A comprehensive conceptual and computational dynamics framework for autonomous regeneration of form and function in biological organisms

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 anatomical and bioelectric homeostasis in an 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

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

Emergent Diversity in an Open-Ended Evolving Virtual Community

Understanding the dynamics of biodiversity has become an important line of research in theoretical ecology and, in particular, conservation biology. However, studying the evolution of ecological communities under traditional modeling approaches based on differential calculus requires species' characteristics to be predefined, which limits the generality of the results. An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. We study here the formation, evolution, and diversity dynamics of a community of virtual plants with a novel individual-centered model involving three different scales: the genetic, the developmental, and the physiological scales. It constitutes an original attempt at combining development, evolution, and population dynamics (based on multi-agent interactions) into one comprehensive, yet simple model. In this world, we observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants. Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions

Aportaciones y Aplicaciones de Disciplinas Bioinspiradas a la Creatividad Computacional

¿Puede una computadora presentar comportamientos creativos? Esta compleja cuestión ha despertado un creciente interés en las últimas décadas. Es un hecho evidente que las computadoras han superado la capacidad humana en múltiples dominios. Sin embargo, alcanzar la creatividad humana sigue suponiendo un reto para las computadoras, siendo considerada como un factor clave en el éxito intelectual de los humanos que los diferencia del resto de seres. Esto permite plantear la cuestión acerca de si los humanos poseen un cierto sentido especial, del cual surge la creatividad, que no puede ser transcrito a un algoritmo y por lo tanto, no puede ser implementado por una computadora. Como respuesta a esto, la creatividad computacional surge como un campo dentro de la inteligencia artificial que se encarga del estudio y desarrollo de sistemas hardware y software que sean capaces de exhibir un comportamiento creativo propio del ser humano. Por otra parte, la observación de la naturaleza ha sido una de las principales fuentes de inspiración para la propuesta de novedosas soluciones creativas en diferentes áreas y contextos. En este sentido, dentro de la inteligencia artificial, el paradigma bioinspirado de la computación evolutiva aborda la resolución de problemas mediante la evolución de poblaciones de individuos. La evolución natural representa un ejemplo extremo de proceso creativo ya que durante millones de años, la evolución de los seres vivos ha hecho posible la emergencia de un número inimaginable de diseños biológicos. Por este motivo e inspirados por la evolución natural, los algoritmos evolutivos, una de las técnicas que conforman la computación evolutiva, han sido empleados ampliamente en procesos creativos. Por definición, la creatividad requiere amplias dosis de innovación y diversidad. En el campo de la biología, recientes hipótesis apuntan a que el proceso de desarrollo, en el que una sola célula se transforma en un organismo complejo, es un mecanismo fundamental en el surgimiento de innovación y diversidad en los seres vivos. Por este motivo, el campo de la biología evolutiva del desarrollo (evo-devo) ha emergido para reclamar su incorporación como componente clave en la evolución de una gran diversidad de comportamientos y diseños estructurales innovadores. En el campo de la computación, la biología evolutiva del desarrollo ha inspirado dos disciplinas: el desarrollo artificial, que incorpora el proceso de desarrollo en los algoritmos evolutivos mediante codificación indirecta del esquema genotipo-fenotipo; y la ingeniería embriomórfica, que, imitando el proceso de desarrollo biológico, persigue el desarrollo de morfologías y comportamientos complejos artificiales mediante la agregación descentralizada y la auto-organización de una gran cantidad de pequeños agentes. Entrelazando la compleja cuestión planteada inicialmente sobre la capacidad creativa de las computadoras y la inspiración de la naturaleza como fuente de creatividad e innovación, este trabajo de tesis explora la aplicación de diferentes disciplinas bioinspiradas, concretamente los algoritmos evolutivos, el desarrollo artificial y la ingeniería embriomórfica, de forma individual o combinada para la generación de productos creativos. Para ello se presentan modelos computacionales que actúan de soporte a la creatividad humana, o que exhiben comportamiento creativo de forma independiente, y cuyas soluciones son aplicables en contextos tan diversos como la composición algorítmica, la medicina, la robótica y la animación por computador

Autonomous self-repair systems : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Regeneration is an important and wonderful phenomenon in nature and plays a key role in living organisms that are capable of recovery from trivial to serious injury to reclaim a fully functional state and pattern/anatomical homeostasis (equilibrium). Studying regeneration can help develop hypotheses for understanding regenerative mechanisms along with advancing synthetic biology for regenerative medicine and development of cancer and anti-ageing drugs. Further, it can contribute to nature-inspired computing for self-repair in other fields. However, despite decades of study, what possible mechanisms and algorithms are used in the regeneration process remain an open question. Therefore, the main goal of this thesis is to propose a comprehensive hypothetical conceptual framework with possible mechanisms and algorithms of biological regeneration that mimics the observed features of regeneration in living organisms and achieves body-wide immortality, similar to the planarian flatworm, about 20mm long and 3mm wide, living in both saltwater and freshwater. This is a problem of collective decision making by the cells in an organism to achieve the high-level goal of returning to normality of both anatomical and functional homeostasis. To fulfil this goal, the proposed framework contains three sub-frameworks corresponding to three main objectives of the thesis: self-regeneration or self-repair (anatomical homeostasis) of a simple in silico tissue and a whole organism consisting of these tissues based on simplified formats of cellular communication, and an extension to more realistic bioelectric communication for restoring both anatomical and bioelectric homeostasis. The first objective is to develop a simple tissue model that regenerates autonomously after damage. Accordingly, we present a computational framework for an autonomous self-repair system that allows for sensing, detecting and regenerating an artificial (in silico) circular tissue containing thousands of cells. This system consists of two sub-models: Global Sensing and Local Sensing that collaborate to sense and repair diverse damages. It is largely a neural system with a perceptron (binary) network performing tissue computations. The results showed that the system is robust and efficient in damage detection and accurate regeneration. The second objective is to extend the simple circular tissue model to other geometric shapes and assemble them into a small virtual organism that regenerates similar to the body-wide immortality of the planarian flatworm. Accordingly, we proposed a computational framework extending the tissue repair framework developed in Objective 1 to model whole organism regeneration that implemented algorithms and mechanisms to achieve accurate and complete regeneration in an (in silico) worm-like organism. The system consists of two levels: tissue and organism levels that integrate to recognise and recover from any damage, even extreme damage cases. The tissue level consists of three tissue repair models for head, body and tail. The organism level connects the tissues together to form the worm. The two levels form an integrated neural feedback control system with perceptron (binary) for tissue computing and linear neural networks for organism-level computing. Our simulation results showed that the framework is very robust in returning the system to the normal state after any small or large scale damage. The last objective is to extend the whole organism regeneration framework developed in Objective 2 by incorporating bioelectricity as the format of communication between cells to make the model better resemble living organisms and to restore not only anatomy but also basic functionality such as restoring body-wide bioelectric pattern needed for physiological functioning in living systems. We greatly extended the second framework by conceptualising and modelling mechanisms and algorithms that mimicked both the pattern and function restoration observed in living organisms and implemented it on the same artificial (in silico) organism developed in Objective 2 but with greater realism of the anatomical structure. This proposed framework consists of three levels that collaborate to fully regenerate the anatomical pattern and maintain bioelectric homeostasis in the in silico worm-like organism. These three levels represent tissue and organism models for regeneration and body-wide bioelectric model for restoring bioelectric homeostasis, respectively. They extend the previous neural feedback control system to integrate another (3rd) level, bioelectric homeostasis. Our simulations showed that the system maintains and restores bioelectric homeostasis accurately under random perturbations of bioelectric status under no damage conditions. It is also very robust and plastic in restoring the system to the normal anatomical pattern and bioelectric homeostasis after any type of damage. Our framework robustly achieves some observations of extreme regeneration of planaria like body-wide immortality. It could also be helpful in engineering for building self-repair robots, biobots and artificial self-repair systems

Spéciation guidée par l'environnement‎ : interactions sur des périodes évolutionnaires de communautés de plantes artificielles

Depuis des décades, les chercheurs en Vie Artificielle on créé une pléthore de créatures en utilisant de multiples schémas d’encodage, capacités motrices et aptitudes cognitives. Un motif récurrent, cependant, est que la focalisation est centrée sur les individus à évoluer, ne laissant que peu de place aux variations environnementales. Dans ce travail, nous argumentons que des contraintes abiotiques plus complexes pourraient diriger un processus évolutionnaire vers des régions de l’espace génétique plus robustes and diverses. Nous avons conçu un modèle morphologique complexe, basé sur les graphes orientés de K. Sims, qui repose sur le moteur physique Bullet pour la précision et utilise des contraintes à 6 Degrés de Liberté pour connecter les paires d’organes. Nous avons ainsi évolué un panel de plantes à l’aspect naturel qui devaient survivre malgré des niveaux de ressources variables induits par une source de lumière mobile et des motifs de pluies saisonnières. En plus de cette expérience, nous avons aussi obtenu une meilleure croissance verticale en ajoutant une contrainte biotique artificielle sous la forme de brins d’herbe statiques. La complexité de ce modèle, cependant, ne permettait pas la mise a l’échelle d’une évolution de populations et a donc été réduit dans l’expérience suivante, notamment en supprimant le moteur physique. Cela nous a amené à l’exploration de la co-évolution de populations composées d’une unique espèce et ayant la capacité de se reproduire de manière autonome grâce à notre Bail-Out Crossover (Croisement avec Désistement). Bien que les populations résultantes n’ont pas démontré un grand intérêt pour cette aptitude, elles ont néanmoins fourni d’importantes informations sur les mécanismes d’auto-reproduction. Ceux-ci ont été mis en action dans un second modèle inspiré des travaux de Bornhofen. Grâce à sa légèreté, cela nous a permis de traiter non seulement de plus grandes populations (de l’ordre de milliers d’individus) mais aussi de plus longues périodes évolutionnaires (100 années, approximativement 5000 générations). Notre première expérience avec ce modèle s’est concentrée sur la possibilité de reproduire des cas d’école de spéciation (allopatrique, parapatrique, péripatrique) sur cette plate-forme. Grâce à APOGet, une nouvelle procédure de regroupement pour l’extraction en parallèle d’espèces à partir d’un arbre généalogique, nous avons pu affirmer que le système était effectivement capable de spéciation spontanée. Cela nous a conduit à une dernière expérience dans laquelle l’environnement était contrôlé par de la Programmation Génétique Cartésienne (CGP), permettant ainsi une évolution automatique d’une population et des contraintes abiotiques auxquelles elle était confrontée. Par une variation du traditionnel algorithme 1 + λ nous avons obtenu 10 populations finales qui ont survécu à de brutales et imprévisibles variations environnementales. En les comparant à un groupe contrôle c pour lequel les contraintes ont été maintenues faibles et constantes, le groupe évolué e a montré des performances mitigées: dans les deux types de tests, une moitié de e surpassait c qui, à son tour, surpassait la moitié restante de e. Nous avons aussi trouvé une très forte corrélation entre les chutes catastrophiques de population et la performance des évolutions correspondantes. Il en résulte que l’évolution de population dans des environnements hostiles et dynamiques n’est pas une panacée bien que ces expériences en démontrent le potentiel et souligne le besoin d’études ultérieures plus approfondies.Artificial Life researchers have, for decades, created a plethora of creatures using numerous encoding schemes, motile capabilities and cognitive capacities. One recurring pattern, however, is that focus is solely put on the evolved individuals, with very limited environmental variations. In this work, we argue that more complex abiotic constraints could drive an evolutionary process towards more robust and diverse regions of the genetic space. We started with a complex morphogenetic model, based on K. Sims’ directed graphs, which relied on the Bullet physics engine for accuracy and used 6Degrees of Freedom constraints to connect pairs of organs. We evolved a panel of natural-looking plants which had to cope with varying resource levels thanks to a mobile light source and seasonal rain patterns. In addition to this experiment, we also obtained improved vertical growth by adding an artificialbiotic constraint in the form of static grass blades. However, the computational cost of this model precluded scaling to a population-level evolution and was reduced in the successive experiment, notably by removing the physical engine. This led to the exploration of co-evolution on single-species populations which, thanks to our Bail-Out Crossover (BOC) algorithm, were able to self-reproduce. The resulting populations provided valuable insight into the mechanisms of self-sustainability. These were put to action in an even more straightforward morphogenetic model inspired by the work of Bornhofen. Due to its light weightness, this allowed for both larger populations (up to thousands of individuals) and longer evolutionary periods (100 years, roughly 5K generations). Our first experiment on this model tested whether text-book cases of speciation could be reproduced in our framework. Such positive results were observed thanks to the species monitoring capacities of APOGeT, a novel clustering procedure we designed for online extraction of species from a genealogic tree. This drove us to a final experiment in which the environment was controlled through Cartesian Genetic Programming thus allowing the automated evolution of both the population and abiotic constraints it is subjected to. Through a variation of the traditional1 + λ algorithm, we obtained 10 populations (evolved group e) which had endured in harsh and unpredictable environments. These were confronted to a control group c, in which the constraints were kept mild and constant, on two types of colonization evaluation. Results showed that the evolved group was heterogeneous with half of e consistently outperforming members of c and the other half exhibiting worse performances than the baseline. We also found a very strong positive correlation between catastrophic drops in population level during evolution with the robustness of their final representatives. From this work, two conclusions can be drawn. First, though the need to fight on both the abiotic and biotic fronts can lead to worse performances, more robust individuals can be found in reasonable time-frames. Second, the automated co-evolution of populations and their environments is essential in exploring counter-intuitive, yet fundamental, dynamics both in biological and artificial life