Machine Learning-Evolved Biophysical Models for Engineering Genetic Circuits and Pathways

Abstract

This project sought to further Synthetic Biology by engineering large genetic circuits and metabolic pathway models that hold true in real world experiments. Hybrid Biophysics-Machine Learning Models were developed to achieve predictive design.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2022
Accession Number
AD1164346

Entities

People

  • Alexander C. Reis
  • Ayaan Hossain
  • Daniel P. Cetnar
  • Grace E. Vezeau
  • Howard M. Salis
  • Sean M. Halper
  • Travis L. LaFleur

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Biotechnology
  • Chemical Reactions
  • Chemical Synthesis
  • Chemistry
  • Computational Biology
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Databases
  • Dimensionality Reduction
  • Engineers
  • Escherichia Coli
  • Genetic Code
  • Genetics
  • Information Science
  • Machine Learning
  • Proteins
  • Rna Stability
  • Synthetic Biology
  • Systems Biology

Readers

  • Integrated Circuit Design and Technology.
  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Biotechnology