Target search on DNA by interacting molecules: First-passage approach

Anatoly B. Kolomeisky; Jaeoh Shin

REVIEW 3 major objections 4 minor 31 references

Target search on DNA by interacting molecules: First-passage approach

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Two interacting molecules can find a DNA target fastest at an intermediate dimer-dissociation rate, rather than as fully bound or fully independent particles.

desk verdict Nice idea and clean analytic limits, but the headline non-monotonic result is built on simulations whose association step is never specified; needs revision before I'd trust it. read the letter →

arxiv 1908.03597 v1 pith:DRN5RT2K submitted 2019-08-09 physics.bio-ph cond-mat.stat-mechq-bio.SC

Jaeoh Shin , Anatoly B. Kolomeisky This is my paper · ORCID

classification physics.bio-phcond-mat.stat-mechq-bio.SC

keywords protein-DNAtargetsearchfirst-passagetimedimerformationprotein-proteininteractionfacilitateddiffusionkineticMonteCarlogeneregulationrandomwalk

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 asks how quickly two interacting protein molecules can find the same site on DNA, the step that precedes gene activation or repression. It models the pair as monomers hopping on a one-dimensional lattice who can meet and reversibly form a dimer complex, and the search ends only when both molecules occupy the target at the same time. The central claim is that the mean search time is not monotonic in the dimer dissociation rate: for intermediate interaction strengths, the search can be faster than either the fully independent or fully complexed extremes, because the system mixes a one-dimensional dimer search with a two-dimensional independent search. Tuning protein-protein interaction strength is therefore a plausible biological control knob for the speed of gene regulation.

What carries the argument

The carrying object is the reversible dimer complex: two monomers hop with rate $\mu_1$, associate at rate $k_a$ when co-located, dissociate at rate $k_d$, and the complex itself hops with rate $\mu_2 < \mu_1$. Detailed balance ties $k_a/k_d$ to the interaction energy $E$, so tuning $k_d$ is physically tuning the strength of protein-protein attraction. The target site has an unbinding rate $k_{\rm off}$, and the search completes only when both molecules are on the target simultaneously. The argument combines first-passage survival-probability calculations in the non-interacting and strong-attraction limits with kinetic Monte Carlo simulations for the general regime.

What would settle it

Repeat the simulations behind Fig. 3 with the dimer hopping rate $\mu_2$ larger than the monomer rate $\mu_1$ while keeping all other rates fixed: if the mean first-passage time versus $k_d$ still shows a minimum, the proposed slow-dimer mechanism cannot be the cause. A cleaner test is a single scan with $\mu_2/\mu_1 > 1$; the paper’s interpretation predicts the mixed-regime minimum should disappear.

Watch

Extended reading notes

Core claim

For two molecules that reversibly form a complex on a one-dimensional DNA segment, the mean first-passage time to simultaneous target occupancy is non-monotonic as a function of the dimer dissociation rate $k_d$, provided the dimer moves more slowly than the monomers. At large $k_d$ the molecules act independently and the search is effectively two-dimensional; at small $k_d$ the search is carried by a long-lived dimer and is effectively one-dimensional. In between, a mixed “1D+2D” regime can be fastest, and the paper explains this by an optimal balance between the dimer’s slow but directed progress and the monomers’ faster but wider exploration. The paper also finds that longer target residence times (smaller $k_{\rm off}$) accelerate the two-molecule search and that targets away from lattice boundaries are reached sooner, with the non-monotonic dependence most visible for short residence times.

Load-bearing premise

The non-monotonic optimum assumes that a two-molecule complex moves more slowly along DNA than a single molecule does; if that ordering is reversed, the mixed-search speed-up predicted here would not hold.

Editorial extensions

If this is right

If the mixed regime is fastest, the cell can accelerate gene activation by tuning transcription-factor dimerization affinity to an intermediate value rather than maximizing or minimizing protein-protein binding.
Longer target residence times for a single molecule speed up the two-molecule completion, so proteins that bind their target strongly once they arrive reduce the time the other molecule needs to find it.
Targets located away from the segment boundaries are found faster, and the effect persists across interaction strengths.
The three search regimes imply distinct trajectory signatures: diagonal-bound motion for dimers, broad independent exploration for repulsion, and intermittent diagonal excursions for the mixed regime.
The non-monotonic optimum appears at short target residence times, so experiments with fast-dissociating transcription factors are the likeliest place to see it.

Reading between the lines

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

Because $k_a/k_d = \exp(-E/k_B T)$, the optimal $k_d$ corresponds to a finite window of binding free energy; mutations that shift $E$ by a few $k_B T$ could move a pair between fast and slow regimes.
With more than two molecules, the same machinery would add another encounter bottleneck, so the optimal dissociation rate may shift or the non-monotonic minimum may sharpen; the paper’s construction can be extended to $N>2$ by simulation.
Real searches include 3D excursions; replacing the one-dimensional lattice by an intermittent 3D+1D walk should preserve the qualitative minimum but change its location, which could be tested in stretched-DNA or single-molecule experiments with two labelled proteins.

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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

Short version: the idea is genuinely nice and the two analytic limits check out, but the paper's headline result—the non-monotonic search time in the dimer dissociation rate—rests entirely on Monte Carlo simulations whose association step is never described. That needs fixing before I'd trust the claim.

What's new: prior multi-particle first-passage papers (Grebenkov; Lawley and Madrid) treat non-interacting particles. Adding reversible dimer formation and showing a 1D+2D mixed regime with an optimal interaction strength is a real extension. The strong-repulsion and strong-attraction limits are clean, and the match with simulations there is good. The physical explanation (slow dimer vs. two fast monomers, with an optimum balance) is plausible and presented honestly.

Soft spots: the simulation method in Section II lists five parameters but never specifies ka or any rule for two monomers forming a complex. The monomer update has three outcomes (stay, hop left, hop right); the complex update has dissociation and hop. There is no 'if two monomers occupy the same site, associate with probability X' anywhere. Since the intermediate regime is exactly where monomers and dimers coexist, the ka/kd ratio controls that coexistence, so leaving ka out is not a cosmetic omission. Fig. 3 plots MFPT vs kd at fixed (unspecified) ka; without the actual value and update rule, the non-monotonic minimum cannot be reproduced, and the claimed 'optimal interaction strength' has no quantitative meaning. The stress-test note is on target. Also, no error bars on the non-monotonic points, but that's secondary.

The analytic sections are fine and cite standard formulas correctly. The model's own limitations paragraph is honest about biological oversimplifications but doesn't mention the missing simulation detail—unfortunate.

Who's this for: people working on facilitated diffusion and multi-particle first-passage problems in biophysics. The idea is worth pursuing; the execution is incomplete as submitted. I'd send it to referees, but instruct them to require a complete simulation protocol and a sensitivity check on ka before publication.

Referee Report

3 major / 4 minor

Summary. The paper proposes a minimal one-dimensional lattice model in which two interacting molecules search for a target site on DNA. The molecules can reversibly associate into a dimer that also diffuses along the lattice, and the search is completed when both molecules are simultaneously present at the target. The authors analyze strong-repulsion and strong-attraction limits with first-passage formulas and perform kinetic Monte Carlo simulations for the general regime. Their central claim is that the mean first-passage time is non-monotonic in the dimer dissociation rate kd, so that an intermediate interaction strength gives the fastest target search. They also study the effects of target residence time and target position, and they interpret the intermediate regime as a beneficial combination of effectively one-dimensional and two-dimensional search mechanisms.

Significance. If the central claim is correct, the paper addresses a real gap in the protein target-search literature, where intermolecular interactions are usually neglected and a non-monotonic optimum in interaction strength would be a nontrivial, potentially testable prediction. The limiting-case analytical expressions, such as the L^2 scaling for two independent particles and the approximate complex-search formula, are transparent and are shown to agree with simulations. The physical interpretation via a 1D+2D trade-off is plausible and connects to facilitated-diffusion ideas. However, the headline non-monotonic result currently rests on simulations whose association protocol is not specified and that are shown without statistical error bars, so the significance is conditional on those simulations being reproducible and statistically robust.

major comments (3)

[Section II, Simulation method] The simulation protocol is incomplete because no association event is specified. The model definition includes the association rate ka and the detailed-balance relation ka/kd = exp(-E/kBT), but the described Monte Carlo algorithm lists only monomer moves, dimer dissociation/moves, monomer unbinding, and the dissociation event; nothing states what happens when two monomers occupy the same site, with what probability per time step, or what value of ka was used for the reported curves. Since the general-regime data in Fig. 3 depend on the coexistence of monomers and dimers, which is governed by ka/kd, the central non-monotonic claim cannot currently be reproduced or independently assessed. The authors should add the association update rule (including how simultaneous arrivals are treated), the ka values used for each figure, and a check that the limits ka >> mu1 and ka << mu1 are realized as claimed.
[Fig. 3 and Section III.C] The headline non-monotonic dependence of the mean first-passage time on the dissociation rate kd is presented as smooth curves without error bars, numbers of Monte Carlo runs, or raw data points. The dip at intermediate kd, especially for mu2/mu1 = 0.2, may be modest compared with stochastic fluctuations, and the reader cannot determine whether the reported minimum is statistically significant. Please add standard errors or other uncertainty measures, report the number of independent runs, and preferably show individual simulation points on the plot.
[Section III.C and Figs. 4-5] The additional general-regime results in Figs. 4 and 5 are generated with the same underspecified association protocol. Once the association rule and ka values are supplied, these figures should be regenerated or at least explicitly linked to the specified parameter set. Without this, the heat map in Fig. 5 and the trajectory interpretation in Fig. 4 inherit the reproducibility problem of the central simulation claim.

minor comments (4)

[Section III.C] The text 'two articles move independently' appears to be a typo for 'two particles move independently'.
[Fig. 5 caption and axes] The axes labeled 'Log[kd]' and 'Log[koff]' do not specify the logarithm base, and the color-bar values have no units; using for example 'log10(kd/mu1)' would improve clarity.
[Section II] The assumption mu2 < mu1 is stated as a general expectation, and the non-monotonic optimum is demonstrated only in this regime. It would be useful to state explicitly that the predicted intermediate optimum is conditioned on mu2 < mu1 and to add a sentence or a simulation showing what happens when mu2 >= mu1.
[Section III.A, Eq. (3)] The exponential approximation for the single-particle survival probability is an assumption; Fig. 2 shows good agreement for the integrated quantities used, but a brief comment on the range of lattice sizes and time scales for which this approximation is reliable would be helpful.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central MFPT results are simulation observations, and the analytical limits use standard random-walk formulas that are independently verified by simulation.

full rationale

The paper's two analytical limits (Secs. III-A and III-B) rest on the single-particle mean first-passage time t1 = (2L^2+L)/(6mu1) and on the encounter-time expression Tn, both attributed to ref. [31] (Veksler and Kolomeisky, including the present author Kolomeisky). This is a self-citation, but it is not load-bearing circularity: the formulas are parameter-free standard results for 1D random walks, and the paper independently verifies them by kinetic Monte Carlo simulation in Fig. 2 and by agreement with the limiting curves in Fig. 3. The formulas are inputs, not predictions derived from the target result. The headline non-monotonic dependence on kd (Fig. 3) is presented as a simulation observation with an explanatory 1D+2D mechanism, not as a fitted prediction: no data are fitted and no output is obtained by construction from an input. The model's assumption mu2 < mu1 is an explicit physical assumption, not a circular redefinition. One genuine weakness exists but it is not circular: Section II's 'Simulation method' never assigns a value or update rule for ka, even though the model defines ka and the detailed-balance relation ka/kd = exp(-E/kBT) ties it to the interaction energy. This leaves the general-regime simulations difficult to reproduce, but reproducibility is a different concern from circularity. The finite-residence-time contribution is taken from ref. [11] (Grebenkov), an external source, and is used only to contextualize the koff dependence. Overall, no prediction in the paper is equivalent by construction to its inputs, and no load-bearing argument reduces to an unverified self-citation chain.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

No parameters are fitted to experimental data. The model uses hand-chosen rates and standard random-walk results. The dimer complex is a known biochemical entity, not an invented one. The main burden is the slow-complex assumption mu2 < mu1 and the unverified exponential-survival approximation; the unspecified association rate ka is a reproducibility gap.

free parameters (5)

mu1 (monomer hopping rate) = 0.25 (time unit)
Set by hand; all other rates are normalized to it.
mu2/mu1 (complex hopping ratio) = 0.05, 0.1, 0.2 (Fig. 3)
Chosen to illustrate the slow-complex assumption; the central non-monotonic effect depends on this ratio.
koff (target unbinding rate) = 0.25 in Figs. 2-4; varied in Fig. 5
Chosen by hand; the residence-time dependence of the search time is a main claim.
kd (dimer dissociation rate) = varied from 10^-5 to 1
Central control parameter for the non-monotonic search time.
ka (association rate) = not stated
Model parameter but no value or Monte Carlo rule is provided; needed to reproduce the general-regime simulations.

assumptions (6)

ad hoc to paper Survival probability of a single particle is exponential, S(1,t) approximately exp(-t/t1)
Used in Eq. (3) to derive t2 = 1.5 t1; an approximation, not exact for 1D finite intervals.
standard math When koff = mu1, the target acts as a reflecting boundary and two independent particles map to a 2D search with MFPT proportional to L^2 ln L
Taken from refs. 7 and 8; used to interpret Fig. 2.
ad hoc to paper Dimer initial encounter positions are uniformly distributed over lattice sites (Eq. 8)
Used to average the complex search time; accuracy is not tested.
domain assumption Complex diffuses slower than monomers, mu2 < mu1
Stated in Section II; this ordering is load-bearing for the intermediate-regime optimum.
domain assumption Detailed balance ka/kd = exp(-E/kBT) links interaction energy to rates
Standard assumption used to interpret interaction strength through rates.
standard math Random-walk first-passage formulas t1=(2L^2+L)/(6mu1) and Tn=(L/2-n)(3L/2+n+1)/(2mu2)
Taken from ref. 31; used in limiting analytical expressions.

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Cite this review

Pith. "Pith review of Target search on DNA by interacting molecules: First-passage approach." pith.science (2026). https://pith.science/paper/DRN5RT2K

@misc{pith2026190803597,
  author       = {Pith},
  title        = {Pith review of: Target search on DNA by interacting molecules: First-passage approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DRN5RT2K}},
  note         = {Machine review of arXiv:1908.03597}
}

read the original abstract

Gene regulation is one of the most important fundamental biological processes in living cells. It involves multiple protein molecules that locate specific sites on DNA and assemble gene initiation or gene repression multi-molecular complexes. While the protein search dynamics for DNA targets has been intensively investigated, the role of inter-molecular interactions during the genetic activation or repression remains not well quantified. Here we present a simple one-dimensional model of target search for two interacting molecules that can reversibly form a dimer molecular complex, which also participates in the search process. In addition, the proteins have finite residence times on specific target sites, and the gene is activated or repressed when both proteins are simultaneously present at the target. The model is analyzed using first-passage analytical calculations and Monte Carlo computer simulations. It is shown that the search dynamics exhibits a complex behavior depending on the strength of inter-molecular interactions and on the target residence times. We also found that the search time shows a non-monotonic behavior as a function of the dissociation rate for the molecular complex. Physical-chemical arguments to explain these observations are presented. Our theoretical approach highlights the importance of molecular interactions in the complex process of gene activation/repression by multiple transcription factor proteins.

Figures

Figures reproduced from arXiv: 1908.03597 by the authors.

**Figure 2.** FIG. 2. The target search time as a function of the lattice [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** FIG. 3. The target search time as a function of the dissocia [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: FIG. 4. The trajectories of the searching molecules. Here the lattice size [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: FIG. 6. Search time as a function of the target position [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages

[1]

Essential cell biology

Bruce Alberts, Dennis Bray, Karen Hopkin, Alexander D Johnson, Julian Lewis, Martin Raﬀ, Keith Roberts, and Peter Walter. Essential cell biology . Garland Science, 2013

work page 2013
[2]

Loops in dna: an overview of experimental and theoretical ap- proaches

J-F Allemand, S Cocco, N Douarche, and G Lia. Loops in dna: an overview of experimental and theoretical ap- proaches. The European Physical Journal E , 19(3):293– 302, 2006

work page 2006
[3]

Generalized fa- cilitated diﬀusion model for dna-binding proteins with search and recognition states

Maximilian Bauer and Ralf Metzler. Generalized fa- cilitated diﬀusion model for dna-binding proteins with search and recognition states. Biophysical journal , 102 (10):2321–2330, 2012

work page 2012
[4]

From ﬁrst-passage times of random walks in conﬁnement to geometry- controlled kinetics

Olivier B´ enichou and R Voituriez. From ﬁrst-passage times of random walks in conﬁnement to geometry- controlled kinetics. Physics Reports , 539(4):225–284, 2014

work page 2014
[5]

Intermittent search strategies

Olivier B´ enichou, C Loverdo, M Moreau, and R Voi- turiez. Intermittent search strategies. Reviews of Modern Physics, 83(1):81, 2011

work page 2011
[6]

Association kinetics with coupled diﬀusional ﬂows: special application to the lac repressor-operator system

Otto G Berg and Clas Blomberg. Association kinetics with coupled diﬀusional ﬂows: special application to the lac repressor-operator system. Biophysical chemistry, 4 (4):367–381, 1976

work page 1976
[7]

First-passage times for random walks in bounded domains

S Condamin, O B´ enichou, and M Moreau. First-passage times for random walks in bounded domains. Physical 7 review letters, 95(26):260601, 2005

work page 2005
[8]

Random walks and brownian motion: A method of computation for ﬁrst-passage times and related quan- tities in conﬁned geometries

Sylvain Condamin, Olivier B´ enichou, and Michel Moreau. Random walks and brownian motion: A method of computation for ﬁrst-passage times and related quan- tities in conﬁned geometries. Physical Review E , 75(2): 021111, 2007

work page 2007

Show all 31 references

[9]

Kinetics of target site localization of a protein on dna: a stochastic approach

M Coppey, O B´ enichou, R Voituriez, and M Moreau. Kinetics of target site localization of a protein on dna: a stochastic approach. Biophysical journal, 87(3):1640– 1649, 2004

2004
[10]

Eukaryotic transcriptional dynamics: from single molecules to cell populations

Antoine Coulon, Carson C Chow, Robert H Singer, and Daniel R Larson. Eukaryotic transcriptional dynamics: from single molecules to cell populations. Nature Reviews Genetics, 14(8):572, 2013

2013
[11]

First passage times for multiple par- ticles with reversible target-binding kinetics.The Journal of Chemical Physics , 147(13):134112, 2017

Denis S Grebenkov. First passage times for multiple par- ticles with reversible target-binding kinetics.The Journal of Chemical Physics , 147(13):134112, 2017

2017
[12]

How do site- speciﬁc dna-binding proteins ﬁnd their targets? Nucleic acids research, 32(10):3040–3052, 2004

Stephen E Halford and John F Marko. How do site- speciﬁc dna-binding proteins ﬁnd their targets? Nucleic acids research, 32(10):3040–3052, 2004

2004
[13]

The lac repressor displays facilitated diﬀusion in living cells

Petter Hammar, Prune Leroy, Anel Mahmutovic, Erik G Marklund, Otto G Berg, and Johan Elf. The lac repressor displays facilitated diﬀusion in living cells. Science, 336 (6088):1595–1598, 2012

2012
[14]

Tuned polymerization of the transcription factor yan limits oﬀ-dna sequestration to confer context-speciﬁc repression

C Matthew Hope, Jemma L Webber, Sherzod A Tokamov, and Ilaria Rebay. Tuned polymerization of the transcription factor yan limits oﬀ-dna sequestration to confer context-speciﬁc repression. eLife, 7:e37545, 2018

2018
[15]

How pro- teins search for their speciﬁc sites on dna: the role of dna conformation

Tao Hu, A Yu Grosberg, and BI Shklovskii. How pro- teins search for their speciﬁc sites on dna: the role of dna conformation. Biophysical journal, 90(8):2731–2744, 2006

2006
[16]

Sequence heterogene- ity and the dynamics of molecular motors

Yariv Kafri and David R Nelson. Sequence heterogene- ity and the dynamics of molecular motors. Journal of Physics: Condensed Matter , 17(47):S3871, 2005

2005
[17]

Physics of protein–dna interac- tions: mechanisms of facilitated target search

Anatoly B Kolomeisky. Physics of protein–dna interac- tions: mechanisms of facilitated target search. Physical Chemistry Chemical Physics , 13(6):2088–2095, 2011

2011
[18]

Motor proteins and molecular motors

Anatoly B Kolomeisky. Motor proteins and molecular motors. CRC press, 2015

2015
[19]

First passage time distribu- tion of multiple impatient particles with reversible bind- ing

SD Lawley and JB Madrid. First passage time distribu- tion of multiple impatient particles with reversible bind- ing. The Journal of Chemical Physics , 150(21):214113, 2019

2019
[20]

Disentangling the many layers of eukaryotic tran- scriptional regulation

Katherine M Lelli, Matthew Slattery, and Richard S Mann. Disentangling the many layers of eukaryotic tran- scriptional regulation. Annual review of genetics , 46:43– 68, 2012

2012
[21]

Molec- ular cell biology

Harvey Lodish, James E Darnell, Arnold Berk, Chris A Kaiser, Monty Krieger, Matthew P Scott, Anthony Bretscher, Hidde Ploegh, Paul Matsudaira, et al. Molec- ular cell biology . Macmillan, 2008

2008
[22]

First- passage phenomena and their applications

Ralf Metzler, Gleb Oshanin, and Sidney Redner. First- passage phenomena and their applications . World Scien- tiﬁc, 2014

2014
[23]

How a protein searches for its site on dna: the mechanism of facilitated diﬀusion

Leonid Mirny, Michael Slutsky, Zeba Wunderlich, Anahita Tafvizi, Jason Leith, and Andrej Kosmrlj. How a protein searches for its site on dna: the mechanism of facilitated diﬀusion. Journal of Physics A: Mathematical and Theoretical, 42(43):434013, 2009

2009
[24]

Physical biology of the cell

Rob Phillips, Julie Theriot, Jane Kondev, and Hernan Garcia. Physical biology of the cell . Garland Science, 2012

2012
[25]

A guide to ﬁrst-passage processes

Sidney Redner. A guide to ﬁrst-passage processes . Cam- bridge University Press, 2001

2001
[26]

The lac represser-operator interaction: Iii

Arthur D Riggs, Suzanne Bourgeois, and Melvin Cohn. The lac represser-operator interaction: Iii. kinetic stud- ies. Journal of molecular biology , 53(3):401–417, 1970

1970
[27]

Classes of fast and speciﬁc search mechanisms for pro- teins on dna

M Sheinman, O B´ enichou, Y Kafri, and R Voituriez. Classes of fast and speciﬁc search mechanisms for pro- teins on dna. Reports on Progress in Physics , 75(2): 026601, 2012

2012
[28]

Molecular search with conformational change: One-dimensional discrete- state stochastic model

Jaeoh Shin and Anatoly B Kolomeisky. Molecular search with conformational change: One-dimensional discrete- state stochastic model. The Journal of Chemical Physics, 149(17):174104, 2018

2018
[29]

Mechanisms of protein search for targets on dna: theoretical insights

Alexey Shvets, Maria Kochugaeva, and Anatoly Kolomeisky. Mechanisms of protein search for targets on dna: theoretical insights. Molecules, 23(9):2106, 2018

2018
[30]

Protein–dna bind- ing: complexities and multi-protein codes

Trevor Siggers and Raluca Gordan. Protein–dna bind- ing: complexities and multi-protein codes. Nucleic Acids Research, 42(4):2099–2111, 2013

2013
[31]

Speed- selectivity paradox in the protein search for targets on dna: is it real or not? The Journal of Physical Chem- istry B, 117(42):12695–12701, 2013

Alex Veksler and Anatoly B Kolomeisky. Speed- selectivity paradox in the protein search for targets on dna: is it real or not? The Journal of Physical Chem- istry B, 117(42):12695–12701, 2013

2013

Pith tools

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

[1] [1]

Essential cell biology

Bruce Alberts, Dennis Bray, Karen Hopkin, Alexander D Johnson, Julian Lewis, Martin Raﬀ, Keith Roberts, and Peter Walter. Essential cell biology . Garland Science, 2013

work page 2013

[2] [2]

Loops in dna: an overview of experimental and theoretical ap- proaches

J-F Allemand, S Cocco, N Douarche, and G Lia. Loops in dna: an overview of experimental and theoretical ap- proaches. The European Physical Journal E , 19(3):293– 302, 2006

work page 2006

[3] [3]

Generalized fa- cilitated diﬀusion model for dna-binding proteins with search and recognition states

Maximilian Bauer and Ralf Metzler. Generalized fa- cilitated diﬀusion model for dna-binding proteins with search and recognition states. Biophysical journal , 102 (10):2321–2330, 2012

work page 2012

[4] [4]

From ﬁrst-passage times of random walks in conﬁnement to geometry- controlled kinetics

Olivier B´ enichou and R Voituriez. From ﬁrst-passage times of random walks in conﬁnement to geometry- controlled kinetics. Physics Reports , 539(4):225–284, 2014

work page 2014

[5] [5]

Intermittent search strategies

Olivier B´ enichou, C Loverdo, M Moreau, and R Voi- turiez. Intermittent search strategies. Reviews of Modern Physics, 83(1):81, 2011

work page 2011

[6] [6]

Association kinetics with coupled diﬀusional ﬂows: special application to the lac repressor-operator system

Otto G Berg and Clas Blomberg. Association kinetics with coupled diﬀusional ﬂows: special application to the lac repressor-operator system. Biophysical chemistry, 4 (4):367–381, 1976

work page 1976

[7] [7]

First-passage times for random walks in bounded domains

S Condamin, O B´ enichou, and M Moreau. First-passage times for random walks in bounded domains. Physical 7 review letters, 95(26):260601, 2005

work page 2005

[8] [8]

Random walks and brownian motion: A method of computation for ﬁrst-passage times and related quan- tities in conﬁned geometries

Sylvain Condamin, Olivier B´ enichou, and Michel Moreau. Random walks and brownian motion: A method of computation for ﬁrst-passage times and related quan- tities in conﬁned geometries. Physical Review E , 75(2): 021111, 2007

work page 2007