Effat S. Emamian

Learning Feedback Molecular Network Models Using Integer Linear Programming

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.

Exploring Extreme Signaling Failures in Intracellular Molecular Networks

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.

A Primer on Modeling and Measurement of Signaling Outcomes Affecting Decision Making in Cells: Methods for Determining Optimal and Incorrect Outcomes in Noisy Biochemical Dynamics

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.