Samarasinghe Sandhya, Minh-Thai Tran Nguyen
Complex Systems, Big Data and Informatics Initiative (CSBII), Lincoln University, Lincoln 7647, New Zealand.
Precision Agriculture Team, Lincoln Agritech Limited, PO Box 69133, Lincoln, New Zealand.
PNAS Nexus. 2023 Jan 9;2(2):pgac308. doi: 10.1093/pnasnexus/pgac308. eCollection 2023 Feb.
In biology, regeneration is a mysterious phenomenon that has inspired self-repairing systems, robots, and biobots. It is a collective computational process whereby cells communicate to achieve an anatomical set point and restore original function in regenerated tissue or the whole organism. Despite decades of research, the mechanisms involved in this process are still poorly understood. Likewise, the current algorithms are insufficient to overcome this knowledge barrier and enable advances in regenerative medicine, synthetic biology, and living machines/biobots. We propose a comprehensive conceptual framework for the engine of regeneration with hypotheses for the mechanisms and algorithms of stem cell-mediated regeneration that enables a system like the planarian flatworm to fully restore anatomical (form) and bioelectric (function) homeostasis from any small- or large-scale damage. The framework extends the available regeneration knowledge with novel hypotheses to propose collective intelligent self-repair machines with multi-level feedback neural control systems driven by somatic and stem cells. We computationally implemented the framework to demonstrate the robust recovery of both form and function (anatomical and bioelectric homeostasis) in an in silico worm that, in a simple way, resembles the planarian. In the absence of complete regeneration knowledge, the framework contributes to understanding and generating hypotheses for stem cell mediated form and function regeneration, which may help advance regenerative medicine and synthetic biology. Further, as our framework is a bio-inspired and bio-computing self-repair machine, it may be useful for building self-repair robots/biobots and artificial self-repair systems.
在生物学中，再生是一种神秘的现象，它启发了自我修复系统、机器人和生物机器人的发展。它是一个集体计算过程，通过细胞间的通讯来达到解剖学设定点，并在再生组织或整个生物体中恢复原始功能。尽管经过了数十年的研究，这个过程所涉及的机制仍然知之甚少。同样，目前的算法也不足以克服这一知识障碍，推动再生医学、合成生物学以及活体机器/生物机器人领域的进展。我们提出了一个关于再生引擎的全面概念框架，并对干细胞介导的再生机制和算法提出了假设，该框架能使像涡虫这样的系统从任何小规模或大规模损伤中完全恢复解剖学（形态）和生物电（功能）稳态。这个框架用新的假设扩展了现有的再生知识，提出了具有由体细胞和干细胞驱动的多级反馈神经控制系统的集体智能自我修复机器。我们通过计算实现了这个框架，以证明在一个以简单方式类似于涡虫的计算机模拟蠕虫中，形态和功能（解剖学和生物电稳态）都能实现稳健恢复。在缺乏完整再生知识的情况下，该框架有助于理解和生成关于干细胞介导的形态和功能再生的假设，这可能有助于推动再生医学和合成生物学的发展。此外，由于我们的框架是一种受生物启发的生物计算自我修复机器，它可能对构建自我修复机器人/生物机器人以及人工自我修复系统有用。
