Complexity Science Hub Vienna, Vienna, Austria.
Laboratoire de physique de l'École normale supérieure, CNRS, PSL Université, Sorbonne Université, Université de Paris, Paris, France.
PLoS Comput Biol. 2022 May 27;18(5):e1010072. doi: 10.1371/journal.pcbi.1010072. eCollection 2022 May.
Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations-ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms-to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, "pivotal" neurons that account for most of the system's sensitivity, suggesting a sparse mechanism of collective control.
生物电路，如神经或基因调控网络，利用内部状态将感觉输入映射到自适应行为组合上。对系统生物学来说，对这种映射进行特征描述是一个主要的挑战。尽管探测内部状态的实验发展迅速，但由于内部状态可以映射到行为的多种可能方式，生物体的复杂性构成了一个基本障碍。我们以秀丽隐杆线虫为例，提出了一种系统地扰动神经状态的方案，该方案限制了实验的复杂性，最终有助于对神经行为图谱的集体方面进行特征描述。我们考虑了受实验启发的小扰动，这些扰动最有可能保留自然动力学，并且更接近内部控制机制，从而影响集体神经活动。然后，我们将这些扰动与集体统计信息的局部信息几何联系起来，这可以通过成对扰动来充分描述。将该方案应用于秀丽隐杆线虫神经活动的最小模型，我们发现集体神经统计对少数几个主要扰动模式最为敏感。主导特征值最初呈幂律衰减，揭示了一种源于单个神经活动和成对相互作用变化的层次结构。排名最高的模式往往由少数几个“关键”神经元主导，这些神经元占系统敏感性的大部分，这表明存在一种稀疏的集体控制机制。
