Predictive Behavior Within Microbial Genetic Networks

By Ilias Tagkopoulos, Yir-Chung Liu, Saeed Tavazoie. Science, 2008

458 citations recorded by the source corpus.

The homeostatic framework has dominated our understanding of cellular physiology. We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intracellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multidimensional structure of diverse environments by forming internal representations that allow prediction of environmental change. We provide evidence for such anticipatory behavior by revealing striking correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations—precisely mirroring the covariation of these parameters upon transitions between the outside world and the mammalian gastrointestinal tract. We further show that these internal correlations reflect a true associative learning paradigm, because they show rapid decoupling upon exposure to novel environments.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/06b11200c9c3f3aaec7200fab3e50129c18974cd

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Citations in the corpus

On Having No Head: Cognition throughout Biological Systems (2016, background): “As a consequence of memory, genetic networks can exhibit predictive ability, enabling anticipatory behavior with respect to physiological stimuli (Tagkopoulos et al., 2008).”
Non-genetic inheritance and changing environments (2013, background): “The study of anticipatory regulation [36-38] may also help in answering these questions.”
Synthetic biology: applications come of age (2010, background): “Furthermore, as adaptive and predictive behaviours are naturally present in all organisms (including microbes”
Natural Agency: The Case of Bacterial Cognition (2017, background): “Cells are able to integrate a variety of internal and external stochastic signals from past and present conditions (Losick and Desplan 2008) to anticipate future conditions and respond accordingly (Tagkopoulos et al. 2008; Perkins and Swain 2009).”
Adaptive laboratory evolution – principles and applications for biotechnology (2013, background): “They allow anticipatory and foraging behavior in pro- and eukaryotic microbial cells [172-174].”