Molecular motors interacting with their own tracks

Artyomov, Max N.; Morozov, A. Yu.; Kolomeisky, Anatoly B.

doi:10.1103/physreve.77.040901

Cited by 8 publications

(20 citation statements)

References 18 publications

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“…Active transport occurs when waves of ATP hydrolysis along the track push a passive element much like ocean waves push a surfer toward the shore. Several studies have examined track-induced transport (Antal and Krapivsky, 2005;Artyomov et al, 2008;Morozov, 2007;Saffarian et al, 2006). Here, we discuss directed transport of a Holliday junction along DNA (Lakhanpal and Chou, 2007).…”

Section: Local Target Signalingmentioning

confidence: 99%

Stochastic models of intracellular transport

2013

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The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an over-damped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of ATP hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review we present a wide range of analytical methods and models of intracellular transport. In the case of diffusive transport, we consider narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion. In the case of active transport, we consider Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasisteady-state reduction methods, and mean field approximations. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures.

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Section: Local Target Signalingmentioning

confidence: 99%

Stochastic models of intracellular transport

2013

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“…As anticipated, when the recovery probability p 2 → 1 and the presence of bridges has no effect, V → u − w = 0 and D → This phenomenon corresponds to a dynamic transition between unbiased and biased diffusion regimes as was argued earlier in [9]. Figure 3(b) is similar to the corresponding plot in [9] for p 1 = 1 case, but for p 1 = 0.1 the gap is prominent only for small p 2 values, and for p 2 > 0.1 it practically disappears. We note that for p 2 = 0 case in figure 3 (b) the correct c → 0 limit must be D(c → 0) = 2/3 [7].…”

Section: -13mentioning

confidence: 75%

“…Namely, comparing p 2 = 0 case [figures 3 (b), 5 (b)] with the result from [7] for u = w = 1 showed the discrepancy in D values of the order of 0.001 for almost entire range of parameters c and p 1 , with the exception of small c 0.02, and p 1 0.01, where discrepancy exceeded 0.008 and 0.006 correspondingly. For figure 4 (b), we compared p 1 = 1 case (not shown) with the corresponding case for u = w = 1 obtained in [9]: typical discrepancy in D values between u = 0.999 and u = 1 cases was ∼ 0.0005 for all c values except c 0.001, where discrepancy exceeded 0.007. As anticipated, when the recovery probability p 2 → 1 and the presence of bridges has no effect, V → u − w = 0 and D → This phenomenon corresponds to a dynamic transition between unbiased and biased diffusion regimes as was argued earlier in [9].…”

Section: -13mentioning

confidence: 99%

“…For figure 4 (b), we compared p 1 = 1 case (not shown) with the corresponding case for u = w = 1 obtained in [9]: typical discrepancy in D values between u = 0.999 and u = 1 cases was ∼ 0.0005 for all c values except c 0.001, where discrepancy exceeded 0.007. As anticipated, when the recovery probability p 2 → 1 and the presence of bridges has no effect, V → u − w = 0 and D → This phenomenon corresponds to a dynamic transition between unbiased and biased diffusion regimes as was argued earlier in [9]. Figure 3(b) is similar to the corresponding plot in [9] for p 1 = 1 case, but for p 1 = 0.1 the gap is prominent only for small p 2 values, and for p 2 > 0.1 it practically disappears.…”

Section: -13mentioning

confidence: 99%

“…It was proposed that a good description of the collagenase dynamics could be provided by the so-called "burnt-bridge model" (BBM) [2][3][4][5][6][7][8][9]. In this model, the motor protein is depicted as a random walker that translocates along the one-dimensional lattice that consists of strong and weak bonds.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamics of molecular motors in reversible burnt-bridge models

Artyomov¹,

Morozov²,

Kolomeisky³

2010

Condens. Matter Phys.

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Dynamic properties of molecular motors whose motion is powered by interactions with specific lattice bonds are studied theoretically with the help of discrete-state stochastic "burnt-bridge" models. Molecular motors are depicted as random walkers that can destroy or rebuild periodically distributed weak connections ("bridges") when crossing them, with probabilities p 1 and p 2 correspondingly. Dynamic properties, such as velocities and dispersions, are obtained in exact and explicit form for arbitrary values of parameters p 1 and p 2 . For the unbiased random walker, reversible burning of the bridges results in a biased directed motion with a dynamic transition observed at very small concentrations of bridges. In the case of backward biased molecular motor its backward velocity is reduced and a reversal of the direction of motion is observed for some range of parameters. It is also found that the dispersion demonstrates a complex, non-monotonic behavior with large fluctuations for some set of parameters. Complex dynamics of the system is discussed by analyzing the behavior of the molecular motors near burned bridges.

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Force Transduction by the Microtubule-Bound Dam1 Ring

Armond

Turner

2010

Biophysical Journal

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The coupling between the depolymerization of microtubules (MTs) and the motion of the Dam1 ring complex is now thought to play an important role in the generation of forces during mitosis. Our current understanding of this motion is based on a number of detailed computational models. Although these models realize possible mechanisms for force transduction, they can be extended by variation of any of a large number of poorly measured parameters and there is no clear strategy for determining how they might be distinguished experimentally. Here we seek to identify and analyze two distinct mechanisms present in the computational models. In the first, the splayed protofilaments at the end of the depolymerizing MT physically prevent the Dam1 ring from falling off the end, and in the other, an attractive binding secures the ring to the microtubule. Based on this analysis, we discuss how to distinguish between competing models that seek to explain how the Dam1 ring stays on the MT. We propose novel experimental approaches that could resolve these models for the first time, either by changing the diffusion constant of the Dam1 ring (e.g., by tethering a long polymer to it) or by using a time-varying load.

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Molecular motors interacting with their own tracks

Cited by 8 publications

References 18 publications

Stochastic models of intracellular transport

Stochastic models of intracellular transport

Dynamics of molecular motors in reversible burnt-bridge models

Force Transduction by the Microtubule-Bound Dam1 Ring

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