A Computational Framework for Autonomous Self-repair Systems

Minh-Thai, Tran Nguyen; Aryal, Jagannath; Samarasinghe, Sandhya; Levin, Michael

doi:10.1007/978-3-030-03991-2_16

Cited by 6 publications

(11 citation statements)

References 14 publications

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“…In our work (Minh-Thai et al, 2018) mentioned previously, we presented an autonomous tissue self-repair system for damage detection and regeneration inspired by biological regeneration. The model considers a circular tissue consisting of a stem cell surrounded by thousands of differentiated cells.…”

Section: Regeneration As a Biological Computing Processmentioning

confidence: 99%

“…Our original tissue repair model in (Minh-Thai et al, 2018) describes regeneration of a virtual tissue which consists of a stem cell surrounded by a thousand differentiated cells in a 2-D circular tissue. Each cell has a pair of polar coordinates (radius, θ) ( Fig.…”

Section: Base Model: Autonomous Self-repair System -Circular Tissuementioning

confidence: 99%

“…These models also carry a large computational burden due to excessive communications. In our previous study (Minh-Thai, Aryal, Samarasinghe, & Levin, 2018), we proposed an autonomous self-repair system for regenerating a simple tissue after damage based on effective collaboration between a stem cell and differentiated cells in the tissue using a neural network framework. The model achieves complete and accurate regeneration with minimum computation.…”

mentioning

confidence: 99%

“…Here, we propose a study of computational dynamics that could guide regenerative mechanisms in evolved or artificial (synthetic) systems. The research focuses on some of the above questions through the extension of our previous autonomous tissue self-repair system in (Minh-Thai et al, 2018) to a regeneration system for a complete organism. Our goal is to develop a computational framework that conceptually mimics planarian regeneration in some important aspects: regeneration of the whole pattern even from a tiny fragment, concurrent regeneration of all affected tissues, complete and accurate regeneration without overgrowth or pruning and minimum computational burden.…”

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confidence: 99%

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A comprehensive conceptual and computational dynamics framework for Autonomous Regeneration Systems

Minh-Thai

Samarasinghe

Levin

2019

Preprint

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This paper presents a new conceptual and computational dynamics framework for damage detection and regeneration in multicellular structures similar to living animals. The model uniquely achieves complete and accurate regeneration from any damage anywhere in the system. We demonstrated the efficacy of the proposed framework on an artificial organism consisting of three tissue structures corresponding to the head, body and tail of a worm. Each structure consists of a stem cell surrounded by a tissue of differentiated cells. We represent a tissue as an Auto-Associative Neural Network (AANN) with local interactions and stem cells as a self-repair network with long-range interactions. We also propose another new concept, Information Field which is a mathematical abstraction over traditional components of tissues, to keep minimum pattern information of the tissue structures to be accessed by stem cells in extreme cases of damage. Through entropy, a measure of communication between a stem cell and differentiated cells, stem cells monitor the tissue pattern integrity, violation of which triggers damage detection and tissue repair. Stem cell network monitors its state and invokes stem cell repair in the case of stem cell damage. The model accomplishes regeneration at two levels: In the first level, damaged tissues with intact stem cells regenerate themselves. Here, stem cell identifies entropy change and finds the damage and regenerates the tissue in collaboration with the AANN. In the second level, involving missing whole tissues and stem cells, the remaining stem cell(s) access the information field to restore the stem cell network and regenerate missing tissues. In the case of partial tissue damage with missing stem cells, the two levels collaborate to accurately restore the stem cell network and tissues. This comprehensive hypothetical framework offers a new way to conceptualise regeneration for better understanding the regeneration processes in living systems. It could also be useful in biology for regenerative medicine and in engineering for building self-repairing biobots.

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Section: Regeneration As a Biological Computing Processmentioning

confidence: 99%

Section: Base Model: Autonomous Self-repair System -Circular Tissuementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A comprehensive conceptual and computational dynamics framework for Autonomous Regeneration Systems

Minh-Thai

Samarasinghe

Levin

2019

Preprint

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“…In our previous work, we developed an in silico circular tissue regeneration model (Minh-Thai et al, 2018). We aim to extend this model, with further enhancements, to other tissue shapes (triangular and rectangular ) and assemble these tissue models into a conceptual regeneration framework for an in silico organism that recovers from any damage anywhere in the system.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration Systems

2021

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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.

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Modeling somatic computation with non-neural bioelectric networks

Manicka

Levin

2019

Sci Rep

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The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

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A Computational Framework for Autonomous Self-repair Systems

Cited by 6 publications

References 14 publications

A comprehensive conceptual and computational dynamics framework for Autonomous Regeneration Systems

A comprehensive conceptual and computational dynamics framework for Autonomous Regeneration Systems

A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration Systems

Modeling somatic computation with non-neural bioelectric networks

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