Giulia Giordano

This paper introduces a synthetic biomolecular controller to guide cell fate decisions by mimicking in vivo regulatory dynamics, achieving tunable bias toward desired cell types with minimal network alteration.

The paper demonstrates temporal dose inversion in adaptive biomolecular circuits using synthetic gene circuits and identifies key parameters for this behavior.

This study analyzes sequestration-based networks to understand and control pulse dynamics through kinetic rates and feedback mechanisms.

The paper demonstrates enzymatic networks can function as molecular perceptrons for linear classification and multi-layer networks for nonlinear classification.

This study compares two synthetic quorum sensing circuit architectures and evaluates three QS inhibition strategies to understand their steady-state behavior and parameter thresholds for effective suppression.

The paper designs synthetic gene circuits that function as band-pass filters using molecular sequestration, showing improved performance with combined Incoherent Feed-Forward Loop and Negative Feedback motifs.

This study designs and models six recombinase-based oscillator circuits, showing they can produce periodic behavior under stochastic conditions using a Gillespie algorithm.

The paper models phosphorylation/dephosphorylation cycles as molecular neurons and shows they can function as biomolecular neural networks capable of performing classification tasks.

The paper classifies biological networks into candidate oscillators and multistationary systems based on feedback loop analysis without parameter knowledge.

This paper introduces a negative autoregulation system for synthetic genelets that matches production and demand, showing improved robustness and scalability compared to cross-activation.