Frank Britto Bisso

CRISPR-based neuromorphic computing for solving regression and classification

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.

Sequestration-based Protein Neural Networks Tolerate the Effects of Shared Translational Resources

This paper develops a framework to analyze how shared translational resources affect protein neural networks, showing that classification remains reliable outside a bounded uncertainty region.

Design principles of neuromorphic computing using genetic circuits

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.

Engineering cell fate with adaptive feedback control

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.

Sequestration-Based Neural Networks That Operate Out of Equilibrium

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

Design of a biomolecular adaptive controller to restore sustained periodic behavior

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