Towards a cognitive framework for understanding cellular behavior

Princeton University, Princeton NJ

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Abstract

Abstract[unreadable] Free-living cells display strong genome-wide transcriptional responses to changes in individual environmental[unreadable] parameters such as oxygen and temperature. Such transcriptional dynamics are thought to be the basis of a[unreadable] homeostatic response that attempts to reverse the immediate intracellular consequences resulting from the[unreadable] specific change in the environment. I present an alternative interpretation where transcriptional responses[unreadable] reflect a multifaceted behavioral program in response to global changes in the environment that are anticipated[unreadable] to follow the perturbation. This results from the fact that native microbial habitats are highly structured, giving[unreadable] rise to strong correlations between environmental parameters. Over geological timescales, such correlations[unreadable] can be internalized through an "associative learning" process that shapes the connectivity and dynamics of[unreadable] regulatory networks. Such internal models should allow microbes to predict the future trajectory of the[unreadable] environment based on immediate sensory information. We have seen evidence of this anticipatory behavior in[unreadable] responses of the bacterium Escherichia coli to changes in temperature and oxygen that correspond to[unreadable] transitions between the outside environment and the mammalian gastrointestinal tract. These internal[unreadable] representations seem to reflect a true associative learning paradigm, since they show plasticity upon exposure[unreadable] to novel environments. These phenomena increasingly demand that we interpret microbial behaviors from a[unreadable] cognitive perspective, much as we do for understanding animal behaviors. I propose a multi-faceted research[unreadable] program aimed at 1) establishing that these phenomena, indeed, represent cognitive modeling of microbial[unreadable] habitats, 2) revealing the underlying network mechanisms, and 3) exploring associative learning of novel[unreadable] environments through laboratory experimental evolution. The proposed work establishes deep connections[unreadable] between the disparate fields of microbial ecology, regulatory network evolution, and behavior. In so doing, it[unreadable] challenges the dominance of the century-old notion of homeostasis, with fundamental implications for how we[unreadable] understand and control microbial behavior, especially in the context of disease.