Signaling network reconstruction from biosensor image fluctuations

Harvard Medical School, Boston MA

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Abstract

DESCRIPTION (provided by applicant): Most human diseases and disorders can be seen as a projection of disrupted cellular components. Such anomalies restrain and/or modify the natural flow of information among the cellular processes, impairing appropriate cell decision. For instance, mutation of the small GTPase proteins can lead to abnormal cell migration. This simplistic description of an aberrant process highlights the importance of probing not only how information is transmitted, e.g. through amino acid phosphorylation, but also the direction it takes from a given point. The discovery of this directionality potentially defines the function of given cellular component. For example, an active form of a protein switches on a specific cellular behavior at a particular region of the cell. Biosensor technology has been proposed to overcome the current limitations of classical biochemical methods on the process of establishing signaling pathway functionality. These fluorescent constructs allow the measurement of the localization and the active state of the studied molecule in living cells. Because proteins can interact with different molecules in different cellular locations, biosensor technology stands out as a powerful tool to address questions of how differently regulated a given protein is in time and space. This proposal focuses on the computational analysis of biosensor imaging data. The main goal is to build a set of computational tools that will infer paths among molecules and specific cellular behavior from biosensor images. As a proof of concept, the second part of this project focuses on the elucidation of Rac1 regulation at the lamellipodia region of cells using the proposed computational tools. This challenge sets up the suitability and relevance of the computational tools proposed here.