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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

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Published on: November 12, 2012

Predictive behavior within microbial genetic networks.

Ilias Tagkopoulos1, Yir-Chung Liu, Saeed Tavazoie

  • 1Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA.

Science (New York, N.Y.)
|May 10, 2008
PubMed
Summary

Microbial cells exhibit predictive behavior beyond simple homeostasis. Intracellular networks in Escherichia coli demonstrate associative learning, anticipating environmental changes like temperature and oxygen shifts.

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Area of Science:

  • Microbial Physiology
  • Systems Biology
  • Computational Biology

Background:

  • The homeostatic framework traditionally explains cellular responses to environmental stimuli.
  • This framework may not fully account for microbial anticipatory behaviors.
  • Metazoan nervous systems exhibit predictive capacities that could be analogous in microbial intracellular networks.

Purpose of the Study:

  • To investigate if microbial intracellular networks possess predictive capabilities beyond homeostasis.
  • To explore the potential for microbial associative learning and internal representations of environmental dynamics.
  • To challenge the sole reliance on homeostasis for understanding microbial environmental responses.

Main Methods:

  • Development of in silico biochemical networks that evolve under complex, defined habitats.
  • Analysis of Escherichia coli transcriptional responses to temperature and oxygen perturbations.
  • Evaluation of the decoupling of internal correlations in novel environments to assess associative learning.

Main Results:

  • In silico networks captured complex environmental structures, forming internal representations for predicting change.
  • Escherichia coli transcriptional responses to temperature and oxygen covariation mirrored transitions between external and gastrointestinal environments.
  • These microbial internal correlations demonstrated characteristics of associative learning, rapidly decoupling in new environments.

Conclusions:

  • Microbial intracellular networks can develop predictive behaviors, extending beyond simple homeostatic regulation.
  • Escherichia coli exhibits associative learning, forming internal representations that anticipate environmental shifts.
  • These findings suggest a more dynamic and predictive model for microbial responses to environmental stimuli is necessary.