108(4):1507–1516, April 1989

1989
[16]

Gumy, Eugene A

Laura F. Gumy, Eugene A. Katrukha, Ilya Grigoriev, Dick Jaarsma, Lukas C. Kapitein, Anna Akhmanova, and Casper C. Hoogenraad. MAP2 Deﬁnes a Pre-axonal Filtering Zone to Regulate KIF1- versus KIF5-Dependent Cargo Transport in Sensory Neurons. Neuron, 94(2):347–362.e7, April 2017

2017
[17]

Gupton and Frank B

Stephanie L. Gupton and Frank B. Gertler. Integrin Signaling Switches the Cytoskeletal and Exocytic Machinery that Drives Neuritogenesis. Developmental Cell , 18(5):725–736, May 2010

2010
[18]

Excitatory cortical neurons with multipolar shape establish neuronal polarity by forming a tangentially oriented axon in the intermediate zone

Yumiko Hatanaka and Kenta Yamauchi. Excitatory cortical neurons with multipolar shape establish neuronal polarity by forming a tangentially oriented axon in the intermediate zone. Cerebral Cortex (New York, N.Y.: 1991) , 23(1):105–113, January 2013

1991
[19]

Catherine Jacobson, Bruce Schnapp, and Gary A. Banker. A change in the selective translo- cation of the kinesin-1 motor domain marks the initial speciﬁcation of the axon. Neuron, 49(6):797 – 804, 2006. 22

2006
[20]

The boundedness-by-entropy method for cross-diﬀusion systems

Ansgar J¨ ungel. The boundedness-by-entropy method for cross-diﬀusion systems. Nonlinearity, 28(6):1963, 2015

1963
[21]

A cellular automata model of proton hopping down a channel

Lemont B Kier, Robert Tombes, Lowell H Hall, and Chao-Kun Cheng. A cellular automata model of proton hopping down a channel. Chemistry & biodiversity , 10(3):338–342, 2013

2013
[22]

Lasiecka and Bettina Winckler

Zoﬁa M. Lasiecka and Bettina Winckler. Mechanisms of polarized membrane traﬃcking in neurons – focusing in on endosomes. Molecular and Cellular Neurosciences , 48(4):278–287, December 2011

2011
[23]

Pierre Mangeol, Bram Prevo, and Erwin J. G. Peterman. KymographClear and KymographDi- rect: two tools for the automated quantitative analysis of molecular and cellular dynamics using kymographs. Molecular Biology of the Cell , 27(12):1948–1957, April 2016

1948
[24]

A dual mechanism controlling the localization and function of exocytic v-snares

Sonia Martinez-Arca, Rachel Rudge, Marcella Vacca, Gra¸ ca Raposo, Jacques Camonis, V´ eronique Proux-Gillardeaux, Laurent Daviet, Etienne Formstecher, Alexandre Hamburger, Francesco Filippini, Maurizio D’Esposito, and Thierry Galli. A dual mechanism controlling the localizati...

2003
[25]

Extracellular and Intracellular Signaling for Neuronal Polarity , volume 95

Takashi Namba, Yasuhiro Funahashi, Shinichi Nakamuta, Chundi Xu, Tetsuya Takano, and Kozo Kaibuchi. Extracellular and Intracellular Signaling for Neuronal Polarity , volume 95. July 2015

2015
[26]

Campbell, and Sandra E

Sylvia Neumann, Romain Chassefeyre, George E. Campbell, and Sandra E. Encalada. Kymo- Analyzer: a software tool for the quantitative analysis of intracellular transport in neurons. Traﬃc (Copenhagen, Denmark), 18(1):71–88, 2017

2017
[27]

Pfenninger

Karl H. Pfenninger. Plasma membrane expansion: a neuron’s Herculean task. Nature Reviews. Neuroscience, 10(4):251–261, April 2009

2009
[28]

Pfenninger, Lisandro Laurino, Diego Peretti, Xiaoxin Wang, Silvana Rosso, Gerardo Morﬁni, Alfredo C´ aceres, and Santiago Quiroga

Karl H. Pfenninger, Lisandro Laurino, Diego Peretti, Xiaoxin Wang, Silvana Rosso, Gerardo Morﬁni, Alfredo C´ aceres, and Santiago Quiroga. Regulation of membrane expansion at the nerve growth cone. Journal of Cell Science , 116(Pt 7):1209–1217, April 2003

2003
[29]

Spontaneous symmetry breaking in two-channel asymmetric exclusion processes with narrow entrances

Ekaterina Pronina and Anatoly B Kolomeisky. Spontaneous symmetry breaking in two-channel asymmetric exclusion processes with narrow entrances. Journal of Physics A: Mathematical and Theoretical, 40(10):2275–2286, 2 2007

2007
[30]

Regulation of plasma membrane expansion during axon formation

Santiago Quiroga, Mariano Bisbal, and Alfredo C´ aceres. Regulation of plasma membrane expansion during axon formation. Developmental Neurobiology, 78(3):170–180, 2018

2018
[31]

Ida Rishal, Naaman Kam, Rotem Ben-Tov Perry, Vera Shinder, Elizabeth M. C. Fisher, Giampietro Schiavo, and Mike Fainzilber. A motor-driven mechanism for cell-length sensing. Cell Reports, 1(6):608–616, June 2012

2012
[32]

Neuronal polarization: From spatiotemporal signaling to cytoskeletal dynamics

Max Schelski and Frank Bradke. Neuronal polarization: From spatiotemporal signaling to cytoskeletal dynamics. Molecular and Cellular Neurosciences , 84:11–28, 2017

2017
[33]

Schlager, Andrea Serra-Marques, Ilya Grigoriev, Laura F

Max A. Schlager, Andrea Serra-Marques, Ilya Grigoriev, Laura F. Gumy, Marta Esteves da Silva, Phebe S. Wulf, Anna Akhmanova, and Casper C. Hoogenraad. Bicaudal d family adaptor proteins control the velocity of Dynein-based movements. Cell Reports, 8(5):1248– 1256, September 2014

2014
[34]

B.A. Schlake. Mathematical Models for Particle Transport: Crowded Motion . PhD thesis, Westf¨ alische Wilhelms-Universit¨ at M¨ unster, 2011. 23

2011
[35]

P¨ uschel

Bhavin Shah, Daniela Lutter, Yaroslav Tsytsyura, Natalia Glyvuk, Akira Sakakibara, J¨ urgen Klingauf, and Andreas W. P¨ uschel. Rap1 GTPases Are Master Regulators of Neural Cell Polarity in the Developing Neocortex. Cerebral Cortex (New York, N.Y.: 1991) , 27(2):1253– 1269, 2017

1991
[36]

Diﬀusion populations: Ghosts or folks

M J Simpson, B D Hughes, and K A Landman. Diﬀusion populations: Ghosts or folks. Australasian Journal of Engineering Education , 15(2100):59–68, 2009

2009
[37]

Exocytic and endocytic membrane traﬃcking in axon development

Takuro Tojima and Hiroyuki Kamiguchi. Exocytic and endocytic membrane traﬃcking in axon development. Development, Growth & Diﬀerentiation , 57(4):291–304, May 2015

2015
[38]

A diﬀusion-based neurite length-sensing mechanism involved in neuronal symmetry breaking

Michinori Toriyama, Yuichi Sakumura, Tadayuki Shimada, Shin Ishii, and Naoyuki Inagaki. A diﬀusion-based neurite length-sensing mechanism involved in neuronal symmetry breaking. Molecular Systems Biology, 6(1):394, 2010

2010
[39]

Quantifying neurite growth mediated by interactions among secretory vesicles, microtubules, and actin networks

Krasimira Tsaneva-Atanasova, Andrea Burgo, Thierry Galli, and David Holcman. Quantifying neurite growth mediated by interactions among secretory vesicles, microtubules, and actin networks. Biophysical Journal, 96(3):840–857, 2 2009

2009
[40]

Urbina, Shawn M

Fabio L. Urbina, Shawn M. Gomez, and Stephanie L. Gupton. Spatiotemporal organization of exocytosis emerges during neuronal shape change. The Journal of Cell Biology , 217(3):1113– 1128, 2018

2018
[41]

Far-from-equilibrium bidirectional transport system with constrained entrances competing for pool of limited resources

Atul Kumar Verma, Natasha Sharma, and Arvind Kumar Gupta. Far-from-equilibrium bidirectional transport system with constrained entrances competing for pool of limited resources. Phys. Rev. E , 97:022105, 2 2018

2018
[42]

Lgl1 activation of rab10 promotes axonal membrane traﬃcking underlying neuronal polarization

Tong Wang, Yang Liu, Xiao-Hui Xu, Cai-Yun Deng, Kong-Yan Wu, Ji Zhu, Xiu-Qing Fu, Miao He, and Zhen-Ge Luo. Lgl1 activation of rab10 promotes axonal membrane traﬃcking underlying neuronal polarization. Developmental Cell , 21(3):431–444, September 2011

2011
[43]

Dendritic traﬃcking faces physiologically critical speed-precision tradeoﬀs

Alex H Williams, Cian O’Donnell, Terrence J Sejnowski, and Timothy O’Leary. Dendritic traﬃcking faces physiologically critical speed-precision tradeoﬀs. eLife, 5:e20556, 12 2016

2016
[44]

Winans, Sean R

Amy M. Winans, Sean R. Collins, and Tobias Meyer. Waves of actin and microtubule polymerization drive microtubule-based transport and neurite growth before single axon formation. eLife, 5:e12387, 2 2016

2016
[45]

Waves of actin and microtubule polymer- ization drive microtubule-based transport and neurite growth before single axon formation

Amy M Winans, Sean R Collins, and Tobias Meyer. Waves of actin and microtubule polymer- ization drive microtubule-based transport and neurite growth before single axon formation. eLife, 5:e12387, 2 2016

2016
[46]

Banker, Takashi Tanii, and Shun Nakamura

Hideaki Yamamoto, Takanori Demura, Mayu Morita, Gary A. Banker, Takashi Tanii, and Shun Nakamura. Diﬀerential neurite outgrowth is required for axon speciﬁcation by cultured hippocampal neurons. Journal of Neurochemistry, 123(6):904–910, December 2012. 24

2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Albus, Ida Rishal, and Mike Fainzilber

Christin A. Albus, Ida Rishal, and Mike Fainzilber. Cell length sensing for neuronal growth control. Trends in Cell Biology , 23(7):305–310, July 2013. 21

work page 2013

[2] [2]

Modeling, simulation and visual analysis of crowds: a multidisciplinary perspective

Saad Ali, Ko Nishino, Dinesh Manocha, and Mubarak Shah. Modeling, simulation and visual analysis of crowds: a multidisciplinary perspective. In Modeling, simulation and visual analysis of crowds, pages 1–19. Springer, 2013

work page 2013

[3] [3]

Bradke and C

F. Bradke and C. G. Dotti. Neuronal polarity: vectorial cytoplasmic ﬂow precedes axon formation. Neuron, 19(6):1175–1186, December 1997

work page 1997

[4] [4]

Bressloﬀ

Paul C. Bressloﬀ. Stochastic model of protein receptor traﬃcking prior to synaptogenesis. Phys. Rev. E , 74:031910, 9 2006

work page 2006

[5] [5]

Bressloﬀ and Ethan Levien

Paul C. Bressloﬀ and Ethan Levien. Synaptic democracy and vesicular transport in axons. Phys. Rev. Lett., 114:168101, 4 2015

work page 2015

[6] [6]

Burger, M

M. Burger, M. Di Francesco, J.F. Pietschmann, and B. Schlake. Nonlinear cross-diﬀusion with size exclusion. SIAM J. Math. Anal. , 42(6):2842 – 2871, 2010

work page 2010

[7] [7]

Nonlinear cross-diﬀusion with size exclusion

Martin Burger, Marco Di Francesco, Jan-Frederik Pietschmann, and B¨ arbel Schlake. Nonlinear cross-diﬀusion with size exclusion. SIAM J. Math. Anal. , 42(6):2842–2871, 2010

work page 2010

[8] [8]

Flow characteristics in a crowded transport model

Martin Burger and Jan-Frederik Pietschmann. Flow characteristics in a crowded transport model. Nonlinearity, 29(11):3528, 2016

work page 2016