Mustafa Ozen

Folding-Driven Control of the Functional PP1 Complex via Multiscale Modeling

The paper develops a multiscale model to study how conformational dynamics regulate the PP1-GADD34-eIF2 complex, showing folding-dependent assembly enhances biochemical function.

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

The paper introduces a deep learning framework to resolve mechanistic ambiguity in complex signaling networks by learning dynamic signatures from competing models.

Intrinsic Molecular Timers and a Biphasic Amplitude Limit Regulate the Integrated Stress Response

The paper develops a theoretical model of the ISR using time-resolved proteomics data to show molecular timers and a translational cliff limit ISR activation.

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

The paper introduces PyDyNo, a framework to identify dominant reaction paths in biochemical networks using Bayesian parameter optimization, revealing distinct signal execution modes in apoptosis and JAK2/STAT5 networks.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor networksuggests potential subtype regulators and transition pathways

The paper analyzes the structure of SCLC transcription factor networks to identify subtype regulators and transition pathways using graph theory and RNA-seq data.

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.