A cellular platform for the development of synthetic living machines

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms

The rich variety of biological forms and behaviours results from one evolutionary history on Earth, via frozen accidents and selection in specific environments. This ubiquitous baggage in natural, familiar model species obscures the plasticity and swarm intelligence of cellular collectives. Significant gaps exist in our understanding of the origin of anatomical novelty, of the relationship between genome and form, and of strategies for control of large-scale structure and function in regenerative medicine and bioengineering. Analysis of living forms that have never existed before is necessary to reveal deep design principles of life as it can be. We briefly review existing examples of chimaeras, cyborgs, hybrots and other beings along the spectrum containing evolved and designed systems. To drive experimental progress in multicellular synthetic morphology, we propose teleonomic (goal-seeking, problem-solving) behaviour in diverse problem spaces as a powerful invariant across possible beings regardless of composition or origin. Cybernetic perspectives on chimaeric morphogenesis erase artificial distinctions established by past limitations of technology and imagination. We suggest that a multi-scale competency architecture facilitates evolution of robust problem-solving, living machines. Creation and analysis of novel living forms will be an essential testbed for the emerging field of diverse intelligence, with numerous implications across regenerative medicine, robotics and ethics.

From the origin of life to pandemics: emergent phenomena in complex systems

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems-from physical to biological and social-and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems' emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments

One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.

Neurons as hierarchies of quantum reference frames

Conceptual and mathematical models of neurons have lagged behind empirical understanding for decades. Here we extend previous work in modeling biological systems with fully scale-independent quantum information-theoretic tools to develop a uniform, scalable representation of synapses, dendritic and axonal processes, neurons, and local networks of neurons. In this representation, hierarchies of quantum reference frames act as hierarchical active-inference systems. The resulting model enables specific predictions of correlations between synaptic activity, dendritic remodeling, and trophic reward. We summarize how the model may be generalized to nonneural cells and tissues in developmental and regenerative contexts.

Relationships Organize Information in Mind and Nature: Empirical Findings of Action–Reaction Relationships (R) in Cognitive and Material Complexity

Diverse phenomena such as feedback, interconnectedness, causality, network dynamics, and complexity are all born from Relationships. They are fundamentally important, as they are transdisciplinary and synonymous with connections, links, edges, and interconnections. The foundation of systems thinking and systems themselves consists of four universals, one of which is action–reaction Relationships. They are also foundational to the consilience of knowledge. This publication gives a formal description of and predictions of action–reaction Relationships (R) or “R-rule”. There are seven original empirical studies presented in this paper. For these seven studies, experiments for the subjects were created on software (unless otherwise noted). The experiments had the subjects complete a task and/or answer a question. The samples are generalizable to a normal distribution of the US population and they vary for each study (ranging from N = 407 to N = 34,398). With high statistical significance the studies support the predictions made by DSRP Theory regarding action–reaction Relationships including its universality as an observable phenomenon in both nature (ontological complexity) and mind (cognitive complexity); mutual dependencies on other universals (i.e., Distinctions, Systems, and Perspectives); role in structural predictions; internal structures and dynamics; efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, parallelism (between cognitive and ontological complexity), universality, and efficacy of action–reaction Relationships (R).

Biology, Buddhism, and AI: Care as the Driver of Intelligence

Intelligence is a central feature of human beings' primary and interpersonal experience. Understanding how intelligence originated and scaled during evolution is a key challenge for modern biology. Some of the most important approaches to understanding intelligence are the ongoing efforts to build new intelligences in computer science (AI) and bioengineering. However, progress has been stymied by a lack of multidisciplinary consensus on what is central about intelligence regardless of the details of its material composition or origin (evolved vs. engineered). We show that Buddhist concepts offer a unique perspective and facilitate a consilience of biology, cognitive science, and computer science toward understanding intelligence in truly diverse embodiments. In coming decades, chimeric and bioengineering technologies will produce a wide variety of novel beings that look nothing like familiar natural life forms; how shall we gauge their moral responsibility and our own moral obligations toward them, without the familiar touchstones of standard evolved forms as comparison? Such decisions cannot be based on what the agent is made of or how much design vs. natural evolution was involved in their origin. We propose that the scope of our potential relationship with, and so also our moral duty toward, any being can be considered in the light of Care-a robust, practical, and dynamic lynchpin that formalizes the concepts of goal-directedness, stress, and the scaling of intelligence; it provides a rubric that, unlike other current concepts, is likely to not only survive but thrive in the coming advances of AI and bioengineering. We review relevant concepts in basal cognition and Buddhist thought, focusing on the size of an agent's goal space (its cognitive light cone) as an invariant that tightly links intelligence and compassion. Implications range across interpersonal psychology, regenerative medicine, and machine learning. The Bodhisattva's vow ("for the sake of all sentient life, I shall achieve awakening") is a practical design principle for advancing intelligence in our novel creations and in ourselves.

Perspectives Organize Information in Mind and Nature: Empirical Findings of Point-View Perspective (P) in Cognitive and Material Complexity

The importance of perspective-taking crosses disciplines and is foundational to diverse phenomena such as point-of-view, scale, mindset, theory of mind, opinion, belief, empathy, compassion, analysis, and problem solving, etc. This publication gives predictions for and a formal description of point-view Perspectives (P) or the “P-rule”. This makes the P-rule foundational to systems, systems thinking and the consilience of knowledge. It is one of four universals of the organization of information as a whole. This paper presents nine empirical studies in which subjects were asked to complete a task and/or answer a question. The samples vary for each study (ranging from N = 407 to N = 34,398) and are generalizable to a normal distribution of the US population. As was evident in Cabrera, “These studies support—with high statistical significance—the predictions made by DSRP Theory (Distinctions, Systems Relationships, Perspectives) point-view Perspectives including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (material complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., Distinctions, Systems, and Relationships); role in structural predictions; and, efficacy as a metacognitive skill”. These data suggest that point-view Perspectives (P) observably and empirically exist, and that universality, efficacy, and parallelism (between cognitive and material complexity) exist as well. The impact of this paper is that it provides empirical evidence for the phenomena of point-view perspective taking (“P-rule”) as a universal pattern/structure of systems thinking, a field in which scholarly debate is often based on invalidated opinioned frameworks; this sets the stage for theory building in the field.

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell-Cell Adhesion

Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell-cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell-cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell-cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell-cell signaling networks and changes in cell-cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the "training set" in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a "testing set" of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell-cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

Systems Organize Information in Mind and Nature: Empirical Findings of Part-Whole Systems (S) in Cognitive and Material Complexity

Part-whole Systems (S) structure is foundational to a diverse array of phenomena such as belonging and containment, networks, statistics, reductionism, holism, etc. and is extremely similar if not synonymous with sets, sorts, groups, combinations and combinatorics, clusters, etc. In Cabrera (1998), part-whole Systems (S) or “S-rule” is established as one of four universals for the organization of information and thus is foundational to systems and systems thinking as well as the consilience of knowledge. In this paper, seven empirical studies are presented in which (unless otherwise noted) subjects completed a task. Ranging from n = 407 to n = 34,398, the sample sizes vary for each study but are generalizeable to a normal distribution of the US population. With high statistical significance, the results of these studies support the predictions made by DSRP Theory regarding part-whole Systems (a.k.a., “S-rule”) including: the universality of S-rule as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); the internal structures and dynamics of S-rule; S-rule’s mutual dependencies on other universals of DSRP (Distinctions, Systems, Relationships, and Perspectives (i.e., Distinctions, Relationships, and Perspectives); the role S-rule plays in making structural predictions; and, S-rule’s efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of part-whole Systems (S).

Some Characteristics and Arguments in Favor of a Science of Machine Behavior Analysis

Researchers and practitioners recognize four domains of behavior analysis: radical behaviorism, the experimental analysis of behavior, applied behavior analysis, and the practice of behavior analysis. Given the omnipresence of technology in every sphere of our lives, the purpose of this conceptual article is to describe and argue in favor of a fifth domain: machine behavior analysis. Machine behavior analysis is a science that examines how machines interact with and produce relevant changes in their external environment by relying on replicability, behavioral terminology, and the philosophical assumptions of behavior analysis (e.g., selectionism, determinism, parsimony) to study artificial behavior. Arguments in favor of a science of machine behavior include the omnipresence and impact of machines on human behavior, the inability of engineering alone to explain and control machine behavior, and the need to organize a verbal community of scientists around this common issue. Regardless of whether behavior analysts agree or disagree with this proposal, I argue that the field needs a debate on the topic. As such, the current article aims to encourage and contribute to this debate.

Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality

The truly surprising thing about evolution is not how it makes individuals better adapted to their environment, but how it makes individuals. All individuals are made of parts that used to be individuals themselves, e.g., multicellular organisms from unicellular organisms. In such evolutionary transitions in individuality, the organised structure of relationships between component parts causes them to work together, creating a new organismic entity and a new evolutionary unit on which selection can act. However, the principles of these transitions remain poorly understood. In particular, the process of transition must be explained by “bottom-up” selection, i.e., on the existing lower-level evolutionary units, without presupposing the higher-level evolutionary unit we are trying to explain. In this hypothesis and theory manuscript we address the conditions for evolutionary transitions in individuality by exploiting adaptive principles already known in learning systems. Connectionist learning models, well-studied in neural networks, demonstrate how networks of organised functional relationships between components, sufficient to exhibit information integration and collective action, can be produced via fully-distributed and unsupervised learning principles, i.e., without centralised control or an external teacher. Evolutionary connectionism translates these distributed learning principles into the domain of natural selection, and suggests how relationships among evolutionary units could become adaptively organised by selection from below without presupposing genetic relatedness or selection on collectives. In this manuscript, we address how connectionist models with a particular interaction structure might explain transitions in individuality. We explore the relationship between the interaction structures necessary for (a) evolutionary individuality (where the evolution of the whole is a non-decomposable function of the evolution of the parts), (b) organismic individuality (where the development and behaviour of the whole is a non-decomposable function of the behaviour of component parts) and (c) non-linearly separable functions, familiar in connectionist models (where the output of the network is a non-decomposable function of the inputs). Specifically, we hypothesise that the conditions necessary to evolve a new level of individuality are described by the conditions necessary to learn non-decomposable functions of this type (or deep model induction) familiar in connectionist models of cognition and learning.

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

Distinctions Organize Information in Mind and Nature: Empirical Findings of Identity–Other Distinctions (D) in Cognitive and Material Complexity

The transdisciplinary importance of distinctions is well-established as foundational to such diverse phenomena as recognition, identification, individual and social identity, marginalization, externalities, boundaries, concept formation, etc., and synonymous general ideas, such as thingness, concepts, nodes, objects, etc. Cabrera provides a formal description of and predictions for identity–other distinctions (D) or “D-rule” as one of four universals for the organization of information that is foundational to systems and systems thinking, as well as the consilience of knowledge. This paper presents seven empirical studies in which (unless otherwise noted) software was used to create an experiment for subjects to complete a task and/or answer a question. The samples varied for each study (ranging from N = 407 to N = 34,398) and were generalizable to a normal distribution of the US population. These studies support—with high statistical significance—the predictions made by DSRP theory regarding identity–other distinctions including its: universality as an observable phenomenon in both mind (cognitive complexity) and nature (ontological complexity) (i.e., parallelism); internal structures and dynamics; mutual dependencies on other universals (i.e., relationships, systems, and perspectives); role in structural predictions; and efficacy as a metacognitive skill. In conclusion, these data suggest the observable and empirical existence, universality, efficacy, and parallelism (between cognitive and ontological complexity) of identity–other distinctions (D).

The “Fish Tank” Experiments: Metacognitive Awareness of Distinctions, Systems, Relationships, and Perspectives (DSRP) Significantly Increases Cognitive Complexity

In the field of systems thinking, there are far too many opinioned frameworks and far too few empirical studies. This could be described as a “gap” in the research but it is more like a dearth in the research. More theory and empirical validation of theory are needed if the field and the phenomenon of systems thinking holds promise and not just popularity. This validation comes in the form of both basic (existential) and applied (efficacy) research studies. This article presents efficacy data for a set of empirical studies of DSRP Theory. According to Cabrera, Cabrera, and Midgley, DSRP Theory has equal or more empirical evidence supporting it than any existing systems theories (including frameworks, which are not theories). Four separate studies show highly statistically relevant findings for the effect of a short (less than one minute) treatment of D, S, R, and P. Subjects’ cognitive complexity and the systemic nature of their thinking increased in all four studies. These findings indicate that even a short treatment in DSRP is effective in increasing systems thinking skills. Based on these results, a longer, more in-depth treatment—such as a one hour or semester long training, such is the norm—would therefore likely garner transformative results and efficacy.

DSRP Theory: A Primer

DSRP Theory is now over 25 years old with more empirical evidence supporting it than any other systems thinking framework. Yet, it is often misunderstood and described in ways that are inaccurate. DSRP Theory describes four patterns and their underlying elements—identity (i) and other (o) for Distinctions (D), part (p) and whole (w) for Systems (S), action (a) and reaction (r) for Relationships (R), and point (ρ) and view (v) for Perspectives (P)—that are universal in both cognitive complexity (mind) and material complexity (nature). DSRP Theory provides a basis for systems thinking or cognitive complexity as well as material complexity (systems science). This paper, as a relatively short primer on the theory, provides clarity to those wanting to understand DSRP and its implications.

New Biomed-Tech Advances Poised to Change the Future

Biomedical and health technology is progressing at breakneck speed. From specialty pharmacies to general discount shops, store shelves are packed with a vast assortment of wearable medical devices that measure glucose levels, heart rate, and other health metrics; and over-the-counter test kits are helping to check for a wide array of infections. At the same time, electronic health records and other data-sharing platforms have smoothed the mass shift from in-person to virtual office visits over the past two years, and new imaging technologies are allowing earlier disease detection so treatments can begin sooner when they are more effective.

Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation

What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal 'genetic' networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions-a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel "causal network" analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns-an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

Tissue Engineering with Mechanically Induced Solid-Fluid Transitions

Epithelia are contiguous sheets of cells that stabilize the shape of internal organs and support their structure by covering their surfaces. They acquire diverse morphological forms appropriate for their specific functions during embryonic development, such as the kidney tubules and the complex branching structures found in the lung. The maintenance of epithelial morphogenesis and homeostasis is controlled by their remarkable mechanics-epithelia can become elastic, plastic, and viscous by actively remodeling cell-cell junctions and modulating the distribution of local stresses. Microfabrication, finite element modelling, light-sheet microscopy, and robotic micromanipulation are used to show that collagen gels covered with an epithelial skin serve as shape-programmable soft matter. The process involves solid to fluid transitions induced by mechanical perturbations, generates spatially distributed surface stresses at tissue interfaces, and is amenable to both additive and subtractive manufacturing techniques. The robustness and versatility of this strategy for engineering designer tissues is demonstrated by directing the morphogenesis of a variety of molded, carved, and assembled forms from the base material. The results provide insight into the active mechanical properties of the epithelia and establish methods for engineering tissues with sustainable architectures.

Behaviorist approaches to investigating memory and learning: A primer for synthetic biology and bioengineering

The fields of developmental biology, biomedicine, and artificial life are being revolutionized by advances in synthetic morphology. The next phase of synthetic biology and bioengineering is resulting in the construction of novel organisms (biobots), which exhibit not only morphogenesis and physiology but functional behavior. It is now essential to begin to characterize the behavioral capacity of novel living constructs in terms of their ability to make decisions, form memories, learn from experience, and anticipate future stimuli. These synthetic organisms are highly diverse, and often do not resemble familiar model systems used in behavioral science. Thus, they represent an important context in which to begin to unify and standardize vocabulary and techniques across developmental biology, behavioral ecology, and neuroscience. To facilitate the study of behavior in novel living systems, we present a primer on techniques from the behaviorist tradition that can be used to probe the functions of any organism – natural, chimeric, or synthetic – regardless of the details of their construction or origin. These techniques provide a rich toolkit for advancing the fields of synthetic bioengineering, evolutionary developmental biology, basal cognition, exobiology, and robotics.

Kinematic self-replication in reconfigurable organisms

All living systems perpetuate themselves via growth in or on the body, followed by splitting, budding, or birth. We find that synthetic multicellular assemblies can also replicate kinematically by moving and compressing dissociated cells in their environment into functional self-copies. This form of perpetuation, previously unseen in any organism, arises spontaneously over days rather than evolving over millennia. We also show how artificial intelligence methods can design assemblies that postpone loss of replicative ability and perform useful work as a side effect of replication. This suggests other unique and useful phenotypes can be rapidly reached from wild-type organisms without selection or genetic engineering, thereby broadening our understanding of the conditions under which replication arises, phenotypic plasticity, and how useful replicative machines may be realized.

Synthetic living machines: A new window on life

Increased control of biological growth and form is an essential gateway to transformative medical advances. Repairing of birth defects, restoring lost or damaged organs, normalizing tumors, all depend on understanding how cells cooperate to make specific, functional large-scale structures. Despite advances in molecular genetics, significant gaps remain in our understanding of the meso-scale rules of morphogenesis. An engineering approach to this problem is the creation of novel synthetic living forms, greatly extending available model systems beyond evolved plant and animal lineages. Here, we review recent advances in the emerging field of synthetic morphogenesis, the bioengineering of novel multicellular living bodies. Emphasizing emergent self-organization, tissue-level guided self-assembly, and active functionality, this work is the essential next generation of synthetic biology. Aside from useful living machines for specific functions, the rational design and analysis of new, coherent anatomies will greatly increase our understanding of foundational questions in evolutionary developmental and cell biology.

A cellular platform for the development of synthetic living machines

Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog (Xenopus laevis) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical, and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage, and show emergent group behaviors. We constructed a computational model to predict useful collective behaviors that can be elicited from a xenobot swarm. In addition, we provide proof of principle for a writable molecular memory using a photoconvertible protein that can record exposure to a specific wavelength of light. Together, these results introduce a platform that can be used to study many aspects of self-assembly, swarm behavior, and synthetic bioengineering, as well as provide versatile, soft-body living machines for numerous practical applications in biomedicine and the environment.