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Convergence Science

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Convergence Science

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Research Frontiers in Synthetic Biology with Machine Learning

Combines computational design algorithms with synthetic biology to predict and engineer novel biological circuits and metabolic pathways for biotechnological applications.

Neural-Guided Genetic Circuit Design and Optimization

Machine Learning-Predicted Protein Folding in Synthetic Pathways

AI-Driven Metabolic Engineering at Scale

Learning Epistasis in Engineered Biological Systems

Deep Learning for De Novo Enzyme Specification

Predictive Modeling of Synthetic Ecosystem Stability

Machine-Learned Gene Regulatory Network Architecture

Algorithmic Design of Orthogonal Biological Parts

AI-Optimized Biosynthetic Pathway Evolution and Assembly

Neural Networks Decoding Cellular Decision Logic

All Convergence Science PhD categories