Bioelectrical Mechanisms for Programming Growth and Form: Taming Physiological Networks for Soft Body Robotics

Biophysical control of plasticity and patterning in regeneration and cancer

Cells and tissues display a remarkable range of plasticity and tissue-patterning activities that are emergent of complex signaling dynamics within their microenvironments. These properties, which when operating normally guide embryogenesis and regeneration, become highly disordered in diseases such as cancer. While morphogens and other molecular factors help determine the shapes of tissues and their patterned cellular organization, the parallel contributions of biophysical control mechanisms must be considered to accurately predict and model important processes such as growth, maturation, injury, repair, and senescence. We now know that mechanical, optical, electric, and electromagnetic signals are integral to cellular plasticity and tissue patterning. Because biophysical modalities underly interactions between cells and their extracellular matrices, including cell cycle, metabolism, migration, and differentiation, their applications as tuning dials for regenerative and anti-cancer therapies are being rapidly exploited. Despite this, the importance of cellular communication through biophysical signaling remains disproportionately underrepresented in the literature. Here, we provide a review of biophysical signaling modalities and known mechanisms that initiate, modulate, or inhibit plasticity and tissue patterning in models of regeneration and cancer. We also discuss current approaches in biomedical engineering that harness biophysical control mechanisms to model, characterize, diagnose, and treat disease states.

Future regenerative medicine developments and their therapeutic applications

Although the currently available pharmacological assays can cure most pathological disorders, they have limited therapeutic value in relieving certain disorders like myocardial infarct, peripheral vascular disease, amputated limbs, or organ failure (e.g. renal failure). Pilot studies to overcome such problems using regenerative medicine (RM) delivered promising data. Comprehensive investigations of RM in zebrafish or reptilians are necessary for better understanding. However, the precise mechanisms remain poorly understood despite the tremendous amount of data obtained using the zebrafish model investigating the exact mechanisms behind their regenerative capability. Indeed, understanding such mechanisms and their application to humans can save millions of lives from dying due to potentially life-threatening events. Recent studies have launched a revolution in replacing damaged human organs via different approaches in the last few decades. The newly established branch of medicine (known as Regenerative Medicine aims to enhance natural repair mechanisms. This can be done through the application of several advanced broad-spectrum technologies such as organ transplantation, tissue engineering, and application of Scaffolds technology (support vascularization using an extracellular matrix), stem cell therapy, miRNA treatment, development of 3D mini-organs (organoids), and the construction of artificial tissues using nanomedicine and 3D bio-printers. Moreover, in the next few decades, revolutionary approaches in regenerative medicine will be applied based on artificial intelligence and wireless data exchange, soft intelligence biomaterials, nanorobotics, and even living robotics capable of self-repair. The present work presents a comprehensive overview that summarizes the new and future advances in the field of RM.

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.

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.

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for artificial systems that must robustly operate in the natural world. Recently, researchers have begun to leverage insights from regenerating and metamorphosing organisms, designing robots capable of editing their own structure to more efficiently perform tasks under changing demands and creating new algorithms to control these changing anatomies. Here, an overview of the literature related to robots that change shape to enhance and expand their functionality is presented. Related grand challenges, including shape sensing, finding, and changing, which rely on innovations in multifunctional materials, distributed actuation and sensing, and somatic control to enable next-generation shape changing robots are also discussed.

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.

From non-excitable single-cell to multicellular bioelectrical states supported by ion channels and gap junction proteins: Electrical potentials as distributed controllers

Endogenous bioelectric patterns within tissues are an important driver of morphogenesis and a tractable component of a number of disease states. Developing system-level understanding of the dynamics by which non-neural bioelectric circuits regulate complex downstream cascades is a key step towards both, an evolutionary understanding of ion channel genes, and novel strategies in regenerative medicine. An important capability gap is deriving rational modulation strategies targeting individual cells' bioelectric states to achieve global (tissue- or organ-level) outcomes. Here, we develop an ion channel-based model that describes multicellular states on the basis of spatio-temporal patterns of electrical potentials in aggregates of non-excitable cells. The model is of biological interest because modern techniques allow to associate bioelectrical signals with specific ion channel proteins in the cell membrane that are central to embryogenesis, regeneration, and tumorigenesis. As a complementary approach to the usual biochemical description, we have studied four biophysical questions: (i) how can single-cell bioelectrical states be established; (ii) how can a change in the cell potential caused by a transient perturbation of the cell state be maintained after the stimulus is gone (bioelectrical memory); (iii) how can a single-cell contribute to the control of multicellular ensembles based on the spatio-temporal pattern of electrical potentials; and (iv) how can oscillatory patterns arise from the single-cell bioelectrical dynamics. Experimentally, endogenous bioelectric gradients have emerged as instructive agents for morphogenetic processes. In this context, the simulations can guide new procedures that may allow a distributed control of the multicellular ensemble.

Soft Robotics in Medical Applications

Soft robotics are robotic systems made of materials that are similar in softness to human soft tissues. Recent medical soft robot designs, including rehabilitation, surgical, and diagnostic soft robots, are categorized by application and reviewed for functionality. Each design is analyzed for engineering characteristics and clinical significance. Current technical challenges in soft robotics fabrication, sensor integration, and control are discussed. Future directions including portable and robust actuation power sources, clinical adoptability, and clinical regulatory issues are summarized.

Perspective: The promise of multi-cellular engineered living systems

Recent technological breakthroughs in our ability to derive and differentiate induced pluripotent stem cells, organoid biology, organ-on-chip assays, and 3-D bioprinting have all contributed to a heightened interest in the design, assembly, and manufacture of living systems with a broad range of potential uses. This white paper summarizes the state of the emerging field of "multi-cellular engineered living systems," which are composed of interacting cell populations. Recent accomplishments are described, focusing on current and potential applications, as well as barriers to future advances, and the outlook for longer term benefits and potential ethical issues that need to be considered.

Effective Differentiation of Induced Pluripotent Stem Cells Into Dental Cells

BACKGROUND

A biotooth is defined as a complete living tooth, made in laboratory cultures from a spontaneous interplay between epithelial and mesenchymal cell-based frontal systems. A good solution to these problems is to use induced pluripotent stem cells (iPSCs). However, no one has yet formulated culture conditions that effectively differentiate iPSCs into dental epithelial and dental mesenchymal cells phenotypes analogous to those present in tooth development.

RESULTS

Here, we tried to induce differentiation methods for dental epithelial cells (DEC) and dental mesenchymal cells from iPSCs. For the DEC differentiation, the conditional media of SF2 DEC was adjusted to embryoid body. Moreover, we now report on a new cultivation protocol, supported by transwell membrane cell culture that make it possible to differentiate iPSCs into dental epithelial and mesenchymal cells with abilities to initiate the first stages in de novo tooth formation.

CONCLUSIONS

Implementation of technical modifications to the protocol that maximize the number and rate of iPSC differentiation, into mesenchymal and epithelial cell layers, will be the next step toward growing an anatomically accurate biomimetic tooth organ. Developmental Dynamics 248:129-139, 2019. © 2018 Wiley Periodicals, Inc.

The bioelectric code: An ancient computational medium for dynamic control of growth and form

What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering.

From cytoskeletal dynamics to organ asymmetry: a nonlinear, regulative pathway underlies left-right patterning

Consistent left-right (LR) asymmetry is a fundamental aspect of the bodyplan across phyla, and errors of laterality form an important class of human birth defects. Its molecular underpinning was first discovered as a sequential pathway of left- and right-sided gene expression that controlled positioning of the heart and visceral organs. Recent data have revised this picture in two important ways. First, the physical origin of chirality has been identified; cytoskeletal dynamics underlie the asymmetry of single-cell behaviour and patterning of the LR axis. Second, the pathway is not linear: early disruptions that alter the normal sidedness of upstream asymmetric genes do not necessarily induce defects in the laterality of the downstream genes or in organ situs Thus, the LR pathway is a unique example of two fascinating aspects of biology: the interplay of physics and genetics in establishing large-scale anatomy, and regulative (shape-homeostatic) pathways that correct molecular and anatomical errors over time. Here, we review aspects of asymmetry from its intracellular, cytoplasmic origins to the recently uncovered ability of the LR control circuitry to achieve correct gene expression and morphology despite reversals of key 'determinant' genes. We provide novel functional data, in Xenopus laevis, on conserved elements of the cytoskeleton that drive asymmetry, and comparatively analyse it together with previously published results in the field. Our new observations and meta-analysis demonstrate that despite aberrant expression of upstream regulatory genes, embryos can progressively normalize transcriptional cascades and anatomical outcomes. LR patterning can thus serve as a paradigm of how subcellular physics and gene expression cooperate to achieve developmental robustness of a body axis.This article is part of the themed issue 'Provocative questions in left-right asymmetry'.

Top-down models in biology: explanation and control of complex living systems above the molecular level

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering.

Mechanisms of urodele limb regeneration

This review explores the historical and current state of our knowledge about urodele limb regeneration. Topics discussed are (1) blastema formation by the proteolytic histolysis of limb tissues to release resident stem cells and mononucleate cells that undergo dedifferentiation, cell cycle entry and accumulation under the apical epidermal cap. (2) The origin, phenotypic memory, and positional memory of blastema cells. (3) The role played by macrophages in the early events of regeneration. (4) The role of neural and AEC factors and interaction between blastema cells in mitosis and distalization. (5) Models of pattern formation based on the results of axial reversal experiments, experiments on the regeneration of half and double half limbs, and experiments using retinoic acid to alter positional identity of blastema cells. (6) Possible mechanisms of distalization during normal and intercalary regeneration. (7) Is pattern formation is a self-organizing property of the blastema or dictated by chemical signals from adjacent tissues? (8) What is the future for regenerating a human limb?

Planarian regeneration in space: Persistent anatomical, behavioral, and bacteriological changes induced by space travel

Regeneration is regulated not only by chemical signals but also by physical processes, such as bioelectric gradients. How these may change in the absence of the normal gravitational and geomagnetic fields is largely unknown. Planarian flatworms were moved to the International Space Station for 5 weeks, immediately after removing their heads and tails. A control group in spring water remained on Earth. No manipulation of the planaria occurred while they were in orbit, and space‐exposed worms were returned to our laboratory for analysis. One animal out of 15 regenerated into a double‐headed phenotype—normally an extremely rare event. Remarkably, amputating this double‐headed worm again, in plain water, resulted again in the double‐headed phenotype. Moreover, even when tested 20 months after return to Earth, the space‐exposed worms displayed significant quantitative differences in behavior and microbiome composition. These observations may have implications for human and animal space travelers, but could also elucidate how microgravity and hypomagnetic environments could be used to trigger desired morphological, neurological, physiological, and bacteriomic changes for various regenerative and bioengineering applications.

Bioelectric regulation of innate immune system function in regenerating and intact Xenopus laevis

Two key inputs that regulate regeneration are the function of the immune system, and spatial gradients of transmembrane potential (Vmem). Endogenous bioelectric signaling in somatic tissues during regenerative patterning is beginning to be understood, but its role in the context of immune response has never been investigated. Here, we show that Vmem levels modulate innate immunity activity in Xenopus laevis embryos. We developed an assay in which X. laevis embryos are infected with a uropathogenic microorganism, in the presence or absence of reagents that modify Vmem, prior to the ontogenesis of the adaptive immune system. General depolarization of the organism's Vmem by pharmacological or molecular genetic (ion channel misexpression) methods increased resistance to infection, while hyperpolarization made the embryos more susceptible to death by infection. Hyperpolarized specimens harbored a higher load of infectious microorganisms when compared to controls. We identified two mechanisms by which Vmem mediates immune function: serotonergic signaling involving melanocytes and an increase in the number of primitive myeloid cells. Bioinformatics analysis of genes whose transcription is altered by depolarization revealed a number of immune system targets consistent with mammalian data. Remarkably, amputation of the tail bud potentiates systemic resistance to infection by increasing the number of peripheral myeloid cells, revealing an interplay of regenerative response, innate immunity, and bioelectric regulation. Our study identifies bioelectricity as a new mechanism by which innate immune response can be regulated in the context of infection or regeneration. Vmem modulation using drugs already approved for human use could be exploited to improve resistance to infections in clinical settings.

Long-Term, Stochastic Editing of Regenerative Anatomy via Targeting Endogenous Bioelectric Gradients

We show that regenerating planarians' normal anterior-posterior pattern can be permanently rewritten by a brief perturbation of endogenous bioelectrical networks. Temporary modulation of regenerative bioelectric dynamics in amputated trunk fragments of planaria stochastically results in a constant ratio of regenerates with two heads to regenerates with normal morphology. Remarkably, this is shown to be due not to partial penetrance of treatment, but a profound yet hidden alteration to the animals' patterning circuitry. Subsequent amputations of the morphologically normal regenerates in water result in the same ratio of double-headed to normal morphology, revealing a cryptic phenotype that is not apparent unless the animals are cut. These animals do not differ from wild-type worms in histology, expression of key polarity genes, or neoblast distribution. Instead, the altered regenerative bodyplan is stored in seemingly normal planaria via global patterns of cellular resting potential. This gradient is functionally instructive, and represents a multistable, epigenetic anatomical switch: experimental reversals of bioelectric state reset subsequent regenerative morphology back to wild-type. Hence, bioelectric properties can stably override genome-default target morphology, and provide a tractable control point for investigating cryptic phenotypes and the stochasticity of large-scale epigenetic controls.

The interplay between genetic and bioelectrical signaling permits a spatial regionalisation of membrane potentials in model multicellular ensembles

The single cell-centred approach emphasises ion channels as specific proteins that determine individual properties, disregarding their contribution to multicellular outcomes. We simulate the interplay between genetic and bioelectrical signals in non-excitable cells from the local single-cell level to the long range multicellular ensemble. The single-cell genetic regulation is based on mean-field kinetic equations involving the mRNA and protein concentrations. The transcription rate factor is assumed to depend on the absolute value of the cell potential, which is dictated by the voltage-gated cell ion channels and the intercellular gap junctions. The interplay between genetic and electrical signals may allow translating single-cell states into multicellular states which provide spatio-temporal information. The model results have clear implications for biological processes: (i) bioelectric signals can override slightly different genetic pre-patterns; (ii) ensembles of cells initially at the same potential can undergo an electrical regionalisation because of persistent genetic differences between adjacent spatial regions; and (iii) shifts in the normal cell electrical balance could trigger significant changes in the genetic regulation.

Physiological inputs regulate species-specific anatomy during embryogenesis and regeneration

A key problem in evolutionary developmental biology is identifying the sources of instructive information that determine species-specific anatomical pattern. Understanding the inputs to large-scale morphology is also crucial for efforts to manipulate pattern formation in regenerative medicine and synthetic bioengineering. Recent studies have revealed a physiological system of communication among cells that regulates pattern during embryogenesis and regeneration in vertebrate and invertebrate models. Somatic tissues form networks using the same ion channels, electrical synapses, and neurotransmitter mechanisms exploited by the brain for information-processing. Experimental manipulation of these circuits was recently shown to override genome default patterning outcomes, resulting in head shapes resembling those of other species in planaria and Xenopus. The ability to drastically alter macroscopic anatomy to that of other extant species, despite a wild-type genomic sequence, suggests exciting new approaches to the understanding and control of patterning. Here, we review these results and discuss hypotheses regarding non-genomic systems of instructive information that determine biological growth and form.

Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine

Bioelectric cell properties have been revealed as powerful targets for modulating stem cell function, regenerative response, developmental patterning, and tumor reprograming. Spatio-temporal distributions of endogenous resting potential, ion flows, and electric fields are influenced not only by the genome and external signals but also by their own intrinsic dynamics. Ion channels and electrical synapses (gap junctions) both determine, and are themselves gated by, cellular resting potential. Thus, the origin and progression of bioelectric patterns in multicellular tissues is complex, which hampers the rational control of voltage distributions for biomedical interventions. To improve understanding of these dynamics and facilitate the development of bioelectric pattern control strategies, we developed the BioElectric Tissue Simulation Engine (BETSE), a finite volume method multiphysics simulator, which predicts bioelectric patterns and their spatio-temporal dynamics by modeling ion channel and gap junction activity and tracking changes to the fundamental property of ion concentration. We validate performance of the simulator by matching experimentally obtained data on membrane permeability, ion concentration and resting potential to simulated values, and by demonstrating the expected outcomes for a range of well-known cases, such as predicting the correct transmembrane voltage changes for perturbation of single cell membrane states and environmental ion concentrations, in addition to the development of realistic transepithelial potentials and bioelectric wounding signals. In silico experiments reveal factors influencing transmembrane potential are significantly different in gap junction-networked cell clusters with tight junctions, and identify non-linear feedback mechanisms capable of generating strong, emergent, cluster-wide resting potential gradients. The BETSE platform will enable a deep understanding of local and long-range bioelectrical dynamics in tissues, and assist the development of specific interventions to achieve greater control of pattern during morphogenesis and remodeling.

On Having No Head: Cognition throughout Biological Systems

The central nervous system (CNS) underlies memory, perception, decision-making, and behavior in numerous organisms. However, neural networks have no monopoly on the signaling functions that implement these remarkable algorithms. It is often forgotten that neurons optimized cellular signaling modes that existed long before the CNS appeared during evolution, and were used by somatic cellular networks to orchestrate physiology, embryonic development, and behavior. Many of the key dynamics that enable information processing can, in fact, be implemented by different biological hardware. This is widely exploited by organisms throughout the tree of life. Here, we review data on memory, learning, and other aspects of cognition in a range of models, including single celled organisms, plants, and tissues in animal bodies. We discuss current knowledge of the molecular mechanisms at work in these systems, and suggest several hypotheses for future investigation. The study of cognitive processes implemented in aneural contexts is a fascinating, highly interdisciplinary topic that has many implications for evolution, cell biology, regenerative medicine, computer science, and synthetic bioengineering.

Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form

Planaria are complex metazoans that repair damage to their bodies and cease remodeling when a correct anatomy has been achieved. This model system offers a unique opportunity to understand how large-scale anatomical homeostasis emerges from the activities of individual cells. Much progress has been made on the molecular genetics of stem cell activity in planaria. However, recent data also indicate that the global pattern is regulated by physiological circuits composed of ionic and neurotransmitter signaling. Here, we overview the multi-scale problem of understanding pattern regulation in planaria, with specific focus on bioelectric signaling via ion channels and gap junctions (electrical synapses), and computational efforts to extract explanatory models from functional and molecular data on regeneration. We present a perspective that interprets results in this fascinating field using concepts from dynamical systems theory and computational neuroscience. Serving as a tractable nexus between genetic, physiological, and computational approaches to pattern regulation, planarian pattern homeostasis harbors many deep insights for regenerative medicine, evolutionary biology, and engineering.

Comparison of the depolarization response of human mesenchymal stem cells from different donors

Bioelectric signaling is currently being explored as a novel regulator of cell processes in non-excitable cells. In particular, stem cells have demonstrated increasing evidence of electrophysiology-mediated regulation of stemness acquisition, proliferation, differentiation, and migration. However, in light of many reports of primary stem cell heterogeneity, it is important to characterize the variability of stem cell response to biophysical manipulations in order to assess the utility of bioelectric modulation as a universal strategy for stem cell control. In this work, human mesenchymal stem cells (hMSCs) from five donors were evaluated for their response to membrane potential (V_mem) depolarization. We compared the inter-donor variability of their osteogenic and adipogenic differentiation potential, as well as their ability to maintain a differentiated phenotype after induction. We identified the markers that responded most consistently across donors and found that calcium deposition and gene expression of bone sialoprotein, lipoprotein lipase, and fatty acid binding protein 4 are the preferred markers for assessing differentiation response to V_mem depolarization. We also note that since there exists variability even among some of these markers, these assays should be performed on any newly acquired hMSC population if their bioelectric properties are to be studied further.

Gap Junctional Blockade Stochastically Induces Different Species-Specific Head Anatomies in Genetically Wild-Type Girardia dorotocephala Flatworms

The shape of an animal body plan is constructed from protein components encoded by the genome. However, bioelectric networks composed of many cell types have their own intrinsic dynamics, and can drive distinct morphological outcomes during embryogenesis and regeneration. Planarian flatworms are a popular system for exploring body plan patterning due to their regenerative capacity, but despite considerable molecular information regarding stem cell differentiation and basic axial patterning, very little is known about how distinct head shapes are produced. Here, we show that after decapitation in G. dorotocephala, a transient perturbation of physiological connectivity among cells (using the gap junction blocker octanol) can result in regenerated heads with quite different shapes, stochastically matching other known species of planaria (S. mediterranea, D. japonica, and P. felina). We use morphometric analysis to quantify the ability of physiological network perturbations to induce different species-specific head shapes from the same genome. Moreover, we present a computational agent-based model of cell and physical dynamics during regeneration that quantitatively reproduces the observed shape changes. Morphological alterations induced in a genomically wild-type G. dorotocephala during regeneration include not only the shape of the head but also the morphology of the brain, the characteristic distribution of adult stem cells (neoblasts), and the bioelectric gradients of resting potential within the anterior tissues. Interestingly, the shape change is not permanent; after regeneration is complete, intact animals remodel back to G. dorotocephala-appropriate head shape within several weeks in a secondary phase of remodeling following initial complete regeneration. We present a conceptual model to guide future work to delineate the molecular mechanisms by which bioelectric networks stochastically select among a small set of discrete head morphologies. Taken together, these data and analyses shed light on important physiological modifiers of morphological information in dictating species-specific shape, and reveal them to be a novel instructive input into head patterning in regenerating planaria.

Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

A major goal of regenerative medicine and bioengineering is the regeneration of complex organs, such as limbs, and the capability to create artificial constructs (so-called biobots) with defined morphologies and robust self-repair capabilities. Developmental biology presents remarkable examples of systems that self-assemble and regenerate complex structures toward their correct shape despite significant perturbations. A fundamental challenge is to translate progress in molecular genetics into control of large-scale organismal anatomy, and the field is still searching for an appropriate theoretical paradigm for facilitating control of pattern homeostasis. However, computational neuroscience provides many examples in which cell networks - brains - store memories (e.g., of geometric configurations, rules, and patterns) and coordinate their activity towards proximal and distant goals. In this Perspective, we propose that programming large-scale morphogenesis requires exploiting the information processing by which cellular structures work toward specific shapes. In non-neural cells, as in the brain, bioelectric signaling implements information processing, decision-making, and memory in regulating pattern and its remodeling. Thus, approaches used in computational neuroscience to understand goal-seeking neural systems offer a toolbox of techniques to model and control regenerative pattern formation. Here, we review recent data on developmental bioelectricity as a regulator of patterning, and propose that target morphology could be encoded within tissues as a kind of memory, using the same molecular mechanisms and algorithms so successfully exploited by the brain. We highlight the next steps of an unconventional research program, which may allow top-down control of growth and form for numerous applications in regenerative medicine and synthetic bioengineering.

Bioelectric memory: modeling resting potential bistability in amphibian embryos and mammalian cells

Background

Bioelectric gradients among all cells, not just within excitable nerve and muscle, play instructive roles in developmental and regenerative pattern formation. Plasma membrane resting potential gradients regulate cell behaviors by regulating downstream transcriptional and epigenetic events. Unlike neurons, which fire rapidly and typically return to the same polarized state, developmental bioelectric signaling involves many cell types stably maintaining various levels of resting potential during morphogenetic events. It is important to begin to quantitatively model the stability of bioelectric states in cells, to understand computation and pattern maintenance during regeneration and remodeling.

Method

To facilitate the analysis of endogenous bioelectric signaling and the exploitation of voltage-based cellular controls in synthetic bioengineering applications, we sought to understand the conditions under which somatic cells can stably maintain distinct resting potential values (a type of state memory). Using the Channelpedia ion channel database, we generated an array of amphibian oocyte and mammalian membrane models for voltage evolution. These models were analyzed and searched, by simulation, for a simple dynamical property, multistability, which forms a type of voltage memory.

Results

We find that typical mammalian models and amphibian oocyte models exhibit bistability when expressing different ion channel subsets, with either persistent sodium or inward-rectifying potassium, respectively, playing a facilitative role in bistable memory formation. We illustrate this difference using fast sodium channel dynamics for which a comprehensive theory exists, where the same model exhibits bistability under mammalian conditions but not amphibian conditions. In amphibians, potassium channels from the Kv1.x and Kv2.x families tend to disrupt this bistable memory formation. We also identify some common principles under which physiological memory emerges, which suggest specific strategies for implementing memories in bioengineering contexts.

Conclusion

Our results reveal conditions under which cells can stably maintain one of several resting voltage potential values. These models suggest testable predictions for experiments in developmental bioelectricity, and illustrate how cells can be used as versatile physiological memory elements in synthetic biology, and unconventional computation contexts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12976-015-0019-9) contains supplementary material, which is available to authorized users.

A conceptual model of morphogenesis and regeneration

This paper is devoted to computer modelling of the development and regeneration of multicellular biological structures. Some species (e.g. planaria and salamanders) are able to regenerate parts of their body after amputation damage, but the global rules governing cooperative cell behaviour during morphogenesis are not known. Here, we consider a simplified model organism, which consists of tissues formed around special cells that can be interpreted as stem cells. We assume that stem cells communicate with each other by a set of signals, and that the values of these signals depend on the distance between cells. Thus the signal distribution characterizes location of stem cells. If the signal distribution is changed, then the difference between the initial and the current signal distribution affects the behaviour of stem cells-e.g. as a result of an amputation of a part of tissue the signal distribution changes which stimulates stem cells to migrate to new locations, appropriate for regeneration of the proper pattern. Moreover, as stem cells divide and form tissues around them, they control the form and the size of regenerating tissues. This two-level organization of the model organism, with global regulation of stem cells and local regulation of tissues, allows its reproducible development and regeneration.

The stability of memories during brain remodeling: A perspective

One of the most important features of the nervous system is memory: the ability to represent and store experiences, in a manner that alters behavior and cognition at future times when the original stimulus is no longer present. However, the brain is not always an anatomically stable structure: many animal species regenerate all or part of the brain after severe injury, or remodel their CNS toward a new configuration as part of their life cycle. This raises a fascinating question: what are the dynamics of memories during brain regeneration? Can stable memories remain intact when cellular turnover and spatial rearrangement modify the biological hardware within which experiences are stored? What can we learn from model species that exhibit both, regeneration and memory, with respect to robustness and stability requirements for long-term memories encoded in living tissues? In this Perspective, we discuss relevant data in regenerating planaria, metamorphosing insects, and hibernating ground squirrels. While much remains to be done to understand this remarkable process, molecular-level insight will have important implications for cognitive science, regenerative medicine of the brain, and the development of non-traditional computational media in synthetic bioengineering.

Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.

Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments. However, existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form, shape, and pattern. We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes, producing network models as testable hypotheses explaining regeneration data. Our system integrates a formalization of the published results in planarian regeneration, an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay, and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results. We applied our method to explain the key experiments in planarian regeneration, and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical, pharmacological, and genetic manipulations. Beyond the planarian data, our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine, and represents the first developmental model discovered de novo from morphological outcomes by an automated system.

Knowing one's place: a free-energy approach to pattern regulation

Understanding how organisms establish their form during embryogenesis and regeneration represents a major knowledge gap in biological pattern formation. It has been recently suggested that morphogenesis could be understood in terms of cellular information processing and the ability of cell groups to model shape. Here, we offer a proof of principle that self-assembly is an emergent property of cells that share a common (genetic and epigenetic) model of organismal form. This behaviour is formulated in terms of variational free-energy minimization-of the sort that has been used to explain action and perception in neuroscience. In brief, casting the minimization of thermodynamic free energy in terms of variational free energy allows one to interpret (the dynamics of) a system as inferring the causes of its inputs-and acting to resolve uncertainty about those causes. This novel perspective on the coordination of migration and differentiation of cells suggests an interpretation of genetic codes as parametrizing a generative model-predicting the signals sensed by cells in the target morphology-and epigenetic processes as the subsequent inversion of that model. This theoretical formulation may complement bottom-up strategies-that currently focus on molecular pathways-with (constructivist) top-down approaches that have proved themselves in neuroscience and cybernetics.