Hense Burkhard A, Schuster Martin
Institute for Computational Biology, Helmholtz Zentrum München, Neuherberg/Munich, Germany
Department of Microbiology, Oregon State University, Corvallis, Oregon, USA
Microbiol Mol Biol Rev. 2015 Mar;79(1):153-69. doi: 10.1128/MMBR.00024-14.
Autoinduction (AI), the response to self-produced chemical signals, is widespread in the bacterial world. This process controls vastly different target functions, such as luminescence, nutrient acquisition, and biofilm formation, in different ways and integrates additional environmental and physiological cues. This diversity raises questions about unifying principles that underlie all AI systems. Here, we suggest that such core principles exist. We argue that the general purpose of AI systems is the homeostatic control of costly cooperative behaviors, including, but not limited to, secreted public goods. First, costly behaviors require preassessment of their efficiency by cheaper AI signals, which we encapsulate in a hybrid "push-pull" model. The "push" factors cell density, diffusion, and spatial clustering determine when a behavior becomes effective. The relative importance of each factor depends on each species' individual ecological context and life history. In turn, "pull" factors, often stress cues that reduce the activation threshold, determine the cellular demand for the target behavior. Second, control is homeostatic because AI systems, either themselves or through accessory mechanisms, not only initiate but also maintain the efficiency of target behaviors. Third, AI-controlled behaviors, even seemingly noncooperative ones, are generally cooperative in nature, when interpreted in the appropriate ecological context. The escape of individual cells from biofilms, for example, may be viewed as an altruistic behavior that increases the fitness of the resident population by reducing starvation stress. The framework proposed here helps appropriately categorize AI-controlled behaviors and allows for a deeper understanding of their ecological and evolutionary functions.
自诱导（AI），即对自身产生的化学信号作出的反应，在细菌界广泛存在。这一过程以不同方式控制着极为不同的目标功能，如发光、养分获取和生物膜形成，并整合了其他环境和生理线索。这种多样性引发了关于所有AI系统背后统一原则的问题。在此，我们认为存在这样的核心原则。我们认为，AI系统的总体目的是对代价高昂的合作行为进行稳态控制，包括但不限于分泌型公共物品。首先，代价高昂的行为需要通过成本较低的AI信号对其效率进行预先评估，我们将其概括为一种混合的“推-拉”模型。“推”的因素，即细胞密度、扩散和空间聚集，决定了一种行为何时变得有效。每个因素的相对重要性取决于每个物种各自的生态背景和生活史。反过来，“拉”的因素，通常是降低激活阈值的应激线索，决定了细胞对目标行为的需求。其次，控制是稳态的，因为AI系统本身或通过辅助机制，不仅启动而且维持目标行为的效率。第三，AI控制的行为，即使看似非合作行为，在适当的生态背景下解读时，本质上通常是合作的。例如，单个细胞从生物膜中逸出可被视为一种利他行为，它通过减轻饥饿压力来提高留存种群的适应性。这里提出的框架有助于对AI控制的行为进行适当分类，并能更深入地理解它们的生态和进化功能。
