Department of Biology, Allen Discovery Center at Tufts University, Medford, MA, USA.
The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, Queen Square, London, UK.
Phys Life Rev. 2020 Jul;33:88-108. doi: 10.1016/j.plrev.2019.06.001. Epub 2019 Jun 12.
Recent advances in molecular biology such as gene editing [1], bioelectric recording and manipulation [2] and live cell microscopy using fluorescent reporters [3], [4] - especially with the advent of light-controlled protein activation through optogenetics [5] - have provided the tools to measure and manipulate molecular signaling pathways with unprecedented spatiotemporal precision. This has produced ever increasing detail about the molecular mechanisms underlying development and regeneration in biological organisms. However, an overarching concept - that can predict the emergence of form and the robust maintenance of complex anatomy - is largely missing in the field. Classic (i.e., dynamic systems and analytical mechanics) approaches such as least action principles are difficult to use when characterizing open, far-from equilibrium systems that predominate in Biology. Similar issues arise in neuroscience when trying to understand neuronal dynamics from first principles. In this (neurobiology) setting, a variational free energy principle has emerged based upon a formulation of self-organization in terms of (active) Bayesian inference. The free energy principle has recently been applied to biological self-organization beyond the neurosciences [6], [7]. For biological processes that underwrite development or regeneration, the Bayesian inference framework treats cells as information processing agents, where the driving force behind morphogenesis is the maximization of a cell's model evidence. This is realized by the appropriate expression of receptors and other signals that correspond to the cell's internal (i.e., generative) model of what type of receptors and other signals it should express. The emerging field of the free energy principle in pattern formation provides an essential quantitative formalism for understanding cellular decision-making in the context of embryogenesis, regeneration, and cancer suppression. In this paper, we derive the mathematics behind Bayesian inference - as understood in this framework - and use simulations to show that the formalism can reproduce experimental, top-down manipulations of complex morphogenesis. First, we illustrate this 'first principle' approach to morphogenesis through simulated alterations of anterior-posterior axial polarity (i.e., the induction of two heads or two tails) as in planarian regeneration. Then, we consider aberrant signaling and functional behavior of a single cell within a cellular ensemble - as a first step in carcinogenesis as false 'beliefs' about what a cell should 'sense' and 'do'. We further show that simple modifications of the inference process can cause - and rescue - mis-patterning of developmental and regenerative events without changing the implicit generative model of a cell as specified, for example, by its DNA. This formalism offers a new road map for understanding developmental change in evolution and for designing new interventions in regenerative medicine settings.
近年来，分子生物学领域取得了一些进展，如基因编辑[1]、生物电记录和操作[2]以及使用荧光报告蛋白的活细胞显微镜技术[3]、[4]，特别是光遗传学的出现使得通过光控蛋白激活来测量和操作分子信号通路具有前所未有的时空精度[5]。这为生物体内发育和再生的分子机制提供了越来越详细的信息。然而，在该领域中，一个能够预测形态出现和复杂解剖结构稳健维持的总体概念却基本缺失。经典（即动态系统和分析力学）方法，如最小作用量原理，在描述生物学中普遍存在的开放、远离平衡的系统时，很难使用。当试图从第一性原理理解神经元动力学时，类似的问题也出现在神经科学中。在这种（神经生物学）背景下，一个基于主动贝叶斯推理的变分自由能原理已经出现。自由能原理最近已被应用于神经科学以外的生物自组织[6]、[7]。对于支持发育或再生的生物过程，贝叶斯推理框架将细胞视为信息处理代理，形态发生的驱动力是最大化细胞的模型证据。这是通过适当表达与细胞内部（即生成）模型相对应的受体和其他信号来实现的，该模型表明细胞应该表达哪种类型的受体和其他信号。形成模式的自由能原理这一新兴领域为理解胚胎发生、再生和癌症抑制背景下的细胞决策提供了必要的定量形式。在本文中，我们推导出了这个框架中理解的贝叶斯推理的数学原理，并使用模拟来显示该形式主义可以再现复杂形态发生的实验、自上而下的操作。首先，我们通过模拟改变前后轴向极性（例如，诱导两个头部或两个尾部）来说明这种“第一原理”方法在形态发生中的应用，这是扁形动物再生中的例子。然后，我们考虑细胞群体中单一个体的异常信号和功能行为——作为癌症发生的第一步，即细胞对“感知”和“做”的内容产生错误的“信念”。我们进一步表明，简单修改推理过程可以导致——并挽救——发育和再生事件的错误模式化，而不会改变细胞的隐含生成模型，例如，由其 DNA 指定。这种形式主义为理解进化中的发育变化以及设计再生医学背景下的新干预措施提供了新的路线图。
