Many biological organisms regenerate structure and function after damage. Despite the long history of research on molecular mechanisms, many questions remain about algorithms by which cells can cooperate towards the same invariant morphogenetic outcomes. Therefore, conceptual frameworks are needed not only for motivating hypotheses for advancing the understanding of regeneration processes in living organisms, but also for regenerative medicine and synthetic biology. Inspired by planarian regeneration, this study offers a novel generic conceptual framework that hypothesizes mechanisms and algorithms by which cell collectives may internally represent an anatomical target morphology towards which they build after damage. Further, the framework contributes a novel nature-inspired computing method for self-repair in engineering and robotics. Our framework, based on past in vivo and in silico studies on planaria, hypothesizes efficient novel mechanisms and algorithms to achieve complete and accurate regeneration of a simple in silico flatwormlike organism from any damage, much like the body-wide immortality of planaria, with minimal information and algorithmic complexity. This framework that extends our previous circular tissue repair model integrates two levels of organization: tissue and organism. In Level 1, three individual in silico tissues (head, body, and tail—each with a large number of tissue cells and a single stem cell at the centre) repair themselves through efficient local communications. Here, the contribution extends our circular tissue model to other shapes and invests them with tissue-wide immortality through an information field holding the minimum body plan. In Level 2, individual tissues combine to form a simple organism. Specifically, the three stem cells form a network that coordinates organism-wide regeneration with the help of Level 1. Here we contribute novel concepts for collective decision-making by stem cells for stem cell regeneration and large-scale recovery. Both levels (tissue cells and stem cells) represent networks that perform simple neural computations and form a feedback control system. With simple and limited cellular computations, our framework minimises computation and algorithmic complexity to achieve complete recovery. We report results from computer simulations of the framework to demonstrate its robustness in recovering the organism after any injury. This comprehensive hypothetical framework that significantly extends the existing biological regeneration models offers a new way to conceptualise the information-processing aspects of regeneration, which may also help design living and non-living self-repairing agents.

许多生物有机体在受损后会再生结构和功能。尽管对分子机制的研究有着悠久的历史，但关于细胞可以通过哪些算法来实现相同的不变形态发生结果的算法仍然存在许多问题。因此，不仅需要概念框架来激发假设以促进对生物体再生过程的理解，而且还需要用于再生医学和合成生物学。受涡虫再生的启发，本研究提供了一个新颖的通用概念框架，该框架假设细胞集合体可能在内部代表一个解剖目标形态，它们在损伤后构建该结构和算法。此外，该框架为工程和机器人技术的自我修复提供了一种新颖的自然启发计算方法。我们的框架基于过去对涡虫的体内和计算机研究，假设了有效的新机制和算法，以实现简单的计算机扁虫类生物体免受任何损伤的完全和准确的再生，就像涡虫的全身永生一样，以最小的信息和算法的复杂性。这个扩展了我们之前的圆形组织修复模型的框架整合了两个层次的组织：组织和有机体。在 1 级中，三个计算机组织（头部、身体和尾部——每个都有大量的组织细胞和一个位于中心的单个干细胞）通过有效的本地通信进行自我修复。在这里，贡献将我们的圆形组织模型扩展到其他形状，并通过保存最小身体计划的信息场为它们提供组织范围的永生。在第 2 级中，单个组织结合形成一个简单的有机体。具体来说，三个干细胞形成了一个网络，在 1 级的帮助下协调整个生物体的再生。在这里，我们为干细胞的集体决策提供了新的概念，以实现干细胞再生和大规模恢复。两个级别（组织细胞和干细胞）都代表执行简单神经计算并形成反馈控制系统的网络。通过简单且有限的蜂窝计算，我们的框架将计算和算法复杂性降至最低，以实现完全恢复。我们报告了该框架的计算机模拟结果，以证明其在任何受伤后恢复有机体方面的稳健性。