Stepping and Crowding of Molecular Motors: Statistical Kinetics from an Exclusion Process Perspective

Luca Ciandrini, M. Carmen Romano, Andrea Parmeggiani

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16 Citations (Scopus)


Motor enzymes are remarkable molecular machines that use the energy derived from the hydrolysis of a nucleoside triphosphate to generate mechanical movement, achieved through different steps that constitute their kinetic cycle. These macromolecules, nowadays investigated with advanced experimental techniques to unveil their molecular mechanisms and the properties of their kinetic cycles, are implicated in many biological processes, ranging from biopolymerization (e.g., RNA polymerases and ribosomes) to intracellular transport (motor proteins such as kinesins or dyneins). Although the kinetics of individual motors is well studied on both theoretical and experimental grounds, the repercussions of their stepping cycle on the collective dynamics still remains unclear. Advances in this direction will improve our comprehension of transport process in the natural intracellular medium, where processive motor enzymes might operate in crowded conditions. In this work, we therefore extend contemporary statistical kinetic analysis to study collective transport phenomena of motors in terms of lattice gas models belonging to the exclusion process class. Via numerical simulations, we show how to interpret and use the randomness calculated from single particle trajectories in crowded conditions. Importantly, we also show that time fluctuations and non-Poissonian behavior are intrinsically related to spatial correlations and the emergence of large, but finite, clusters of comoving motors. The properties unveiled by our analysis have important biological implications on the collective transport characteristics of processive motor enzymes in crowded conditions.
Original languageEnglish
Pages (from-to)1176-1184
Number of pages9
JournalBiophysical Journal
Issue number5
Early online date2 Sept 2014
Publication statusPublished - 2 Sept 2014

Bibliographical note

The authors thank I. Stansfield for useful discussions, and thank the anonymous referees. L.C. also thanks the Institute for Complex Systems and Mathematical Biology at the University of Aberdeen for computational time support.

L.C. is supported by an EMBO long-term fellowship cofunded by the European Commission (EMBOCOFUND2010, No. GA-2010-267146). A.P. is financially supported by the Scientific Council of the University of Montpellier 2 and Labex NUME. M.C.R. thanks the Biotechnology and Biological Sciences Research Council (No. BB/F00513/X1) and the Scottish Universities Life Science Alliance for financial support.


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