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Transport Molecules play a crucial role for cell viability. Amongst others, linear motors transport cargos along rope-like structures from one location of the cell to another in a stochastic fashion. Thereby each step of the motor, either forwards or backwards, bridges a fixed distance. While moving along the rope the motor can also detach and is lost. We give here a mathematical formalization of such dynamics as a random process which is an extension of Random Walks, to which we add an absorbing state to model the detachment of the motor from the rope. We derive particular properties of such processes that have not been available before. Our results include description of the maximal distance reached from the starting point and the position from which detachment takes place. Finally, we apply our theoretical results to a concrete established model of the transport molecule Kinesin V.
Amongst the many complex processes taking place in living cells, transport of cargoes across the cytosceleton is fundamental to cell viability and activity. To move cargoes between the different cell parts, cells employ Molecular Motors. The motors operate by transporting cargoes along the so-called cellular micro-tubules, namely rope-like structures that connect, for instance, the cell-nucleus and outer membrane. We introduce a new Markov Chain, the killed Quasi-Random-Walk, for such transport molecules and derive properties like the maximal run length and time. Furthermore we introduce permuted balance, which is a more flexible extension of the ordinary reversibility and introduce the notion of Time Duality, which compares certain passage times pathwise. We give a number of sufficient conditions for Time Duality based on the geometry of the transition graph. Both notions are closely related to properties of the killed Quasi-Random-Walk.
Transport processes in and of cells are of major importance for the survival of the organism. Muscles have to be able to contract, chromosomes have to be moved to opposing ends of the cell during mitosis, and organelles, which are compartments enclosed by membranes, have to be transported along molecular tracks. Molecular motors are proteins whose main task is moving other molecules.For that purpose they transform the chemical energy released in the hydrolysis of ATP into mechanical work. The motors of the cytoskeleton belong to the three super families myosin, kinesin and dynein. Their tracks are filaments of the cytoskeleton, namely actin and the microtubuli. Here, we examine stochastic models which are used for describing the movements of these linear molecular motors. The scale of the movements comprises the regime of single steps of a motor protein up to the directed walk along a filament. A single step bridges around 10 nm, depending on the protein, and takes about 10 ms, if there is enough ATP available. Our models comprise M states or conformations the motor can attain during its movement along a one-dimensional track. At K locations along the track transitions between the states are possible. The velocity of the protein depending on the transition rates between the single states can be determined analytically. We calculate this velocity for systems of up to four states and locations and are able to derive a number of rules which are helpful in estimating the behaviour of an arbitrary given system. Beyond that we have a look at decoupled subsystems, i.e., one or a couple of states which have no connection to the remaining system. With a certain probability a motor undergoes a cycle of conformational changes, with another probability an independent other cycle. Active elements in real transport processes by molecular motors will not be limited to the transitions between the states. In distorted networks or starting from the discrete Master equation of the system, it is possible to specify horizontal rates, too, which furthermore no longer have to fulfill the conditions of detailed balance. Doing so, we obtain unique, complete paths through the respective network and rules for the dependence of the total current on all the rates of the system. Besides, we view the time evolutions for given initial distributions. In enzymatic reactions there is the idea of a main pathway these reactions follow preferably. We determine optimal paths and the maximal flow for given networks. In order to specify the dependence of the motor's velocity on its fuel ATP, we have a look at possible reaction kinetics determining the connection between unbalanced transitions rates and ATP-concentration. Depending on the type of reaction kinetics and the number of unbalanced rates, we obtain qualitatively different curves connecting the velocity to the ATP-concentration. The molecular interaction potentials the motor experiences on its way along its track are unknown. We compare different simple potentials and the effects the localization of the vertical rates in the network model has on the transport coefficients in comparison to other models.