TY - JOUR T1 - A Stochastic Model for Microtubule Motors Describes the In Vivo Cytoplasmic Transport of Human Adenovirus A1 - Gazzola, Mattia A1 - Burckhardt, Christoph J. A1 - Bayati, Basil A1 - Engelke, Martin A1 - Greber, Urs F. A1 - Koumoutsakos, Petros Y1 - 2009/12/24 N2 - Author Summary Molecular motors, due to their transportation function, are essential to the cell, but they are often hijacked by viruses to reach their replication site. Imaging of virus trajectories provides information about the patterns of virus transport in the cytoplasm, leading to improved understanding of the underlying mechanisms. In turn improved understanding may suggest actions that can be taken to interfere with the transport of pathogens in the cell. In this work we use in vivo imaging of virus trajectories to develop a computational model of virus transport in the cell. The model parameters are identified by an optimization procedure to minimize the discrepancy between in vivo and in silico trajectories. The model explains the in vivo trajectories as the result of a stochastic interaction between motors. Furthermore it enables predictions on the number of motors and binding sites on pathogens, quantities that are difficult to obtain experimentally. Beyond the understanding of mechanisms involved in pathogen transport, the present paper introduces a systematic parameter identification algorithm for stochastic models using in vivo imaging. The discrete and noisy characteristics of biological systems have led to increased attention in stochastic models and this work provides a methodology for their systematic development. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 5 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1000623 SP - e1000623 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000623 ER -