Dynamics and Evolution of Associative Memory in Bacterial Populations

Abstract

This project addressed two fundamental questions in systems microbiology. The first is, can predict microbial expression and phenotypes in novel conditions if we use past measurements to model their cellular state and behavior? The second is, how cells adapt in the presence of stress combinations that are either sequential or simultaneous. The project has been highly successful providing answers in both, creating the most accurate and integrative model for microbial phenotype prediction and validating findings in our experimental lab.

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Document Details

Document Type
Technical Report
Publication Date
Jan 23, 2017
Accession Number
AD1058584

Entities

People

  • Ilias Tagkopoulos

Organizations

  • University of California, Davis

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Algorithms
  • Biology
  • Chemistry
  • Computational Biology
  • Computational Science
  • Content Addressable Memory
  • Data Integration
  • Deep Learning
  • Department Of Defense
  • Engineering
  • Escherichia
  • Escherichia Coli
  • Gene Expression
  • Glycoside Hydrolases
  • Information Science
  • Machine Learning
  • Mathematics
  • Microbiology
  • Microorganisms
  • Models
  • Predictive Modeling
  • Systems Biology

Fields of Study

  • Biology

Readers

  • Molecular Biology and Genetics
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • Biotechnology