Optimization of Transcription Factor Genetic Circuits

Abstract

Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day–night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 31, 2022
Source ID
10.3390/biology11091294

Entities

People

  • Steven Frank

Organizations

  • National Science Foundation
  • University of California

Tags

Readers

  • Circadian Sleep-Wake Regulation and Chronobiology
  • Manufacturing Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology