Generalization of a Procedure for Computing Transcription Factor Profiles

The limited amount of quantitative experimental data generated from life-science experiments poses a major challenge in systems biology. The reason for this is that many systems approaches, such as parameter estimation, simulation, and sensitivity analysis make use of models or analyze quantitative data. However, these techniques are only of limited use if only qualitative or semi-quantitative information is available about a system. Therefore, procedures that generate quantitative data from experiments in the life sciences can greatly expand the use of systems approaches to biological problems. This paper addresses this issue as it introduces a procedure that computes quantitative transcription factor profiles from fluorescent microscopy data collected from green fluorescent protein (GFP) reporter cells. This technique forms a generalization of a method that has recently been introduced for monitoring NF-kB profiles. The contribution made in this work is that the assumption that the transcription factor profile exhibits damped oscillations is relaxed, as transcription factors, other than the previously investigated NF-kB, may exhibit different profiles. This is achieved by investigating a variety of potential profiles and solving the inverse problem for the model describing transcription, translation, and activation of GFP for each one. The transcription factor profile that results in the best fit among the potential candidates, for the measured fluorescent intensity data, is then chosen as the most likely concentration profile. The technique is illustrated in two detailed case studies, where one case study involves simulation data while the other one uses experimentally-derived fluorescent intensity data.