M. Ali Al-Radhawi

This paper combines mechanistic and machine learning models to address resource competition in synthetic biology, demonstrating improved predictions and physical consistency.

The paper develops a dynamical system model to analyze how Notch signaling and epigenetic modifications influence melanoma cell fate transitions during metastasis.

This paper improves heuristic logic synthesis by using a database of optimal 4-input Boolean implementations to reduce gate count in larger circuits, achieving average improvements of 5.16% and 4.54% for 5- and 6-input circuits respectively.

The paper demonstrates that increasing activator expression can reduce target gene expression by displacing repressor from decoy sites, explaining paradoxical gene regulation through competition for genomic binding sites.

This paper models and minimizes dCas9 competition in CRISPRi-based genetic circuits to improve their performance and dynamic range.

This paper classifies resource competition structures in synthetic gene circuits and shows how their response to competitors and interventions can distinguish between different competition mechanisms.

This paper designs a synthetic genetic multi-bit counter using repressor-based circuits that can count input pulses modulo 2^N.

The paper develops a method to derive analytic stationary distributions for biochemical reaction networks by transforming them to be complex balanced, and introduces a computational tool for this purpose.

The paper introduces a mathematical model that integrates DNA methylation with gene regulatory networks to quantify its role in gene silencing stability.

The paper develops a mathematical model to explain immune recruitment and drug resistance in metronomic chemotherapy using a mouse glioma model and validates it across different drug regimens.

This paper develops a method to synthesize distributed Boolean circuits using diffusible small molecules, enabling scalable computation beyond single-cell limitations.

This paper introduces a computational framework to analyze biochemical reaction networks for structural attractiveness using Robust Lyapunov functions, demonstrating its application to various biological systems.

The paper extends previous work by analyzing the time-evolution of probability mass functions in gene regulatory networks with slow promoter kinetics.

The paper analyzes the periodic gain of the Ribosome Flow Model (RFM) and derives conditions for its optimality in translation elongation.