Towards a cognitive framework for understanding cellular behavior

摘要 自由生活的细胞对个体环境的变化表现出强烈的全基因组转录反应 例如氧气和温度。这样的转录动力学被认为是一个基因的基础。 内稳态反应，试图逆转由细胞内分泌引起的直接细胞内后果。 环境的具体变化。我提出了另一种解释， 反映了一个多方面的行为计划，以应对预期的全球环境变化， 跟随扰动。这是因为原生微生物栖息地是高度结构化的， 环境参数之间存在很强的相关性。在地质时间尺度上，这种相关性 可以通过一个“联想学习”过程来内化， 监管网络。这样的内部模型应该允许微生物预测未来的轨迹， 基于即时感官信息的环境。我们已经看到了这种预期行为的证据， 大肠杆菌对温度和氧气变化的反应， 外部环境和哺乳动物胃肠道之间的过渡。这些内部 表征似乎反映了一个真正的联想学习范式，因为它们在暴露后显示出可塑性 to novel新environments环境.这些现象越来越要求我们从一个特定的角度来解释微生物的行为。 认知的角度，就像我们理解动物行为一样。我建议进行多方面的研究 该计划旨在1）建立这些现象，确实，代表微生物的认知模型 栖息地，2）揭示潜在的网络机制，3）探索新的联想学习 通过实验室实验进化的环境。拟议的工作建立了深刻的联系 微生物生态学、调控网络进化和行为的不同领域之间的联系。这样做 挑战了占主导地位的百年历史的概念稳态，与根本的影响，我们如何 了解和控制微生物的行为，特别是在疾病的背景下。

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