Computing Transcription Factor Distribution Profiles from Green Fluorescent Protein Reporter Data

Signal transduction pathways play a key role in many cellular functions as well as intercellular communication. However, elucidating the exact mechanisms involved in signal transduction pathways is non-trivial due to limited measurement capabilities for observing intracellular signals and stochasticity inherent in signaling pathways. This work will address part of these challenges by quantifying the distribution of important components in signaling pathways, e.g., transcription factors, from data. Specifically, this work presents a technique that computes the time-varying distribution of transcription factor concentrations inside cells from fluorescent images of green fluorescent protein (GFP) reporter systems. The presented approach consists of an algorithm for identifying individual fluorescent cells from fluorescent images, and an algorithm to compute the distribution of transcription factor profiles from the fluorescence intensity distribution by solving an inverse problem. The technique is applied to experimental data to derive the NF-kB concentration distribution from fluorescent images of a NF-kB GFP reporter system. The presented image analysis method is able to correctly identify individual fluorescent cells from fluorescent images which are characterized by low contrast and a significant noise level. The derived heterogeneity in the NF-kB distribution not only matches available qualitative experimental data but can also be used for parameter estimation of mathematical models that describe the stochasticity inherent in TNF-a - NF-kB signaling.