Christian Cuba Samaniego

This paper develops a CRISPR-based neuromorphic computing system using dCas9 and guide RNAs to perform classification and regression tasks, showing invariance in decision boundaries and preserved node thresholds under resource constraints.

This paper analyzes how ribosome competition affects protein neural networks in bacterial systems, showing it alters decision boundaries and output magnitudes.

This paper establishes design principles for implementing neuromorphic computing in living cells using genetic circuits, demonstrating how four chemical reaction networks can function as perceptrons and be layered to create decision boundaries for cell classification.

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.

This paper designs and experimentally validates biomolecular neural networks using molecular sequestration, demonstrating their ability to function as linear classifiers in living systems.

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 paper designs a biomolecular adaptive controller using an IFFL-like topology to restore sustained oscillations in systems that have lost periodicity.

This paper designs sigma factor-based biomolecular classifiers and investigates how resource competition affects their performance and design principles.

This paper designs a portable regulatory RNA array for tunable gene regulation across diverse bacteria, enabling complex circuitry without requiring large regulatory libraries.

The paper demonstrates how subtraction can be achieved through molecular sequestration in a chemical reaction network and presents two feedback control architectures based on this principle.

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.

This paper introduces RNA compensation, a positive feedback strategy to mitigate RNA signal consumption in RNA-based circuits, improving composability and modularity.

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 demonstrates how bistable switches and quorum sensing can enable ultrasensitive feedback control in microbial populations while avoiding cellular burden.

This paper designs molecular quasi-integral controllers using a reaction network called Brink motif to achieve robust homeostasis in biological systems.