Analysis of Cell Adhesion Experiments Based on Hidden Markov Models

• Activity Number: 25
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yijie Wang*+ and Ying Hung and C. F. Jeff Wu
• Companies: Georgia Institute of Technology and Rutgers University and Georgia Institute of Technology
• Keywords: hidden Markov chain ; cell adhesion ; EM ; Mixture Model

Abstract

Cell adhesion plays an important role in physiological and pathological processes. It is mediated by specific interactions between cell adhesion proteins (called receptors) and the molecules to which they bind (called ligands). This study is motivated by cell adhesion experiment conducted at Georgia Tech, which uses decrease/resumption of thermal fluctuations of a biomembrane probe to pinpoint association/dissociation of receptor-ligand bonds. More than one type of bond is commonly observed and they correspond to different fluctuation decrease due to their string strength difference. Existing approach is not robust in estimating the association/dissociation points and can only detect one type of bond. A hidden Markov model is developed to tackle the problems. It works by assuming that the probe fluctuates differently according to the underlying binding states of the cells, i.e., no bond or distinct types of bonds. These states are unobservable but their changes can be captured by a Markov chain. Applications of the proposed approach to real data demonstrate robustness and accuracy of estimating bond lifetimes and waiting times, which form basis for estimation of kinetic parameters.