A key question in developmental biology and regenerative medicine concerns the physiological mechanisms by which cells coordinate their behaviors toward the construction and repair of complex anatomical structures. Gap junctional communication among cells enables bioelectrical signaling within a network that enables collections of cells to cooperate during morphogenesis. During regeneration in amputated planarian flatworms, cells capable of dividing must migrate to areas where new tissue is needed. Moreover, these cells must stop proliferating when the needed structures are completed. We previously proposed a cell-cell communication mechanism that enables structure discovery and regeneration by cell networks. In this paper, we further develop the mechanism to address two important simplifications of the previous model: cell division was not limited to adult stem cells (as it is in vivo ), and adult stem cells did not migrate to injured areas to initiate the regeneration process. Thus, here we limit cell division to a specific cell type (neoblasts) and propose a second message type that guides neoblasts to locations where cell division is needed. Our results show that even after incorporating these two constraints, our cell-cell communication model maintained its regeneration capabilities against a large tissue removal.

Complex organisms, such as multi-cellular ones, have neither emerged spontaneously, nor evolved directly, from a disorganised mass of quarks. Stable intermediary sub-systems, like atoms and uni-cellular organisms, had to occur first and serve as reusable blocks for more complex systems to build upon. The occurrence of structured systems, featuring internal diversity , from uniform self-adaptive sub-systems is a key phenomenon to study in this context. We believe this phenomenon relies on the interactions among self-adaptive sub-systems, both at the micro -level (directly between sub-systems) but most importantly via macro -levels (indirectly via aggregate information and control from/to all sub-systems). To study this, we have developed a hierarchical control simulator based on self-adaptive cellular automata (CA). This paper presents our Holonic Cellular Automata (HCA) simulator, and the preliminary results showing the occurrence of structure / diversity from micro-macro feedback loops among self-adaptive CAs starting in the same states. This provides a promising basis for further investigations into the range of possibilities concerning structure creation, as a key enabler for the emergence of complex systems.

Many organisms such as planaria, axolotls and deer exhibit prodigious regenerative abilities, being capable of regenerating complex organs or entire body plans. An understanding of how these organisms store and modify their morphological patterning information is necessary to identify modes of control and intervention. Insight into this process is key to the development of novel biomedical applications. In this work, we present the CANN( k ) model: an abstract computational model of pattern regeneration which couples an artificial neural network (ANN) with a k -color cellular automaton (CA). The ANN provides a global information processing system which generates state-dependent update rules for the CA. The CANN( k ) models are constructed to generate target patterns which are stable under perturbations of the pattern. We generate ensembles of CANN(4) models for each of the 4-color patterns, assess their sensitivity to changes of the ANN structure. This provides a novel model for understanding the important biological phenomenon of neural control of cellular morphogenesis in development or regeneration.

Self-organization is a fundamental principle of the development and function of living systems. Decentralized self-assembly of neurons that act as autonomous agents leads to complicated neural networks in the brain without the need of a blueprint, i.e. without endpoint information. Key principles of the self-assembly of neural networks are (1) algorithmic growth based on limited input information, (2) reliance on iterations of simple rules that often utilize stochastic dynamic processes, and (3) non-deterministic variability, yet functional robustness of the resulting network. Approaches to morphogenetic engineering of functionally robust computational networks through self-assembly may benefit from an understanding of such principles from biological systems. The extraction of such principles is dependent on our ability to observe the self-assembly of neural networks at sufficient spatiotemporal resolution in order to aid data-driven computational modeling. Here, I present quantitative 4D microscopic video data and computational modeling of the self-assembly process of a neural network with more than a million synaptic connections in the fly visual system. Based on long-term imaging we have previously extracted a set of self-assembly rules and engineered a deterministic computational model that recapitulates the network’s self-organization at the cellular (autonomous agents) level. In a second step, we have now measured and modelled the underlying stochastic dynamics at subcellular levels. Our analyses indicate that stochastic dynamics of neuronal extensions are a prerequisite for flexible and robust self-assembly through algorithmic growth based on simple rules.