Dynamics of Intracellular Nano-Transport by Kinesins

Transport in neurons is realized by motor proteins (kinesins) which walk along intracellular tracks. Healthy mechanical transport is necessary for neurons to maintain their normal functions. Degradation of this transport is believed to result in neurodegenerative diseases such as Alzheimer's disease. One possible cause for the degradation is the presence of molecules such as tau proteins attached to the surfaces of the intracellular tracks. It is observed that tau proteins interrupt the motion of kinesin both in vitro and in vivo experiments. In this dissertation, a new physics-based mathematical model is developed to study the effects of obstacles on the intracellular transport. This newly developed model considers various motions of kinesins in cells; walking on the track, unbinding from the track, thermally fluctuating motion (when they are not bound on the track), and binding again to the track. Kinesins unbind from the track when they encounter clusters of tau proteins. However, kinesins can bind to other locations of the track in a very short time, especially the kinesins which are attached to small cargoes. A quantitative study reveals that the decrease in the average velocity is not considerable until the tracks are crowded with large numbers of obstacles. Despite the small change in the average velocity, kinesin motion is modified by the obstacles. The model predicted that the transport of the cargo is delayed in front (upstream) of clusters of tau proteins, and the transport is likely to be fast behind (downstream) the clusters. Also, the results show that the regional delays can be detected with a low precision which is much longer than the step size of kinesins. The predicted behavior of kinesins near obstacles and the proposed method to characterize that behavior can open the door to an easier means to estimate the state of health of the transport system in neuron.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111618/1/wcnam_1.pd

The effects of viscoelastic fluid on kinesin transport

Kinesins are molecular motors which transport various cargoes in the cytoplasm of cells and are involved in cell division. Previous models for kinesins have only targeted their in vitro motion. Thus, their applicability is limited to kinesin moving in a fluid with low viscosity. However, highly viscoelastic fluids have considerable effects on the movement of kinesin. For example, the high viscosity modifies the relation between the load and the speed of kinesin. While the velocity of kinesin has a nonlinear dependence with respect to the load in environments with low viscosity, highly viscous forces change that behavior. Also, the elastic nature of the fluid changes the velocity of kinesin. The new mechanistic model described in this paper considers the viscoelasticity of the fluid using subdiffusion. The approach is based on a generalized Langevin equation and fractional Brownian motion. Results show that a single kinesin has a maximum velocity when the ratio between the viscosity and elasticity is about 0.5. Additionally, the new model is able to capture the transient dynamics, which allows the prediction of the motion of kinesin under time varying loads.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98600/1/0953-8984_24_37_375103.pd

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Effects of Obstacles on the Dynamics of Kinesins, Including Velocity and Run Length, Predicted by a Model of Two Dimensional Motion.

Kinesins are molecular motors which walk along microtubules by moving their heads to different binding sites. The motion of kinesin is realized by a conformational change in the structure of the kinesin molecule and by a diffusion of one of its two heads. In this study, a novel model is developed to account for the 2D diffusion of kinesin heads to several neighboring binding sites (near the surface of microtubules). To determine the direction of the next step of a kinesin molecule, this model considers the extension in the neck linkers of kinesin and the dynamic behavior of the coiled-coil structure of the kinesin neck. Also, the mechanical interference between kinesins and obstacles anchored on the microtubules is characterized. The model predicts that both the kinesin velocity and run length (i.e., the walking distance before detaching from the microtubule) are reduced by static obstacles. The run length is decreased more significantly by static obstacles than the velocity. Moreover, our model is able to predict the motion of kinesin when other (several) motors also move along the same microtubule. Furthermore, it suggests that the effect of mechanical interaction/interference between motors is much weaker than the effect of static obstacles. Our newly developed model can be used to address unanswered questions regarding degraded transport caused by the presence of excessive tau proteins on microtubules

Highly loaded behavior of kinesins increases the robustness of transport under high resisting loads.

Kinesins are nano-sized biological motors which walk by repeating a mechanochemical cycle. A single kinesin molecule is able to transport its cargo about 1 μm in the absence of external loads. However, kinesins perform much longer range transport in cells by working collectively. This long range of transport by a team of kinesins is surprising because the motion of the cargo in cells can be hindered by other particles. To reveal how the kinesins are able to accomplish their tasks of transport in harsh intracellular circumstances, stochastic studies on the kinesin motion are performed by considering the binding and unbinding of kinesins to microtubules and their dependence on the force acting on kinesin molecules. The unbinding probabilities corresponding to each mechanochemical state of kinesin are modeled. The statistical characterization of the instants and locations of binding are captured by computing the probability of unbound kinesin being at given locations. It is predicted that a group of kinesins has a more efficient transport than a single kinesin from the perspective of velocity and run length. Particularly, when large loads are applied, the leading kinesin remains bound to the microtubule for long time which increases the chances of the other kinesins to bind to the microtubule. To predict effects of this behavior of the leading kinesin under large loads on the collective transport, the motion of the cargo is studied when the cargo confronts obstacles. The result suggests that the behavior of kinesins under large loads prevents the early termination of the transport which can be caused by the interference with the static or moving obstacles

Parent Nested Optimizing Structure for Vibration Reduction in Floating Wind Turbine Structures

A tuned mass damper (TMD) is a system that effectively reduces the vibrations of floating offshore wind turbines (FOWTs). To maximize the performance of TMDs, it is necessary to optimize their design parameters (i.e., stiffness, damping, and installation location). However, this optimization process is challenging because of the existence of multiple local minima. Although various methods have been proposed to determine the global minimum (e.g., exhaustive search, genetic algorithms, and artificial fish swarm algorithms), they are computationally intensive. To address this issue, a novel optimization approach based on a parent nested optimizing structure and approximative search is proposed in this paper. The approximative search determines an initial parameter set (close to the optimal set) with fewer calculations. Then, the global minimum can be rapidly determined using the nested and parent optimizers. The effectiveness of this approach was verified with an FOWT exposed to stochastic winds. The results show that this approach is 30–55 times faster than a conventional global optimization method

The kinesin cycle is depicted.

<p>The states in the upper box relate to the walking cycle of the kinesin. K denotes the kinesin molecule. The lower box relates to the unbound state of the kinesin. The variables denoted by k are transition rates between states, and <i>P</i>_<i>D</i>0 and <i>P</i>_<i>D</i>1 represent the probability of unbinding from the MT when the kinesin is in the state [K + MT] and [K.ATP + MT]₁. (a) An ATP molecule binds to the leading head of the kinesin. (b) The binding of ATP to the kinesin head results in a structural changes in the head [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003981#pcbi.1003981.ref038" target="_blank">38</a>]. This change induces the docking of the neck linker to its head. The docking of the neck linker to the leading head generates a force to move the trailing head toward the plus end of MT. Then, the trailing head diffuses to the next binding site of MT by Brownian motion. (c) The moving head binds to the MT and releases ADP. (d) ATP in the rear head is hydrolyzed, and then this hydrolysis enables the release of phosphate (Pi) from the head. Then, the neck linker returns to the disordered state from the docked state.</p

Lattice array of MTs and the distance between adjacent binding sites.

<p>(a) shows the binding sites for MTs of a helical structure. (b) depicts the binding sites in the absence of a mismatch in the lattice array.</p

Binding sites occupied by kinesin heads.

<p>The black ellipses represent the binding sites occupied by the kinesin. The gray and white ellipses are <i>α</i> and <i>β</i> tubulins of the MT. Note that the kinesin head can only bind to <i>β</i> tubulins. (a1) Two sites are occupied by the kinesin when its two heads are strongly bound. (b) depicts possible scenarios when one of the kinesin heads can be unbound and the other head is strongly bound. The kinesin can occupy one (b1), three (b2), five (b3), or nine (b4) binding sites. (c1)-(c2) shows two examples of cases where sites occupied by two kinesin molecules are adjacent, allowing for interference. Both kinesins have one unbound head and one bound head. The dotted circles represent the sites occupied by kinesin heads.</p