Department of Physics, Physics of Living Systems Group, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Complexity Sciences Center and Department of Physics, University of California at Davis, Davis, California 95616, USA.
Phys Rev E. 2018 Jul;98(1-1):012408. doi: 10.1103/PhysRevE.98.012408.
Experimentalists observe phenotypic variability even in isogenic bacteria populations. We explore the hypothesis that in fluctuating environments this variability is tuned to maximize a bacterium's expected log-growth rate, potentially aided by epigenetic (all inheritable nongenetic) markers that store information about past environments. Crucially, we assume a time delay between sensing and action, so that a past epigenetic marker is used to generate the present phenotypic variability. We show that, in a complex, memoryful environment, the maximal expected log-growth rate is linear in the instantaneous predictive information-the mutual information between a bacterium's epigenetic markers and future environmental states. Hence, under resource constraints, optimal epigenetic markers are causal states-the minimal sufficient statistics for prediction-or lossy approximations thereof. We propose new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments.
实验人员观察到即使在同基因细菌群体中也存在表型可变性。我们探讨了这样一种假设,即在波动的环境中,这种可变性被调整为使细菌的预期对数增长率最大化,这可能得益于能够存储有关过去环境信息的表观遗传(所有可遗传的非遗传)标记。至关重要的是,我们假设在感应和行动之间存在时间延迟,因此过去的表观遗传标记被用来产生当前的表型可变性。我们表明,在复杂的、有记忆的环境中,最大的预期对数增长率与瞬时预测信息——细菌的表观遗传标记与未来环境状态之间的互信息——呈线性关系。因此,在资源有限的情况下,最优的表观遗传标记是因果状态——预测的最小充分统计量,或者是其有损耗的近似值。我们提出了新的理论研究和关于在波动的复杂环境中细菌表型避险的新实验。