Ali Abdi

The paper applies a Neyman-Pearson framework to single-cell TNF-NF-κB signaling data to quantify decision-making performance and detect pathway failures.

The paper models and computes decision making errors in a molecular signaling system with two outputs using single cell measurements of NFκB and ATF-2 concentrations.

The paper proposes an integer linear programming approach to learn molecular network models that incorporate feedback paths, improving alignment with experimental data and enabling more reliable biological predictions.

The paper introduces algorithms to identify extreme signaling failures in molecular networks and finds that specific molecules, not random ones, cause the most detrimental network dysfunction.

This paper introduces decision-theoretic and statistical methods to model signaling outcomes in cells, using single-cell data to analyze decision accuracy and dynamics in biological systems.