Stochastic modeling of intracellular signaling dynamics for the purpose of regulating endothelial cell migration
Author(s)Mayalu, Michaëlle Ntala
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
H. Harry Asada.
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Effective control of cellular behaviors has serious implications in the study of biological processes and disease. However, phenotypic changes may be difficult to detect instantaneously and are usually associated with noticeable delay between input cue and output cellular response. Because of this, relying on detection of phenotypic behaviors for use in feedback control may lead to instability and decreased controller performance. In order to alleviate these issues, a new approach to regulating cell behaviors through control of intracellular signaling events is presented. Many cell behaviors are mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation of cell surface receptors. Multiple protein signaling transductions occur concurrently through diverse pathways triggered by different extracellular cues. Cell behavior differs, depending on the chronological order of multiple signaling events. This thesis develops several modeling frameworks for an intracellular signaling network specific to endothelial cell migration in angiogenesis. Unlike previous works, the models developed in this thesis exploit the effect of signaling order on extracellular response. Our approach examines the transduction time associated with each pathway of a cascaded signaling network. Transduction times of multiple pathways are compared, and the probability that the multiple signaling events occur in a desired chronological order is evaluated. We begin our development with an input-output time-delay model derived from simulated data that is used to predict the optimal extracellular input intensity for a desired response. We then present a stochastic "pseudo-discrete" model of the signal transduction time. We conclude by presenting several control strategies for control of intracellular signaling events.
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 56-59).
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology